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Identification of cancer relevant synthetic genetic interactions with cohesin mutations in Saccharomyces… Reytan, Sivan 2017

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IDENTIFICATION OF CANCER RELEVANT SYNTHETIC GENETIC INTERACTIONS WITH COHESIN MUTATIONS IN SACCHAROMYCES CEREVISIAE  by  Sivan Reytan  B.Sc., The Hebrew University of Jerusalem, 2014  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Medical Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  May 2017  © Sivan Reytan, 2017 ii ABSTRACT  Cancer therapy is changing. Whole genome sequencing technologies are advancing at an unprecedented pace, opening new opportunities for the genotype-driven personalized treatment of cancer. Synthetic Lethality (SL) based therapeutics have emerged as promising approaches to target cancer-specific somatic mutations, by targeting a second gene that is required for viability in the presence of a tumor-specific mutation. The targetable set of SL partner genes can be expanded by screening for a conditional SL interaction, in which loss of function of two genes results in sensitivity to low doses of a DNA-damaging agent, a concept we have called Synthetic Cytotoxicity (SC). SC also has the potential to expand the number of genotypes that can be treated with existing chemotherapeutics and to improve the efficacy of these therapeutics. In contrast to SL and SC negative genetic interactions, Phenotypic Suppression (PS) describes  a genetic interaction in which the double mutant cell is more fit than anticipated based on the fitness of each single mutant.   The model organism, Saccharomyces cerevisiae was used to screen for SC interactions with cohesin-mutated genes, with the aim of identifying cross species candidate genes that could be followed up in subsequent studies as SL-based cancer-drug targets. The cohesin complex is frequently mutated across a wide range of tumors and is conserved from yeast to man. We used Synthetic Genetic Array (SGA) technology, a high-throughput genetic method available in yeast, to screen cohesin-mutated strains for synthetic lethal genetic interactions against an array of 310 deletions affecting mainly DNA damage response genes. The screens were done in the presence and absence of four clinically-relevant genotoxic agents. We screened and analyzed 4,650  iii potential genetic interactions, identifying hundreds of negative and positive interactions, belonging to conserved biological pathways, and potentially relevant to cancer. Using ScanLag, a new validation method, we re-tested and validated several genetic interactions that represent potential therapeutic candidates. These strong SL, SC and PS interactions can be further analyzed in mammalian cells to potentially inform and improve individual cancer therapies as personalized medicine treatments, and lead to the discovery of new pathways or candidates for anti-cancer treatments.                    iv LAY SUMMARY Cancer cells contain unique DNA alterations that distinguish them from normal cells, representing vulnerabilities that can be exploited for more precise therapy. Currently, cancer therapies heavily rely on DNA-damaging treatments that affect normal cells and lead to various side effects. Exploiting the genetic distinctness of cancer cells to sensitize them to lower doses of DNA-damaging therapy has the potential to improve cancer treatment while minimizing side effects. The cohesin complex of proteins is frequently altered in cancer and can contribute to the initiation and progression of the disease. Since cohesin, and many cancer-relevant processes are similar in yeast and humans, yeast could be used to model cancer and anti-tumor therapy and allow for large scale testing that is impractical in higher organisms. Here, I identified combinations of potential therapeutic targets with specific DNA-damaging agents in yeast, that could guide current cancer treatments. .   v PREFACE This thesis is original, unpublished work by Sivan Reytan.  Dr. Philip Hieter conceptualized this project along with Dr. Nigel O'Neil and Sivan Reytan. All experiments were performed by Sivan Reytan. The creation of the DDR mutant array (DDR-MA) was a combined effort of Hunter Li, Megan Kofoed, Erik Tammpere and Sivan Reytan. Collection and analysis of data was completed by Sivan Reytan under the guidance of Hunter Li, Dr. Nigel O'Neil and Dr. Philip Hieter.   vi TABLE OF CONTENTS ABSTRACT ................................................................................................................................... ii LAY SUMMARY ......................................................................................................................... iv PREFACE .......................................................................................................................................v TABLE OF CONTENTS ............................................................................................................ vi LIST OF TABLES ....................................................................................................................... ix LIST OF FIGURES .......................................................................................................................x LIST OF SYMBOLS .................................................................................................................. xii LIST OF ABBREVIATIONS ................................................................................................... xiii ACKNOWLEDGEMENTS ........................................................................................................xv DEDICATION............................................................................................................................ xvi Chapter 1: INTRODUCTION ......................................................................................................1 1.1 Overview ......................................................................................................................... 1 1.2 Cancer: disease and therapeutics .................................................................................... 2 1.2.1 Synthetic Lethality (SL) .............................................................................................. 8 1.2.2 Synthetic Cytotoxicity (SC) ........................................................................................ 9 1.3 The cohesin complex .................................................................................................... 12 1.4 Yeast genetics, cohesin and cancer therapeutics ........................................................... 17 1.4.1 Synthetic Genetic Array (SGA) ................................................................................ 18 1.5 Thesis objective ............................................................................................................ 22 Chapter 2: MATERIALS AND METHODS .............................................................................24 2.1 Yeast strains .................................................................................................................. 24 2.1.1 DDR-MA .................................................................................................................. 25  vii 2.2 SGA screen ................................................................................................................... 27 2.2.1 SGA bioinformatic analysis ...................................................................................... 30 2.3 DDAs ............................................................................................................................ 31 2.4 Tecan liquid growth curves ........................................................................................... 32 2.5 ScanLag solid growth curves ........................................................................................ 33 2.5.1 ImageJ analysis ......................................................................................................... 34 2.6 AUC calculation............................................................................................................ 34 Chapter 3: RESULTS ..................................................................................................................35 3.1 Systematic identification of cohesin genetic interactions ............................................. 35 3.1.1 SL and SC genetic interaction networks ................................................................... 50 3.1.2 PS genetic interactions .............................................................................................. 56 3.2 Validation of hits- retesting in ScanLag and Tecan ...................................................... 61 3.2.1 Validation of genetic interactions using ScanLag .................................................... 66 Chapter 4: DISCUSSION............................................................................................................77 4.1 Summary of findings..................................................................................................... 77 4.1.1 Overview ................................................................................................................... 77 4.2 Significance of findings ................................................................................................ 86 4.3 Future directions ........................................................................................................... 87 Bibliography .................................................................................................................................89 Appendices ..................................................................................................................................100 Appendix A ............................................................................................................................. 100 A.1 List of yeast strains ................................................................................................. 100 A.2 List of initial hit strains and frequency of significance. .......................................... 120  viii A.3 Mathematical explanation of genetic interaction formula ...................................... 122 A.4 Final lists of validated genetic interactions with scc1 query mutation ................... 123   ix LIST OF TABLES  Table ‎1-1. Cohesin subunits, regulatory proteins and associated cohesinopathies....................... 16 Table ‎2-1. The physical map of the new DDR-MA...................................................................... 26 Table ‎2-2. DNA-damaging agents used in the project. ................................................................. 31 Table ‎2-3. DDAs and concentrations used for ScanLag validation process. ................................ 33 Table ‎3-1. Primary SGA screen results using the three cohesin queries. ..................................... 48 Table ‎3-2. Results of retested initial SGA scc1 hits. .................................................................... 71 Table ‎4-1. List of array yeast strains and human homologous. .................................................. 119 Table ‎4-2. List of initial hit strains and frequency of significance. ............................................ 121 Table ‎4-3. Validated genetic interactions based on SGA data.................................................... 124 Table ‎4-4. New negative genetic interactions. ............................................................................ 125   x LIST OF FIGURES  Figure ‎1-1. Types of cancer therapies. ............................................................................................ 8 Figure ‎1-2. The concept of SL and SC. ........................................................................................ 11 Figure ‎1-3. The cohesin complex in yeast (bold) and human. ...................................................... 13 Figure ‎1-4. Frequency of cohesin mutations in human cancers.................................................... 13 Figure ‎1-5. Copy number variations (CNA) of RAD21 across a variety of cancer types. ........... 15 Figure ‎1-6. Quantitative genetic interactions determination......................................................... 19 Figure ‎1-7. The structure of the SGA screen conducted in this project. ....................................... 21 Figure ‎1-8. Thesis project flow. .................................................................................................... 23 Figure ‎2-1. SGA screen structure scheme..................................................................................... 29 Figure ‎3-1. Example for fitness-defect distribution. ..................................................................... 36 Figure ‎3-2. Venn diagrams representing the number of initial hits per cohesin query. ................ 50 Figure ‎3-3.  Synthetic Lethal initial hits network. ........................................................................ 52 Figure ‎3-4. Synthetic Cytotoxic initial hits network with MMS. ................................................. 53 Figure ‎3-5. Synthetic Cytotoxic initial hits network with CPT. ................................................... 54 Figure ‎3-6. Synthetic Cytotoxic initial hits network with bleomycin. .......................................... 55 Figure ‎3-7. Synthetic Cytotoxic initial hits network with benomyl.............................................. 56 Figure ‎3-8. Phenotypic Suppression initial hits network without a DDA. ................................... 57 Figure ‎3-9. Phenotypic Suppression initial hits network with MMS............................................ 58 Figure ‎3-10. Phenotypic Suppression initial hits network with CPT............................................ 59 Figure ‎3-11. Phenotypic Suppression initial hits network with bleomycin. ................................. 60 Figure ‎3-12. Phenotypic Suppression initial hits network with benomyl. .................................... 61  xi Figure ‎3-13. ScanLag method components. ................................................................................. 63 Figure ‎3-14. Tecan (top) vs. ScanLag (bottom) parallel experiments. ......................................... 65 Figure ‎3-15. Double-mutant hits score distribution under CPT, ScanLag vs. SGA. .................... 68 Figure ‎3-16. Growth curves of SC interaction on MMS. ............................................................. 72 Figure ‎3-17. Growth curves of SC interaction on CPT. ............................................................... 73 Figure ‎3-18. Growth curves of SC interaction on benomyl. ......................................................... 74 Figure ‎3-19. Growth curves of SC interaction on benomyl. ......................................................... 74 Figure ‎3-20. Growth curves of SL interaction. ............................................................................. 75 Figure ‎3-21.  Growth curves of PS interaction. ............................................................................ 76 Figure ‎4-1. Distribution of double-mutant in ScanLag vs. SGA .................................................. 81 Figure ‎4-1. The calculation of genetic interaction in an SGA. ................................................... 122   xii LIST OF SYMBOLS  Δ:‎deletion  xiii LIST OF ABBREVIATIONS  AUC: area under the curve CIN: chromosome instability CPT: camptothecin DDA: DNA-damaging agent DDR: DNA damage response DDR-MA: DNA-damage response mutant array DMSO: dimethyl sulfoxide DSB: double-strand DNA break G418: geneticin HR: homologous recombination LOF: loss of function M: cell cycle mitotic phase MMS: methyl methanesulfonate ND: no drug NI: negative (genetic) interaction OD: optical density PARP: poly (ADP ribose) polymerase PI: positive (genetic) interaction PS: phenotypic suppression S: cell cycle synthesis phase SC: synthetic cytotoxicity  xiv SD: standard deviation SGA: synthetic genetic array SL: synthetic lethality SS: synthetic sickness SSB: single-strand DNA break SSC: sister chromatid cohesion SS/L: synthetic sickness/ lethality ts: temperature sensitive WT: wild type    xv ACKNOWLEDGEMENTS I would like to thank my supervisor, Dr. Philip Hieter, for the opportunity to pursue this project under his supervision and for his highly appreciated support, guidance and generosity.   Thank you to my committee members, Dr. Michel Roberge, Dr. William Gibson and Dr. Marco Marra, for their willingness to oversee my scientific progress despite their busy schedule, and for their valuable intellectual contributions.   I would like to express my sincere gratitude to the wonderful past and present members of the Hieter Lab: Nigel‎O’Neil,‎Megan‎Kofoed,‎Hunter‎Li‎, Leanne Amitzi, Akil Hamza, Frank Ye, Tejomayee Singh, Shay Ben-Aroya, Supipi Duffy, Joshua Regal, Melanie Bailey, Sidney Ang, Walid Omar and Jan Stoepel, for steering me in the right direction and offering useful comments, remarks and engagement through the learning process of this master thesis.   A special thanks to Cheryl Bishop, graduate secretary of the Medical Genetics program, for her admirable care for the graduate students of the program, beyond her professional requirements.   I offer my enduring gratitude to Dr. Shay Ben-Aroya and his lab, and professor Nathalie Balaban and her lab, for their valuable guidance in establishing ScanLag in the Hieter lab.  Finally, I would like to thank my beloved family and friends for your continuous encouragement and love that helped me overcome hardships and accomplish my goals.   Thank you all, for supporting me throughout my years of study in Canada and through the process of researching and writing this thesis.    ivx  NOITACIDED esahc dna noitibma ym wollof ot ytinutroppo eht em gnidivorp rof ,stnerap raed ym ,uoy knahT  .uoy tuohtiw elbissop neeb evah ton dluow tnemhsilpmocca sihT .smaerd ym .traeh ym fo mottob eht morf ,uoy knahT   , למשפחתי האהובה והיקרהעל שנתתם לי את האמצעים לרדוף אחרי השאיפות שלי ולנתב את חיי , ורד ויגאל, הוריי היקרים, תודה לכם, התמיכה, האהבה על, תודה על השורשים העמוקים שנטעתם בי ועל הכנפיים שהענקתם לי .בדרך שבחרתי   .הגבולות-חוצי ההקשבה והעזרה  . על הדאגה והעידוד מרחוק, ראחי היק, תודה ליאור  .האכפתיות והברכות הרבות, על האהבה המתמדת, סבתות יקרות, תודה לכן  . משפחתי הנפלאה, אני מקדישה את התזה הזו לכם  1 Chapter 1: INTRODUCTION  1.1 Overview  Cancer is regarded as a global epidemic and is among the leading causes of death worldwide. Cancer is not a single disease, but rather, a collection of perhaps hundreds of diseases, in which the accumulation of specific sets of genetic alterations enables a number of abnormal phenotypes that define a particular cancer subtype1. Genome instability, which results in increased frequency of alterations in chromosome number, structure and sequence, is a key tumor-enabling process that drives the accumulation of genetic changes that facilitate tumor survival and progression2. The cohesin complex, when mutated, leads to genome instability and is frequently mutated across a variety of cancer types3, making cohesin an attractive genetic vulnerability to target with new anti-cancer therapeutics.   Synthetic Lethality (SL)-based therapeutics have emerged as a promising approach to target cancer specific somatic mutations4,5. SL exploits the genetic distinctness of cancer cells by targeting a second gene that is required for viability in the presence of a tumor specific mutation, resulting in specific killing of the tumor cells while normal cells remain viable. Another approach to leverage genetic interactions to specifically kill cancer cells is Synthetic Cytotoxicity (SC), in which the disruption of two gene products results in increased sensitivity to low dose of a cytotoxic agent6. SC has the potential to expand the number of genotypes that can be treated in combination with existing cytotoxic therapies, and to substantially improve their efficacy, which could result in lower effective doses of potentially harmful cytotoxic drugs. However, SC genetic interactions have not been broadly explored.    SL/SC genetic interactions are rare and large scale screening is needed in order to find relevant interactions. The budding yeast, Saccharomyces cerevisiae, has been proven to be a  2 valuable model organism for studying eukaryotic gene function and interaction7. Nearly 20% of yeast genes are members of orthologous gene families associated with human disease8, and together with advanced genetic technologies available for yeast, this organism serves as a powerful model to screen for conserved genetic interactions with yeast homologs of known cancer-associated mutated genes, and for sensitivity to clinically-relevant DNA-damaging agents (DDAs). This, in turn, has the potential to identify therapeutic candidates in humans that can improve current cancer treatment.   The aim of my thesis is to use the cohesin complex as a test case to screen for SC genetic interactions in yeast, and discover how SC can broaden the number of genes that can be targeted in the context of specific DDAs. In the course of this study, a new growth measurement system, ScanLag, was assessed as a method to validate genetic interactions discovered in large-scale genetic screens.   In this chapter, I will introduce the concept of targeted therapy in cancer, describe how genetic interactions can be used to develop new anti-cancer therapeutics, and describe the yeast genetic technology that allows for the high-throughput screening of genetic interactions. I will also provide an introduction to the function and cancer-mutation frequencies of the cohesin complex. The genetic approaches discussed here, have implications for both the advancement of personalized medicine in the field of cancer, as well as in the field of rare genetic diseases associated with mutations in the cohesin complex.  1.2 Cancer: disease and therapeutics  Cancer is the second leading cause of death worldwide, accounting for 8.8 million deaths in 2015, and for which the number of new cases is expected to rise by ~70% over the next 2 decades9.   3 Cancer is a genetic disease  Cancer is a complex genetic disease, that is caused by the genetic composition of the cell, as well as by epigenetics and environmental factors (e.g. diet and lifestyle). It describes a situation in which certain cells in the body acquire certain capabilities that enable them to become tumorigenic and ultimately malignant, via a series of enabling mutations and genomic alterations2. The genomic landscape of cancer cells usually consists of two distinct types of mutations: 1) several driver mutations (accumulation of as many as 20 mutations per individual cancer10), which are genetic alterations that provide a selective proliferative advantage (directly or indirectly) to the malignant cell, and, 2) many passenger mutations, which confer no selective growth advantage11,12. Driver mutations can be further distinguished by the type of gene affected, either oncogenes or tumor-suppressor genes. Oncogenes are altered genes (proto-oncogenes) that have acquired mutations that drive the process of tumorigenesis when activated, usually by a dominant gain-of-function mutation11,13. On the other hand, tumor-suppressor genes can drive tumorigenesis when inactivated, usually by a recessive loss-of-function mutation, as their expression inhibits cell-proliferation and the development of cancer11,13. The unique accumulation of genomic changes and mutations among malignant cells, between and within patients, contributes to cancer heterogeneity and the complexity of the disease.  Hallmarks of cancer  Despite being heterogeneous, several common phenotypes, known as "the hallmarks of cancer" 2, characterize cancer. These phenotypes include: eluding growth suppressors, which includes the bypassing of negative regulation of cell proliferation, mainly via LOF mutations in tumor-suppressor genes; continued proliferative signaling, which is characterized by the ability of cancer cells to sustain chronic proliferation, independent of external growth signals (either by  4 an autocrine proliferative stimulation, stimulation of surrounding cells to secrete various growth factors, or by dysregulated receptor signaling); resistance to apoptosis, which is the avoidance of cell death by circumventing or limiting the effects of intrinsic (e.g. due to DNA damage) or extrinsic (e.g. via Fas receptor) cell-death signals; angiogenesis, which is the formation of new blood vessels that help sustain the expanding neoplastic growth via delivery of oxygen and nutrients, as well as enabling cells to secrete metabolic waste and carbon dioxide; enabled invasion and metastasis, which is the ability of tumor cells to migrate to other tissues and spread throughout the body (e.g. via loss of cell adhesion); genome instability and mutation, which is an enabling feature of cancer cells that is due to the acquisition of genomic alterations that contribute to an increased aneuploidy rate and/or mutation-rate; tumor-promoting inflammation, that is an enabling feature of cancer cells in which the attempt of the immune system (mainly the innate system) to eradicate the tumor results in the opposite effect, leading to other hallmark features (e.g. by releasing mutagenic reactive oxygen species); escaping immune detection and eradication; reprogrammed energy metabolism, which describes the adjustments of energy metabolism inside the tumor cell to support cell growth and division (e.g. switching to glycolysis as the primary energy source, especially under hypoxia (Warburg effect)); and finally, uncontrolled replication leading to immortality, such as by protection of telomere length. These hallmarks of cancer cells, which differentiate them from normal cells, also represent potential vulnerabilities that can be leveraged to kill cancer cells selectively.  Cytotoxic anti-cancer therapeutics  While some cancers (specifically those that form solid tumors) can be treated locally with surgery, others require taking a different approach, including the use of radiation and pharmaceutical compounds. Chemotherapy (i.e. the use of drugs) and radiation were  5 incorporated into cancer treatment in the 20th century, when clinical scientists discovered the negative effects of certain compounds (e.g. nitrogen mustard and aminopterin) and x-ray irradiation on one of the known abnormal phenotypes of cancer cells- uncontrolled proliferation14. While accomplishing the goal of killing cancer cells, these traditional therapeutics target important cellular processes, such as DNA replication and cell division, that are essential for both cancer and normal cells, especially rapidly dividing cells14,15. Thus, chemotherapy and radiation therapy can be classified as cytotoxic therapy, as they are toxic to both healthy and malignant cells and can be accompanied by a range of negative side effects.  Major classes of current standard (or traditional) cytotoxic chemotherapy include: 1) Alkylating agents (e.g. temozolomide, lomustine, oxalaplatin), which damage DNA bases by cross linkage, disrupting abnormal base pairing, or by attaching an alkyl group to DNA bases. 2) Antimetabolites (e.g. 5-FU, 6-MP), which are pyrimidine or purine analogues that interfere with the biosynthesis of DNA and RNA. 3) Anti-tumor antibiotics (e.g. doxorubicin, bleomycin), which function by altering the DNA of cancer cells, for example, by introducing double-strand breaks. 4) Topoisomerase inhibitors (e.g. topotecan, etoposide), which bind to either topoisomerase I or II, stabilize the complex onto the DNA, and stimulate DNA cleavage. 5) Mitotic inhibitors (e.g. paclitaxel, vinblastine, ixabepilone), which interfere with microtubule stability and structure, thus, preventing the completion of cell division during M-phase.15    Next generation cancer therapies  A new generation of chemotherapeutic drugs was designed to exploit other hallmarks of cancer cells besides rapid cell division. These treatments can be general or more specific as part of the concept of targeted therapy based on cancer sub-typing16. Examples for non-sub-typed treatments that target cancer hallmarks include: drugs that induce apoptosis (e.g. infliximab,  6 oncrasin-1) to address the hallmark capability of escaping cell death, and angiogenesis inhibitors (e.g. bevacizumab) to address the hallmark capability of forming new blood vessels to provide a tumor with oxygen and necessary nutrients. Treatments that target more specific hallmarks are usually based on tumor-profiling and include: specific growth signal inhibitors (e.g. erlotinib, gefitinib) to address the hallmark of dysregulated signaling pathways of cancer cells17, and immunotherapy, that has become an exciting and promising method to treat cancer, to address the hallmark capability of avoiding immune destruction18.    As more has been learned about the biology underlying tumor development and progression, treatments have been developed based on molecular classification of tumors in order to stratify them and choose a more specific therapy. Breast cancer, for example, can be classified by the presence or absence of specific hormone receptors on the tumor cell surface that are associated with the development of this type of cancer19. If the cancer cells have estrogen (ER) or progesterone (PR) receptors, they are classified as either ER+ or PR+, respectively, and can be treated with hormonal therapy to limit or inhibit the level of hormone signaling19. However, if the cells are ER- or PR- (i.e. hormone-receptor negative), hormonal therapy will most likely not be effective. In addition, breast cancer cells can be tested for human epidermal growth factor receptor 2 (HER2) status, another receptor that when overexpressed, contributes to cancer development and to more aggressive malignancy19,20. Cancers that are HER2+ can also be specifically treated. However, 10-20% of breast cancers test negative for all three receptors, hence classified as triple-negatives, and require a different treatment that can involve chemotherapy, radiotherapy, or a combinational therapy of specific inhibitors such as PARP inhibitors (discussed later) and cytotoxic agents19,20,21. More recently, breast cancers have been further stratified by DNA sequencing or microarray profiling, in which specific genes (known to  7 be associated with the disease) are being assayed for predisposing mutations. BRCA status assays, for example, are designed to screen for cancer-predisposing BRCA1 and BRCA2 mutations22. Mutations in BRCA1 or BRCA2 can result in sensitivity to certain DNA damaging therapeutics23.  The advancement of next-generation sequencing, that has facilitated low cost sequencing of whole genomes and exomes, has expanded the mutational profiling of tumors and has directly contributed to the rise of personalized medicine, allowing to identify cancer vulnerabilities including mutations in oncogenes and tumor- suppressors of specific cancer types, at an unprecedented pace16. Cancer cells accumulate a unique set of genomic alterations and mutations, contributing to the different response to treatment between cancer patients, even those who are diagnosed with the same type or subtype of cancer11. Personalized medicine addresses this challenge by taking into consideration the unique genetic composition of the individual tumor and tailoring specific cancer treatments and preventive measures based on the tumor genotype16,24. One example of a genotype-associated cancer vulnerability is oncogene addiction, which describes a situation in which tumor cells depend on a certain oncogene (and its gain-of-function mutation) for survival25. Exploiting oncogene addiction, as part of targeted therapy, aims to inhibit the expression of that specific oncogene to selectively kill the tumor cells25. An example of a therapeutic that targets oncogene addiction is the drug Gleevec (generic imatinib mesylate) in the treatment of chronic myeloid leukemia (CML)26. The BCR-ABL fusion protein is the product of the Philadelphia (Ph+) chromosome, a reciprocal translocation between chromosome 9 (ABL gene) and chromosome 22 (BCR gene), and is present in  >90% of CML patients27. The fused parts of the two genes generate a tyrosine kinase protein that causes an irregular signaling inside hematopoietic stem cells that alters their normal function. Gleevec  8 revolutionized CML treatment by specifically inhibiting the oncogenic tyrosine kinase, resulting in a significant improvement of patient outcome and survival28.     Figure ‎1-1. Types of cancer therapies.  Cancer can be treated in a non-targeted manner via surgery, chemotherapy or radiation therapy. Targeted therapy can be directed to either a general phenotype of cancer cells or to a specific genotype.  1.2.1 Synthetic Lethality (SL)   Not all cancers have identified targetable oncogenes. Targeting tumor-suppressor gene mutations poses a greater challenge than targeting oncogenes because tumor-suppressor gene mutations are usually loss- or reduction-of-function and, therefore, are not targets for inhibition5. However, it may be possible to exploit tumor specific mutations by identifying and utilizing synthetic lethal interactions between tumor-suppressor gene mutations and a synthetic lethal partner.   9  Synthetic Lethality (SL), a concept first developed in model organisms, is a genetic interaction between two mutated genes in which a mutation in either gene alone is viable yet mutations in both genes simultaneously is lethal29,30,31–33 (Figure ‎1-2). The protein product of a gene that, when mutated, has a SL interaction with a known cancer mutation should be an excellent anti-cancer drug target4,5,34. An example of a cancer-relevant SL interaction is between mutated BRCA genes (BRCA1 or BRCA2) and Poly (ADP-ribose) polymerase (PARP) inhibitors. PARP1 and PARP2 are active at single-strand break (SSB) sites, where they contribute to efficient repair35. BRCA1 and BRCA2 are key proteins in the homologous recombination (HR) repair pathway, and account for the majority of families with hereditary susceptibility to ovarian and breast cancers36. Mutation in either BRCA1 or BRCA2, results in a compromised or non-functional HR pathway, leading to DNA-damage repair via an error-prone pathway and to genetic instability. Deficiency in either pathway, HR or PARP-mediated repair, is viable, however, loss of both repair pathways was found to result in cell death37,38. The implementation of PARP inhibitors in the clinic39,40, has stimulated attempts to identify other SL interactions. 1.2.2 Synthetic Cytotoxicity (SC)  Genetic interactions are condition-dependent and can depend on or be suppressed by intrinsic (e.g. genetics, cellular metabolism, cellular microenvironment) and extrinsic (e.g. exposure to cytotoxic therapy) conditions. In the case of tumor cells, therapeutic exposure can affect SL interactions and conversely, genetic interactions can affect the cellular sensitivity to therapeutics.‎Studies‎in‎yeast‎have‎shown‎that‎genetic‎interactions‎can‎‘re-wire’‎the‎DNA‎damage response pathway resulting in increased or decreased sensitivity to DNA damaging agents41,42. Genetic interactions resulting in sensitivity to a DNA damaging agent is a class of  10 conditional SL that we have called Synthetic Cytotoxicity (SC)6 (Figure ‎1-2). Genetic interactions resulting in resistance to DNA damaging agents would be characterized as Phenotypic Suppression (PS), or conditional viability.    SC, the combining of an inhibitor of a secondary gene and lower doses of cytotoxic therapies, in the context of a specific cancer mutation, has the potential to expand targetable tumor specific mutations beyond those for which SL interactions can be found. A proof-of-concept screen used a tel1 null mutant, the yeast orthologue of the human tumor-suppressor ATM, to identify SC interactions in yeast, using the topoisomerase I inhibitor camptothecin (CPT)6. There are few SL interactions with tel1 in yeast. However, the SC screen found several SC interactions that were not present in the absence of the cytotoxic agent, thereby demonstrating that SC, in principle, could be used to expand the number of genetic interaction partners that could be targeted to affect the specific killing of ATM mutated tumors6. Another example of  SC is the effect of inhibiting PARP in cohesin-mutated glioblastoma cells in the presence of temozolomide (alkylating agent)43. Bailey and colleagues, used matched glioblastoma cell lines, with either a mutated STAG2 (a cohesin core subunit) or a restored wild-type STAG2, to investigate the combined effect of PARP inhibitor and temozolomide. It was found that using a monotherapeutic approach of only PARP inhibitor, can affect genome stability of STAG2-mutated glioblastoma cells. However, the combination of a temozolomide and PARP inhibitor, enhanced the killing of STAG2 mutated cells. Thus, a SC combination, consisting of an inhibitor and a cytotoxic agent, was more effective in killing cohesin-mutated cells compared to the presence of only the inhibitor.  11   Figure ‎1-2. The concept of SL and SC.  For the purpose of this project, gene A is a CIN-causing gene that is frequently mutated in tumors (e.g. cohesin gene), and gene B is a negative interaction partner gene (e.g. DNA-damage response gene).  *This figure was adapted from Xuesong L et al. 20146.     The main goal of cancer treatment is to selectively eliminate the malignant cells (maximizing the therapeutic effect) while reducing adverse reactions (minimizing the toxic effect). Finding a secondary gene, that upon inhibition, will sensitize only cancer cells to lower dose of a cytotoxic agent, is highly valuable in terms of minimizing side effects while maintaining efficacy and, in principle, expanding the spectrum of cancer genotypes that can be targeted.        12 1.3 The cohesin complex  The cohesin complex is involved in various essential cellular functions which, when mutated, can contribute to tumorigenesis. These functions include DNA repair, DNA replication, chromosome segregation, chromatin structure and gene expression44,45. Somatic mutations in cohesin-encoding genes occur frequently across many tumor types, including colorectal cancer (one of the leading cancers), acute myeloid leukemia (AML), bladder cancer, glioblastoma, Ewing’s‎sarcoma,‎melanoma, and Down syndrome related acute megakaryocytic leukemia3,44 (Figure ‎1-4). These somatic cohesin mutations could thereby, represent an excellent genetic vulnerability for SC-based therapeutic approaches. The high frequency of cancer mutations in cohesin, an essential protein complex, combined with SL data that is available for its mutated subunits, make cohesin a good candidate for SC screening with DNA damaging agents. To date, no therapeutics have been identified that exploit cohesin mutations in cancer cells.   The cohesin complex is highly conserved from yeast to humans and is composed of four core subunits, including SMC3, SMC1A, RAD21 and STAG1/2 in human somatic cells46,47 (Figure ‎1-3). Cohesin-associated proteins support the function and regulation of the cohesin complex46,47 (Table ‎1-1).      13  Figure ‎1-3. The cohesin complex in yeast (bold) and human. The SMC protein family displays both a hinge domain at one end of the protein and an ATPase head domain at the other end. SMC1 and SMC3 bind together in a V shape through their hinge domains, and interact with SCC1 (RAD21) through their head domains. The SCC3 protein (either STAG1 or STAG2 in humans) binds to the central region of SCC1.   Figure ‎1-4. Frequency of cohesin mutations in human cancers.   14 Based on all current TCGA published data from cBioPortal database, the graph shows the percentage of cases in which an altered gene was identified in a given study, organizing the data based on cancer type and plotting it into charts using Excel. Cohesin mutations include mutations in the core subunits of the complex, SMC1A, SMC3, RAD21, STAG1 and STAG2.     The frequency of mutations in the cohesin core subunits is notably higher than other well described tumor-suppressors for many cancer types. For example, more samples of breast cancer contain mutations in cohesin as compared to BRCA1 or BRCA2 genes. In breast and ovarian cancers, cohesin gene alteration frequencies are even higher when including copy number variations, due to amplifications of the RAD21 subunit (Figure ‎1-5). Recurrent mutations in the cohesin genes, especially in STAG2, are associated with different myeloid neoplasms such as AML, and were found to coexist frequently with common mutated epigenetic modifiers in the initiation of this type of cancer48–51. Although AML and glioblastoma have relative low mutation rates52, cohesin mutations are found in more than 10% and 5% of the number of samples studied, respectively, consistent with an important contribution to the development of these cancers.  15  Figure ‎1-5. Copy number variations (CNA) of RAD21 across a variety of cancer types.  Data was extracted from cBioPortal database and was based on TCGA published data.     Cohesin is essential for cell viability, therefore, most cohesin subunit genes that are mutated in tumors carry hypomorphic mutations which most likely lead to a reduced function rather than a complete loss of function. The exception is STAG2, which does exhibit frequent loss-of-function mutations in tumors. This can be explained by the fact that STAG2 has a paralog, STAG144.  In vertebrates, the two somatic cohesin complexes are differentiated by the presence of either STAG1 or STAG2, and can vary in their cellular abundance, between cell types, and/or the stage of development44. The STAG2 gene is among the most frequently mutated Amplification  16 genes in at least four tumor types44, and is highly mutated in bladder cancer (10-30%)53,51,54,55. Due to its location on the X-chromosome, only one copy is being expressed in each cell, therefore, a loss of STAG2 requires only a single mutational event. Loss of STAG2 is not lethal because STAG1 is thought to have some overlapping function with STAG244.   Mutations in cohesin and cohesin-associated genes can also result in a spectrum of rare inherited human diseases, termed cohesinopathies. The cohesinopathies include Cornelia de Lange syndrome, Roberts syndrome, Wilson-Turner syndrome, Warsaw breakage syndrome, and other medical conditions such as premature ovarian failure and chronic atrial and intestinal dysrhythmia56–60. Table ‎1-1, summarizes the association of the different cohesin subunits with these conditions.    Table ‎1-1. Cohesin subunits, regulatory proteins and associated cohesinopathies. S. cerevisiae Human Function Disease Smc1 Smc1a Cohesin subunit Cornelia de Lange syndrome  Smc1b Cohesin subunit (meiosis)  Smc3 Smc3 Cohesin subunit Cornelia de Lange syndrome Mcd1/Scc1 Rad21 Cohesin‎subunit‎(α-kleisin) Cornelia de Lange syndrome Rec8 Rad21L1 Cohesin subunit (meiotic α-kleisin)  Scc3/Irr1 STAG1 Cohesin subunit   STAG2    STAG3 Cohesin subunit (meiosis) Premature ovarian failure Pds5 Pds5a Cohesin maintenance   Pds5b   Wpl1/Rad61 Wapal Cohesin dissociation  Scc2 Nipbl Cohesin loading Cornelia de Lange syndrome Scc4 Mau2 Cohesin loading  Eco1/Ctf7 Esco1 Cohesin establishment   Esco2  Roberts syndrome  17 S. cerevisiae Human Function Disease Esp1 Espl1 Separase  Pds1 Pttg1 Securin  Hos1 HDAC8 Smc3 deacetylase Wilson-Turner syndrome, Cornelia de Lange syndrome Sgo1 Sgol1 Protection of centromeric cohesin Chronic Atrial and Intestinal Dysrhythmia  Sgol2     Interestingly, an increased risk of cancer among patients with these rare cohesinopathies is not obvious56,61. These congenital disorders are very rare (1:10,000 live births for Cornelia de Lange syndrome, unknown prevalence for Roberts syndrome and <1:1,000,000 live birth for both Wilson-turner and Warsaw breakage syndromes), and patients often die young from other medical complications before tumors can be identified. However, in a retrospective analysis done on 295 deceased Cornelia de Lange individuals with a known cause of death, it was found that 2% of death cases were accounted by cancer (1 lymphoma out of 117 children, 4 gastrointestinal and 1 unspecified cancer deaths out of 97 adults)62. In addition, two cases of Cornelia de Lange syndrome associated with infantile hemangioendothelioma of the liver and Wilms' tumor were also reported63.  1.4 Yeast genetics, cohesin and cancer therapeutics  Yeast is a single-cell eukaryotic organism that shares many fundamental genes and pathways with humans. Studies in yeast have provided major insights on gene function, cellular pathways and interactions relevant to human cancer64–66. Of particular relevance to this thesis, many human CIN-causing genes were first found in yeast, demonstrating the power and benefit of using model organisms for studying biological mechanisms67–71. For example, the cohesin complex was initially discovered in yeast72,73, and later was found to be mutated in colorectal cancer samples exhibiting a CIN phenotype74,75, and several other tumor types (see above). In  18 1997, Hartwell suggested that model organisms, such as yeast, could be harnessed to discover cancer-relevant SL interactions, which could identify potential anti-cancer therapeutic targets4. Using the highly conserved cohesin genes as queries for high-throughput synthetic lethal genetic interactions screening, yeast can serve as a platform to identify genetic interactions of value for the development of novel cancer drug targets for selective killing of tumors carrying cohesin somatic mutations74,76.  1.4.1 Synthetic Genetic Array (SGA)  Finding potential SL and SC targets requires large scale unbiased screening. Yeast affords the opportunity for high-throughput genetic screening to identify therapeutically-relevant genetic interactions using approaches such as Synthetic Genetic Array (SGA)77,78. SGA is a powerful genetic screening technique in yeast that can identify a vast number of genetic interactions, both positive and negative, in a fast and systematic way. It involves using robotics to efficiently mate and manipulate high-density arrays of yeast single mutants in order to construct double mutants for a reference query gene mutation of interest, via a sequence of replica-pinning procedures78,79. By observing the differences in growth rate between the single and the double mutants under diverse conditions (e.g. chemical compounds), quantitative scores can be generated to reflect the magnitude of genetic interactions. Negative genetic interactions (i.e. SL and SC) will result in a more severe fitness defect than anticipated under a certain condition (Figure ‎1-6), while positive genetic interactions (i.e. PS) will result in a better fitness than anticipated. Another important benefit of SGA is its flexibility, as any genetic alteration can be used as the reference query mutation (e.g. point mutation, knockout, gene over expression, etc.), and genetic interactions can be tested under various conditions (different temperatures, media, drugs, etc.).    19  Figure ‎1-6. Quantitative genetic interactions determination.  In the absence of a genetic interaction, the fitness of a double mutant is expected to be the product of the individual fitness values of the corresponding single mutants. For example, a yeast strain that carries a mutation in gene A, which confers a defective response and consequent increased sensitivity to a certain DNA-damaging agent (DDA), demonstrates 20% growth rate reduction compared to wild-type strain at the same dose of the DDA. Likewise, mutant B shows a sensitivity with a 10% growth-rate reduction. The double mutant, however, grows 90% slower than the wild-type in the presence of the DDA, indicating that the genetic combination is more than additive (i.e. causes a greater growth defect than expected for the combination of mutation A and B) (0.8 *0.9 = 0.72, or a 28% expected reduced growth rate). One interpretation of this type of genetic interaction is that both genes might be involved in the same biological process, such as DNA repair, but occur in separate pathways. The cell can tolerate loss-of-function mutations in either pathway but not both.   In a typical SGA screen (Figure ‎1-7), a haploid query yeast strain, harboring the reference mutation of interest, is mated to an array of haploid mutants. Diploid cells are selected for the selective markers of both query gene and array null mutation. After two rounds of diploid selection, the diploid strains are induced to go through meiosis and produce spores (via nutrient starvation). Sporulated haploids are then selected based on their genotype. Haploid double mutants bearing the query mutation and the array deletion, are analyzed based on strain spot size on the final plate relative to the array single mutant. SL genetic interactions are identified based on the non-drug condition, while SC interactions are identified in the presence of sub-lethal  20 doses of  DDAs. Comparing the area of the strain spots between the single and the double mutants, will demonstrate the growth rate differential of each single and double mutant strain under the specific environment. SC genetic interactions are those for which the double mutants demonstrates a greater growth defect in the presence of DDA compared to each single mutant or to the double mutant in the absence of a DDA.   These genetic interactions, identified by SGA, can indicate functional dependency and pathway redundancy in yeast that might be conserved in humans. This, in turn, can be exploited for developing new inhibitory drugs or guiding the modification of current medical therapies16. SGA technology enables an investigator to collect and analyze large sets of genetic interaction data77,78,80 that can be used to construct and map a large number of genetic interactions into networks, all in an efficient and robust way.     21  Figure ‎1-7. The structure of the SGA screen conducted in this project.  22 1.5 Thesis objective  The aim of my project was to identify novel genetic interactions, both negative (SL, SC) and positive (PS), with the frequently-mutated cohesin complex genes in yeast using SGA. Over the span of a decade, this technology has been used to screen genome-wide for SL genetic partners of all non-essential and essential yeast genes, under a defined laboratory condition81. Screening for SC is a powerful and an innovative concept with potential clinical relevance.  By using different conditions, including the presence of four distinct cytotoxic agents (MMS, CPT, bleomycin, and benomyl), SC interactions could be assessed for frequency and strength of the negative interaction. We also wanted to determine whether cohesin mutations affect the efficacy of these DDAs and whether any gene mutations could suppress the sensitivity of cohesin mutations to the DDAs (i.e. act as phenotypic suppressors, PS).  High-throughput methods such as SGA generate false positives and false negatives, and therefore, SGA data needs to be validated to identify bona fide genetic interactions. A new method, called ScanLag, was also assessed for validating initial genetic interaction hits, as captured in the SGA screens. The ScanLag method was compared to liquid growth curves, that have been used as the gold standard in the field for validating genetic interactions. ScanLag score data was also compared to the magnitude of genetic interactions from the preliminary SGA score data to understand the correlation between the two.   The SGA genomic technology can enable the identification of many SL and SC genetic interactions with a mutated cohesin gene, that can be tested for evolutionary conservation in mammalian cells. Creating maps of these interactions can further expand our knowledge about the biological importance of these genes and their potential role in cancer.   23 Any SL and SC genetic interactions that are validated in mammalian cells have the potential to provide improved individual cancer therapies for tumors carrying cohesin gene mutations, while PS genetic interactions can indicate potential molecular mechanism for chemotherapy resistance and potential targets for treating the cohesinopathies.   Figure ‎1-8. Thesis project flow.  24 Chapter 2: MATERIALS AND METHODS 2.1 Yeast strains  Strains used are BY4743 background82. All strains used in this project were confirmed by PCR and were checked for temperature-sensitive (ts) phenotype and auxotrophic markers. Temperature-sensitive cohesin subunit alleles, smc1-259 and scc1-73, and cohesin loader allele scc2-4, are marked with URA383 and were used as the query genes in the SGA screens, as an expansion of a previously published screen76. The full genotypes of the haploid query strains are: scc1-73::URA3/SCC1 or smc1-259::URA3/SMC1 or scc2-4:URA3/SCC2, ura3Δ0 his3Δ1 LYS2 can1Δ::STE2pr_pombeHIS5 lyp1Δ. All query strains are MATα‎haploids met15Δ0 orMET15 in addition to the genotype specified.   For validation, double mutants from the scc1-73 screen were retested, as the scc1-73 data captured many previously reported hits. In addition, the scc1-73 strain exhibits a better fitness than the smc1-259 strain, and we wished to follow-up with one of the core subunits of the complex. The scc1-double mutants, that were retested for validation, originated from SGA screen plates following re-sporulation and haploid selection of the original screen double heterozygous diploids. The double mutant spot on the haploid double-selection SGA plate, containing a mixed population, was streaked for single colonies on a -URA+G418 selective plate. One single colony was chosen for retesting and validation to ensure a cloned population of cells with a homogeneous genetic background. To generate MATa single mutants from the DDR-MA carrying both selective markers, double heterozygotes were generated through a separate SGA screen with a "WT query", marked with the NatMX cassette inserted into the URA3 gene. Following sporulation, single mutants representing the array deletion were picked from the final haploid double selection plate. This separate SGA procedure was needed since the  25 spots on the single haploid selection plates generated in the cohesin allele SGA screens contain a mixed population of single and double mutants (due to selection of only the array marker). SGA spots were streaked for single colonies on double-selection plates, from which one single colony was chosen for validation. Haploid double mutant strains were tested simultaneously with both single mutant strains (i.e. array deletion and query mutated parental strains). All strains, tested in both SGA and in the validation process, were grown at 25°C until the final experimental stage (to prevent long exposure to cellular stress), unless otherwise indicated.    2.1.1  DDR-MA  In order to focus on highly conserved genes and pathways associated with a CIN phenotype and relevant to cancer survival and progression (rather that the whole-genome array), we built the DNA-damage response mutant-array (DDR-MA), which is a collection of 310 haploid yeast strains, bearing knock-outs of non-essential genes which function or are mainly associated with the cellular process of DDR. These strains are marked with a KanMX cassette, which confers resistance to the drug G418. The overall genotype of the haploid gene deletion strains is MATa ura3Δ leu2Δ his3Δ lys2Δ met15Δ ykoΔ::KanMX.  The DDR-MA was arrayed in a 384 density format for the SGA screen. Many of these genes, when mutated, are known to cause a CIN phenotype71 and almost all genes have at least one human homolog or functional analog (Table ‎4-1). Array strains were either originated from the Deletion Mutant Array (DMA) Collection84 or the Heterozygous Diploid Collection85.  Before any screen could be conducted, quality control (QC) of the array strains used in the build of the new DDR-MA was performed. This was accomplished by a group effort that included making genomic DNA preparations of 313 array yeast strains, and confirming that the gene deletions were correct by using PCR to identify the specific deleted gene, and compiling a  26 collection of correct strains. The goal of this process was to verify the identity of the mutated strains on the array. Sixty-two strains were incorrect in the genome wide DMA (obtained from a commercial source) and were, therefore, needed to be re-created. Out of these 62 strains, 11 were created via direct DNA-mediated transformation, and the other 51 were generated from the heterozygous collection (via tetrad dissection) and were incorporated into the new array (Table ‎2-1).   Table ‎2-1. The physical map of the new DDR-MA     The new DDR-MA, was designed to also account for certain artifacts that could affect growth fitness such as the position of the strains on the plate and their growth characteristics (slow or fast growers). The new array, DDR-MAv1, now contains 289 DDR genes, 21 CIN genes, 63 WT strains (his3Δ and met15Δ) as control, and 4 empty spots as contamination control. Every strain, except WT, is represented once (i.e. one spot) on the array plate.1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24WT YER051WYDL155W YLR247C WT YLL019C YBR158W YLR085C WT YLR107WYER016WYML011C WT #REF! YJR047C YMR186W WT YMR216CYER070WYDR014W WT Blank Blank WTWT JHD1 CLB3 IRC20 WT KNS1 AMN1 ARP6 WT REX3 BIM1 RAD33 WT #REF! ANB1 HSC82 WT SKY1 RNR1 RAD61 WT Blank Blank WTWT/BlankYOR025WYDL013WYOL072W YDL042C YPL241C YDL070W YPL129W YER164W YDR378C YER169WYEL003W YER179W YEL061C YDR523C YGR171C YFL003C YGR184C YGR271WYCR008W YIL018W Blank WT BlankWT/Blank HST3 SLX5 THP1 SIR2 CIN2 BDF2 TAF14 CHD1 LSM6 RPH1 GIM4 DMC1 CIN8 SPS1 MSM1 MSH4 UBR1 SLH1 SAT4 RPL2B Blank WT BlankWT/BlankYMR284WYAR002W YNL330C YLL002WYOR073WYML061CYOR304WYML032C YOR351CYMR167WYOL006C YMR190C YOL043C YMR201C YOL068C YNL299W YPL240C YNL246W YPL181WYOR026WYPL096WYOR080W WTWT/Blank YKU70 NUP60 RPD3 RTT109 SGO1 PIF1 ISW2 RAD52 MEK1 MLH1 TOP1 SGS1 NTG2 RAD14 HST1 TRF5 HSP82 VPS75 CTI6 BUB3 PNG1 DIA2 WTWT YLR418C YJL013C YGL066W WT YKR024C YGL100W YDR289C WT YGR270WYML028WYML124C WT YJL115W YOR308C YJR063W WT YBR009C YOL012C YBR189W WT YDL082W YGR180C WTWT CDC73 MAD3 SGF73 WT DBP7 SEH1 RTT103 WT YTA7 TSA1 TUB3 WT ASF1 SNU66 RPA12 WT HHF1 HTZ1 RPS9B WT RPL13A RNR4 WTWT/BlankYBR186WYOL004W YBR195C YPL164C YBR223C YBR231C YBR228WYDR075WYDR121WYDR092WYDR379WYDR363WYEL056W YDR364C YAL019W YDR369C YGR129WYGR163W YHL022C YHR120WYHR086W YCL016C WT/BlankWT/Blank PCH2 SIN3 MSI1 MLH3 TDP1 SWC5 SLX1 PPH3 DPB4 UBC13 RGA2 ESC2 HAT2 CDC40 FUN30 XRS2 SYF2 GTR2 SPO11 MSH1 NAM8 DCC1 WT/BlankWT/BlankYGL240WYHR066W YJL006C YCL029C YBR026C YKL117W YAL040C YGR108WYHR154W YPR141C YLR210W YBL058W YJL176C YGL003C YDR176W YGR285C YPR119W YHR064C YGL043W YNL252C YJR104C YJR074WWT/BlankWT/Blank DOC1 SSF1 CTK2 BIK1 ETR1 SBA1 CLN3 CLB1 RTT107 KAR3 CLB4 SHP1 SWI3 CDH1 NGG1 ZUO1 CLB2 SSZ1 DST1 MRPL17 SOD1 MOG1 WT/BlankWT YHR200W YKL057C WT YGR056W YCR014C YLR357W WT YLR270W YLR135W YLR288C WT YLR320W YKL017C YGL194C WT YGL211W YKL025C YPL042C WT YPL008W YKL190W YPR164W WTWT RPN10 NUP120 WT RSC1 POL4 RSC2 WT DCS1 SLX4 MEC3 WT MMS22 HCS1 HOS2 WT NCS6 PAN3 SSN3 WT CHL1 CNB1 MMS1 WTWT/BlankYKR028WYNR023WYPL183W-AYKL114C YJL101C YBL019W YNR052CYML060WYER177WYMR106C YJR066W YOR005C YDL047W YIL132C YGL070C YHL006C YCR044C YDR078C YGR252WWT/Blank YPL256C WT/BlankWT/BlankWT/Blank SAP190 SNF12 RTC6 APN1 GSH1 APN2 POP2 OGG1 BMH1 YKU80 TOR1 DNL4 SIT4 CSM2 RPB9 SHU1 PER1 SHU2 GCN5 WT/Blank CLN2 WT/BlankWT/BlankWT/Blank YOR144CYDR363W-AYOR191W YBL088C YOR258WYGL033W YLR376C YGL086W YAL015C YGL087C YLR265C YNL201C YLR306W YKL213C YGL251C YOL090W YPL046C YER095W YBL046WYHL025WYGL090W YHR191C WT/BlankWT/Blank ELG1 SEM1 ULS1 TEL1 HNT3 HOP2 PSY3 MAD1 NTG1 MMS2 NEJ1 PSY2 UBC12 DOA1 HFM1 MSH2 ELC1 RAD51 PSY4 SNF6 LIF1 CTF8 WT/BlankWT YPL022W YBR245C YGR258C WT YEL037C YER045C YNL082W WT YCR092C YHR082C YDR097C WT YDR419WYMR156CYOR346W WT YPL167C YGR109C YGL058W WT YCR066W YOR156C WTWT RAD1 ISW1 RAD2 WT RAD23 ACA1 PMS1 WT MSH3 KSP1 MSH6 WT RAD30 TPP1 REV1 WT REV3 CLB6 RAD6 WT RAD18 NFI1 WTWT/Blank YGL094C YLR032WYNL218W YLR035C YKR056WYML102WYDR314C YPR018WYDR334WYPR101W YFR014C YJL047C YFR031C-AYGR188C YIR019C YJR043C YOL115W YDL101C YPR052C YDL116W YJL030W YNL021WWT/BlankWT/Blank PAN2 RAD5 MGS1 MLH2 TRM2 CAC2 RAD34 RLF2 SWR1 SNT309 CMK1 RTT101 RPL2A BUB1 MUC1 POL32 PAP2 DUN1 NHP6A NUP84 MAD2 HDA1 WT/BlankWT/Blank YIL139C YLR233C YMR224C YLR318WYNL250WYDR225W YOR033C YGL229C YGL163C YGR003WYDR076WYPL001WYDR004WYFR040W YJL092W YFR034C YER041W YBL003C YDR386W YBL067C YBR098W YNL230C WT/BlankWT/Blank REV7 EST1 MRE11 EST2 RAD50 HTA1 EXO1 SAP4 RAD54 CUL3 RAD55 HAT1 RAD57 SAP155 SRS2 PHO4 YEN1 HTA2 MUS81 UBP13 MMS4 ELA1 WT/BlankWT YJR082C YNL025C WT YBR073W YKR092C YIL112W WT YIL128W YML021CYNL107W WT YGR063C YDL230W YKL139W WT YBR289W YDR030C YGL115W WT YGL173C YBR274W YGL175C WTWT EAF6 SSN8 WT RDH54 SRP40 HOS4 WT MET18 UNG1 YAF9 WT SPT4 PTP1 CTK1 WT SNF5 RAD28 SNF4 WT KEM1 CHK1 SAE2 WTWT/Blank YDR217C YDR263C YER173WYIL009C-AYPL194W YMR080CYOR368WYOL087C YCL061C YER098WYNL273WYMR127CYMR048W YJL065C YDL200C YDL216C YPR135WYMR036C YKL113C YMR199WYHR031C YNL030WWT/BlankWT/Blank RAD9 DIN7 RAD24 EST3 DDC1 NAM7 RAD17 DUF1 MRC1 UBP9 TOF1 SAS2 CSM3 DLS1 MGT1 RRI1 CTF4 MIH1 RAD27 CLN1 RRM3 HHF2 WT/BlankWT/BlankYBR278W YER116C YCR065W YER142C YDL154W YER162C YBR010W YDL074C YIL153W YLR240W YIR002C YLR399C YNL068C YPL024W YBR272C YJR090C YMR137CYDR079C-AYNL138W YAL021C YPR023C YOR014WWT/BlankWT/Blank DPB3 SLX8 HCM1 MAG1 MSH5 RAD4 HHT1 BRE1 RRD1 VPS34 MPH1 BDF1 FKH2 RMI1 HSM3 GRR1 PSO2 TFB5 SRV2 CCR4 EAF3 RTS1 WT/BlankWT WT/Blank YNL031C WT YBR034C WT/BlankYNL136W WT YNL116WWT/BlankYLR394W WT YMR173WWT/BlankYER176W WT YOR386WWT/Blank YIL066C WT YBL002WWT/Blank YNL307C WTWT WT/Blank HHT2 WT HMT1 WT/Blank EAF7 WT DMA2 WT/Blank CST9 WT DDR48 WT/Blank ECM32 WT PHR1 WT/Blank RNR3 WT HTB2 WT/Blank MCK1 WT 27 2.2 SGA screen  Ts cohesin-mutated alleles, marked with URA376, were used as queries to perform three high-throughput SGA screens (Figure ‎2-1). These mutants were screened against the DDR-MA using a Singer ROTOR automated method79. During the SGA pinning steps, all strains were grown at 25oC until the final step, in which the smc1-259 strain was screened at 25oC while scc1-73 and scc2-4 were screened at 30oC, based on their growth defects, as described76.   Using the ROTOR robotic arm, each haploid‎MATα‎query‎strain‎(bearing a cohesin mutation) was pinned to mate with every MATa haploid array strain to generate heterozygous diploid cells. The mating plate was put in the 25oC incubator for 1 day. A single mating plate was split into three biological replicates for the next step, in which diploids were selected for the presence of both the URA3 (marking the cohesin query gene) and the KanMX (marking the array deletion) markers, by growing them on SC-URA+G418 media. Plates were put in the 25oC incubator for 1 day. This step was repeated twice to increase confidence (with second diploid selection plate being put in the 25oC incubator for 1.5 days). By pinning to sporulation media and inducing nutrient starvation, the cells on each of the three plates were triggered to sporulate and generate haploid spores. Sporulation plates were put in the 25oC incubator for 7 days. Following sporulation, cells on each plate were pinned onto 2 plates: single-selection (synthetic complete medium lacking histidine,‎lysine‎and‎arginine‎but‎containing‎50μg/ml‎thialysine‎and‎50μg/ml‎canavanine, and 200μg/ml‎G418)‎and‎double-selection (synthetic complete medium lacking uracil,‎histidine,‎lysine‎and‎arginine‎but‎containing‎50μg/ml‎thialysine‎and‎50μg/ml‎canavanine,‎and 200μg/ml‎G418)‎haploid‎media‎plates.‎In‎this‎step,‎two‎important‎selections‎are‎performed:‎1.Selecting against diploids by providing the recessive resistance to canavanine and thialysine through CAN1 and LYP1 KO genes. 2. Via HIS3 haploid selection marker, selecting for MATa  28 haploids to prevent selfing/mating between different mating type germinated spores that could result in new diploids. Single haploid selection media (selecting for ura+ cells) enables the growth of both the single mutants carrying a cohesin ts-allele::URA3, and the double mutants carrying a cohesin ts-allele::URA3 and the DDRΔ::‎KanMX allele. Double haploid selection media (selecting for ura+ and G418 resistant cells) enables only cohesin ts-allele::URA3‎DDRΔ::‎KanMX double mutants to grow. Plates were put in the 25oC incubator for 3 days. This step of haploid selection was repeated twice to increase confidence, where each plate was pinned onto 2 plates, containing the same-media (either single or double haploid selection). Plates were put in the 25oC incubator for 2 days. Final step involved each biological replicate non-drug (ND) plate being replicated onto 3 plates, representing technical replicates to increase statistical power. Plates were put at 25oC or 30oC, depending on the semi-permissive temperature for the particular query strain, for 1-2 days. Another copy of the haploid selection ND plate was pinned onto a similar haploid selection media (either single or double selection), containing 1 of 4 DNA-damaging agents (DDAs) in 1 of 2 different sub-lethal concentrations (high or low), to test sensitivity. Plates were put in the 25oC incubator for 1 day. Final step for the drug plates includes every DDA plate being replicated into 3 identical selection plates (technical replicates) to increase statistical power and incubated at semi-permissive temperature, either 250C or 300C, for 1-2 days.   All SGA plates were scanned after 48 hours from the incubation time point of the final steps, and analyzed using the bioinformatic tools Balony and R software.     29  Figure ‎2-1. SGA screen structure scheme. 30 2.2.1 SGA bioinformatic analysis  Using well established bioinformatics methodologies (Balony and R softwares), I quantified the effect of the genetic interaction on the growth of each double mutant yeast strain compared to the relevant single mutant strains. The analysis involved 3 steps: Balony, Excel (normalization), and the R software.   Balony86, a freely available software, follows a multi-step process, starting with the preparation of plate scans. The strain spots on each plate are identified and the pixel area of each is measured to provide a raw score. Scores are copied to a designated Excel template, ordered by the type of mutants on the plate (single mutants or double mutants), the number of biological replicate (out of 3), and the number of technical replicate (out of 3). Normalizing the raw score of each mutated strain relative to either WT (on haploid single selection plate) or cohesin query (on haploid double selection plate) strains, is then performed manually in Excel.   Using the raw scores acquired from Balony, 36 of the WT or query spot scores are averaged and compared to the raw score of each single or double mutant on the plate, respectively (by dividing the score of the mutant strain in the parental spots average). Though the Balony software can also be used for normalization,  it is not used because it will normalize the score of each spot relative to all the spots on the specific plate. This will not suit an array such as the DDR-MA, where many strains are slow growers, because it will lead to a distorted normalization.   Normalized values are further processed in Excel to generate an (e-c)/c score, by averaging the normalized scores of all 9 replicates (3 biological x 3 technical) of each strain, in the control (single mutants) and in the experimental (double mutants) plates, per condition (-/+ DDA). The e-c/c score is used to determine SGA initial hits. In a previous SGA screen, which  31 used the same cohesin-mutated queries76, initial hits were determined based on an e-c calculation (rather than e-c/c), using a whole-genome yeast array. A whole-genome array will mainly consist of strains that grow similar to WT, therefore, there is no need to take into account the fitness of the array strains. However, the smaller DDR-MA, consists of ~56% sensitive and slow growing strains (based on unpublished data), therefore, (e-c)/c calculation is more appropriate. This type of calculation enables the capture of more genetic interactions that otherwise would not have passed the cutoff. In addition, the (e-c)/c formula also enables comparison of double mutants between conditions without having to take into account the change in fitness of the query strain (see Figure ‎4-1). However, the limitation of this calculation is that it can also increase the false positive rate.   Using R, we compared the scores of all 9 replicates of the double mutants to all 9 replicates of the single mutants to check for variability and reproducibility. Via R, we generated the significance of the SGA results (i.e. P value). P value was determined by comparing the variability between replicates (technical and biological) and between the experiment and the control sets (detailed in Stoepel et al.- manuscript in preparation). R requires 3 files, including a data file with normalized pixel counts obtained from Balony, a plate information file containing information about whether each plate is a control or an experimental plate, and information about biological and technical replicates, and a script file. 2.3 DDAs For this project, four DDAs were used, as summarized in Table ‎2-2.    32  Table ‎2-2. DNA damaging agents used in the project.           High concentration was defined as the concentration that results in ~80% fitness of the wild-type strain. Low concentration was defined as the concentration that results in ~80% averaged fitness of the top 30 sensitive strains on the array for that condition, while having no effect on the wild-type strain. The three query strains are not hypersensitive under these concentrations.  2.4 Tecan liquid growth curves  Strains were grown to saturation at 25oC in 5ml YPD. The next day, 100µl of the saturated over-night culture was diluted into fresh 5ml YPD and was incubated at 25oC for ~3 hours. OD600 measurements were taken and each culture was diluted to an OD of 0.1 in a 96-well plate containing YPD or YPD+DDA to reach a final volume of 200µl. The plate was inserted into Tecan M200 plate reader, where OD600 measurements are taken at 30 minute intervals for 24 or 48 hours at 30oC. To analyze the growth curves, the area under the curve (AUC) was calculated for each curve and compared to WT strain for that specific condition. Agent name Action Standard drug stock Low SGA concentration High SGA concentration  Camptothecin (CPT) topoisomerase I inhibitor 5mg/mL DMSO 1 µg/mL 4 µg/mL Bleomycin  SSB and DSB via free radicals 10mg/mL H2O 2.5 µg/mL 10 µg/mL Methyl methanesulfonate (MMS) alkylating agent  100% 0.00001% 0.00005% Benomyl Destabilizing microtubules (mitotic inhibition) 25mg/mL DMSO 5 µg/mL (0.02%DMSO) 20µg/mL (0.08%DMSO)  33 Each strain was tested in triplicate. Each replicate generated an independent growth curve that was averaged to generate an average AUC value per strain and condition.  2.5 ScanLag solid growth curves  Using ScanLag software87, the program serves as a communication method between a designated computer and a scanner to periodically acquire images of the spot of cells growing on the plate. Scanning intervals were set to two hours over the period of 48 hours (overall, 25 scans including 0 time point). Preparation of strains is the same as for liquid growth curves using Tecan. Each tested strain was inoculated into 5ml YPD media and grown overnight at 250C. The next day, 100µl of the saturated culture was inoculated into a fresh 5ml YPD media and incubated for 3 hours at 250C. Each culture was measured for OD600 and was diluted into YPD-containing 96-well plate to reach an OD600 of 0.1. Diluted strains were then spotted in a volume of 4µl onto solid media plates (-/+ DDA) (Table ‎2-3) in triplicates, and were placed onto the scanner surface with lids on facing up, inside the 300C incubator for 48 hours.    Table ‎2-3. DDAs and concentrations used for ScanLag validation process.  DNA-damaging agent Concentration used  methyl methanesulfonate (MMS)  0.00005% Camptothecin (CPT) 4 µg/mL bleomycin 10 µg/mL, 3 µg/mL benomyl 20µg/mL   34 2.5.1 ImageJ analysis  The software ImageJ88, relying on the Time Series Analyzer plugin, was used to analyze the 2D time-lapse scans and to measure the increase in brightness over time. The intensity of each yeast spot of interest on the plate was monitored using a particle tracking tool, for which the dimensions stay consistent for the analysis of every strain spot and every plate in the course of a certain experiment. Periodic scores for each spot were obtained and analyzed in Excel to create growth curves over time and the AUC was calculated. Each strain was tested in triplicate. Each replicate generated an independent growth curve that was averaged to generate an average AUC value per strain and condition. For every plate, the scores of a blank spot (i.e. a spot on the plate that does not contain yeast) over time were obtained and subtracted from the scores of every yeast spot at that time point.  2.6 AUC calculation  AUC calculation was performed on normalized values, by reducing each periodic score from the first score of a specific strain replicate, averaging of every two sequential scores divided by 2 (averaging serves as multiplying by 2, which is the time difference between every score), summing all values per replicate and averaging final values per triplicate. The final averaged value of the WT strain, for every condition, was set to 1. The final averaged value of every other strain replicate was compared to that value. High values of AUC, compared to WT, are associated with increased growth and hence high strain fitness of the mutant strain, and vice versa.    35 Chapter 3: RESULTS  3.1 Systematic identification of cohesin genetic interactions   Synthetic Genetic Array (SGA) technology was used to screen temperature sensitive alleles of two cohesin core-subunits (smc1-259, scc1-73) and a cohesin loader subunit (scc2-4)76, against a curated array of 310 yeast strains carrying gene deletions affecting DNA damage response (DDR) genes, termed DDR-MA (MATERIALS AND M). The screens were done in the presence and absence of 4 distinct genotoxic agents, representing commonly used chemotherapeutic classes: Benomyl (microtubule inhibitor), Bleomycin (radiomimetic), Camptothecin (CPT) (TOP1 inhibitor), and Methyl Methanesulfonate (MMS) (alkylating agent) (MATERIALS AND M). Therefore, each SGA screen consisted of five conditions; four DDAs and one no-DDA control (termed no-DDA condition, ND). Thus, 4,650 potential genetic interactions (3 query genes X 310 array genes X 5 conditions) were generated and analyzed. SGA screens were performed as previously described (MATERIALS AND M).  Using Balony and R software (MATERIALS AND M), SGA data was sorted using two criteria to identify potential interactions. In concordance with the goal of identifying novel SC genetic interactions that could be translated to human cancers, the focus was on the magnitude of the genetic interaction. Potential interactions were defined as double mutants with an e-c/c score (experimental set value – control set value/ control set value) of‎≤-0.2 for negative genetic interactions‎(NI)‎with‎the‎cohesin‎mutation,‎or‎‎≥0.2‎for‎positive‎genetic‎interactions‎(PI)‎(Table ‎3-1). The experimental set represents the overall growth of the double mutant spots (normalized to the cohesin query), while the control set represents the overall growth of the array single mutant spots (normalized to WT), per condition. The formula uses the fitness of the two parental  36 strains (i.e. each single mutant) and calculates the growth of the double mutant colony relative to the predicted growth based on the growth of the combined two single mutations (i.e. the multiplicative model). In the analysis of a typical SGA screen, most double mutants would not exhibit an interaction. This is expressed in an e-c/c score of 0. However, we focused on the tails of the distribution that passed the cutoffs (Figure ‎3-1). These initial hits represent double mutants that due to the genetic interaction and the condition used, exhibit a reduced growth rate that is lower than expected (i.e. SL or SC, classified as negative genetic interactions) or an improved growth rate that is greater than expected (i.e. PS, classified as positive genetic interaction).    Figure ‎3-1. Example for fitness-defect distribution.  e (experiment) represents the fitness of the double mutant, c (control) represents the fitness of the single array mutant. e-c/c=0 means no interaction, e-c/c>o means positive interaction, e-c/c<0 means negative interaction. Each dot on the graph represents one double mutant yeast strain. This graph was based on the results of the scc1-73 screen using camptothecin (CPT) (analyzed after 48 hours). There are, overall, 59 negative interaction hits and 96 positive interaction hits.   -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 0 50 100 150 200 250 300 350 e-c/c value Number of Array Genes Fitness Distribution of Double Mutants e-c/c  37  Due to the high-quality and small size of the array, we used a less stringent cutoff value of ≤-0.2, relative to the cutoff chosen for a previous SL screen using these queries against a whole-genome array which was‎≤-0.376. This cutoff was chosen based on experience with previous SGA screens done in the Hieter lab using the DDR-MA (H. Li, personal communication).                  38   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  ACA1 0.02 0.66 -0.01 0.91 -0.18 0.01 -0.07 0.26 -0.09 0.30 N/A NA N/A NA N/A NA N/A NA N/A NA 0.12 0.12 0.10 0.19 0.12 0.18 0.07 0.33 0.04 0.71  AMN1 0.10 0.02 0.01 0.64 -0.10 0.04 0.05 0.51 0.13 0.03 0.01 0.91 -0.19 0.09 -0.04 0.29 0.09 0.41 -0.22 0.18 -0.11 0.09 -0.18 0.06 -0.06 0.73 -0.05 0.45 0.31 0.11  ANB1 -0.01 0.76 0.07 0.20 -0.03 0.61 0.00 1.00 0.09 0.27 0.01 0.75 -0.03 0.55 0.07 0.65 0.04 0.63 0.17 0.05 -0.09 0.05 -0.15 0.15 -0.08 0.54 -0.01 0.65 0.25 0.02  APN1 0.01 0.63 -0.35 0.00 -0.25 0.00 0.11 0.40 -0.06 0.67 0.11 0.03 -0.27 0.03 0.10 0.04 0.08 0.10 -0.07 0.47 -0.06 0.14 -0.20 0.04 -0.01 0.85 -0.03 0.40 -0.05 0.38  APN2 -0.02 0.61 -0.07 0.13 -0.23 0.01 -0.13 0.12 -0.20 0.04 0.12 0.12 -0.08 0.39 0.05 0.42 0.03 0.51 0.07 0.47 -0.02 0.36 -0.16 0.00 -0.04 0.12 -0.07 0.01 -0.11 0.07  ARP6 0.09 0.67 1.19 0.07 0.29 0.56 -0.02 0.95 0.09 0.90 -0.25 0.02 1.67 0.46 0.35 0.68 -0.42 0.09 -0.74 0.07 0.32 0.01 1.50 0.00 1.66 0.00 0.53 0.08 1.14 0.01  ASF1 0.10 0.17 0.63 0.51 -0.66 0.05 -0.05 0.43 -0.65 0.00 -0.26 0.40 0.14 0.86 0.34 0.79 -0.15 0.62 -0.74 0.04 -0.33 0.00 -0.12 0.66 -0.33 0.67 -0.25 0.00 -0.59 0.03  BDF1 0.16 0.53 0.73 0.16 0.40 0.45 -0.33 0.48 0.04 0.88 0.52 0.55 1.80 0.27 1.35 0.32 1.14 0.36 1.19 0.37 0.34 0.00 1.16 0.00 1.57 0.00 0.10 0.33 0.73 0.00  BDF2 -0.01 0.68 -0.03 0.24 -0.13 0.02 -0.04 0.42 0.06 0.60 -0.01 0.81 0.71 0.45 0.18 0.60 0.00 0.96 -0.03 0.89 -0.07 0.15 0.11 0.42 0.16 0.26 -0.08 0.19 0.19 0.05  BIK1 -0.03 0.57 0.00 0.99 0.08 0.80 -0.18 0.09 -0.07 0.33 -0.17 0.00 -0.08 0.08 -0.04 0.67 -0.33 0.01 -0.32 0.01 -0.15 0.02 -0.01 0.94 0.12 0.55 -0.38 0.00 0.09 0.69  BIM1 -0.33 0.01 0.22 0.32 -0.22 0.23 -0.53 0.13 -0.56 0.00 0.15 0.76 0.66 0.59 0.27 0.66 -0.63 0.02 0.26 0.81 -0.62 0.05 -0.55 0.03 -0.53 0.00 -0.92 0.00 -0.75 0.02  BMH1 -0.08 0.33 0.35 0.03 -0.21 0.01 -0.15 0.29 -0.06 0.85 0.20 0.06 1.17 0.00 0.18 0.06 0.02 0.84 1.15 0.01 0.07 0.58 0.48 0.21 0.30 0.55 -0.32 0.06 -0.35 0.40  BRE1 0.37 0.00 -0.20 0.20 -0.18 0.41 0.60 0.01 0.00 0.99 0.08 0.13 0.00 0.98 -0.77 0.00 0.42 0.00 -0.33 0.06 0.40 0.14 2.02 0.06 2.39 0.01 -0.18 0.01 0.80 0.19  BUB1 -0.22 0.01 -0.50 0.01 -0.02 0.81 2.89 0.01 -0.80 0.00 -0.17 0.68 0.85 0.55 0.11 0.87 -0.41 0.24 0.33 0.80 -0.76 0.00 -0.24 0.59 -0.11 0.87 -0.01 0.98 -0.88 0.00  BUB3 -0.65 0.00 -0.64 0.00 -0.36 0.00 0.49 0.62 -0.76 0.00 0.71 0.30 1.21 0.29 0.70 0.43 1.84 0.33 1.20 0.39 -0.25 0.41 -0.28 0.17 -0.57 0.01 4.33 0.32 -0.24 0.65  CAC2 0.09 0.03 0.19 0.63 -0.09 0.70 0.07 0.26 -0.04 0.79 -0.30 0.16 1.26 0.51 0.29 0.69 -0.28 0.01 0.06 0.92 -0.02 0.82 -0.06 0.67 0.08 0.64 -0.05 0.23 -0.14 0.61  CCR4 0.06 0.21 0.17 0.03 0.30 0.01 0.25 0.09 0.04 0.67 0.16 0.13 0.57 0.05 0.33 0.13 0.08 0.61 0.19 0.08 -0.42 0.13 -0.15 0.56 -0.15 0.72 -0.17 0.59 -0.39 0.25  CDC40 0.85 0.07 0.18 0.34 -0.36 0.02 0.16 0.72 -0.62 0.20 0.14 0.80 0.33 0.76 0.24 0.77 0.10 0.92 -1.00 0.37 0.07 0.93 2.01 0.36 1.77 0.33 0.64 0.17 0.18 0.89  CDC73 N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA 17.98 0.27 5.43 0.24 9.10 0.27 20.33 0.31 14.85 0.22  CDH1 0.18 0.51 0.21 0.67 0.45 0.49 -0.69 0.01 0.39 0.54 -0.06 0.91 0.43 0.75 0.34 0.63 -0.65 0.02 0.28 0.83 -0.57 0.00 -0.50 0.04 -0.52 0.04 -0.76 0.00 -0.78 0.00  CHD1 0.03 0.43 -0.08 0.09 -0.29 0.00 0.16 0.06 0.19 0.16 -0.21 0.02 -0.17 0.10 -0.40 0.00 0.00 0.99 -0.38 0.00 -0.32 0.00 -0.41 0.00 -0.44 0.00 -0.25 0.00 -0.15 0.09  CHK1 0.07 0.05 0.02 0.71 -0.11 0.28 0.04 0.53 -0.05 0.59 0.03 0.24 -0.02 0.69 -0.05 0.17 0.06 0.12 0.08 0.46 -0.15 0.03 -0.11 0.03 -0.22 0.01 -0.05 0.02 0.05 0.47  CHL1 0.37 0.06 1.19 0.03 1.07 0.02 0.07 0.91 0.81 0.08 -0.95 0.00 -1.00 0.00 -0.98 0.00 -1.00 0.00 -1.00 0.00 -0.23 0.59 0.00 1.00 0.53 0.50 -0.09 0.87 -0.26 0.73  CIN2 -0.09 0.04 -0.10 0.04 -0.12 0.00 2.23 0.03 -0.07 0.37 -0.24 0.00 -0.28 0.02 0.03 0.55 -0.45 0.55 -0.70 0.01 -0.32 0.00 -0.37 0.00 -0.29 0.00 -0.14 0.91 -0.28 0.01  CIN8 -0.52 0.00 -0.39 0.15 -0.27 0.02 -0.44 0.20 -0.54 0.05 -0.54 0.00 -0.24 0.01 -0.24 0.01 -0.43 0.00 -0.87 0.00 -0.41 0.05 -0.31 0.39 -0.10 0.74 -0.45 0.04 -0.61 0.05  CLB1 0.12 0.11 0.08 0.30 -0.14 0.36 0.08 0.12 -0.04 0.53 0.18 0.01 0.18 0.04 0.07 0.40 0.17 0.02 0.14 0.12 0.09 0.04 0.02 0.41 0.08 0.14 0.04 0.09 -0.19 0.14  CLB2 -0.18 0.00 -0.42 0.00 -0.79 0.00 -0.64 0.00 -0.97 0.00 -0.61 0.00 -0.65 0.00 -0.72 0.00 -0.60 0.00 -0.97 0.00 -0.31 0.02 -0.08 0.59 -0.24 0.26 -0.43 0.01 -0.58 0.03  CLB3 0.00 0.98 0.05 0.03 -0.16 0.08 0.09 0.30 0.09 0.18 -0.05 0.19 -0.24 0.00 -0.27 0.00 -0.20 0.01 -0.25 0.02 -0.12 0.36 -0.08 0.71 0.08 0.82 -0.20 0.06 0.22 0.09  CLB4 -0.03 0.55 -0.06 0.20 -0.23 0.01 -0.15 0.02 -0.13 0.22 0.12 0.11 -0.04 0.43 -0.01 0.87 0.06 0.34 -0.11 0.19 -0.05 0.20 -0.11 0.10 -0.19 0.00 -0.16 0.01 -0.39 0.03  CLB6 0.13 0.04 0.05 0.40 -0.16 0.01 0.05 0.31 0.02 0.70 0.30 0.00 0.40 0.00 0.31 0.01 0.17 0.04 0.39 0.01 0.03 0.65 0.10 0.20 0.21 0.11 0.14 0.01 -0.09 0.25   39   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  CLN1 0.02 0.64 0.11 0.39 0.20 0.29 0.11 0.07 0.03 0.60 0.10 0.06 0.21 0.02 0.09 0.07 0.06 0.34 0.52 0.03 -0.13 0.12 -0.05 0.41 -0.12 0.08 -0.07 0.09 0.09 0.12  CLN2 0.22 0.00 0.38 0.00 0.09 0.40 0.55 0.00 0.20 0.02 0.48 0.00 0.52 0.00 0.47 0.00 0.59 0.00 0.47 0.02 0.16 0.02 0.20 0.01 0.24 0.01 0.24 0.00 0.29 0.09  CLN3 0.11 0.26 0.18 0.44 -0.02 0.92 0.02 0.87 -0.14 0.15 -0.15 0.14 -0.27 0.04 -0.15 0.02 -0.09 0.22 -0.84 0.00 -0.05 0.35 -0.04 0.33 -0.06 0.02 -0.13 0.00 -0.28 0.09  CMK1 0.06 0.20 0.03 0.78 -0.07 0.72 -0.03 0.54 0.09 0.63 -0.01 0.89 0.11 0.45 -0.01 0.81 0.03 0.35 -0.20 0.30 -0.05 0.04 -0.07 0.13 -0.17 0.00 -0.10 0.02 -0.29 0.01  CNB1 -0.03 0.19 -0.18 0.01 0.05 0.77 0.12 0.09 -0.08 0.32 0.17 0.05 -0.17 0.00 0.04 0.18 0.11 0.02 -0.13 0.25 -0.10 0.09 -0.16 0.02 -0.02 0.65 -0.04 0.17 -0.03 0.71  CSM2 0.00 0.94 0.07 0.61 -0.10 0.35 -0.04 0.36 -0.09 0.58 0.10 0.08 -0.39 0.01 -0.23 0.00 0.05 0.38 -0.20 0.25 0.02 0.47 0.00 1.00 -0.04 0.83 -0.01 0.78 -0.09 0.47  CSM3 0.29 0.27 0.12 0.72 0.37 0.11 0.41 0.19 -0.02 0.96 -0.62 0.02 -0.70 0.02 -0.72 0.01 -0.87 0.00 -1.00 0.00 -0.74 0.00 -0.06 0.83 0.30 0.09 -0.11 0.55 -0.97 0.00  CST9 0.13 0.02 -0.17 0.00 -0.09 0.22 0.20 0.04 -0.05 0.39 -0.05 0.29 -0.25 0.01 -0.13 0.13 -0.12 0.03 -0.35 0.15 -0.07 0.40 -0.14 0.06 -0.20 0.06 -0.04 0.45 -0.64 0.00  CTF4 -0.32 0.02 3.16 0.12 6.60 0.00 -0.30 0.52 -0.55 0.08 -0.77 0.00 0.45 0.73 0.37 0.71 -0.89 0.00 -1.00 0.00 0.14 0.76 5.04 0.12 6.37 0.25 0.08 0.50 0.44 0.71  CTF8 -0.11 0.21 0.70 0.16 0.98 0.27 0.18 0.32 -0.45 0.10 0.01 0.98 1.74 0.44 1.00 0.54 -0.12 0.73 0.35 0.80 -0.16 0.40 0.06 0.76 -0.08 0.77 -0.08 0.35 -0.33 0.51  CTI6 -0.08 0.06 -0.09 0.07 -0.24 0.00 -0.31 0.00 -0.32 0.00 -0.07 0.21 0.13 0.41 -0.18 0.02 -0.05 0.31 -0.27 0.02 -0.01 0.93 0.15 0.21 -0.08 0.55 -0.04 0.40 0.06 0.64  CTK1 0.21 0.01 -0.02 0.81 -0.16 0.06 -0.35 0.05 -0.44 0.06 -0.20 0.46 0.94 0.43 0.48 0.51 -0.34 0.15 -0.80 0.12 -0.37 0.05 -0.20 0.20 -0.35 0.03 -0.54 0.00 -0.58 0.19  CTK2 0.45 0.00 0.55 0.02 0.12 0.04 -0.06 0.66 2.84 0.13 0.29 0.26 2.94 0.32 1.11 0.34 0.20 0.21 34.39 0.32 -0.01 0.90 0.34 0.08 0.43 0.01 0.05 0.78 0.65 0.37  CUL3 -0.05 0.18 -0.19 0.03 -0.16 0.00 0.08 0.02 -0.07 0.24 0.10 0.14 0.18 0.19 0.17 0.01 0.13 0.06 0.03 0.84 0.02 0.73 -0.01 0.94 -0.07 0.19 -0.03 0.09 -0.17 0.07  DBP7 -0.02 0.85 -0.02 0.93 -0.09 0.69 -0.32 0.01 -0.36 0.19 -0.12 0.31 0.79 0.32 0.62 0.40 -0.25 0.01 -0.02 0.98 -0.32 0.01 -0.28 0.01 -0.23 0.00 -0.41 0.00 -0.54 0.04  DCC1 0.00 0.99 0.36 0.16 0.58 0.02 0.14 0.12 0.31 0.16 -0.01 0.91 0.80 0.01 0.42 0.01 -0.07 0.23 0.34 0.01 -0.40 0.22 -0.02 0.96 0.14 0.72 -0.22 0.60 0.43 0.58  DCS1 -0.07 0.15 -0.20 0.01 -0.28 0.00 -0.14 0.05 -0.36 0.01 0.13 0.11 0.06 0.33 0.15 0.10 0.10 0.11 0.21 0.05 0.01 0.76 0.00 0.99 0.11 0.04 0.03 0.19 -0.23 0.07  DDC1 0.03 0.22 -0.52 0.00 -0.65 0.01 -0.09 0.26 -0.26 0.09 0.09 0.11 0.10 0.39 -0.72 0.00 0.10 0.11 -0.37 0.21 0.04 0.61 0.00 0.99 2.69 0.40 0.02 0.52 0.04 0.80  DDR48 0.15 0.01 -0.12 0.08 0.04 0.80 0.19 0.05 -0.01 0.87 0.02 0.64 -0.11 0.22 -0.05 0.44 -0.01 0.84 0.11 0.68 -0.10 0.10 -0.15 0.06 -0.25 0.07 -0.10 0.02 -0.29 0.01  DIA2 0.21 0.04 3.44 0.28 1.00 0.41 -0.03 0.90 -0.10 0.86 -0.24 0.00 -0.58 0.14 -0.58 0.03 -0.17 0.16 -0.90 0.12 -0.19 0.25 0.46 0.60 0.02 0.98 -0.34 0.02 -0.16 0.62  DIN7 0.00 0.97 -0.10 0.03 -0.13 0.05 -0.06 0.11 0.24 0.05 0.09 0.12 -0.01 0.85 0.18 0.12 0.07 0.45 0.01 0.87 -0.03 0.77 -0.10 0.00 -0.12 0.02 -0.06 0.13 -0.13 0.05  DLS1 0.08 0.07 0.22 0.01 0.07 0.39 0.14 0.01 0.06 0.53 0.06 0.02 0.32 0.00 0.21 0.00 0.26 0.01 0.34 0.00 0.02 0.71 -0.01 0.67 0.08 0.25 0.02 0.71 0.10 0.33  DMA2 0.03 0.21 -0.10 0.02 -0.11 0.13 0.13 0.24 0.10 0.10 -0.02 0.70 -0.10 0.04 -0.07 0.39 -0.01 0.85 0.18 0.15 -0.09 0.18 -0.11 0.11 -0.22 0.02 -0.05 0.08 -0.02 0.84  DMC1 0.12 0.02 0.09 0.22 -0.11 0.05 0.21 0.03 -0.07 0.13 0.07 0.05 -0.04 0.34 0.02 0.73 0.15 0.09 -0.25 0.08 0.05 0.46 0.02 0.89 0.13 0.32 0.08 0.03 0.09 0.60  DNL4 0.03 0.61 -0.09 0.14 -0.09 0.43 0.00 0.94 -0.17 0.03 0.05 0.44 0.08 0.03 0.05 0.26 0.09 0.13 -0.05 0.57 0.02 0.32 0.06 0.49 0.13 0.49 0.01 0.58 -0.02 0.89  DOA1 0.11 0.02 -0.67 0.02 -0.60 0.01 -0.66 0.00 -0.58 0.01 -0.22 0.09 -0.46 0.20 -0.70 0.00 -0.50 0.00 -0.87 0.00 0.23 0.00 -0.13 0.53 -0.36 0.00 -0.20 0.04 -0.56 0.08  DOC1 0.08 0.09 0.04 0.59 0.01 0.98 -0.27 0.50 -0.44 0.01 -0.38 0.03 -0.33 0.16 -0.52 0.09 -0.40 0.01 -0.98 0.00 -0.61 0.00 -0.38 0.11 0.70 0.64 -0.61 0.00 -0.33 0.35  DPB3 0.12 0.00 0.00 0.92 -0.02 0.63 0.02 0.73 0.05 0.41 -0.08 0.07 -0.07 0.21 0.00 0.94 -0.05 0.34 -0.22 0.03 -0.05 0.55 -0.24 0.00 -0.21 0.00 -0.13 0.01 -0.09 0.19  DPB4 0.15 0.02 0.31 0.00 0.01 0.91 0.26 0.00 0.01 0.96 0.09 0.28 0.41 0.02 0.20 0.03 0.20 0.01 0.20 0.48 -0.01 0.75 0.01 0.82 -0.02 0.45 -0.05 0.08 -0.32 0.04  DST1 -0.17 0.01 -0.18 0.05 -0.05 0.79 -0.04 0.78 -0.23 0.11 0.15 0.09 0.52 0.22 0.49 0.03 0.15 0.10 -0.25 0.06 -0.10 0.32 -0.02 0.76 0.00 0.99 -0.13 0.06 -0.15 0.28   40   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  DUF1 0.16 0.03 0.09 0.11 -0.05 0.70 0.26 0.04 0.06 0.40 0.10 0.04 0.06 0.21 -0.02 0.63 0.07 0.47 0.08 0.57 0.10 0.01 0.01 0.72 0.00 0.98 0.15 0.05 -0.16 0.06  DUN1 0.26 0.06 0.12 0.72 0.41 0.46 0.30 0.01 -0.06 0.75 0.20 0.30 0.31 0.49 0.21 0.56 0.30 0.00 -0.27 0.39 0.04 0.62 -0.23 0.00 -0.27 0.00 0.11 0.01 -0.37 0.32  EAF3 0.21 0.01 0.14 0.22 0.36 0.06 0.28 0.03 -0.05 0.58 0.00 0.99 0.25 0.03 0.12 0.09 0.14 0.14 -0.16 0.07 0.07 0.61 0.13 0.39 0.42 0.07 0.03 0.52 0.29 0.01  EAF6 0.20 0.01 0.19 0.01 0.02 0.51 0.42 0.01 0.23 0.01 -0.02 0.69 -0.21 0.03 0.04 0.30 0.12 0.06 -0.27 0.25 0.06 0.38 0.04 0.51 0.12 0.02 0.16 0.01 -0.08 0.47  EAF7 0.11 0.11 0.19 0.03 0.19 0.07 0.36 0.00 0.00 0.95 -0.09 0.10 -0.05 0.22 0.09 0.28 -0.01 0.91 -0.66 0.08 -0.08 0.44 0.04 0.38 0.24 0.01 0.09 0.09 -0.02 0.90  ECM32 0.00 0.98 0.05 0.72 0.28 0.24 0.01 0.79 0.09 0.26 -0.05 0.19 -0.12 0.02 -0.16 0.00 -0.10 0.11 0.32 0.07 -0.10 0.20 -0.10 0.45 0.07 0.83 -0.13 0.04 -0.16 0.11  ELA1 0.03 0.20 0.02 0.52 -0.06 0.31 0.01 0.81 -0.10 0.18 0.04 0.09 -0.01 0.86 -0.02 0.37 0.01 0.76 0.05 0.48 -0.06 0.13 -0.09 0.14 -0.15 0.10 -0.09 0.03 0.07 0.45  ELC1 0.11 0.01 0.09 0.04 -0.10 0.05 0.08 0.37 -0.25 0.01 0.03 0.57 0.27 0.03 0.10 0.15 0.03 0.78 -0.20 0.15 0.06 0.23 0.03 0.69 0.02 0.51 0.06 0.08 -0.12 0.36  ELG1 0.00 0.99 -0.07 0.68 -0.11 0.69 -0.13 0.19 -0.30 0.28 0.48 0.25 2.02 0.14 0.90 0.29 -0.10 0.65 1.32 0.30 -0.33 0.00 -0.44 0.00 -0.42 0.00 -0.38 0.00 -0.59 0.00  ESC2 0.12 0.17 0.38 0.26 -0.31 0.01 -0.07 0.39 -0.45 0.00 0.18 0.17 0.83 0.00 0.38 0.00 0.10 0.38 -0.76 0.06 0.17 0.00 0.80 0.03 0.17 0.23 0.08 0.04 0.01 0.99  EST1 0.04 0.42 -0.09 0.10 0.00 0.98 0.02 0.86 0.05 0.48 0.17 0.25 0.28 0.43 0.48 0.33 0.04 0.39 0.23 0.56 0.01 0.77 -0.09 0.09 -0.01 0.77 0.03 0.06 -0.12 0.06  EST2 0.13 0.14 0.09 0.47 0.28 0.27 0.15 0.31 0.12 0.07 0.14 0.03 0.02 0.72 0.07 0.29 0.15 0.01 -0.08 0.51 -0.01 0.77 -0.09 0.01 -0.10 0.03 -0.01 0.45 -0.33 0.01  EST3 0.09 0.02 -0.01 0.92 0.00 0.98 0.03 0.74 0.11 0.30 0.09 0.01 0.07 0.29 0.25 0.00 0.15 0.09 0.05 0.71 0.04 0.75 0.11 0.51 0.23 0.45 0.03 0.42 0.13 0.29  ETR1 -0.04 0.75 -0.18 0.32 -0.05 0.87 0.12 0.67 -0.24 0.26 0.01 0.94 -0.18 0.21 0.06 0.27 0.24 0.01 -0.68 0.00 0.01 0.88 -0.15 0.02 0.22 0.11 -0.10 0.00 -0.20 0.03  EXO1 0.01 0.65 -0.09 0.11 -0.44 0.00 0.00 0.98 -0.12 0.06 0.06 0.15 0.05 0.62 -0.24 0.00 0.06 0.33 0.02 0.81 0.04 0.14 0.06 0.15 -0.22 0.00 0.01 0.86 -0.02 0.91  FKH2 0.15 0.01 -0.24 0.01 -0.52 0.00 0.27 0.00 -0.40 0.01 -0.03 0.92 0.06 0.92 -0.30 0.49 -0.18 0.01 -0.23 0.65 -0.01 0.89 0.16 0.59 0.17 0.64 0.07 0.01 -0.41 0.09  FUN30 0.01 0.77 -0.16 0.00 -0.27 0.00 0.01 0.91 -0.22 0.01 -0.12 0.04 -0.20 0.16 -0.30 0.03 -0.16 0.01 -0.58 0.00 0.07 0.25 -0.04 0.66 -0.11 0.02 0.00 0.88 -0.14 0.08  GCN5 0.33 0.02 1.18 0.01 0.91 0.01 -0.29 0.44 0.68 0.02 0.40 0.50 1.68 0.25 0.96 0.25 0.20 0.76 0.46 0.57 -0.02 0.91 0.31 0.56 0.87 0.09 0.37 0.03 -0.20 0.69  GIM4 -0.25 0.00 -0.12 0.28 -0.05 0.87 0.92 0.30 -0.53 0.04 -0.45 0.00 -0.23 0.05 -0.35 0.06 0.23 0.65 -0.75 0.00 -0.47 0.00 -0.34 0.00 -0.41 0.00 -0.69 0.02 -0.64 0.00  GRR1 0.04 0.94 -0.34 0.48 -0.34 0.43 0.33 0.71 -0.65 0.30 2.47 0.48 5.62 0.44 2.14 0.49 3.66 0.48 -1.00 0.37 -0.20 0.61 0.32 0.64 0.41 0.42 -0.06 0.91 N/A 0.37  GSH1 0.02 0.56 0.06 0.57 0.78 0.01 -0.06 0.78 -0.28 0.16 0.09 0.70 -0.39 0.36 0.21 0.58 -0.41 0.74 -1.00 0.14 0.18 0.36 0.93 0.49 1.32 0.22 0.14 0.64 1.06 0.10  GTR2 0.01 0.70 0.36 0.00 0.02 0.91 0.10 0.21 0.05 0.48 0.36 0.00 0.33 0.00 0.29 0.00 0.24 0.01 0.31 0.01 0.07 0.38 0.12 0.07 0.30 0.00 0.10 0.12 0.52 0.01  HAT1 0.05 0.36 0.07 0.55 0.16 0.39 -0.02 0.57 0.10 0.55 0.18 0.01 0.13 0.05 0.07 0.24 0.11 0.11 0.12 0.19 0.11 0.01 0.11 0.19 0.08 0.21 0.02 0.46 -0.07 0.35  HAT2 0.02 0.60 -0.05 0.13 -0.14 0.02 0.00 0.90 -0.03 0.71 0.17 0.10 0.06 0.46 0.09 0.37 0.09 0.17 0.16 0.16 0.03 0.31 0.06 0.56 0.12 0.44 0.01 0.91 -0.07 0.45  HCM1 -0.06 0.33 0.06 0.28 0.04 0.46 0.05 0.22 0.00 0.95 -0.05 0.69 0.52 0.39 0.51 0.17 0.01 0.91 0.06 0.93 -0.12 0.17 0.02 0.82 0.15 0.03 0.01 0.90 -0.31 0.04  HCS1 -0.01 0.70 -0.07 0.20 -0.22 0.02 0.01 0.87 -0.20 0.01 0.14 0.06 0.10 0.10 0.04 0.66 0.05 0.40 0.09 0.48 0.05 0.16 -0.05 0.36 -0.09 0.02 -0.05 0.01 -0.25 0.05  HDA1 0.04 0.33 0.16 0.34 -0.18 0.24 0.15 0.04 -0.03 0.50 -0.06 0.44 0.28 0.52 -0.17 0.16 -0.06 0.18 -0.26 0.25 -0.13 0.03 -0.32 0.00 -0.35 0.00 -0.22 0.00 -0.03 0.70  HFM1 0.06 0.57 -0.04 0.66 0.00 0.99 -0.01 0.93 -0.11 0.20 -0.03 0.60 -0.04 0.40 0.05 0.45 0.01 0.87 -0.19 0.23 -0.03 0.20 -0.05 0.47 -0.12 0.11 -0.09 0.05 -0.25 0.09  HHF1 -0.06 0.23 -0.01 0.72 -0.04 0.77 -0.08 0.17 -0.08 0.19 -0.06 0.61 0.52 0.38 0.21 0.61 -0.06 0.22 -0.36 0.24 0.02 0.88 0.16 0.17 0.05 0.66 -0.10 0.08 -0.17 0.03  HHF2 0.05 0.04 0.04 0.50 0.04 0.57 0.25 0.09 -0.04 0.37 0.11 0.09 0.24 0.18 0.27 0.07 0.21 0.04 0.26 0.10 -0.06 0.27 -0.07 0.26 -0.13 0.08 -0.01 0.85 0.17 0.05   41   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  HHT1 0.05 0.23 0.02 0.80 0.00 0.99 0.09 0.29 0.06 0.20 -0.18 0.00 -0.07 0.24 -0.16 0.00 -0.13 0.02 -0.38 0.00 -0.07 0.04 0.04 0.77 0.02 0.84 -0.06 0.04 -0.02 0.84  HHT2 -0.02 0.49 -0.03 0.65 -0.01 0.95 -0.09 0.04 0.15 0.07 -0.11 0.00 -0.07 0.19 -0.09 0.04 -0.07 0.12 0.12 0.22 -0.14 0.10 -0.29 0.01 -0.22 0.00 -0.08 0.07 -0.07 0.48  HMT1 0.12 0.12 -0.01 0.72 -0.04 0.79 0.18 0.02 0.16 0.55 0.02 0.70 -0.06 0.26 -0.04 0.47 -0.01 0.89 -0.17 0.55 -0.19 0.04 -0.25 0.02 -0.03 0.85 -0.12 0.05 -0.46 0.00  HNT3 0.79 0.01 0.76 0.12 1.04 0.11 0.72 0.63 1.87 0.17 1.11 0.41 2.24 0.26 2.15 0.27 0.29 0.76 1.95 0.30 0.45 0.02 1.25 0.00 1.60 0.00 0.59 0.02 0.82 0.00  HOP2 0.39 0.14 0.35 0.47 1.11 0.12 2.86 0.01 0.75 0.41 -0.71 0.02 -0.48 0.28 -0.29 0.42 -0.38 0.34 -0.98 0.00 0.41 0.08 1.00 0.01 1.33 0.03 0.42 0.18 0.60 0.10  HOS2 -0.03 0.31 -0.32 0.02 -0.26 0.01 -0.32 0.00 -0.50 0.01 0.27 0.02 -0.08 0.65 -0.04 0.37 0.01 0.87 -0.82 0.00 0.06 0.39 -0.19 0.27 -0.24 0.02 -0.26 0.01 -0.14 0.41  HOS4 0.09 0.16 0.02 0.73 0.00 0.98 0.19 0.06 0.17 0.07 0.32 0.00 0.44 0.01 0.37 0.00 0.29 0.00 0.45 0.13 0.18 0.01 0.15 0.00 0.17 0.00 0.07 0.04 -0.03 0.77  HSC82 0.05 0.37 0.12 0.51 0.18 0.57 0.21 0.00 0.09 0.74 0.08 0.64 0.36 0.58 0.51 0.42 0.06 0.42 0.51 0.53 -0.03 0.39 -0.20 0.23 0.00 1.00 0.14 0.13 0.39 0.02  HSM3 0.09 0.17 0.01 0.76 -0.03 0.59 -0.03 0.66 -0.06 0.52 0.08 0.37 -0.02 0.87 0.01 0.85 0.11 0.16 0.06 0.57 -0.01 0.78 -0.08 0.06 0.08 0.13 0.11 0.00 0.17 0.19  HSP82 -0.06 0.09 0.09 0.09 0.10 0.71 -0.02 0.76 -0.03 0.43 0.16 0.26 0.85 0.28 0.48 0.24 0.15 0.09 0.69 0.28 -0.01 0.92 0.20 0.21 0.37 0.34 0.03 0.42 0.38 0.10  HST1 -0.01 0.73 -0.16 0.01 -0.24 0.00 -0.16 0.06 0.01 0.68 0.04 0.08 -0.07 0.25 0.02 0.55 0.03 0.54 -0.13 0.02 -0.09 0.29 -0.14 0.13 -0.23 0.01 -0.15 0.02 -0.11 0.10  HST3 0.01 0.86 -0.13 0.10 -0.20 0.02 0.23 0.06 -0.35 0.01 -0.25 0.04 -0.33 0.00 -0.32 0.00 -0.14 0.01 -0.77 0.04 -0.36 0.00 -0.44 0.00 -0.33 0.01 -0.28 0.01 -0.26 0.09  HTA1 0.24 0.02 0.00 0.94 -0.18 0.02 0.07 0.41 0.20 0.26 -0.21 0.04 -0.28 0.01 -0.16 0.01 -0.06 0.31 -0.56 0.10 -0.03 0.34 -0.09 0.24 -0.26 0.00 -0.08 0.02 0.01 0.96  HTA2 0.13 0.03 0.01 0.88 0.06 0.74 -0.05 0.28 0.00 0.94 0.10 0.06 0.07 0.18 -0.04 0.12 0.09 0.16 -0.02 0.92 0.04 0.45 0.28 0.22 0.52 0.07 -0.07 0.13 -0.14 0.09  HTB2 0.04 0.16 0.03 0.56 -0.05 0.20 0.10 0.08 0.07 0.09 -0.15 0.00 -0.13 0.12 -0.17 0.02 -0.11 0.04 0.09 0.37 -0.09 0.09 -0.19 0.07 -0.24 0.03 -0.13 0.01 -0.04 0.65  HTZ1 -0.10 0.02 -0.03 0.81 -0.35 0.00 -0.45 0.05 -0.61 0.01 -0.28 0.00 -0.44 0.02 -0.50 0.00 -0.58 0.01 -0.91 0.03 -0.22 0.03 -0.18 0.42 0.15 0.50 -0.22 0.12 0.01 0.98  IRC20 -0.07 0.04 -0.04 0.20 -0.11 0.06 0.06 0.39 0.12 0.02 -0.05 0.47 0.09 0.68 0.01 0.93 -0.10 0.03 0.15 0.31 -0.15 0.10 -0.29 0.01 -0.28 0.01 -0.20 0.04 0.02 0.68  ISW1 0.01 0.79 -0.15 0.09 0.01 0.95 0.02 0.77 0.13 0.04 0.02 0.84 -0.10 0.55 0.05 0.80 0.00 0.99 0.29 0.26 -0.20 0.03 -0.31 0.00 -0.29 0.00 -0.28 0.00 -0.41 0.04  ISW2 0.12 0.02 0.34 0.00 -0.01 0.86 0.42 0.00 -0.05 0.53 -0.05 0.17 0.01 0.92 0.13 0.06 0.26 0.05 -0.26 0.20 -0.08 0.04 0.35 0.21 0.41 0.03 0.19 0.01 0.03 0.83  JHD1 0.00 0.95 0.02 0.44 -0.11 0.07 0.07 0.33 0.17 0.02 -0.10 0.01 -0.18 0.00 -0.13 0.12 -0.15 0.03 -0.02 0.87 -0.05 0.54 -0.04 0.87 0.03 0.92 -0.05 0.74 0.44 0.03  KAR3 -0.36 0.11 -0.54 0.03 -0.41 0.00 -0.65 0.03 -0.75 0.00 -0.50 0.04 -0.64 0.00 -0.61 0.00 -0.56 0.00 -1.00 0.00 -0.89 0.00 -0.21 0.62 0.12 0.85 -0.74 0.00 -0.99 0.00  KEM1 0.01 0.94 0.04 0.84 -0.04 0.73 -0.02 0.95 -0.60 0.18 -0.25 0.07 0.06 0.63 0.00 0.99 -0.37 0.00 -0.75 0.10 0.91 0.34 1.47 0.23 1.65 0.25 -0.22 0.60 3.07 0.23  KNS1 -0.01 0.61 0.00 0.96 -0.15 0.02 -0.11 0.17 0.08 0.11 -0.08 0.14 0.03 0.42 -0.24 0.00 -0.01 0.91 0.19 0.44 -0.13 0.15 0.04 0.69 -0.08 0.67 -0.21 0.03 0.18 0.05  KSP1 0.17 0.01 0.02 0.64 -0.10 0.16 0.21 0.00 0.10 0.34 0.13 0.04 0.20 0.17 0.02 0.73 0.19 0.00 -0.15 0.02 0.07 0.02 0.01 0.85 -0.06 0.60 -0.01 0.64 -0.12 0.19  LIF1 -0.01 0.87 0.03 0.56 -0.04 0.81 -0.01 0.90 -0.12 0.20 0.03 0.45 -0.07 0.01 -0.05 0.24 -0.01 0.67 -0.14 0.09 0.00 0.93 -0.10 0.16 -0.16 0.04 -0.10 0.01 -0.11 0.26  LSM6 0.02 0.72 -0.08 0.19 0.03 0.89 0.01 0.96 0.19 0.18 -0.21 0.00 -0.43 0.00 -0.36 0.00 -0.01 0.89 -0.70 0.00 -0.36 0.11 -0.11 0.86 0.15 0.85 -0.46 0.00 0.23 0.65  MAD1 -0.11 0.15 -0.08 0.42 0.34 0.31 -0.12 0.62 -0.61 0.02 0.24 0.00 0.41 0.01 0.23 0.06 -0.17 0.16 0.62 0.01 -0.31 0.00 0.02 0.88 -0.06 0.78 -0.54 0.00 -0.36 0.01  MAD2 -0.14 0.01 -0.07 0.12 -0.20 0.01 0.08 0.67 -0.64 0.00 0.17 0.00 0.31 0.20 0.15 0.35 -0.28 0.04 -0.07 0.72 -0.14 0.45 0.11 0.68 0.30 0.62 -0.33 0.03 -0.34 0.04  MAD3 -0.13 0.01 -0.11 0.02 -0.23 0.01 0.14 0.13 -0.18 0.01 0.21 0.01 0.17 0.02 0.19 0.00 -0.14 0.02 0.23 0.08 -0.28 0.01 -0.17 0.04 -0.22 0.00 -0.35 0.00 -0.27 0.00  MAG1 0.01 0.85 -0.07 0.74 -0.10 0.03 -0.05 0.25 0.14 0.05 0.09 0.12 2.12 0.25 0.07 0.02 0.03 0.56 0.05 0.68 -0.03 0.65 0.42 0.05 -0.11 0.01 -0.06 0.08 -0.05 0.21   42   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  MCK1 0.00 0.97 -0.08 0.66 -0.66 0.00 -0.04 0.38 -0.22 0.14 -0.10 0.17 -0.36 0.00 -0.84 0.00 -0.25 0.01 -0.64 0.12 -0.18 0.03 -0.35 0.01 -0.59 0.00 -0.19 0.01 -0.25 0.22  MEC3 0.06 0.21 -0.15 0.41 0.25 0.79 -0.07 0.25 -0.27 0.25 0.28 0.03 0.09 0.39 -0.47 0.02 0.16 0.02 -0.62 0.13 0.07 0.08 -0.12 0.13 -0.60 0.12 0.01 0.47 0.44 0.44  MEK1 0.04 0.35 0.03 0.53 -0.15 0.00 0.04 0.17 0.08 0.56 0.19 0.06 0.01 0.85 0.35 0.29 0.26 0.21 0.29 0.47 0.05 0.26 -0.13 0.08 -0.08 0.18 -0.07 0.06 -0.19 0.27  MET18 0.04 0.38 -0.02 0.71 -0.09 0.01 0.27 0.08 0.09 0.33 0.10 0.17 0.47 0.20 0.51 0.00 0.18 0.20 0.37 0.43 0.26 0.04 0.04 0.67 0.14 0.10 0.12 0.43 0.11 0.46  MGS1 0.11 0.04 0.00 0.96 -0.08 0.52 0.08 0.11 0.09 0.22 0.20 0.00 0.27 0.03 0.27 0.02 0.21 0.00 0.39 0.05 0.10 0.13 0.08 0.16 0.07 0.02 0.12 0.00 0.10 0.13  MGT1 0.06 0.11 -0.16 0.00 -0.11 0.02 -0.02 0.60 -0.09 0.18 0.09 0.05 0.28 0.54 0.15 0.26 0.08 0.01 0.22 0.27 -0.03 0.23 -0.08 0.24 -0.03 0.62 -0.01 0.64 -0.08 0.11  MIH1 0.20 0.01 -0.02 0.65 -0.20 0.00 0.12 0.04 -0.11 0.28 -0.01 0.88 -0.29 0.00 -0.22 0.01 -0.01 0.82 -0.50 0.05 -0.10 0.03 -0.01 0.70 -0.11 0.10 0.01 0.88 -0.20 0.06  MLH1 0.32 0.07 0.72 0.09 0.55 0.24 -0.01 0.93 0.09 0.79 0.64 0.05 2.77 0.13 1.49 0.07 0.72 0.05 6.08 0.20 -0.10 0.28 0.21 0.31 0.39 0.04 0.10 0.06 -0.19 0.55  MLH2 0.01 0.69 0.03 0.58 0.06 0.62 0.02 0.42 0.10 0.25 0.10 0.06 0.03 0.72 0.10 0.18 0.09 0.08 -0.02 0.91 0.03 0.55 -0.06 0.27 -0.03 0.46 -0.04 0.21 -0.17 0.04  MLH3 -0.02 0.56 0.00 0.99 -0.03 0.88 0.00 0.90 -0.01 0.80 0.05 0.32 0.12 0.02 0.01 0.74 0.09 0.06 -0.06 0.76 -0.01 0.89 -0.06 0.18 -0.05 0.03 -0.06 0.09 -0.13 0.05  MMS1 0.04 0.38 0.60 0.07 -0.45 0.28 0.29 0.01 -0.61 0.01 -0.23 0.04 0.43 0.03 -0.41 0.09 -0.23 0.05 -0.65 0.00 -0.23 0.04 0.27 0.19 0.12 0.81 -0.15 0.00 -0.42 0.06  MMS2 -0.04 0.19 0.85 0.55 0.00 0.99 0.08 0.37 0.03 0.94 0.02 0.78 0.44 0.33 0.07 0.40 0.17 0.00 -0.32 0.02 -0.08 0.12 -0.26 0.39 -0.12 0.03 -0.11 0.01 -0.44 0.01  MMS22 -0.04 0.43 -0.52 0.44 -0.74 0.34 -0.14 0.05 -0.80 0.00 0.38 0.58 20.64 0.36 26.55 0.38 -0.05 0.72 3.86 0.45 -0.09 0.44 -0.76 0.02 -0.98 0.00 -0.21 0.01 0.25 0.83  MMS4 0.06 0.22 0.13 0.34 -0.70 0.01 0.12 0.37 -0.02 0.87 0.06 0.12 0.78 0.00 -0.23 0.31 0.00 0.93 0.02 0.76 -0.03 0.45 0.25 0.16 0.35 0.26 -0.04 0.16 0.21 0.02  MOG1 0.22 0.17 0.29 0.15 0.29 0.51 0.25 0.29 0.88 0.43 -0.04 0.95 0.37 0.73 0.11 0.89 -0.37 0.34 0.00 1.00 0.29 0.19 1.13 0.00 1.95 0.00 -0.14 0.55 2.33 0.00  MPH1 0.11 0.03 -0.03 0.73 -0.11 0.06 0.16 0.03 -0.04 0.44 0.05 0.22 -0.23 0.15 -0.06 0.07 0.01 0.77 -0.35 0.06 0.05 0.37 -0.05 0.22 -0.01 0.92 0.06 0.33 -0.17 0.30  MRC1 0.10 0.58 -0.04 0.82 0.11 0.79 -0.29 0.05 0.06 0.84 -0.26 0.03 -0.11 0.28 -0.26 0.04 -0.33 0.01 0.03 0.95 -0.56 0.00 -0.30 0.06 -0.33 0.27 -0.53 0.00 -0.51 0.01  MRE11 0.13 0.02 0.17 0.60 -0.29 0.58 -0.02 0.79 -0.38 0.34 -0.16 0.09 1.87 0.18 1.01 0.10 -0.14 0.02 1.16 0.02 -0.01 0.92 0.96 0.08 2.54 0.01 -0.18 0.01 -0.13 0.55  MRPL17 0.03 0.86 -0.09 0.47 -0.02 0.87 -0.18 0.02 -0.18 0.40 0.24 0.03 -0.07 0.14 0.35 0.01 0.22 0.02 -0.43 0.22 0.08 0.09 0.11 0.15 0.50 0.12 0.16 0.02 0.35 0.06  MSH1 0.00 0.96 0.04 0.76 0.43 0.40 -0.09 0.19 0.22 0.54 0.33 0.04 0.92 0.13 1.07 0.05 0.39 0.00 1.84 0.46 0.01 0.90 0.25 0.02 0.27 0.02 0.07 0.01 -0.05 0.85  MSH2 0.31 0.04 0.49 0.18 0.56 0.21 -0.10 0.64 0.32 0.40 0.39 0.43 1.55 0.38 0.75 0.44 0.45 0.15 1.65 0.44 0.35 0.18 0.67 0.07 1.19 0.07 0.13 0.04 1.03 0.17  MSH3 0.05 0.30 -0.06 0.11 -0.25 0.01 0.03 0.57 -0.01 0.92 0.05 0.48 0.14 0.06 0.07 0.48 0.07 0.23 0.22 0.04 -0.02 0.54 -0.03 0.49 -0.08 0.04 -0.02 0.30 -0.03 0.33  MSH4 0.11 0.02 0.19 0.05 -0.05 0.69 0.08 0.25 0.01 0.92 0.03 0.42 -0.06 0.43 0.00 0.97 0.00 0.97 -0.32 0.08 0.10 0.04 0.02 0.90 -0.20 0.21 -0.04 0.16 -0.51 0.01  MSH5 0.04 0.28 -0.10 0.06 -0.16 0.01 -0.09 0.12 0.20 0.05 0.17 0.11 0.11 0.41 0.16 0.07 0.08 0.38 0.22 0.10 -0.01 0.83 0.00 0.98 0.06 0.70 0.00 0.94 -0.07 0.24  MSH6 0.07 0.14 0.08 0.22 -0.17 0.02 0.20 0.01 0.02 0.90 0.00 0.99 0.13 0.20 0.00 0.95 0.06 0.33 0.17 0.41 0.07 0.10 0.11 0.42 0.03 0.70 0.04 0.38 0.02 0.80  MSI1 -0.02 0.59 -0.19 0.06 -0.22 0.13 0.21 0.01 -0.04 0.30 -0.38 0.00 -0.32 0.00 -0.36 0.00 -0.25 0.00 -0.80 0.00 -0.06 0.25 -0.18 0.32 -0.10 0.06 -0.07 0.01 -0.43 0.01  MSM1 0.17 0.08 0.11 0.03 0.28 0.20 -0.08 0.59 0.57 0.00 0.29 0.03 0.03 0.78 0.44 0.00 0.35 0.01 0.10 0.76 0.05 0.74 0.46 0.44 0.99 0.19 0.12 0.05 4.70 0.18  MUC1 0.13 0.03 0.05 0.33 -0.14 0.05 0.00 0.99 0.01 0.91 0.27 0.01 0.30 0.09 0.18 0.12 0.15 0.13 0.43 0.05 0.01 0.82 0.01 0.88 0.02 0.86 0.00 0.92 0.00 0.96  MUS81 0.05 0.06 0.02 0.64 -0.08 0.59 -0.02 0.55 -0.30 0.00 0.07 0.17 0.77 0.00 -0.09 0.63 0.09 0.06 -0.24 0.15 0.05 0.60 0.63 0.10 2.08 0.32 -0.02 0.20 0.07 0.57  NAM7 0.00 0.93 -0.16 0.01 -0.02 0.56 -0.28 0.01 -0.20 0.01 -0.09 0.05 0.12 0.24 0.09 0.08 -0.01 0.77 -0.46 0.02 -0.03 0.65 0.08 0.10 0.00 0.93 -0.08 0.01 -0.19 0.11   43   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  NAM8 -0.09 0.10 0.00 0.96 -0.04 0.09 0.05 0.36 -0.07 0.18 0.00 0.91 0.07 0.46 0.05 0.14 0.03 0.41 -0.21 0.03 -0.17 0.01 -0.09 0.29 -0.10 0.04 -0.06 0.07 -0.11 0.08  NCS6 -0.01 0.71 -0.01 0.85 0.07 0.34 0.01 0.93 -0.67 0.00 0.14 0.05 0.05 0.54 0.34 0.02 0.20 0.01 -0.37 0.18 0.17 0.05 0.15 0.31 0.62 0.00 0.28 0.00 0.39 0.62  NEJ1 0.09 0.21 0.23 0.15 0.25 0.50 -0.01 0.67 0.22 0.36 0.07 0.15 0.16 0.07 0.03 0.71 0.06 0.44 0.00 0.97 -0.01 0.84 -0.02 0.70 -0.12 0.06 -0.02 0.36 -0.53 0.06  NFI1 0.00 0.94 -0.09 0.03 -0.03 0.70 0.08 0.13 -0.19 0.01 0.03 0.28 -0.16 0.04 -0.04 0.13 0.03 0.40 -0.05 0.54 -0.06 0.01 -0.02 0.85 -0.06 0.60 -0.03 0.49 0.08 0.29  NGG1 0.39 0.01 1.51 0.05 2.22 0.19 0.21 0.13 0.63 0.09 -0.07 0.65 -0.31 0.69 -0.44 0.28 0.21 0.73 -1.00 0.37 0.16 0.84 2.87 0.17 2.85 0.04 0.60 0.08 29.82 0.18  NHP6a 0.07 0.19 0.08 0.14 0.05 0.82 0.02 0.80 -0.23 0.02 -0.13 0.15 0.27 0.42 0.07 0.73 -0.12 0.02 -0.10 0.83 0.12 0.06 0.05 0.26 0.06 0.34 0.04 0.18 -0.06 0.52  NTG1 -0.03 0.48 -0.02 0.49 -0.23 0.01 -0.06 0.32 -0.12 0.16 0.06 0.45 0.40 0.19 0.29 0.18 0.04 0.24 0.33 0.18 0.01 0.86 0.23 0.26 0.17 0.22 -0.02 0.63 -0.08 0.34  NTG2 0.02 0.69 0.02 0.49 0.04 0.85 -0.03 0.65 0.00 0.97 0.14 0.01 0.03 0.42 0.06 0.14 0.12 0.09 -0.13 0.18 -0.01 0.72 -0.08 0.13 -0.03 0.56 -0.03 0.17 -0.07 0.55  NUP120 0.29 0.09 0.33 0.37 0.10 0.69 0.30 0.16 0.14 0.72 -0.02 0.57 0.26 0.16 0.18 0.22 0.23 0.05 -0.17 0.65 -0.24 0.00 -0.18 0.05 0.11 0.64 -0.22 0.00 -0.20 0.10  NUP60 0.18 0.91 -0.03 0.98 0.00 1.00 0.33 0.85 0.25 0.88 0.02 0.99 -0.21 0.88 0.05 0.97 -0.06 0.96 0.06 0.97 2.05 0.28 1.50 0.32 0.97 0.39 0.02 0.97 9.74 0.15  NUP84 0.25 0.05 1.25 0.02 0.17 0.66 0.42 0.01 -0.42 0.13 1.03 0.00 2.74 0.00 1.51 0.00 1.08 0.00 1.50 0.00 -0.27 0.05 -0.01 0.95 0.72 0.03 -0.08 0.10 -0.53 0.01  OGG1 -0.01 0.73 0.06 0.21 -0.10 0.17 -0.02 0.64 -0.04 0.56 0.15 0.02 0.25 0.01 0.23 0.06 0.10 0.02 0.23 0.02 0.01 0.54 0.03 0.59 0.09 0.01 -0.02 0.26 0.05 0.62  PAN2 -0.08 0.05 -0.31 0.00 -0.13 0.55 0.29 0.17 -0.07 0.26 0.17 0.00 0.02 0.50 0.17 0.10 0.06 0.71 0.08 0.48 -0.19 0.01 -0.01 0.95 0.13 0.60 -0.42 0.00 -0.10 0.20  PAN3 -0.10 0.01 -0.28 0.01 -0.06 0.81 -0.14 0.20 -0.14 0.43 0.24 0.01 0.25 0.01 0.16 0.14 -0.20 0.42 0.03 0.79 -0.12 0.01 -0.25 0.00 -0.25 0.00 -0.64 0.00 -0.39 0.00  PAP2 0.10 0.13 0.06 0.31 -0.07 0.27 0.06 0.38 -0.11 0.03 0.18 0.08 0.71 0.29 0.45 0.19 0.02 0.82 0.40 0.51 0.04 0.18 -0.03 0.31 0.04 0.55 0.01 0.68 -0.08 0.23  PCH2 -0.06 0.04 -0.10 0.02 -0.16 0.01 0.13 0.22 -0.02 0.44 -0.06 0.11 -0.16 0.00 -0.10 0.05 -0.05 0.24 -0.32 0.06 -0.12 0.07 0.00 0.97 0.08 0.61 -0.17 0.01 -0.13 0.34  PER1 0.06 0.04 0.16 0.01 -0.64 0.00 -0.04 0.28 -0.53 0.04 -0.01 0.82 0.01 0.77 -0.51 0.00 -0.03 0.59 -0.80 0.01 0.06 0.40 0.20 0.04 -0.13 0.44 0.04 0.29 -0.53 0.10  PHO4 0.06 0.08 -0.03 0.46 -0.15 0.00 -0.02 0.68 -0.22 0.02 0.14 0.03 0.18 0.01 0.17 0.37 0.14 0.14 -0.09 0.28 0.06 0.18 0.00 0.91 0.08 0.10 0.01 0.50 -0.09 0.04  PHR1 0.01 0.97 0.62 0.20 0.38 0.19 0.17 0.03 -0.11 0.77 -0.49 0.41 -0.43 0.50 -0.15 0.79 -0.52 0.37 -0.95 0.16 -0.44 0.52 -0.67 0.31 -0.02 0.97 -0.38 0.64 -0.91 0.17  PIF1 0.12 0.04 0.51 0.00 0.37 0.05 0.19 0.04 0.08 0.42 0.21 0.28 1.36 0.03 0.85 0.02 0.41 0.03 0.91 0.01 0.19 0.07 0.88 0.00 1.20 0.00 -0.02 0.72 0.55 0.01  PMS1 0.05 0.41 -0.05 0.34 -0.25 0.01 -0.07 0.16 -0.06 0.26 0.10 0.10 -0.01 0.92 0.01 0.91 0.03 0.47 0.15 0.47 -0.04 0.63 -0.01 0.71 0.05 0.24 0.01 0.74 0.10 0.60  PNG1 0.02 0.60 0.16 0.17 0.06 0.74 -0.12 0.29 -0.03 0.64 -0.02 0.48 -0.11 0.02 -0.10 0.04 -0.11 0.13 -0.11 0.49 -0.02 0.78 -0.14 0.03 -0.22 0.00 -0.13 0.03 0.00 0.97  POL32 0.10 0.05 0.47 0.54 0.20 0.55 0.23 0.08 0.03 0.94 -0.06 0.19 0.28 0.25 -0.18 0.01 0.04 0.53 -0.79 0.00 -0.01 0.90 -0.48 0.02 -0.04 0.73 -0.05 0.34 -0.65 0.02  POL4 0.04 0.38 0.08 0.04 -0.21 0.00 0.12 0.05 -0.06 0.22 0.12 0.20 0.12 0.11 0.10 0.02 0.09 0.17 -0.12 0.35 0.00 0.96 0.01 0.84 0.07 0.23 0.10 0.03 -0.17 0.19  POP2 0.40 0.32 0.17 0.70 0.25 0.80 3.61 0.18 0.39 0.80 0.33 0.01 0.53 0.01 0.04 0.83 0.44 0.55 1.69 0.08 0.38 0.61 0.52 0.53 1.51 0.31 0.51 0.70 4.62 0.44  PPH3 0.11 0.14 -0.01 0.88 0.10 0.15 0.05 0.56 0.05 0.61 0.24 0.01 -0.29 0.01 0.07 0.32 0.16 0.02 -0.44 0.00 0.01 0.54 -0.17 0.03 0.06 0.17 -0.10 0.02 -0.42 0.09  PSO2 0.16 0.01 0.00 0.95 -0.09 0.03 0.41 0.01 -0.21 0.13 0.04 0.49 -0.17 0.03 -0.11 0.01 -0.13 0.06 -0.27 0.35 -0.08 0.03 -0.01 0.92 -0.11 0.25 0.05 0.39 -0.09 0.58  PSY2 0.10 0.03 0.02 0.95 0.07 0.56 0.06 0.30 -0.36 0.01 0.03 0.74 -0.15 0.73 0.00 1.00 -0.07 0.55 -0.72 0.05 0.05 0.24 -0.10 0.22 0.00 0.99 -0.06 0.33 -0.40 0.15  PSY3 0.09 0.18 0.01 0.96 -0.12 0.61 0.17 0.02 -0.14 0.56 0.05 0.46 -0.41 0.01 -0.29 0.00 0.10 0.09 -0.48 0.02 0.06 0.24 0.11 0.56 0.00 0.97 0.09 0.08 -0.37 0.14  PSY4 0.05 0.33 0.09 0.28 0.09 0.37 -0.01 0.65 -0.10 0.31 0.05 0.44 0.14 0.34 0.02 0.71 0.07 0.15 -0.12 0.06 -0.03 0.47 0.06 0.62 0.06 0.62 -0.09 0.00 -0.20 0.10   44   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  PTP1 0.03 0.12 0.01 0.96 -0.10 0.64 -0.09 0.07 0.08 0.45 0.31 0.01 0.77 0.04 0.26 0.16 0.21 0.02 0.98 0.03 0.09 0.04 -0.17 0.00 -0.26 0.00 -0.11 0.00 -0.30 0.02  RAD1 0.12 0.02 -0.17 0.01 -0.19 0.00 0.17 0.01 0.15 0.18 -0.02 0.52 -0.25 0.01 0.09 0.25 0.12 0.15 0.04 0.68 0.01 0.92 -0.18 0.01 -0.08 0.04 0.04 0.28 0.00 0.91  RAD14 0.09 0.25 0.02 0.59 -0.04 0.19 0.04 0.64 0.03 0.45 0.29 0.23 1.29 0.44 0.62 0.31 0.10 0.26 0.41 0.63 -0.13 0.10 -0.14 0.22 0.01 0.78 -0.05 0.06 0.04 0.62  RAD17 0.02 0.46 -0.51 0.00 -0.74 0.00 -0.02 0.85 -0.48 0.00 0.11 0.01 0.08 0.36 -0.66 0.02 0.11 0.18 -0.48 0.04 -0.03 0.54 -0.04 0.60 -0.73 0.00 0.02 0.57 -0.10 0.40  RAD18 0.10 0.05 0.39 0.28 -0.08 0.18 0.08 0.04 -0.51 0.02 -0.05 0.47 0.22 0.62 -0.07 0.04 -0.08 0.10 -0.51 0.03 -0.04 0.04 1.48 0.01 0.23 0.28 -0.06 0.12 0.15 0.41  RAD2 0.10 0.01 0.17 0.37 0.23 0.42 0.11 0.28 0.10 0.41 0.06 0.16 -0.09 0.41 0.14 0.09 0.16 0.01 0.23 0.11 0.05 0.49 0.00 0.95 0.04 0.50 0.03 0.61 0.10 0.33  RAD23 0.06 0.12 0.12 0.29 0.13 0.46 0.12 0.01 -0.08 0.13 -0.20 0.01 -0.32 0.02 -0.29 0.00 -0.12 0.04 -0.41 0.02 0.22 0.02 0.16 0.01 0.23 0.01 0.24 0.00 0.12 0.05  RAD24 0.08 0.11 -0.51 0.00 -0.74 0.00 0.09 0.42 -0.54 0.00 0.05 0.54 0.01 0.96 -0.69 0.00 0.00 0.93 0.09 0.80 0.00 0.97 -0.13 0.04 -0.55 0.01 0.04 0.16 -0.26 0.17  RAD27 0.10 0.12 0.46 0.12 0.16 0.03 0.14 0.26 -0.08 0.22 -0.08 0.38 0.76 0.10 -0.06 0.28 0.02 0.60 -0.09 0.43 -0.41 0.01 -0.19 0.38 0.05 0.35 -0.26 0.00 0.00 0.99  RAD28 0.05 0.11 -0.05 0.14 -0.06 0.68 0.04 0.55 0.03 0.72 -0.03 0.29 -0.08 0.16 -0.10 0.10 -0.01 0.92 0.10 0.50 -0.02 0.76 -0.08 0.18 -0.01 0.95 -0.08 0.03 -0.28 0.16  RAD30 0.05 0.30 -0.19 0.01 -0.19 0.01 -0.07 0.05 -0.08 0.09 0.10 0.25 -0.15 0.01 0.10 0.05 0.09 0.05 0.18 0.21 0.07 0.06 -0.07 0.39 0.03 0.48 0.00 0.85 0.06 0.38  RAD33 0.05 0.23 0.04 0.32 0.03 0.87 -0.03 0.61 0.08 0.33 -0.03 0.17 -0.15 0.02 0.01 0.91 0.15 0.19 0.04 0.59 -0.06 0.44 -0.07 0.77 0.02 0.92 -0.03 0.61 0.43 0.01  RAD34 0.05 0.45 0.20 0.27 0.10 0.59 0.07 0.26 0.14 0.32 0.15 0.01 0.14 0.21 0.09 0.36 0.06 0.33 0.08 0.53 0.05 0.34 0.02 0.50 0.01 0.83 -0.01 0.61 -0.10 0.13  RAD4 0.37 0.04 0.49 0.16 0.45 0.16 -0.17 0.75 0.07 0.87 0.51 0.54 1.92 0.27 0.90 0.41 0.48 0.59 1.62 0.32 0.29 0.30 0.77 0.20 0.88 0.13 0.21 0.13 -0.04 0.94  RAD5 0.12 0.02 0.02 0.85 -0.07 0.58 0.13 0.18 -0.35 0.01 -0.09 0.12 2.47 0.00 -0.07 0.21 -0.02 0.73 -0.39 0.52 -0.07 0.15 -0.15 0.52 -0.01 0.84 0.01 0.83 -0.33 0.17  RAD50 0.11 0.11 1.11 0.03 2.57 0.04 -0.06 0.32 -0.47 0.14 -0.02 0.64 1.33 0.17 2.09 0.51 -0.15 0.01 0.94 0.43 -0.19 0.01 1.58 0.09 4.61 0.00 -0.16 0.11 -0.15 0.43  RAD51 0.05 0.19 -0.56 0.03 -0.70 0.00 -0.12 0.20 -0.77 0.00 0.00 0.99 0.09 0.88 -0.12 0.80 -0.07 0.49 -0.24 0.67 0.06 0.26 1.24 0.05 3.82 0.03 0.04 0.40 1.59 0.34  RAD52 0.13 0.07 0.05 0.90 -0.77 0.09 0.07 0.29 -0.44 0.38 0.09 0.68 4.05 0.01 1.94 0.08 0.03 0.75 -0.20 0.79 -0.26 0.01 1.33 0.32 0.03 0.96 0.00 0.85 0.75 0.03  RAD54 0.02 0.42 0.22 0.43 -0.32 0.49 -0.11 0.11 -0.28 0.06 0.08 0.19 1.82 0.05 0.12 0.74 0.00 0.98 -0.53 0.06 0.05 0.26 0.35 0.32 1.25 0.10 -0.11 0.00 -0.10 0.44  RAD55 0.04 0.27 0.08 0.46 -0.21 0.16 -0.06 0.40 -0.23 0.08 0.06 0.32 1.10 0.03 0.34 0.66 -0.06 0.30 -0.71 0.00 0.07 0.18 -0.05 0.91 -0.59 0.54 0.03 0.59 -0.25 0.28  RAD57 0.09 0.01 0.10 0.37 0.62 0.18 0.02 0.75 -0.23 0.33 0.31 0.31 14.00 0.24 21.65 0.02 0.12 0.67 -0.60 0.11 0.12 0.09 1.75 0.21 22.61 0.20 -0.06 0.08 0.62 0.28  RAD6 0.03 0.49 -0.03 0.10 -0.10 0.01 0.00 0.89 -0.10 0.18 0.08 0.05 0.09 0.01 0.10 0.06 0.11 0.05 -0.08 0.10 0.05 0.18 0.02 0.62 0.08 0.09 0.04 0.03 0.11 0.07  RAD61 -0.67 0.00 -0.51 0.01 -0.18 0.04 -0.94 0.00 -0.79 0.00 -0.94 0.00 -0.20 0.81 -0.42 0.49 -1.00 0.00 -1.00 0.00 -0.53 0.24 -0.05 0.94 0.00 1.00 -0.44 0.41 -0.13 0.87  RAD9 0.07 0.10 -0.59 0.00 -0.82 0.00 0.02 0.77 -0.42 0.06 0.03 0.55 -0.19 0.03 -0.58 0.00 0.02 0.76 -0.49 0.09 0.03 0.63 -0.43 0.00 -0.89 0.00 0.00 0.92 -0.12 0.49  RDH54 0.07 0.49 -0.63 0.00 -0.51 0.11 -0.17 0.13 -0.28 0.15 0.11 0.02 -0.12 0.54 -0.13 0.60 0.05 0.36 0.12 0.74 -0.07 0.10 -0.26 0.07 -0.30 0.24 -0.11 0.07 -0.63 0.04  REV1 0.03 0.31 -0.39 0.13 -0.16 0.03 0.04 0.29 -0.31 0.01 0.07 0.24 -0.40 0.01 0.08 0.27 0.10 0.17 -0.18 0.27 0.04 0.31 -0.55 0.00 -0.03 0.57 -0.02 0.31 0.00 1.00  REV3 0.04 0.27 -0.60 0.00 -0.19 0.01 0.02 0.76 -0.34 0.04 0.08 0.21 -0.31 0.01 0.10 0.09 0.04 0.46 -0.14 0.10 -0.01 0.76 -0.48 0.00 0.11 0.10 -0.01 0.74 -0.05 0.40  REV7 0.10 0.07 0.19 0.07 -0.14 0.01 -0.08 0.09 0.27 0.04 0.12 0.08 -0.19 0.07 0.05 0.48 -0.05 0.39 -0.64 0.02 -0.08 0.12 -0.15 0.12 -0.15 0.00 -0.13 0.03 0.13 0.04  REX3 -0.02 0.54 -0.01 0.87 0.17 0.60 -0.10 0.11 0.09 0.27 0.02 0.64 -0.08 0.01 -0.06 0.04 0.17 0.22 0.03 0.86 -0.15 0.05 -0.18 0.01 -0.12 0.37 -0.08 0.12 0.25 0.14  RFX1 -0.12 0.01 0.10 0.06 0.08 0.01 0.00 0.94 -0.02 0.80 -0.06 0.67 0.36 0.54 0.32 0.45 0.00 0.94 0.49 0.36 -0.20 0.04 -0.20 0.00 -0.12 0.07 -0.22 0.01 0.15 0.07   45   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  RGA2 0.10 0.28 -0.35 0.28 -0.38 0.05 -0.12 0.39 -0.17 0.44 0.18 0.06 0.06 0.48 0.20 0.12 0.00 0.94 -0.27 0.59 0.08 0.08 -0.11 0.58 -0.09 0.59 -0.01 0.92 -0.82 0.00  RLF2 0.13 0.05 -0.23 0.02 -0.15 0.18 0.28 0.00 -0.12 0.25 -0.23 0.46 0.66 0.64 0.34 0.71 -0.29 0.02 0.68 0.65 0.13 0.05 -0.18 0.09 0.05 0.65 0.05 0.24 -0.31 0.03  RMI1 0.10 0.14 -0.04 0.89 -0.40 0.02 0.10 0.15 -0.17 0.09 0.18 0.01 0.72 0.04 0.14 0.21 0.18 0.01 -0.04 0.87 -0.02 0.77 0.32 0.18 -0.08 0.08 0.01 0.77 -0.19 0.15  RNR1 0.25 0.01 0.56 0.00 0.75 0.00 0.54 0.00 0.55 0.01 0.50 0.00 -0.03 0.90 0.65 0.02 1.09 0.00 -0.61 0.36 0.12 0.55 0.00 0.98 0.76 0.04 0.63 0.00 0.27 0.53  RNR3 0.04 0.33 0.02 0.64 -0.01 0.88 0.04 0.47 0.05 0.44 0.02 0.83 -0.16 0.30 -0.08 0.48 -0.01 0.96 0.00 0.99 -0.13 0.09 0.10 0.67 0.35 0.54 -0.17 0.00 0.04 0.77  RNR4 0.28 0.03 0.54 0.06 0.75 0.09 0.95 0.00 -0.21 0.68 0.35 0.17 0.82 0.48 1.31 0.11 1.35 0.00 0.39 0.79 0.16 0.04 -0.12 0.44 0.99 0.00 0.40 0.00 2.68 0.02  RPA12 0.06 0.06 0.03 0.37 -0.21 0.02 0.11 0.18 -0.07 0.25 0.01 0.71 0.11 0.08 -0.16 0.00 0.03 0.39 -0.31 0.01 0.08 0.49 0.14 0.28 0.09 0.50 0.06 0.03 0.14 0.31  RPB9 0.07 0.30 -0.12 0.54 0.36 0.09 -0.24 0.13 -0.53 0.04 0.10 0.25 -0.64 0.00 -0.02 0.86 0.05 0.61 -0.77 0.13 0.01 0.80 -0.48 0.02 -0.19 0.01 -0.38 0.00 -0.25 0.43  RPD3 0.33 0.03 0.08 0.43 -0.31 0.00 0.83 0.00 0.32 0.06 -0.35 0.00 -0.07 0.81 -0.26 0.08 -0.09 0.20 -0.67 0.04 0.12 0.03 0.38 0.10 -0.05 0.52 0.23 0.00 0.51 0.19  RPH1 -0.06 0.06 -0.08 0.05 -0.10 0.03 -0.13 0.04 -0.10 0.02 0.04 0.10 0.01 0.69 0.01 0.88 0.10 0.23 -0.03 0.61 -0.19 0.00 -0.13 0.05 -0.15 0.05 -0.15 0.01 0.14 0.03  RPL13a -0.03 0.49 0.13 0.03 0.12 0.18 0.09 0.16 -0.18 0.25 -0.02 0.74 0.20 0.00 0.09 0.16 -0.05 0.61 0.19 0.12 0.00 1.00 0.07 0.23 0.11 0.05 -0.02 0.64 -0.11 0.23  RPL2a 0.20 0.05 -0.03 0.51 0.03 0.73 0.37 0.01 -0.09 0.27 0.13 0.25 0.79 0.16 0.32 0.09 0.24 0.02 -0.06 0.72 0.24 0.01 0.25 0.06 0.40 0.00 0.17 0.01 0.26 0.14  RPL2b 0.04 0.47 0.06 0.21 -0.06 0.19 0.00 0.96 0.04 0.34 -1.00 0.37 -1.00 0.37 -1.00 0.37 -1.00 0.37 -1.00 0.37 -0.21 0.32 -0.04 0.73 0.04 0.72 -0.09 0.57 0.01 0.97  RPN10 0.01 0.78 0.03 0.55 -0.38 0.00 -0.25 0.00 -0.28 0.00 0.44 0.36 1.85 0.16 1.29 0.22 -0.33 0.00 0.96 0.38 -0.19 0.01 0.21 0.02 -0.09 0.43 -0.41 0.00 -0.37 0.02  RPS9b 0.37 0.07 0.24 0.03 0.22 0.27 0.21 0.04 0.57 0.10 -0.24 0.00 -0.30 0.00 -0.16 0.00 -0.17 0.04 -0.68 0.01 0.15 0.54 0.37 0.37 0.80 0.30 0.01 0.87 0.44 0.31  RRD1 0.19 0.00 0.03 0.55 0.02 0.79 0.19 0.12 0.18 0.12 -0.01 0.83 0.18 0.36 0.25 0.07 -0.06 0.58 0.10 0.73 0.02 0.70 0.06 0.56 0.25 0.15 0.00 0.91 -0.23 0.31  RRI1 0.11 0.03 -0.04 0.27 0.02 0.89 0.04 0.33 0.02 0.69 0.10 0.19 -0.01 0.77 -0.04 0.45 0.05 0.11 0.23 0.45 -0.08 0.03 -0.12 0.02 -0.12 0.26 -0.07 0.07 -0.34 0.02  RRM3 0.18 0.00 0.14 0.07 0.24 0.01 0.46 0.00 0.16 0.04 0.05 0.26 -0.03 0.76 0.15 0.04 0.21 0.03 0.05 0.72 0.04 0.33 0.08 0.61 0.28 0.31 0.12 0.04 0.16 0.08  RSC1 0.37 0.41 1.15 0.09 0.83 0.16 0.15 0.87 -0.14 0.84 1.50 0.32 4.52 0.18 1.84 0.28 0.87 0.50 1.72 0.46 0.10 0.43 1.16 0.00 1.51 0.00 0.91 0.11 0.93 0.01  RSC2 0.37 0.45 1.15 0.00 0.80 0.12 0.63 0.59 0.13 0.87 -0.69 0.12 -0.50 0.18 -0.69 0.03 -0.92 0.01 -0.92 0.05 0.44 0.00 1.27 0.00 1.85 0.00 0.52 0.05 0.79 0.00  RTC6 -0.05 0.18 -0.19 0.00 -0.25 0.00 -0.07 0.19 -0.06 0.50 0.09 0.16 0.08 0.34 0.24 0.01 0.19 0.01 -0.62 0.00 -0.01 0.83 0.04 0.52 0.21 0.00 0.06 0.08 -0.25 0.11  RTS1 0.03 0.84 0.34 0.38 0.26 0.54 0.17 0.64 -0.29 0.39 0.05 0.40 -0.08 0.34 -0.35 0.00 -0.29 0.00 -0.26 0.53 -0.32 0.00 -0.20 0.02 -0.33 0.09 -0.41 0.00 0.02 0.86  RTT101 0.21 0.12 1.48 0.47 2.06 0.48 0.06 0.41 -0.18 0.39 0.01 0.91 13.38 0.12 6.04 0.17 -0.07 0.16 -0.44 0.24 -0.08 0.22 -0.10 0.75 -0.38 0.23 -0.23 0.00 -0.52 0.02  RTT103 0.09 0.08 -0.12 0.03 -0.12 0.07 0.27 0.00 -0.14 0.17 0.28 0.00 -0.09 0.23 0.01 0.90 0.35 0.00 -0.05 0.71 -0.16 0.09 -0.19 0.06 -0.12 0.01 -0.12 0.00 -0.02 0.78  RTT107 0.12 0.01 -0.32 0.05 -0.56 0.00 0.19 0.03 -0.12 0.32 0.01 0.86 -0.38 0.11 -0.65 0.00 0.05 0.42 -0.77 0.00 0.02 0.18 -0.06 0.72 0.06 0.51 -0.01 0.79 -0.41 0.10  RTT109 0.02 0.66 -0.01 0.98 -0.38 0.47 -0.05 0.22 -0.32 0.03 0.20 0.69 1.60 0.51 1.97 0.53 -0.02 0.91 0.71 0.67 -0.30 0.01 -0.22 0.50 0.20 0.86 -0.11 0.11 -0.11 0.34  SAE2 0.06 0.12 -0.05 0.47 -0.62 0.00 0.13 0.07 -0.21 0.19 0.03 0.46 -0.12 0.33 -0.74 0.00 0.04 0.27 -0.08 0.45 -0.02 0.78 -0.04 0.88 -0.15 0.74 -0.05 0.58 0.27 0.12  SAP155 -0.06 0.03 -0.12 0.22 -0.08 0.10 -0.26 0.01 -0.29 0.03 -0.27 0.00 -0.32 0.00 -0.32 0.00 -0.41 0.00 -0.66 0.01 -0.22 0.01 -0.33 0.00 -0.40 0.00 -0.46 0.00 -0.58 0.02  SAP190 0.16 0.10 0.08 0.14 0.04 0.87 0.11 0.09 0.34 0.25 0.24 0.00 0.58 0.28 0.67 0.16 0.24 0.01 0.53 0.20 0.14 0.18 0.07 0.53 0.18 0.16 0.04 0.33 -0.11 0.34  SAP4 0.05 0.67 -0.44 0.01 -0.39 0.01 -0.14 0.26 0.05 0.76 0.03 0.60 -0.10 0.18 0.07 0.25 -0.04 0.37 -0.38 0.23 0.01 0.89 -0.16 0.03 -0.19 0.04 -0.05 0.34 -0.82 0.00   46   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  SAS2 0.40 0.83 0.01 0.99 0.14 0.93 0.51 0.79 0.13 0.93 0.32 0.74 2.24 0.30 0.99 0.45 0.07 0.96 -0.95 0.39 -0.20 0.77 -0.03 0.97 0.17 0.81 -0.04 0.96 -0.23 0.75  SAT4 0.16 0.06 0.15 0.14 -0.06 0.16 0.19 0.10 -0.65 0.00 -0.05 0.46 -0.25 0.00 -0.20 0.02 0.08 0.35 2.68 0.01 0.03 0.44 0.12 0.07 -0.04 0.59 0.13 0.09 0.33 0.43  SBA1 0.06 0.23 -0.08 0.03 -0.11 0.48 0.15 0.01 0.00 0.98 0.19 0.08 0.05 0.40 0.18 0.03 0.16 0.06 0.03 0.67 -0.03 0.43 -0.05 0.35 0.09 0.01 0.01 0.56 0.02 0.72  SEH1 0.18 0.02 0.16 0.42 0.10 0.77 -0.02 0.80 0.06 0.71 0.06 0.24 0.26 0.07 0.06 0.50 0.09 0.35 -0.17 0.17 -0.23 0.01 -0.21 0.01 -0.20 0.00 -0.19 0.00 -0.33 0.00  SEM1 0.02 0.66 -0.22 0.01 -0.19 0.02 0.14 0.03 -0.51 0.00 -0.19 0.02 0.13 0.11 -0.07 0.32 0.08 0.08 -0.65 0.01 0.10 0.04 -0.03 0.56 0.24 0.00 0.18 0.02 -0.43 0.07  SGF73 0.02 0.76 0.13 0.03 -0.36 0.00 0.29 0.01 -0.25 0.01 -0.32 0.02 -0.36 0.00 -0.37 0.02 -0.10 0.10 -0.74 0.00 0.06 0.04 -0.11 0.13 -0.05 0.21 0.12 0.02 -0.28 0.03  SGO1 0.00 0.99 0.22 0.31 0.22 0.47 -0.65 0.00 -0.02 0.97 -0.10 0.18 0.02 0.91 0.07 0.77 -0.45 0.01 -0.34 0.16 -0.50 0.00 -0.31 0.01 -0.33 0.00 -0.69 0.00 -0.33 0.01  SGS1 0.06 0.06 0.22 0.33 -0.17 0.27 0.05 0.40 -0.16 0.03 0.19 0.00 0.16 0.62 0.04 0.44 0.25 0.03 -0.26 0.12 -0.04 0.69 0.17 0.34 -0.06 0.40 0.04 0.20 -0.01 0.95  SHP1 0.46 0.00 0.81 0.12 0.16 0.52 0.22 0.02 0.02 0.82 -0.46 0.28 -0.73 0.02 -0.75 0.01 -0.88 0.01 N/A NA -0.34 0.00 -0.48 0.11 -0.25 0.03 -0.40 0.01 -0.55 0.36  SHU1 -0.01 0.78 -0.14 0.08 -0.33 0.02 0.03 0.58 -0.21 0.00 0.03 0.56 -0.34 0.01 -0.20 0.01 0.08 0.17 -0.16 0.02 0.02 0.70 -0.07 0.47 -0.13 0.05 0.01 0.83 0.01 0.93  SHU2 -0.01 0.65 -0.11 0.04 -0.32 0.00 0.00 0.98 -0.23 0.00 0.06 0.31 -0.15 0.10 -0.18 0.00 0.06 0.20 -0.13 0.01 0.07 0.09 -0.08 0.27 -0.08 0.10 -0.01 0.77 0.02 0.70  SIN3 0.27 0.01 0.21 0.17 -0.14 0.13 0.83 0.00 0.20 0.07 -0.03 0.72 0.16 0.72 -0.23 0.27 0.00 0.99 -0.79 0.25 0.06 0.64 0.08 0.35 -0.32 0.05 0.30 0.00 0.18 0.28  SIR2 N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA N/A NA  SIT4 0.50 0.21 0.52 0.30 -0.17 0.59 -0.67 0.14 -0.72 0.20 0.58 0.70 2.01 0.47 0.95 0.59 -0.25 0.69 1.30 0.62 0.62 0.13 1.56 0.15 -0.07 0.67 -0.28 0.51 -0.02 0.99  SKY1 0.13 0.02 0.17 0.06 -0.01 0.84 0.35 0.02 0.36 0.00 0.06 0.20 -0.10 0.18 -0.23 0.01 0.11 0.07 0.32 0.09 0.00 0.94 -0.25 0.02 -0.21 0.09 0.12 0.13 0.34 0.00  SLH1 -0.09 0.09 -0.02 0.66 -0.14 0.01 -0.07 0.10 0.04 0.27 0.04 0.33 0.05 0.16 -0.05 0.13 0.18 0.03 -0.07 0.47 -0.13 0.03 -0.08 0.33 -0.25 0.01 -0.14 0.03 0.00 0.96  SLX1 0.08 0.12 -0.04 0.10 -0.21 0.00 0.08 0.09 -0.01 0.70 0.12 0.08 0.07 0.32 0.03 0.64 0.15 0.06 -0.09 0.08 0.06 0.27 0.08 0.50 0.15 0.29 0.07 0.33 0.06 0.38  SLX4 0.09 0.03 -0.05 0.91 -0.16 0.48 0.10 0.29 -0.16 0.29 -0.06 0.29 -0.14 0.81 -0.37 0.04 0.02 0.69 -0.47 0.01 0.13 0.07 0.24 0.60 0.28 0.38 0.08 0.15 -0.02 0.88  SLX5 0.31 0.39 0.08 0.85 0.26 0.66 0.03 0.86 0.25 0.69 0.43 0.53 1.36 0.34 0.92 0.32 -0.09 0.67 0.14 0.91 -0.15 0.14 0.21 0.76 0.34 0.67 -0.09 0.30 0.24 0.45  SLX8 0.14 0.34 -0.09 0.74 0.08 0.85 0.09 0.34 -0.20 0.29 -0.14 0.09 0.06 0.90 -0.21 0.19 -0.24 0.01 -0.65 0.01 0.20 0.39 0.66 0.47 0.64 0.51 -0.04 0.59 0.40 0.53  SNF12 1.27 0.16 1.35 0.08 1.28 0.39 0.05 0.82 6.49 0.35 0.70 0.01 0.93 0.05 -0.18 0.60 0.43 0.05 0.75 0.12 0.23 0.08 0.70 0.01 0.64 0.14 0.04 0.71 0.47 0.03  SNF4 0.20 0.17 0.63 0.11 0.69 0.02 0.06 0.64 1.44 0.19 0.14 0.12 -0.06 0.59 -0.09 0.10 0.16 0.01 -0.84 0.00 -0.03 0.72 -0.10 0.45 0.03 0.93 -0.03 0.35 -0.61 0.02  SNF5 0.44 0.14 0.54 0.01 -0.03 0.87 0.01 0.98 -0.32 0.54 0.97 0.00 2.62 0.00 1.29 0.00 1.16 0.00 0.85 0.02 0.23 0.27 0.42 0.13 0.46 0.17 0.27 0.01 0.37 0.29  SNF6 0.26 0.01 0.12 0.02 -0.51 0.00 -0.47 0.00 -0.78 0.02 0.43 0.09 0.53 0.00 0.04 0.39 0.39 0.03 -0.62 0.17 0.09 0.13 0.14 0.13 0.34 0.23 -0.05 0.49 0.16 0.63  SNT309 0.02 0.49 0.06 0.75 0.12 0.52 -0.10 0.27 0.06 0.75 0.44 0.00 0.86 0.00 0.64 0.00 -0.24 0.06 0.78 0.02 0.26 0.00 0.33 0.20 0.67 0.00 -0.09 0.60 0.37 0.27  SNU66 0.04 0.44 -0.04 0.60 0.01 0.92 0.14 0.25 -0.07 0.32 0.12 0.01 0.07 0.13 0.11 0.15 -0.06 0.25 -0.19 0.01 0.02 0.59 0.02 0.76 0.19 0.07 0.04 0.41 -0.14 0.19  SOD1 0.11 0.18 -0.16 0.16 -0.36 0.00 0.01 0.95 -0.63 0.19 -0.11 0.89 1.00 0.60 0.16 0.87 -0.66 0.11 -0.30 0.75 -0.22 0.42 -0.34 0.42 -0.43 0.07 -0.19 0.60 0.59 0.77  SPO11 0.01 0.79 0.09 0.04 -0.20 0.00 0.03 0.72 -0.19 0.12 -0.02 0.73 -0.06 0.11 -0.11 0.03 0.05 0.58 -0.35 0.04 0.05 0.49 0.01 0.79 -0.15 0.01 0.02 0.76 -0.06 0.43  SPS1 0.02 0.60 0.08 0.40 0.31 0.38 -0.01 0.92 0.10 0.41 0.11 0.04 0.14 0.24 0.12 0.08 0.23 0.00 0.02 0.41 0.00 0.90 0.04 0.68 0.01 0.96 0.02 0.75 0.25 0.00  SPT4 0.32 0.01 0.33 0.04 0.25 0.25 0.65 0.00 0.26 0.01 0.64 0.00 2.02 0.00 1.16 0.01 0.82 0.00 1.37 0.00 0.45 0.41 1.13 0.17 1.61 0.17 0.30 0.14 0.75 0.24   47   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  SRP40 0.00 0.91 0.13 0.15 0.16 0.20 0.06 0.35 0.37 0.01 0.00 0.97 0.54 0.32 0.32 0.30 -0.04 0.55 0.40 0.11 0.12 0.01 0.12 0.36 0.34 0.04 0.15 0.00 -0.03 0.85  SRS2 -0.11 0.01 -0.58 0.00 -0.68 0.00 -0.18 0.02 -0.63 0.00 -0.20 0.02 -0.43 0.04 -0.74 0.00 -0.17 0.02 -0.62 0.00 -0.22 0.00 -0.26 0.17 -0.23 0.14 -0.16 0.04 -0.29 0.01  SRV2 -0.09 0.86 -0.23 0.02 0.59 0.27 -0.02 0.96 -0.25 0.63 0.09 0.91 0.38 0.70 0.43 0.66 -0.39 0.66 0.52 0.77 0.04 0.95 0.28 0.76 0.70 0.54 -0.20 0.72 0.47 0.64  SSF1 0.05 0.23 0.10 0.46 0.21 0.46 0.22 0.00 0.15 0.45 -0.06 0.21 0.02 0.33 0.18 0.02 0.16 0.01 -0.33 0.01 0.05 0.50 0.11 0.01 0.30 0.00 0.18 0.00 0.04 0.72  SSN3 0.83 0.30 2.88 0.02 1.74 0.06 6.42 0.33 1.67 0.37 -0.45 0.58 0.65 0.73 -0.01 1.00 -0.33 0.77 -0.76 0.25 -0.13 0.76 0.58 0.34 0.81 0.05 0.40 0.45 -0.22 0.62  SSN8 0.22 0.10 -0.23 0.72 0.73 0.05 0.68 0.02 0.48 0.78 -0.70 0.06 -0.83 0.09 -0.42 0.16 -0.65 0.02 N/A NA -0.35 0.34 0.46 0.54 1.21 0.22 -0.17 0.18 -0.88 0.17  SSZ1 -0.06 0.25 0.36 0.13 -0.41 0.04 0.04 0.74 -0.85 0.00 0.15 0.19 0.08 0.87 -0.52 0.18 0.10 0.21 -0.97 0.38 -0.09 0.64 0.18 0.34 -0.28 0.41 0.17 0.30 -0.17 0.82  SWC5 0.48 0.25 1.07 0.03 1.19 0.12 -0.55 0.26 0.84 0.42 0.67 0.53 3.86 0.19 1.63 0.30 -0.07 0.94 0.20 0.86 0.56 0.00 1.20 0.00 1.76 0.00 0.79 0.01 0.76 0.00  SWI3 0.27 0.04 0.32 0.01 -0.38 0.08 -0.33 0.05 -0.80 0.14 1.62 0.08 4.09 0.16 1.65 0.30 0.15 0.76 16.48 0.33 0.09 0.04 0.11 0.27 0.05 0.44 0.05 0.08 -0.01 0.95  SWR1 -0.04 0.67 0.71 0.22 0.14 0.72 -0.38 0.19 -0.69 0.00 -0.07 0.81 0.79 0.60 0.28 0.73 -0.28 0.43 0.22 0.87 0.10 0.35 0.62 0.04 1.03 0.00 -0.16 0.47 0.46 0.02  SYF2 -0.01 0.84 -0.06 0.37 0.04 0.85 -0.19 0.01 -0.19 0.02 0.10 0.03 0.01 0.82 0.05 0.38 -0.05 0.43 -0.35 0.01 -0.01 0.91 -0.04 0.53 -0.06 0.18 -0.11 0.01 -0.25 0.04  TAF14 0.01 0.97 0.40 0.45 0.57 0.13 3.28 0.18 0.25 0.68 0.82 0.69 6.58 0.44 2.38 0.53 0.21 0.88 13.71 0.40 0.30 0.02 1.14 0.00 1.59 0.00 0.71 0.01 0.86 0.00  TDP1 0.09 0.31 -0.04 0.68 -0.25 0.01 0.02 0.69 -0.20 0.25 0.32 0.73 0.61 0.61 0.31 0.75 0.38 0.69 0.05 0.97 -0.72 0.05 -0.52 0.15 -0.49 0.17 -0.39 0.31 -0.86 0.01  TEL1 0.04 0.43 -0.30 0.00 -0.56 0.00 0.11 0.15 -0.12 0.14 -0.03 0.55 -0.27 0.02 -0.50 0.00 0.01 0.87 -0.30 0.26 0.05 0.28 -0.21 0.01 -0.41 0.00 -0.02 0.63 -0.37 0.03  TFB5 0.33 0.09 0.49 0.39 0.45 0.25 0.30 0.26 0.42 0.45 0.52 0.33 0.94 0.51 0.66 0.38 0.38 0.37 0.24 0.85 -0.06 0.50 -0.42 0.08 -0.11 0.40 -0.18 0.00 -0.35 0.13  THP1 0.09 0.81 0.54 0.39 0.03 0.94 1.15 0.01 -0.47 0.28 0.50 0.65 2.01 0.37 1.26 0.42 -0.21 0.61 1.55 0.57 -0.03 0.90 1.21 0.35 0.58 0.53 -0.18 0.05 0.91 0.09  TOF1 -0.67 0.00 -0.72 0.00 -0.41 0.01 -0.97 0.00 -0.65 0.09 -0.93 0.13 -1.00 0.12 -1.00 0.12 -1.00 0.12 -1.00 0.12 -0.99 0.00 0.12 0.92 0.49 0.71 -0.88 0.01 -1.00 0.19  TOP1 0.16 0.02 0.31 0.00 0.86 0.00 0.16 0.07 -0.07 0.34 0.07 0.20 0.15 0.06 0.77 0.00 0.25 0.04 -0.59 0.05 0.05 0.41 0.37 0.00 1.46 0.00 0.36 0.00 0.48 0.03  TOR1 0.03 0.32 -0.01 0.84 -0.17 0.03 -0.07 0.13 -0.12 0.11 0.22 0.12 0.69 0.22 0.34 0.21 0.09 0.25 1.48 0.06 0.00 0.95 -0.08 0.14 -0.12 0.00 -0.12 0.01 -0.24 0.18  TPP1 0.00 0.89 -0.10 0.01 -0.18 0.06 -0.10 0.36 -0.03 0.75 0.08 0.03 0.08 0.54 0.04 0.69 0.04 0.28 -0.08 0.65 0.01 0.88 0.02 0.88 -0.09 0.38 -0.05 0.01 -0.31 0.03  TRF5 -0.08 0.05 -0.03 0.40 -0.05 0.43 -0.11 0.28 -0.41 0.02 0.05 0.07 0.03 0.30 0.08 0.35 0.02 0.74 -0.64 0.00 0.02 0.87 0.25 0.46 0.41 0.40 0.10 0.02 0.22 0.63  TRM2 0.01 0.70 -0.03 0.29 -0.14 0.02 -0.06 0.22 0.04 0.52 0.17 0.30 0.38 0.30 0.34 0.35 0.02 0.73 0.58 0.26 0.01 0.77 0.07 0.16 0.12 0.02 0.01 0.86 -0.06 0.45  TSA1 0.11 0.24 -0.35 0.01 0.10 0.05 0.01 0.84 0.01 0.89 0.00 0.94 -0.23 0.06 0.02 0.89 0.17 0.08 -0.44 0.00 -0.05 0.57 -0.18 0.64 -0.08 0.64 -0.15 0.02 -0.47 0.02  TUB3 0.00 0.98 -0.08 0.01 -0.10 0.04 1.75 0.02 -0.12 0.03 -0.03 0.34 0.08 0.08 0.07 0.19 1.64 0.01 -0.36 0.00 -0.09 0.08 -0.15 0.05 -0.07 0.14 -0.43 0.03 -0.14 0.06  UBC12 0.09 0.13 0.01 0.73 -0.38 0.01 0.02 0.74 0.08 0.51 0.23 0.01 -0.02 0.62 -0.18 0.00 0.15 0.08 0.13 0.27 0.05 0.21 -0.15 0.18 -0.42 0.00 -0.02 0.36 -0.66 0.02  UBC13 0.15 0.00 0.27 0.18 0.05 0.72 0.38 0.03 -0.15 0.28 0.04 0.69 1.80 0.00 0.16 0.36 0.18 0.10 -0.64 0.01 0.15 0.01 0.99 0.40 0.34 0.00 0.11 0.04 0.33 0.30  UBP13 0.14 0.01 0.04 0.26 -0.07 0.20 -0.09 0.20 0.20 0.10 0.41 0.03 1.47 0.10 0.70 0.17 0.11 0.07 1.80 0.08 0.00 0.97 0.00 0.97 -0.04 0.82 -0.10 0.04 0.07 0.61  UBP9 0.05 0.53 0.00 1.00 0.00 1.00 0.08 0.52 -0.02 0.63 0.09 0.27 0.06 0.30 -0.01 0.82 0.00 0.94 -0.16 0.26 0.07 0.26 0.20 0.00 0.38 0.03 0.06 0.40 -0.16 0.13  UBR1 0.01 0.94 -0.20 0.50 -0.40 0.02 -0.37 0.04 -0.44 0.04 0.02 0.75 0.04 0.77 -0.07 0.69 0.09 0.31 -0.54 0.27 -0.33 0.00 -0.66 0.00 -0.85 0.00 -0.72 0.00 -0.92 0.00  ULS1 0.12 0.05 0.05 0.43 -0.03 0.80 0.29 0.00 0.00 0.97 -0.02 0.55 -0.10 0.17 0.03 0.44 0.05 0.31 -0.38 0.15 0.03 0.55 0.02 0.69 0.15 0.00 0.13 0.03 -0.16 0.02   48   SMC1 SCC1 SCC2    ND MMS CPT BEN BLE ND MMS CPT BEN BLE ND MMS CPT BEN BLE  Gene ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval ec/c pval  UNG1 0.12 0.09 0.09 0.09 -0.06 0.52 0.28 0.08 -0.02 0.80 0.11 0.09 0.41 0.35 0.35 0.25 0.09 0.44 0.24 0.63 0.11 0.07 0.03 0.38 0.07 0.27 0.11 0.07 -0.23 0.19  VPS34 0.08 0.82 -0.14 0.31 -0.19 0.24 0.41 0.46 -0.71 0.14 4.36 0.39 9.25 0.31 4.09 0.37 4.25 0.35 N/A 0.37 -0.73 0.00 -0.62 0.02 -0.85 0.00 -0.80 0.02 -1.00 0.02  VPS75 -0.09 0.08 0.01 0.73 -0.05 0.37 -0.08 0.06 -0.25 0.00 0.04 0.51 0.14 0.01 0.10 0.03 0.09 0.24 -0.09 0.19 -0.04 0.61 0.20 0.06 0.17 0.01 0.08 0.18 0.31 0.00  XRS2 0.06 0.23 2.04 0.06 0.94 0.30 -0.21 0.04 0.04 0.75 -0.16 0.02 0.08 0.84 -0.25 0.68 -0.15 0.12 0.68 0.13 -0.20 0.03 1.07 0.20 7.93 0.38 -0.16 0.02 0.54 0.22  YAF9 0.69 0.09 0.78 0.28 1.00 0.04 1.18 0.31 1.17 0.33 -0.98 0.11 -1.00 0.12 -0.97 0.08 -1.00 0.12 -1.00 0.12 0.14 0.74 0.72 0.37 1.38 0.09 0.27 0.67 0.26 0.71  YEN1 0.07 0.10 0.05 0.24 -0.17 0.00 0.05 0.38 -0.08 0.17 0.13 0.03 0.17 0.03 0.11 0.11 0.15 0.02 0.11 0.14 0.05 0.11 0.00 0.92 0.06 0.19 0.01 0.81 0.08 0.04  YKU70 0.02 0.69 -0.07 0.52 0.13 0.51 0.06 0.31 0.09 0.35 0.04 0.09 -0.18 0.21 0.07 0.19 0.03 0.42 -0.41 0.05 -0.11 0.12 -0.12 0.19 -0.07 0.75 -0.11 0.21 -0.01 0.83  YKU80 0.41 0.09 0.68 0.15 0.68 0.12 -0.27 0.66 -0.16 0.73 0.29 0.75 1.81 0.45 0.92 0.49 0.12 0.89 0.49 0.62 0.05 0.83 0.72 0.17 0.91 0.19 0.20 0.61 -0.25 0.57  YTA7 0.04 0.49 -0.09 0.07 -0.07 0.04 -0.27 0.01 -0.32 0.00 0.00 0.99 0.36 0.59 0.33 0.58 -0.22 0.03 0.08 0.93 0.06 0.57 0.15 0.47 0.25 0.20 -0.14 0.06 -0.18 0.05  ZUO1 0.08 0.42 0.30 0.07 0.12 0.73 0.07 0.53 1.71 0.00 0.22 0.10 0.09 0.74 -0.01 0.97 0.08 0.30 -0.66 0.56 0.21 0.13 0.44 0.07 0.29 0.04 0.31 0.01 0.74 0.06   Table ‎3-1. Primary SGA screen results using the three cohesin queries.  Green indicates hits with a score greater than 0.2 (i.e. PS hits). Red indicates hits with a score lower than -0.2 (i.e. SL or SC hits). Yellow filling indicates hits with p-value <0.05 (significant).          49  The number of initial genetic interactions, per query and per condition, are indicated in Figure ‎3-2.      50  Figure ‎3-2. Venn diagrams representing the number of initial hits per cohesin query. Diagrams are based on the raw data from the SGA analysis.   3.1.1 SL and SC genetic interaction networks  In order to reduce false positives, interactions observed in at least two out of the three independent cohesin SGA screens were prioritized for further analysis. As a consequence, mutated genes that interacted with only one of the mutated cohesin query genes were excluded at this stage.   Out of 4,650 possible genetic interactions, 124 genes that scored as negative interaction in two of three screens were identified, participating in 234 interactions across all five  51 conditions. Many hits belonged to other cellular pathways and processes, in addition to the DDR pathway, such as cell cycle progression, chromatin organization, transcription, translation and pathways in other organelles (e.g. mitochondria, endoplasmic reticulum, etc.).    Using Cytoscape software, genetic interaction maps for the three SGA screens were created (Figure ‎3-3 to 3-7). Seven genes that are known to be false positives were filtered out from these maps. These false positive interactions appear in all screens to date (H. Li, personal communication), regardless of the condition and may be due to sporulation or meiosis defects associated with these particular array genes.                    52   Figure ‎3-3.  Synthetic Lethal initial hits network.  Network in the absence of a DNA-damaging agent. Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, three hits (center) are shared between all cohesin queries. Three genes are shared between smc1-259 and scc2-4, while 18 are shared between scc1-73 and scc2-4 and one gene between scc1-73 and smc1-259. Green indicates previously validated SL hits76. Out of the core subunits. scc1-73 SGA screen recapitulated 7 previously validated SL hits, compared to 6 for smc1-259. The cohesin loader, scc2-4, recapitulated 8 previously validated SL hits.    53  Figure ‎3-4. Synthetic Cytotoxic initial hits network with MMS. Network in the presence of the DDA methyl methanesulfonate (MMS). Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, five hits (center) are shared between all three cohesin queries. Six genes are shared between smc1-259 and scc2-4, while seven are shared between scc1-73 and scc2-4 and five genes between scc1-73 and smc1-259. The concentration of MMS used in the SGA (high concentration) is indicated in the MATERIALS AND M chapter.    54 Figure ‎3-5. Synthetic Cytotoxic initial hits network with CPT. Network in the presence of the DDA camptothecin (CPT). Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, 10 hits (center) are shared between all three cohesin queries. The smc1-259 and scc2-4 screens share 11 gene hits, while eight are shared between scc1-73 and scc2-4 and 14 genes between scc1-73 and smc1-259. The concentration of CPT used in the SGA (high concentration) is indicated in the MATERIALS AND M chapter.     55 Figure ‎3-6. Synthetic Cytotoxic initial hits network with bleomycin. Network in the presence of the DDA bleomycin. Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, 18 hits (center) are shared between all three cohesin queries. The smc1-259 and scc2-4 screens share 12 gene hits, while 23 are shared between scc1-73 and scc2-4 and 29 genes between scc1-73 and smc1-259. The concentration of bleomycin used in the SGA (high concentration) is indicated in the MATERIALS AND M chapter. The large number of gene interactions in the presence of bleomycin might be due to low fitness values for the single array mutants in the presence of the bleomycin concentration used.    56  Figure ‎3-7. Synthetic Cytotoxic initial hits network with benomyl. Network in the presence of the DDA benomyl. Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, 10 hits (center) are shared between all three cohesin queries. The smc1-259 and scc2-4 screens share three gene hits, while seven are shared between scc1-73 and scc2-4 and three genes between scc1-73 and smc1-259. The concentration of benomyl used in the SGA (high concentration) is indicated in the MATERIALS AND M chapter.   3.1.2 PS genetic interactions  A positive genetic interaction is defined by better than expected growth of the double mutant as predicted by the growth of the individual single mutants. Out of 4,650 possible genetic interactions, 111 genes representing positive interaction hits were identified, participating in 254 interactions across all five conditions and shared between at least two out of the three cohesin queries (Figure ‎3-8 to 3-12).   57  Figure ‎3-8. Phenotypic Suppression initial hits network without a DDA. Network in the absence of  a DNA-damaging agent. Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, nine hits (center) are shared between all cohesin queries. Four genes are shared between smc1-259 and scc2-4, while four are shared between scc1-73 and scc2-4 and 14 gene2 between scc1-73 and smc1-259.    58  Figure ‎3-9. Phenotypic Suppression initial hits network with MMS. Network in the presence of the DDA methyl methanesulfonate (MMS). Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, 30 hits (center) are shared between all three cohesin queries. Nine genes are shared between smc1-259 and scc2-4, while 17 are shared between scc1-73 and scc2-4 and 16 genes between scc1-73 and smc1-259. The concentration of MMS used in the SGA (high concentration) is indicated in the MATERIALS AND M chapter.    59  Figure ‎3-10. Phenotypic Suppression initial hits network with CPT. Network in the presence of the DDA camptothecin (CPT). Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, 25 hits (center) are shared between all three cohesin queries. The smc1-259 and scc2-4 screens share 15 gene hits, while 19 are shared between scc1-73 and scc2-4 and eight genes between scc1-73 and smc1-259. The concentration of CPT used in the SGA (high concentration) is indicated in the MATERIALS AND M chapter.   60  Figure ‎3-11. Phenotypic Suppression initial hits network with bleomycin. Network in the presence of the DDA bleomycin. Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, 10 hits (center) are shared between all three cohesin queries. The smc1-259 and scc2-4 screens share 10 gene hits, while 16 are shared between scc1-73 and scc2-4 and seven genes between scc1-73 and smc1-259. The concentration of bleomycin used in the SGA (high concentration) is indicated in the MATERIALS AND M chapter.    61  Figure ‎3-12. Phenotypic Suppression initial hits network with benomyl. Network in the presence of the DDA benomyl. Pink indicates cohesin query genes. Width of connecting arrows indicates the magnitude of the genetic interaction (the wider the arrow-the stronger the interaction). In this network map, nine hits (center) are shared between all three cohesin queries. The smc1-259 and scc2-4 screens share six gene hits, while five are shared between scc1-73 and scc2-4 and 15 genes between scc1-73 and smc1-259. The concentration of benomyl used in the SGA (high concentration) is indicated in the MATERIALS AND M chapter.   3.2 Validation of hits- retesting in ScanLag and Tecan  Double mutants from the scc1-73 SGA screen were chosen for further analysis and validation. Validation was performed with scc1-73, as it is a core cohesin and exhibits better fitness compared to smc1-259 76.   A new growth measurement method named ScanLag87,89 was used to validate interactions identified in SGA screens. Previously, validation was performed in liquid growth assays using a  62 Tecan M1000 plate reader. ScanLag measures colony growth on solid media plates to generate growth curves. One advantage of ScanLag is that the same conditions used in the SGA screen can be applied (media, concentration of DDAs, incubator, and temperature fluctuation). Tecan, on the other hand, might require adjustments to find an appropriate DDA concentration that is comparable to the concentrations in SGA plates. In addition, ScanLag is more efficient in terms of the quantity of plates that can be analyzed at the same time (up to five plates, as opposed to one 96 well plate in the Tecan). ScanLag enables the validation of 12 genetic interactions on all four DDAs and in the absence of a DDA (control) at the same time, whereas Tecan can only assess two genetic interactions under all five conditions. Furthermore, ScanLag provides the opportunity to test 4-5 different drug concentrations simultaneously to find a therapeutic window. This means that in a single experiment, the concentration range for a strong negative effect on the fitness of the double mutant without killing the single mutant can be determined. Preparation of liquid cultures of strains being analyzed, however, is the same for both methods. Thus, ScanLag is beneficial for retesting SGA screen hits, as an efficient and adjustable follow-up method.       63  Figure ‎3-13. ScanLag method components.  The ScanLag software installed on the designated computer communicates with the scanner inside the incubator to acquire images of the plates over time. Running the program and accessing the stored images can be done through any computer via TeamViewer software.     The ScanLag system was set up by placing a scanner inside a 300C incubator, and connecting it to a designated computer located outside of the incubator. This computer has several programs installed on it, including ScanLag87,89, to enable the recognition and communication between the two devices (i.e. scanner and computer). The activity of the scanner was monitored to ensure that it did not affect the temperature inside the incubator by placing thermometers inside the incubator and near the scanner, and comparing the results to the temperature-indicating screen on the incubator's exterior door.   The ScanLag script was programmed to instruct the scanner to acquire images every two hours, for a period of 48 hours. The computer connected to the scanner, stores the periodic scans  64 and can be accessed by both lab and personal computers through a remote control/access software named TeamViewer.   In order to compare ScanLag to Tecan, a parallel experiment using the same yeast cultures for both methods was performed. At least three replicates for each strain were present for each experiment, and included a WT strain, a positive control in the presence of a DDA (rad52Δ ), the double mutant hit from the scc1-73 screen, rev1Δscc1-73,  and the respective single mutants, scc1-73 and rev1Δ. Strains were grown overnight in YPD, diluted to an OD of ~0.1 by adding 100µl of the culture to a new tube containing 5 ml of fresh YPD media, and allowed to grow for 3 hours while rotating at 25oC. After 3 hours, cultures were diluted precisely to an OD600 of 0.1 in a single 96-well plate filled with YPD. Since the double mutant was putative SC in the presence of MMS, strains were tested in the absence of a DDA by plating them on YPD plates or inoculating into YPD liquid media, and in the presence of a DDA by plating on YPD+MMS plates or inoculating into YPD+MMS liquid media. Both experiments were performed at 30oC for 48 hours for ScanLag and 24 hours for Tecan. All strains were analyzed as described in MATERIALS AND M, to calculate the AUC and generate growth curves.      65    Figure ‎3-14. Tecan (top) vs. ScanLag (bottom) parallel experiments.    The analysis of the rev1Δscc1-73 double mutant demonstrated a larger growth differential when assessed using ScanLag compared to Tecan under the DDA concentration used. The effect  66 on the fitness of the strains was measured by using an observed-predicted calculation (o-p for short), which is generated by calculating the predicted (i.e. multiplying the fitness of both single mutants under a specific condition) growth rate from the observed double mutant growth to assess any growth defect. The growth differential between the two methods used for double mutants that originated from the same colony (and overnight culture), might be attributed to a lower effective MMS concentration and different exposure of the cells to it in liquid compared to solid media.   Given that ScanLag resulted in similar results to the Tecan measurements, especially after a  24 hours analysis, and given that ScanLag allowed for more interactions to be tested in the same amount of time and allowed us to use the same drug concentration as in the SGA screen, ScanLag was used for further validation of genetic interactions identified in the SGA experiments.  3.2.1 Validation of genetic interactions using ScanLag   ScanLag was used to retest digenic interactions from the SGA data. Initially, 24 double mutants from the SGA plates were isolated for validation assays. These double mutant strains exhibited inconsistent phenotypes, most likely due to the gain of additional modifying mutations during extended storage of the plates at 4oC. New double mutants were therefore isolated via resporulation of the original scc1-73 DMA double heterozygous diploids. We then tested 28 new double mutants, and their respective singles, and found 6 double mutant strains exhibiting different phenotypes than predicted by SGA. Since the origin of the double mutants was from sporulated strains and a single round of haploid selection, we were concerned that one round of selection was not sufficient to eliminate diploids and that the phenotypic differences we observed  67 might be due to the presence of heterozygous diploid strains. Indeed, testing for haploidy using a mating type test revealed that the majority of strains were actually diploids.  A new set of double mutants was generated after two rounds of haploid selection. To test whether the selected double mutants were haploid, 51 strains (rev1Δscc1-73 and ctk1Δscc1-73 were not retested as they had recapitulated the genetic interaction) were tested for haploidy using a mating-type test. Fifty verified double mutant haploid strains were then carried on for validation. One strain, clb2Δscc1-73 was found to be a heterozygous diploid and was removed from the list.   Twenty-five SL interactions were chosen for validation (Figure ‎3-7). These SL interactions include gene pairs that were previously tested and validated for the SL phenotype using the same cohesin queries76, and new interactions that were not previously identified. Each SL double mutant was tested under all five conditions (no-DDA, termed ND, and on all 4 DDAs), using ScanLag (MATERIALS AND M).   For the validation of the SC network, genetic interactions that were observed for all three cohesin queries were prioritized. Each SC interaction was retested in all conditions for two reasons: 1) to assess whether a SC interaction seen initially under a particular DDA was true positive and 2) to assess whether SC interactions with other DDA were missed (i.e. false negative) in the SGA screen. As a consequence, some interactions that appeared in only two out of the three SGA screens in the presence of a certain DDA, were retested under those conditions. This expanded the number of genetic interactions being tested for validation.   Growth of 25 SL interaction double mutants, 43 SC interaction double mutants (including 5 on MMS, 10 on CPT, 18 on Bleomycin and 10 on Benomyl) and 4 chosen PS interaction double mutants, were measured using ScanLag. Overall, 48 negative interaction gene pairs and 4  68 positive interaction gene pairs were tested in all 5 conditions (YPD, MMS, CPT, Bleomycin and Benomyl) using the scc1 query mutant (MATERIALS AND M).   Figure ‎3-15. Double-mutant hits score distribution under CPT, ScanLag vs. SGA.  Each dot represents a single strain. ScanLag score is calculated as o-p (observed - predicted). SGA score is calculated as e-c/c.      Genetic interactions were assessed by analyzing the growth of the spotted cell cultures using ScanLag over time, which were then used to calculate the fitness of the mutant strains. Growth was quantified by calculating the AUC. Genetic interaction scores were normalized to the WT strain growth on the same plate. These AUC values were used to calculate o-p scores. In this method, o is the observed fitness relative to WT and p is the calculated predicted fitness of the double mutant, based on the combined growth of the two single mutants (i.e. multiplying their observed fitness) relative to WT. Since the ScanLag method (specifically ImageJ software) provides arbitrary units for measuring of colony size over time, we wished to determine the correlation of growth measurement between the two methods (i.e. SGA and ScanLag). To compare ScanLag and SGA, observed minus predicted (o-p) growth scores were calculated using ScanLag and compared to the e-c/c SGA scores.  69  The numerical range of growth values from ScanLag (o-p) and SGA (e-c/c) were quite different, with the SGA scores having a larger range than ScanLag values. In order to adjust the ScanLag window to make it more comparable to SGA scores, a different cutoff was needed to identify interactions. Since scc1-73 is a slow grower that is highly sensitive to induced DNA-damage, different cutoff values were determined for each condition, a stricter cutoff in their absence (no-DDA, ND), and a less stringent cutoff in the presence of DDAs.  Double mutants tested without a DDA (SS/SL) that showed a negative growth greater than expected, were considered 'validated' if the o-p ≤-0.20. Double mutants tested with a DDA (SC) that showed a negative growth greater than expected, were considered 'validated' if the o-p ≤-0.1. Double mutants that showed growth greater than expected with or without a DDA (PS), were considered 'validated' if the o-p ≥0.20.‎For the 52 scc1-73 DMA double mutants tested, we found 25 double mutant strains that were validated as having either SL or SC interactions and five double mutant strains that were validated as having PS interactions with a mutated cohesin, under the condition used.    70 Gene Hit Type of predicted GI YPD ScanLag MMS ScanLag CPT  ScanLag Bleomycin  ScanLag Benomyl  ScanLag CTF4 SL 0.08 0.1 0.02 0.02 -0.19 GIM4 SL -0.32 -0.09 -0.29 -0.06 0.2? KAR3 SL -0.17 -0.16 -0.32 -0.06 -0.22 RAD61 SL -0.21 0.16 0.08 0.02 0.39 BUB3 SL 0.2 0.1 0.18 -0.05 -0.01 BIM1 SL -0.17 -0.18 -0.22 -0.07 -0.26 CHL1 SL -0.14 -0.01 0.08 0.13 0.1 DOC1 SL -0.3 -0.13 -0.19 -0.07 -0.24 CSM3 SL -0.32 -0.16 -0.18 0 0.01 BUB1 SL 0.65 0.61 0.67 0.47 0.25 SHP1 SL 0.13 0.08 0.02 -0.08 0.1 MMS1 SL -0.33 0 -0.01 -0.77 0.05 SRS2 SL -0.38 -0.18 -0.28 DG x HTZ1 SL -0.26 -0.25 -0.16 -0.06 0.17 SAP155 SL -0.25 -0.26 -0.19 -0.97 -0.1 LSM6 SL -0.2 -0.08 -0.15 -0.07 -0.16 CIN2 SL -0.27 -0.13 -0.15 DG x PHR1 SL -0.12 0.02 -0.16 DG x ASF1 SL -0.01 0.11 0.16 -0.05 0.05 MRC1 SL -0.36 -0.21 -0.26 -0.08 -0.47 CHD1 SL -0.1 -0.31 -0.2 DG x SSN8 SL -0.15 0 -0.05 DG x HST3 SL -0.18 -0.14 -0.18 DG x CTK1 SL -0.34 -0.18 -0.23 x -0.08 REV1 SC (mms) 0.02 -0.29 0.06 x x REV3 SC (mms) -0.13 -0.18 -0.15 -0.3 0.07 APN1 SC (mms) -0.3 -0.28 -0.26 DG x TEL1 SC (mms, cpt) -0.37 -0.29 -0.37 DG x MCK1 SC (cpt, bleo) -0.26 -0.38 -0.25 DG x RAD17 SC (cpt) -0.04 0.03 -0.07 x x RAD24 SC (cpt) -0.17 -0.16 -0.14 -0.43 -0.17  71 Gene Hit Type of predicted GI YPD ScanLag MMS ScanLag CPT  ScanLag Bleomycin  ScanLag Benomyl  ScanLag RAD9 SC (cpt) -0.17 -0.04 -0.12 -0.1 -0.03 DOA1 SC (cpt, bleo) -0.26 -0.25 -0.26 0.13 -0.33 EXO1 SC (cpt) -0.39 -0.45 -0.47 DG x UBR1 SC (bleo) -0.28 -0.19 -0.09 x x RAD5 SC (bleo) -0.2 0.05 -0.16 -0.69 0.03 PER1 SC (bleo) -0.12 -0.11 0.01 x x SEM1 SC (bleo) -0.1 -0.03 -0.01 0.02 0.17 SGF73 SC (bleo) -0.17 -0.11 -0.07 -0.85 0 FKH2 SC (bleo) -0.29 -0.21 -0.21 -0.52 -0.02 RAD55 SC (bleo) -0.17 0.01 0.02 0.01 -0.02 PSY2 SC (bleo) -0.19 -0.11 -0.25 x x RPB9 SC (bleo) -0.06 0.05 0.07 0.03 -0.05 SIT4 SC (beno) -0.27 -0.15 -0.37 -0.14 -0.33 CDH1 SC (beno) -0.13 -0.17 -0.37 -0.02 -0.22 RPN10 SC (beno) -0.25 -0.06 -0.23 -0.25 -0.07 DBP7 SC (beno) -0.24 -0.22 -0.36 -0.12 -0.05 SGO1 SC (beno) -0.29 -0.38 -0.4 -0.02 -0.32 SPT4 PS 0.18 0.11 0.16 x x RNR4 PS -0.2 -0.24 -0.32 -0.12 -0.35 SWC5 PS 0.54 0.61 0.79 0.35 -0.07 CLN2 PS 0.26 -0.04 0.09 x x Table ‎3-2. Results of retested initial SGA scc1 hits.  Gene name filled in yellow indicated previously validated negative interaction hits using the cohesin query76. Gene name filled in blue were verified for haploidy. Red filled scores are negative interaction that passed the specific cutoff (i.e. validated). Green filled scores are phenotypic suppressors (PS) validated hits. GI stands for genetic interaction as predicted by SGA primary data. The 'x' symbolizes that the experiment has not been done. DG indicates an experiment in which scc1 query mutant did not grow.  72  Several strong genetic interactions were validated. The rev3Δscc1-73 interaction was a good example in which the genetic interaction of the double mutant is stronger in the presence of a DDA, specifically MMS, compared to in the absence of a DDA. Thus, we validated rev3Δ as an SC interaction with a mutated cohesin complex. The o-p score (normalized to the WT strain under the same condition) of the double mutant on YPD was -0.13, compared to -0.18 on MMS. While the predicted growth defect was 0.25 on MMS, the observed fitness of the double mutant was 0.08.    Figure ‎3-16. Growth curves of SC interaction on MMS.  The double mutant rev3Δscc1-73 without a DDA (left) and with MMS (right), respective to a WT strain and the two single mutants. Growth curves were generated after 24 hours. Images from scanned plates were taken after 24 hours, presenting the single mutant rev3Δ (top) and the double mutant rev3Δscc1-73 (bottom) under each condition.    Another validated SC interaction was cdh1Δscc1-73 on CPT, where the differential in fitness of the double mutant, both with and without CPT, was substantial. The o-p score (normalized to the WT strain under the same condition) of the double mutant was -0.13 on YPD  73 compared to -0.37 on CPT. While the predicted growth defect was -0.39 on CPT, the observed fitness of the double mutant was 0.02.  Figure ‎3-17. Growth curves of SC interaction on CPT.  The double mutant cdh1Δscc1-73 without a DDA (left) and with CPT(right), respective to a WT strain and the two single mutants. Growth curves were generated after 24 hours.    A strong SC interaction with scc1 was observed on benomyl for both sit4Δ and sgo1Δ. The o-p score (normalized to the WT strain under the same condition) of sit4Δscc1-73 double mutant was -0.27 on YPD and -0.33 on benomyl. While the predicted growth defect was -0.41 on benomyl, the observed fitness of the double mutant was 0.08. The o-p score of sgo1Δscc1-73 was -0.29 on YPD and -0.32 on benomyl. While the predicted growth defect was 0.4 on benomyl, the observed fitness of the double mutant was 0.08. These interactions are good examples of weak interactions that occur in the absence of a DDA, while exhibiting a strong SC interaction with the addition of a DDA.   74  Figure ‎3-18. Growth curves of SC interaction on benomyl.  The double mutant sit4Δscc1-73 without a DDA (left) and with benomyl (right), respective to a WT strain and the two single mutants. Growth curves were generated after 24 hours. Images from scanned plates (left) were taken after 24 hours, presenting the single mutant sit4Δ (top) and the double mutant sit4Δscc1-73 (bottom) under each condition. Images from scanned plates (right) were taken after 72 hours (three days).   Figure ‎3-19. Growth curves of SC interaction on benomyl.  The double mutant sgo1Δscc1-73 without a DDA (left) and with benomyl (right), respective to a WT strain and the two single mutants. Growth curves were generated after 24 hours. Images from scanned plates were taken after 48 hours, presenting the single mutant sgo1Δ (top) and the double mutant sgo1Δscc1-73 (bottom).   75   As for the reassessment of SL hits, we found several validated hits, including examples that were previously validated (e.g. gim4Δscc1-73), as well as novel genetic interactions (e.g. rpn10Δscc1-73, lsm6Δscc1-73). For all of these examples, the lethal interaction was maintained with the addition of the DDAs.   Figure ‎3-20. Growth curves of SL interaction.  The double mutants gim4Δscc1-73, rpn10Δscc1-73, lsm6Δscc1-73,  without a DDA, respective to a WT strain and the two single mutants. Growth curves were generated after 24 hours. Images from scanned plates were taken after 24 hours, presenting the single mutant (top) and the double mutant (bottom).    A strong PS genetic interaction was observed between scc1 and swc5Δ across four out of the five conditions. The predicted fitness was 0.35 (normalized to the WT strain on YPD), while the observed fitness of the double mutant was 0.88.  76  Figure ‎3-21.  Growth curves of PS interaction.  The double mutants swc5Δscc1-73 without a DDA, relative to a WT strain and the two single mutants. Growth curves were generated after 24 hours. The image from scanned plates was taken after 24 hours, and present in the upper right corner with the single mutant (top) and the double mutant (bottom).    Data obtained from the bleomycin experiments were highly variable. This was due, in part, to the high sensitivity of the query strain to bleomycin. Growth of single and double mutants was tested under different concentrations of bleomycin in order to find the optimum concentration to maximize the effect on double mutant strains without strongly affect the growth of the query, scc1. However, such optimal concentration was not found, suggesting that the concentration range of bleomycin for scc1 was narrow.     77 Chapter 4: DISCUSSION  4.1 Summary of findings 4.1.1 Overview  Aim #1- Identifying synthetic cytotoxic interactions with cohesin  The aim of my thesis was to identify genetic interactions with cohesin mutations that result in synthetic cytotoxicity (SC) to DNA damaging agents (DDAs) . SC interactions with cohesin mutations could be used to leverage the high frequency of cohesin mutations in cancer to improve the efficacy, safety and selectivity of current anti-cancer treatments. Four DDAs, representing common classes of traditional anti-cancer cytotoxic agents, were used to discover new DDA-associated genetic interactions, thereby expanding on previous genetic screens for genetic interactions with cohesin mutations76,81. To achieve this aim, I performed high-throughput SGA screens, using three different cohesin mutations as query genes with a curated array of 310 DNA-associated mutations, in the presence and absence of four distinctive DDAs (five conditions overall).    Determining interactions + SGA limitations  In order to determine initial SGA interactions, an e-c/c score threshold was chosen that identified hundreds of genetic interactions, both negative (i.e. SL, SC) and positive (i.e. PS), with the cohesin-mutated queries (carrying ts-mutations in the core-subunits SMC1 and SCC1, and in the loader SCC2). For each screen an average of 46 negative genetic interactions and 65 positive genetic interactions were obtained. As with any large-scale genetic screen, such as SGA, one must consider false positive and false negative results. Despite using a relatively small, recently confirmed yeast array (see MATERIALS AND M) that contains many functionally related genes  78 which would be expected to interact with the query, false positive and false negative results are inevitable. False results can be due to technical pinning errors (i.e. missing spots), the emergence of suppressor mutations, variation in media stocks and condition, and temperature fluctuations that can affect the phenotype of temperature sensitive query mutations.  We expected that overlapping the three preliminary data sets would enrich for robust biologically-meaningful interactions and potentially minimize the false positive rate. As predicted, a subset of genetic interactions, both negative and positive, was shared among the different cohesin mutations. A total of 124 negative interaction (NI) and 111 positive interaction (PI) mutated genes were found that were shared between at least two out of the three cohesin query mutations. These interactions spanned a number of biological processes in addition to DDR (which is enriched for in our array), including transcription, translation, protein modification, folding and degradation, cell cycle progression, chromatin modification and metabolism.   Cohesin mutations have been previously screened by SGA for genetic interactions in both large81 and small scale76 efforts. The interactions found in these studies can be used to compare against the results from the three SGA experiments I performed using the DDR-MA in the absence of DDA. A comparison between the large genetic interaction database81 (known as The CellMap) using the three cohesin queries and my SGA results was made. By using the cutoff  used in the database for determining hits (<-0.08, and using the e-c formula instead of e-c/c used in this study), and filtering for shared genetic interactions between at least 2 out of the 3 cohesin queries, I found 23 shared negative genetic interactions out of 41 genes that are on the DDR-MA, with CellMap hit genes (including 2 false positives). For example, csm3Δscc1-73, mrc1Δscc1-73 , chl1Δscc1-73 and kar3Δscc1-73 were shared negative genetic interactions. Out of 32 PS genetic  79 interactions identified in my SGA screen data (in the absence of DDAs and shared between at least 2 out of the 3 queries), no shared interactions (that passed CellMap cutoff of >0.08) were found. This may be due to the fact that while NI tend to occur between genes with clear functional relationships, a feature represented in our selective small array, PI often map to more general pathways such as cellular proteostasis and cell cycle progression that might be underrepresented in the array81. In addition,  PIs can also occur due to spontaneous suppressor mutations that correct the fitness defects of the strain carrying a deletion or hypomorphic mutant allele. It is also possible, that the combination of the two mutations in these identified interactions increases mutation rate and contributes to the appearance of new mutations that can improve the growth defect of the double mutant.   Aim #2- Development of ScanLag as a tool for validation of genetic interactions  The second goal of my thesis was to use and assess ScanLag as a technique to validate the genetic interactions identified in the large-scale SGA screen. After filtering out seven genetic interactions that are known to be false positives, initial genetic interaction network maps were created, in the absence and presence of the four distinctive DDAs, from which the interactions for validation were chosen. Given that many genetic interactions profoundly affect growth, double mutant selection must be rigorous to prevent the selection of heterozygous diploids. During the process of our early validation attempts, it was apparent that SGA strains need two haploid-selection rounds to avoid diploid double heterozygous mutants that escaped selection due to strong selection against haploid double-mutants with strong fitness defects.    The utility of ScanLag lies in its ability to combine the advantages of two well-known yeast techniques, quantitative growth curves and qualitative spot assays, which have been extensively used to validate the identified genetic interactions following an SGA screen.  80 ScanLag was originally designed by the Balaban lab to measure in parallel the delay in growth (Lag time) and growth rate (i.e. the speed at which the number of cells in the population increases) of bacteria cells87,89. Using an optical scanner to measure bacterial colony size over time, ScanLag software is able to identify and quantify different parameters, such as the time of appearance and growth time of each colony. I repurposed ScanLag to quantify the effect on the growth rate of yeast double mutants, compared to the parental single mutant strains, on a variety of conditions per single experiment, using a different bioinformatic analysis tool (i.e. ImageJ instead of MATLAB). Using solid agar plates containing the same conditions as in the SGA screen, ScanLag allows for a fast retesting process of many genetic interactions simultaneously. The major advantage of ScanLag over Tecan is its ability to retest ~6-fold more genetic interactions in one round of experiment, in the absence and presence of DDAs, and thus is suitable for retesting the hundreds of genetic interactions that can be identified in an SGA screen.  This use of ScanLag for measuring yeast growth has not been published.   In order to validate genetic interactions, a growth differential threshold needed to be assigned. ScanLag uses different measurements for growth, therefore, it is important to understand how ScanLag growth curves correlate with SGA. In order to choose a threshold for ScanLag, observed - predicted (o-p) scores for the tested interactions were plotted and the distributions of the overall double mutant scores were compared to values determined by SGA. The distribution of scores in ScanLag were approximately half those observed for SGA. For example, on camptothecin (CPT), double-mutant e-c/c values range from -0.98 to 1.63 with SGA, while ScanLag values ranged from -0.47 to 0.8 using (Figure ‎4-1).   81  Figure ‎4-1. Distribution of double-mutant in ScanLag vs. SGA Double-mutant scores distribution, ScanLag vs. SGA, under CPT.  ScanLag scores are calculated using o-p formula, while e-c/c formula for SGA.     Selecting two different thresholds for the absence and presence of a DDA, was based on the observation that the chosen query strain, carrying the scc1-73 allele, was sensitive to DDAs resulting in a lesser maximum growth value. Therefore, a different cutoff was needed in order to capture these interactions. Furthermore, it was found that some interactions required longer growth periods to maximize the differential between single mutants and double mutants. ScanLag experiments were analyzed after 24 hours in order to maximize the number of genetic interactions that could be tested in a given time period. However, it is possible to run a ScanLag experiment for longer time periods. However, if WT is used as the reference, analyzing at a time point less than 48 hours is ideal, as WT will reach saturation in less than 48 hours. Ideally, growth over longer periods would allow for a wider range over which to screen for growth differentials when using sensitive or slow-growing mutant strains. Due to the fact that the single scc1-73 is a slow-growing and a DDA-sensitive strain, the growth window for identifying SC interactions is small within a 24 hours period. The small growth window could be expanded by lowering the DDA concentration, and/or extending growth periods, to increase the differential -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 0 20 40 60 ScanLag double mutant -1.5 -1 -0.5 0 0.5 1 1.5 2 0 20 40 60 SGA double mutant  82 between the double mutant and single mutants in the presence of the DDA. For some genetic interactions, it may be necessary to perform multiple ScanLag assays to identify ideal growth periods and DDA concentrations to determine if interactions are additive or SC. In some cases, it might be difficult to discern additive genetic interactions in the presence of a DDA from true SC interactions, due to the fact that scc1-73 is sensitive to many DDAs and frequently exhibits SS interactions with many different array gene mutations even in the absence of DDA. However, even if the effect on the fitness of the double mutant is additive, it still might be of interest to pursue in human cancer cell lines as an additive cytotoxic interaction results in a greater cytotoxic effect than either single mutant with DDA or the double mutant without DDA.   Fifty-two double mutants were retested by ScanLag. Genetic interactions with bleomycin resulted in variable responses and were, therefore, removed from further analysis. By considering only interactions that passed the specific cutoff for the absence and presence of a DDA, 30 of 44 (not including bleomycin) genetic interactions recapitulated the genetic interaction (negative or positive) observed by SGA and were deemed validated (Table ‎4-3). These 44 expected genetic interactions included 24 SL interactions, 16 SC interactions and 4 PS interactions. Validated interactions that were recapitulated as predicted by SGA were, for example, csm3Δscc1-73, doc1Δscc1-73 and gim4Δscc1-73 as SL interactions. The strong SC interaction rev3Δscc1-73 on MMS was also recapitulated. These results demonstrate that ScanLag is a viable approach for retesting genetic interactions following a large-scale genetic screen.   Some genetic interactions that were not recapitulated upon retesting with ScanLag, included five double mutants that were previously validated as SL genetic interactions76, such as ctf4Δscc1-7 and bub3Δscc1-73. Seven other double mutants initially identified to be SLs in my  83 SGA screen data, also failed to validate, with three of them being SCs (phr1Δscc1-73, chd1Δscc1-73, hst3Δscc1-73). In addition, two PS interactions, spt4Δscc1-73 and rnr4Δscc1-73, also failed to validate using ScanLag and in the case of the latter double mutant, changed to a SL/SS across all conditions (Table ‎3-2). A possible explanation could be that the rnr4Δscc1-73 double mutant exhibits a negative genetic interaction but gained a suppressor mutation in the course of the SGA, leading to its spread throughout the strain spot on the plate. Fifteen double mutants predicted to be SC on MMS, CPT and/or benomyl, showed 30 new genetic interactions by ScanLag which differed from the one seen by SGA, on these DDAs (Table ‎4-4). Out of 9 double mutants expected to be SC on only bleomycin, 6 demonstrated new genetic interactions on other conditions (Table ‎4-4). These new (not observed by SGA) genetic interactions include 2 new SCs on benomyl, 10 new SCs on CPT, 13 new SCs on MMS and 19 genetic interactions that were observed as SC by SGA but SL/SS by ScanLag.   SC interactions that were found to be SL/SS upon retesting with ScanLag, were probably SLs that were missed initially in the course of the SGA, suggesting that these SL genetic interactions were false negatives in the SGA screen. Since in the course of a ScanLag experiment, strains that originated from single colonies are tested, rather than from a mixed population as on the SGA plates, the effect of the interaction can be detected without being masked by colonies with suppressor mutations that could take over the spot. Examples that were predicted to be SCs and were found to have a strong genetic interaction in the absence of a DDA (SS) are tel1Δscc1-73, and ubr1Δscc1-73, which was a previously observed strong negative interaction 81. Other double mutants that exhibited reduced fitness in the absence of DDA that was increased to lethality in the presence of DDAs (SC) were rev3Δscc1-73 on MMS, cdh1Δscc1-73 on CPT, sit4Δscc1-73 and sgo1Δscc1-73 on benomyl.    84  In the set of validated genetic interactions were some new and unexpected interactions. Cohesin interacts directly with SGO1 to retain SGO1 at the inner centromeres90,91. This interaction can suppress CIN by stabilizing kinetochore-microtubule attachment, and by protecting centromeric Scc1/Rad21 from prophase dissociation during M-phase. The validated SC interaction of sgo1Δscc1-73 with benomyl is new but not unexpected, as mutations affecting spindle checkpoint genes such as SGO1 might be expected to be sensitive to the spindle poison benomyl and to defective cohesin function. This is because both stabilized kinetochore-microtubule attachment and functioning cohesin complex are important in ensuring faithful chromosome tethering and separation during cell division92. Another new yet not unexpected SL interaction is rpn10Δscc1-73. The RPN10 gene functions as a non-ATPase base subunit of the 26S proteasome in yeast93–96. It was found that proteasome activity is necessary to complete cell division in human cells, independent of cohesin cleavage97, therefore, it is possible that a combination of both a deficient cohesin and a deficient proteasome activity results in cell death due to inability to complete cell division.   I also found that loss of the translesion synthesis (TLS) polymerases REV1 or REV3 in cohesin mutated strains resulted in SC genetic interactions with MMS. The DNA repair gene REV1 is a deoxycytidyl transferase that is involved in TLS98–101.  REV1 functions in an error-prone translesion pathway, recruiting the DNA polymerase zeta complex (that includes REV3 and REV7 as subunits) to sites of damaged DNA102,103. A null mutation in either REV1 or REV3 results in sensitivity to the alkylating agent MMS104. It has been suggested, that upon induced DNA damage by a DDA such as MMS during S phase, REV1 is needed to switch to TLS polymerase and bypass the damaged site. Since cohesin also functions in DNA repair via mediating homologous recombination (HR) between two sister chromatids to repair double- 85 strand breaks (DSBs), this might serve as an alternate model to bypass the DNA damage caused by MMS in the absence of the REV proteins. However, a combination of a null REV1 or REV3 and a compromised cohesin, might lead to cellular death in the presence of MMS due to unrepaired damage. This model can be further supported by the observations that HR is impaired in Drosophila lacking DNA polymerase zeta105 and‎that‎REV1−/−‎vertebrate‎cells‎exhibit a decreased level of immunoglobulin gene conversion which is mediated by HR106, suggesting participation of  REV1 in the recombination-based pathway.  Positive interactions (i.e. PS), could confer an advantage to cohesin-mutated cells, and therefore, could be relevant to some cases of resistance to chemotherapeutic agents. The genetic interaction swc5Δscc1-73, for example, was recapitulated and validated as a positive genetic interaction in the course of this study. The null mutation in SWC5 (CFDP1 gene in humans), which affects histone exchange and chromatin remodeling, together with a cohesin mutation, resulted in a better than expected growth relative to either single mutant under all conditions except benomyl. The SWC5 gene is a component of the SWR1 complex, which exchanges histone variant Htz1p (homolog of human H2AZ) for chromatin-bound histone H2A98,107–110. This dynamic exchange of histones can affect transcription, epigenetic silencing, DNA damage repair and chromosome segregation111. In the absence of SWC5, the complex is able to bind to the chromatin but the replacement of histones is abolished111. The PS may be due, in part, to a change in transcription profile that stimulates other pathways or proteins that overcome the cellular stress. Other subunits of the complex in yeast also include SWR1 and YAF9, which were both present on the DDR-MA and were PS hits based on the preliminary SGA data. The observed PS, which is observed in the presence of all four DDAs except benomyl, will need further studies in order to understand its mechanism.   86  In summary, the aim of this study was to identify SC interactions with a compromised cohesin complex, in the presence of DDAs that represent different chemotherapeutic classes. Strong candidates, both with negative and positive genetic interactions with a mutated cohesin, were identified and validated. Synthetic sick (SS) interactions were found to be highly prevalent in this study, as most of the gene deletion mutations on the DDR-MA, as well as the query mutation, scc1, cause CIN and/or sensitivity to DDAs, thus resulting in a narrow window for more accurate analysis with the condition used in this study. Further investigation will be required to determine if these interactions are due to general or specific synergistic defects and if they are conserved in human cell lines as well.  4.2 Significance of findings  In this work, I exploited the SGA method available in yeast, which enables rapid screening of thousands of possible double mutant combinations on one plate, to identify SL, SC and PS genetic interactions with a mutated cohesin complex. Previous studies had identified SL interactions with cohesin mutations. My work extended the range of genetic interactions with cohesin mutations by introducing DNA damaging agents, demonstrating the potential of exploiting SC to broaden the spectrum of potential anticancer drug targets. Given that the array used was curated to represent DDR and CIN genes that are highly conserved between yeast and human, the identified genetic interactions in this study can thereby translate into a potential therapeutic candidates for inhibition while exploiting existing traditional cytotoxic agents. In addition to identifying potential anticancer drug targets, the genetic interactions I found also have the potential to shed light on the biological mechanisms underlying the role of cohesin in cancer and genome stability.  87  ScanLag was found to be a useful method for validating genetic interactions identified in high throughput SGA screens. ScanLag was previously used to measure bacterial growth rate and lag time. My work demonstrates that it is a powerful tool for measuring yeast growth as well. This method is efficient and enabled the validation of many genetic interactions. ScanLag could be incorporated into SGA workflows to validate genetic interactions found in high-throughput screens.  4.3 Future directions  The interesting SC interactions, such as rev3Δscc1-73 and rev1Δscc1-73 with MMS, and the positive genetic interaction swc5Δscc1-73, can be characterized and tested with other mutated cohesin alleles to confirm a consistent effect on growth fitness. Understanding the biological mechanism behind these interactions could provide further valuable insights on the function of cohesin under different cellular stresses. While the smaller-scale mutant-array used in this study is a proof of principle, SGA can also be expanded to test whole genome. This will enable to broaden the number of potential interactions that can be identified, as well as to discover novel genetic interactions that otherwise might not have been expected to interact with a compromised cohesin. Similarly, other DDAs could be used in additional cohesin SGA screens, such as antimetabolites, which represent another class of chemotherapeutic agents used in the clinic and induce DNA-damage by, for example, inhibiting one or more enzymes that are critical for DNA synthesis (such as needed for DNA repair)112, and UV radiation, which introduces a specific type of damage and represents a highly common environmental factor. The genes identified as validated SC interaction partners with cohesin mutations can also be used as SGA queries themselves to determine if these genes are highly connected SL/SC hubs that might be relevant to other cancer associated mutations.   88  ScanLag can be further exploited as a follow-up method for SGA. However, determining an optimal time and condition for analysis to enable for better separation between growth curves will be needed when using query strains with strong fitness defects, as seen in this work with cohesin query mutations.   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Rev. 109, 2880–93 (2009).  100 Appendices  Appendix A    A.1 List of yeast strains ORF GENE Function Human  pathway fitness YGR163W GTR2 EGO and GSE complexes RagB,RagC,RagD activates transcription decreased YLR085C ARP6 SWR1 complex ARP6 ATP-dependent histone exchange decreased YML021C UNG1 mtDNA UNG,UNG2 BER decreased YOL115W PAP2 TRAMP complex TRF4-1,POLK BER increased YAL015C NTG1 DNA N-glycosylase, AP lyase  NTHL1 BER(mitochondria),SSR increased YER142C MAG1 3-methyl-adenine DNA glycosylase Aag BER,DR increased YKL113C RAD27 5' to 3' exonuclease, 5' flap endonuclease Fen1 BER,NHEJ decreased YBL019W APN2 AP lyase 2 HAP1 BER,SSR normal YKL114C APN1 AP lyase 1  APEX1, APEX2 BER,SSR decreased YML060W OGG1  OGG1 BER,SSR increased YOL043C NTG2 DNA N-glycosylase, AP lyase  NTHL1 BER,SSR increased YOR258W HNT3 DNA 5' AMP hydrolase Aprataxin,PNKP BER,SSR decreased YMR156C TPP1  PNKP BER,SSR,NHEJ viable  101 ORF GENE Function Human  pathway fitness YPR135W CTF4  AND-1 BIR,sister chromatid cohesion decreased YGR270W YTA7  ATAD2B chromatin viable YKL117W SBA1  PTGES3 chromatin viable YLR418C CDC73  CDC73 chromatin viable YBL002W HTB2 Histone H2B, H2A-H2B heterodimer Histone chromatin assemble viable YBL003C HTA2 Histone H2A, H2A-H2B heterodimer Histone chromatin assemble normal YBR009C HHF1 Histone H4, H3-H4 heterodimer Histone chromatin assemble decreased YBR010W HHT1 Histone H3, H3-H4 heterodimer H3.3 chromatin assemble decreased YBR195C MSI1 CAF-1 complex  RbAp48,RbAp46 chromatin assemble decreased YDL070W BDF2  Brd2,BAZ1B chromatin assemble decreased YDR225W HTA1 Histone H2A, H2A-H2B heterodimer Histone chromatin assemble Decreased YJR082C EAF6 NuA4 HAT complex FLJ11730 chromatin assemble Decreased YLR399C BDF1  Brd2 chromatin assemble Decreased YML102W CAC2 CAF-1 complex  Cac2 chromatin assemble Decreased YNL030W HHF2 Histone H4, H3-H4 heterodimer Histone chromatin assemble Decreased YNL031C HHT2 Histone H3, H3-H4 heterodimer Histone chromatin assemble Decreased YNL107W YAF9 NuA4 HAT complex AF9,ENL,GAS41 chromatin assemble Decreased  102 ORF GENE Function Human  pathway fitness YNL136W EAF7 NuA4 HAT complex MRGBP chromatin assemble Decreased YOR191W ULS1  TTF2 chromatin assemble Decreased YPR018W RLF2 CAF-1 complex  p150,CHAF1A chromatin assemble Decreased YPR023C EAF3 NuA4 HAT complex MORF4,MRG15/X chromatin assemble Decreased YAL019W FUN30  SMARCAD1 chromatin remodeling Decreased YDR334W SWR1 SWR1 complex SRCAP chromatin remodeling Decreased YNL330C RPD3 Histone deacetylase hda2 chromatin remodeling Viable YOR304W ISW2 ATP-dependent DNA translocase SMARCA1 chromatin remodeling Decreased YBR245C ISW1  SMARCA5 Chromatin remodelling Viable YBR289W SNF5 SWI/SNF chromatin remodeling complex Snf5(SMARCB1), BAF47/INI1 Chromatin remodelling Decreased YGR056W RSC1  RSC chromatin remodeling complex BAF180,PBRM1 Chromatin remodelling Decreased YJL065C DLS1 ISW2/yCHRAC chromatin accessibility complex CHRAC1 Chromatin remodelling Viable YJL176C SWI3 SWI/SNF chromatin remodeling complex SMARCC1 Chromatin remodelling Decreased YLR357W RSC2  RSC chromatin remodeling complex BAF180 Chromatin remodelling Decreased YNR023W SNF12  RSC chromatin remodeling complex SMARCD1 Chromatin remodelling Viable YNL068C FKH2 Forkhead family transcription factor FOXF2 chromatin silencing Increased  103 ORF GENE Function Human  pathway fitness YBL058W SHP1  NSFL1C chromosome segregation Viable YEL061C CIN8  KIF11 chromosome segregation Viable YER016W BIM1  MAPRE1 chromosome segregation Viable YER177W BMH1  YWHAE chromosome segregation Viable YGL003C CDH1  FZR1 chromosome segregation Viable YGL240W DOC1  ANAPC10 chromosome segregation Viable YML124C TUB3  TUBA1A chromosome segregation Viable YOR014W RTS1  PPP2R5C chromosome segregation Viable YGR285C ZUO1  DNAJC2 cin Viable YHR064C SSZ1  HSPA8 cin Viable YGL194C HOS2 Set3 complex HDAC11 Covalent modifications of histones Decreased YIL112W HOS4 Set3 complex ANKRD50 Covalent modifications of histones Decreased YNL021W HDA1 class II histone deacetylase complex HDAC5 Covalent modifications of histones Decreased YAL021C CCR4  CNOT6 ddc Viable YAL040C CLN3  CCNB1 DDC Viable YBL046W PSY4 phosphatase PP4 complex R2 DDC Viable YBR158W AMN1  AMN1 ddc Viable  104 ORF GENE Function Human  pathway fitness YBR186W PCH2 pachytene checkpoint TRIP13 DDC Increased YBR274W CHK1  Chk1 DDC Increased YCL029C BIK1  KIF13B ddc Viable YCL061C MRC1  Claspin DDC Decreased YCR008W SAT4  CHEK1 ddc Viable YCR044C PER1  PER1 DDC Viable YDL155W CLB3 B-type cyclin CCNA1 DDC Viable YDR364C CDC40  CDC40 DDC Viable YDR379W RGA2  C5orf4 DDC Increased YGL086W MAD1 Mad1p-Mad2p complex MAD1L1 DDC Increased YGR108W CLB1 B-type cyclin CCNA1 DDC Decreased YGR109C CLB6 B-type cyclin CCNA1 DDC Viable YGR188C BUB1  spindle checkpoint  Bub1 DDC Decreased YGR252W GCN5  KAT2A DDC Viable YHR082C KSP1  Chk2 DDC Viable YJL013C MAD3  spindle checkpoint  BubR1 DDC Decreased YJL030W MAD2 Mad1p-Mad2p complex MAD2L1,MAD2L2 DDC Decreased  105 ORF GENE Function Human  pathway fitness YLR210W CLB4 B-type cyclin CCNA1 DDC Viable YMR036C MIH1  CDC25B DDC Viable YMR199W CLN1 G1 cyclin CCNB1 DDC Viable YNL201C PSY2 phosphatase PP4 complex R3 DDC Viable YOR026W BUB3  spindle checkpoint  BUB3 DDC Decreased YOR073W SGO1 spindle checkpoint SGOL1 DDC Decreased YPL256C CLN2  CCNB1 DDC Viable YPR119W CLB2 B-type cyclin CCNA1 DDC Decreased YLR288C MEC3 Rad17p-Mec3p-Ddc1p Hus1 DDC,BER Decreased YNL273W TOF1 Tof1p-Mrc1p-Csm3p Tof1,TIMELESS DDC,DNA repair Viable YMR048W CSM3 Replication fork associated factor TIPIN DDC,DNA replication Decreased YBL088C TEL1  ATM DDC,DSB Decreased YOR368W RAD17 Rad17p-Mec3p-Ddc1p Rad1 DDC,DSB,BER Decreased YPL194W DDC1 Rad17p-Mec3p-Ddc1p RAD9 DDC,HR,BER Decreased YDL101C DUN1  Chk2 DDC,PRR Decreased YJR090C GRR1 SCF ubiquitin-ligase complex FBXL20 divalent cation transport Decreased YER169W RPH1  JMJD2A DNA damage-response Decreased  106 ORF GENE Function Human  pathway fitness YMR173W DDR48  DSPP DNA damage-response Increased YDL013W SLX5 Slx5-Slx8 STUbL complex RNF4,TTF2 DNA repair Decreased YDL116W NUP84 Nup84 subcomplex Nup107 DNA repair Decreased YDR263C DIN7 homolog of the RAD2 and RAD27 EXO1 DNA repair Normal YER116C SLX8 Slx5-Slx8 STUbL complex RNF4 DNA repair Decreased YGL100W SEH1 Nup84 subcomplex SEH1 DNA repair Viable YGR129W SYF2 nineteen complex Prp19/CDC5 DNA repair Decreased YHR200W RPN10  19S subunit S5a DNA repair Decreased YIL153W RRD1  PTPA DNA repair Decreased YKL057C NUP120 Nup84 subcomplex Nup160 DNA repair Decreased YNL218W MGS1  WHIP DNA repair Decreased YOR386W PHR1  CRY1/2 DNA repair Increased YPR052C NHP6A  HMGB1,HMGB2 DNA repair Viable YPR101W SNT309 nineteen complex Prp19/CDC5 DNA repair Decreased YBR223C TDP1  Tdp1 DNA repair,BER,SSR Decreased YOL006C TOP1 Topoisomerase I TOPI DNA replication Decreased YOR080W DIA2 SCF ubiquitin ligase complex AC069113.1 DNA replication Decreased  107 ORF GENE Function Human  pathway fitness YHR031C RRM3 5′-3′DNA helicase PIF1 DNA replication,(HR) Decreased YML061C PIF1 5′-3′DNA helicase PIF1 DNA replication,mtDNA repair,(HR) Decreased YKL017C HCS1 Hexameric DNA polymerase alpha-associated DNA helicase A AQR,IGHMBP2 DNA synthesis Increased YJR043C POL32 DNA polymerase δ Cdc27,Polδ,POLD3 DNA synthesis,BER,NER,MMR,PRR Decreased YDR121W DPB4 DNA polymerase ε/II Polε,POLE DNA synthesis,BER,SSR Increased YBR278W DPB3 DNA polymerase ε/II Polε,POLE DNA synthesis,HR,BER,NER,MMR Decreased YDL200C MGT1  MGMT DR Increased YAR002W NUP60 nuclear pore complex (NPC) Nup153 DSB Decreased YDL047W SIT4  PPP6C DSB Viable YEL056W HAT2  Hat1p-Hat2p HAT complex RbAp48,RbAp46 DSB Decreased YFR040W SAP155  PPP6R DSB Viable YGL229C SAP4  PPP6R DSB Viable YHR154W RTT107  MDC1 DSB Decreased YKR028W SAP190  PPP6R DSB Viable YKR056W TRM2  TRMT2A DSB Increased YMR127C SAS2  KAT5 DSB Viable  108 ORF GENE Function Human  pathway fitness YPL001W HAT1  Hat1p-Hat2p HAT complex HAT1 DSB Decreased YLR320W MMS22 Mms1-Mms22 complex MMS22L DSB,HR Decreased YBL067C UBP13  USP1 DSR,FA Viable YER098W UBP9  USP1 DSR,FA Viable YFR034C PHO4  HES1,STRA13 DSR,FA Viable YOL087C DUF1  WDR48 DSR,FA Viable YPL183W-A RTC6  BRIP1 DSR,FA Viable YBR026C ETR1  cin Fatty acid biogenesis Viable YPL008W CHL1  FANCJ,RTEL1 genome integrity,(HR) Decreased YOL012C HTZ1  H2AFZ Histone variant Viable YOR025W HST3  cin Histones and Chromatin Viable YBR073W RDH54  RAD54B HR Decreased YDR004W RAD57 Rad55p-Rad57p heterodimer  Rad51B,Rad51C,Rad51D,XRCC2,XRCC3 HR Decreased YDR076W RAD55 Rad55p-Rad57p heterodimer  Rad51B,Rad51C,Rad51D,XRCC2,XRCC3 HR Decreased YDR078C SHU2 Shu1-Psy3-Shu2-Csm2 Xrcc2-Rad51D-Sws1 HR Decreased  109 ORF GENE Function Human  pathway fitness YER041W YEN1 Holliday junction resolvase GEN1 HR Decreased YGL175C SAE2 Endonuclease CtIP HR Decreased YHL006C SHU1 Shu1-Psy3-Shu2-Csm2 Xrcc2-Rad51D-Sws0 HR Increased YIL132C CSM2 Shu1-Psy3-Shu2-Csm2 Xrcc2-Rad51D-Sws2 HR Decreased YIR002C MPH1  FANCM HR Decreased YJL047C RTT101  Cul4 HR Decreased YLR376C PSY3 Shu1-Psy3-Shu2-Csm2 Xrcc2-Rad51D-Sws1 HR Increased YOR144C ELG1 alternative replication factor C complex Rfc1 HR Decreased YPR164W MMS1 Mms1-Mms22 complex DDB1 HR Decreased YDL074C BRE1  Bre1,RFWD3 HR,DDC Decreased YDR075W PPH3 phosphatase PP4 complex Ppp4c HR,DDC Increased YBR228W SLX1 Slx1-Slx4 SLX1A HR,DSBR Increased YPL024W RMI1 Sgs1-TopIII-Rmi1 BLAP75/Rmi1 HR,DSBR Decreased YMR190C SGS1 Sgs1-TopIII-Rmi1,3'-5'DNA helicase RecQL,RecQ4,RecQ5,BLM,WRN,RTS HR,DSBR,BER,SSR Decreased YML032C RAD52  Rad52,Rad52B HR,DSBR,SDSA,SSA,BIR,PRR Decreased YOR033C EXO1 5'-3' exonuclease and flap-endonuclease Exo1,Hex1 HR,MMR Increased  110 ORF GENE Function Human  pathway fitness YDR369C XRS2 MRX complex NBS1 HR,NHEJ Decreased YMR137C PSO2  Artemis,DCLRE1C HR,NHEJ Decreased YMR224C MRE11 MRX complex Mre11 HR,NHEJ Decreased YNL250W RAD50 MRX complex Rad50 HR,NHEJ Decreased YER095W RAD51  Rad51B,Rad51C,Rad51D,XRCC2,XRCC3 HR,SDSA Decreased YJL092W SRS2 3'-5'DNA helicase Fbh1 HR,SDSA Decreased YGL163C RAD54  RAD54 HR,SDSA,NHEJ Decreased YBR098W MMS4 Mms4p-Mus81p endonuclease Eme1,Eme2 meiotic HR Decreased YDR386W MUS81 Mms4p-Mus81p endonuclease Mus81 meiotic HR Decreased YER179W DMC1 recombinase Dmc1 meiotic HR Decreased YGL033W HOP2  Hop2,GT198 meiotic HR Viable YGL251C HFM1 3'-5'DNA helicase HFM1 meiotic HR Decreased YHL022C SPO11  SPO11 meiotic HR Decreased YHR086W NAM8  PABP-1 meiotic HR Decreased YOR351C MEK1  Chk2 meiotic HR Normal YHR120W MSH1  MSH1 mitochondrial repair, MMR Decreased  111 ORF GENE Function Human  pathway fitness YBR272C HSM3  19S subunit S5b MMR Decreased YCR092C MSH3 Msh2p-Msh3p heterodimer MSH3 MMR Decreased YDL154W MSH5 Msh4p-Msh5p heterodimer MSH5 MMR Decreased YDR097C MSH6 Msh2p-Msh6p heterodimer MSH6,GTBP MMR Decreased YFL003C MSH4 Msh4p-Msh5p heterodimer MSH4 MMR Normal YLR035C MLH2  PMS1 MMR Increased YOL090W MSH2 Msh2p-Msh6p heterodimer, Msh2p-Msh3p heterodimer MSH2 MMR Decreased YMR167W MLH1 Pms1p-Mlh1p dimer MLH1 MMR,meiotic HR Decreased YNL082W PMS1 Pms1p-Mlh1p dimer PMS2 MMR,meiotic HR Decreased YPL164C MLH3 Mlh1–Mlh3 complex MLH1 MMR,meiotic HR Increased YER070W RNR1  RRM1 Modulation of nucleotide pools Viable YGR180C RNR4  RRM2 Modulation of nucleotide pools Viable YIL066C RNR3  RRM1 Modulation of nucleotide pools Viable YLR270W DCS1 hydrolase DcpS mRNA decapping Decreased YGL173C KEM1 5'-3' exonuclease XRN1 mRNA decay Decreased YDR378C LSM6  cin mRNA decay (LSM) Viable  112 ORF GENE Function Human  pathway fitness YDR030C RAD28  CSA,CKN1,ERCC8 NER Normal YDR079C-A TFB5  GTF2H5 NER Decreased YDR314C RAD34  XPC NER Decreased YEL037C RAD23 NEF2 HR23a,HR23b NER Increased YER162C RAD4 NEF2 XPC NER Decreased YGL070C RPB9 RNA polymerase II POLR3 NER Viable YGR003W CUL3 Ubiquitin-protein ligase CUL3 NER Viable YGR258C RAD2  XPG,ERCC5 NER Viable YHL025W SNF6 SWI/SNF chromatin remodeling complex  SWI/SNF NER Decreased YIL128W MET18  MMS19 NER Decreased YML011C RAD33 Rad4-Rad23 complex HR23B NER Decreased YMR201C RAD14 NEF1(Rad1p-Rad10p-Rad14p) XPA NER Increased YNL230C ELA1  elongin C,TCEB2 NER Viable YPL046C ELC1  elongin A,TCEB3 NER Increased YPL096W PNG1 PNGASE-HR23 complex PNG NER Increased YER173W RAD24 Rad24-RFC, Rad17p-Mec3p-Ddc1p Rad17,RS1 NER,DDC Increased YDR217C RAD9  53BP1 NER,DDC,(NHEJ) Decreased  113 ORF GENE Function Human  pathway fitness YPL022W RAD1 NEF1(Rad1p-Rad10p-Rad14p) XPF,ERCC4 NER,DSB,HR,ICLR,SSA Decreased YDL042C SIR2 NAD+-dependent deacetylase SIRT1,SIR2L1,Sir2α NHEJ Viable YGL090W LIF1  XRCC4  NHEJ Increased YJR066W TOR1  PRKDC NHEJ Viable YKL213C DOA1  PLAA NHEJ Decreased YLL002W RTT109 Rtt109/Vps75 complex p300 NHEJ Decreased YLR265C NEJ1  XLF/Cernunnos NHEJ Increased YMR106C YKU80 telomeric Ku complex (Yku70p-Yku80p) Ku80 NHEJ Increased YNL116W DMA2 E3 RNF8 NHEJ Viable YNL246W VPS75 Rtt109/Vps75 complex SET NHEJ Decreased YNL307C MCK1  GSK3A/B NHEJ Decreased YOL004W SIN3 Sin3p-Rpd3p histone deacetylase complex SIN3b NHEJ Decreased YOR005C DNL4 DNA ligase IV Lig4 NHEJ Decreased YCR014C POL4 DNA polymerase IV POLL,POLB,Polλ NHEJ,BER,SSR Decreased YMR284W YKU70 telomeric Ku complex (Yku70p-Yku80p) Ku70 NHEJ,telomere functions Decreased YNL299W TRF5 TRAMP complex PAPD5,POLK nuclear RNA degradation Decreased YJR074W MOG1  cin Nucleocytoplasmic transport Viable  114 ORF GENE Function Human  pathway fitness YML028W TSA1  cin Oxidative Stress Viable YMR186W HSC82  HSP90 pheromone signaling Decreased YPL240C HSP82  HSP90 pheromone signaling Decreased YKR092C SRP40  NHN1 preribosome assembly or transport Decreased YGR184C UBR1  cin Proteasome Viable YLR306W UBC12  UBE2F protein degradation Decreased YDL230W PTP1  PTPn9 protein dephosphorylation Decreased YCR066W RAD18 E2,RAD6-RAD18 heterodimer Rad18 PRR Decreased YDR092W UBC13 Mms2-Ubc13 ubiquitin UBE2N,UBE2T PRR Increased YGL087C MMS2 Mms2-Ubc13 ubiquitin MMS2,CROC1 PRR Decreased YGL094C PAN2 Pan2p-Pan3p poly(A)-ribonuclease complex PAN2 PRR Increased YKL025C PAN3 Pan2p-Pan3p poly(A)-ribonuclease complex PAN3 PRR Increased YDR419W RAD30 DNA polymerase ζ POLH,Rad30 PRR,BER,SSR Decreased YLR032W RAD5  HLTF,SHPRH PRR,DSB Decreased YIL139C REV7 DNA polymerase ζ REV7/MAD2B PRR,DSB,ICL Increased  115 ORF GENE Function Human  pathway fitness YOR346W REV1 DNA polymerase ζ Rev1 PRR,DSB,ICL,BER,SSR Decreased YPL167C REV3 DNA polymerase ζ REV3L,Polζ,POLD1 PRR,DSB,ICL,BER,SSR Increased YGL058W RAD6 E2,RAD6-RAD18 heterodimer HHR6A,HHR6B PRR,HR Decreased YIR019C MUC1  NOLC1 pseudohyphal formation Increased YER164W CHD1 SAGA and SLIK complexes CHD8 regulate transcription Decreased YGR063C SPT4  SPT4 regulate transcription Decreased YCR065W HCM1 Forkhead transcription factor FOXB1 regulates the late S-phase specific expression of genes Decreased YNL138W SRV2 adenylyl cyclase complex CAP1 regulation of actin Decreased YBR189W RPS9B  cin Ribosomal core component Viable YDL082W RPL13A  cin Ribosomal core component Viable YHR066W SSF1  cin Ribosome Biogenesis Viable YKR024C DBP7  cin Ribosome Biogenesis Viable YFR031C-A RPL2A large (60S) ribosomal subunit RPL8 Ribosomes Decreased YIL018W RPL2B large (60S) ribosomal subunit RPL8 Ribosomes Decreased YDR289C RTT103  RPRD1B RNA  processing Viable YOL072W THP1  PCID2 RNA  processing Viable  116 ORF GENE Function Human  pathway fitness YMR216C SKY1 SR protein kinase (SRPK)  CLK3 RNA metabolism Decreased YJL006C CTK2  cin RNA polymerase and TFIIs Viable YJR063W RPA12  cin RNA polymerase and TFIIs Viable YKL139W CTK1  CDK11B RNA processing Decreased YLR107W REX3 RNA exonuclease REXO1 RNA processing Normal YDR363W-A SEM1 subunit of 26S proteasome DSS1 RNA processing,(HR) Viable YGL066W SGF73  cin SAGA complex Viable YLR240W VPS34  PIK3C2A signal transduction Viable YCL016C DCC1 Ctf18p complex DSCC1 sister chromatid cohesion Decreased YHR191C CTF8 Ctf18p complex CTF8 sister chromatid cohesion Decreased YPR141C KAR3  cin Spindle Viable YOR308C SNU66  cin Spliceosome Viable YDR523C SPS1 Putative protein serine/threonine kinase TAO1 spore wall synthesis Decreased YLR135W SLX4 5'flap endonuclease BTBD12 SSA Decreased YFR014C CMK1 Calmodulin-dependent protein kinase CAMK1 stress response Increased YKL190W CNB1 calcineurin CIB1/2 stress response Decreased  117 ORF GENE Function Human  pathway fitness YDR363W ESC2  NIP45,SUMO1 SUMO Decreased YLR394W CST9 SUMO E3 ligase RNF212 synaptonemal complex formation Decreased YIL009C-A EST3 TERT TERT Telomere maintenance Viable YLR233C EST1 TERT TERT Telomere maintenance Viable YLR318W EST2 TERT TERT Telomere maintenance Viable YNL025C SSN8 RNA polymerase II holoenzyme CCNK telomere maintenance Decreased YOL068C HST1 Sum1p/Rfm1p/Hst1p complex  SIRT4 telomere maintenance Decreased YPL129W TAF14  cin TFIID Viable YPL181W CTI6  MLL5 transcriptional activation Increased YPL042C SSN3 RNA polymerase II holoenzyme CDK8 transcriptional control Decreased YGL115W SNF4 AMP-activated Snf1p kinase complex PRKAG2 Transcriptional regulation Decreased YGR171C MSM1  cin Translation Viable YJR047C ANB1  cin Translation Viable YNL252C MRPL17  cin Translation Viable YGR271W SLH1 Putative RNA helicase POLH,ASCC3,HELQ translation regulation,BER,SSR Decreased YGL211W NCS6  CTU1 tRNA binding Decreased YEL003W GIM4  cin Tubulin folding Viable  118 ORF GENE Function Human  pathway fitness YPL241C CIN2  cin Tubulin folding Viable YDL216C RRI1 COP9 signalosome (CSN) complex COPS5 Ubiquitin response Viable YBR034C HMT1  PRMT6 unknown Viable YBR231C SWC5  SWR1 complex CFDP1 unknown Decreased YDR176W NGG1  TADA3 unknown Viable YER045C ACA1  ATF2 unknown Viable YER051W JHD1  FBXL10 unknown Decreased YER176W ECM32  UPF1 unknown Viable YGL043W DST1  TCEA1 unknown Viable YJL101C GSH1  GCLC unknown Viable YJL115W ASF1  ASF1A unknown Viable YJR104C SOD1  SOD1 unknown Viable YLL019C KNS1  CLK2 unknown Decreased YLR176C RFX1  RFX unknown Viable YLR247C IRC20  SHPRH unknown Viable YMR080C NAM7  UPF1 unknown Viable YNR052C POP2  CNOT7 unknown Viable  119 ORF GENE Function Human  pathway fitness YOR156C NFI1  PIAS1 unknown Viable  Table ‎4-1. List of array yeast strains and human homologous.     120  A.2 List of initial hit strains and frequency of significance.    GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequencyASF1 2 * APN1 3 ** ASF1 2 *** ASF1 3 BIK1 2 *BIM1 2 * BUB1 2 * BIM1 2 BIM1 2 *** BIM1 3 **BUB1 2 ** BUB3 2 * BUB3 2 BUB1 2 CDH1 3BUB3 2 * CIN2 2 CHD1 3 * BUB3 2 * CIN8 3 **CHD1 2 ** CIN8 3 CIN8 2 ** CDC40 2 * CLB2 3CHL1 2 * CLB2 2 *** CLB2 3 * CHL1 2 * CTF4 2CIN2 2 *** DOA1 2 * DDC1 2 * CIN2 2 * CTK1 3 **CIN8 3 *** DOC1 2 DOA1 3 * CIN8 3 DBP7 3CLB2 2 *** GIM4 2 EXO1 3 *** CLB2 3 DOA1 2CSM3 2 ** HST3 2 FKH2 2 * CLN3 2 * DOC1 3 **CTF4 2 ** KAR3 3 * FUN30 2 CMK1 2 * HOS2 2 *CTK1 2 ** MCK1 2 * GIM4 2 CSM3 2 HTZ1 3 *DOC1 2 ** MMS22 2 HOS2 2 ** CST9 2 * KAR3 3 *GIM4 3 *** PAN3 2 * HST1 2 ** CTF4 2 * KEM1 2 **HST3 2 ** PHR1 2 HST3 2 * CTF8 2 ** MAD2 2HTZ1 2 *** RAD61 2 * HTZ1 2 *** CTI6 2 MRC1 3KAR3 3 ** RAD9 2 KAR3 2 * CTK1 3 ** PHR1 2 ***LSM6 2 * RDH54 2 * MAD3 2 DBP7 2 RAD61 3MMS1 2 ** REV1 3 MCK1 3 DCS1 2 * RPB9 2MRC1 2 ** REV3 3 * MEC3 2 ** DDC1 2 RPN10 3PHR1 2 RPB9 2 * MIH1 2 ** DOA1 3 RTS1 2 *RAD61 3 ** RTT107 2 ** MMS1 2 ** DOC1 3 * SAP155 3RPL2B 2 SAP155 2 * MMS22 2 DST1 2 * SGO1 3 *SAP155 2 *** SHP1 2 MMS4 2 DUN1 2 * SHP1 2 *SHP1 2 ** SRS2 3 MRC1 2 * ELC1 2 *** SIT4 3 *SRS2 2 *** SSN8 2 * MSI1 2 ** ELG1 2 * SRV2 2 *SSN8 2 TEL1 3 * PER1 2 ESC2 2 ** SWR1 2TOF1 3 ** TOF1 2 RAD17 3 * ETR1 2 TOF1 3TSA1 2 * RAD24 3 FKH2 3 UBR1 2 *UBR1 2 *** RAD55 2 FUN30 2 YTA7 2 ***RAD9 3 *** GIM4 3 *RDH54 2 * GRR1 2 *RPD3 2 GSH1 2RTS1 2 HOS2 2RTT107 2 * HST3 3 **SAE2 2 HTZ1 2 *SAP155 2 *** KAR3 3SGF73 2 * KEM1 2 *SHP1 2 ** MAD1 2SHU1 2 *** MAD2 2 *SIN3 2 * MCK1 3 *SKY1 2 MEC3 2SOD1 2 ** MMS1 3SRS2 3 * MMS2 2 *SSZ1 3 ** MSH4 2 **TDP1 2 MSI1 2 *TEL1 3 MUS81 2 **TOF1 2 NAM7 2UBC12 2 * NCS6 2 *UBR1 2 * NUP84 2PER1 3PHR1 2 *POL32 2 *PPH3 2PSO2 2PSY2 3PSY3 2 **RAD17 2RAD18 2RAD24 2 **RAD5 3 **RAD51 2RAD54 2RAD55 3 **RAD57 2 *RAD61 2 *RAD9 2 *RDH54 2 **RGA2 2RPB9 3 **RPN10 2 ***RTC6 2 *RTS1 2 *RTT101 2RTT107 2SAP155 3 *SAP4 2 **SAS2 2 *SEM1 3 **SGF73 3SGO1 2SLX8 2SNF4 2 *SNF6 2 *SOD1 2 *SRS2 3 *SSN3 2SSZ1 2 **SYF2 2 *TDP1 2TEL1 2 ***TOF1 3 *TRF5 2 *TSA1 2 *UBR1 3VPS34 2 *ND MMS CPT Bleomycin Benomyl 121   Table ‎4-2. List of initial hit strains and frequency of significance.  Gene hits filled in orange indicate validated negative genetic interactions. Gene hits filled in green indicate false positive hits. Number of shared interactions is based on the number a certain gene was identifies as hit out of the three screens. Frequency of significance indicates in how many screens (out of the three) the hit strain had a p-value <0.05.   GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequencyASF1 2 * APN1 3 ** ASF1 2 *** ASF1 3 BIK1 2 *BIM1 2 * BUB1 2 * BIM1 2 BIM1 2 *** BIM1 3 **BUB1 2 ** BUB3 2 * BUB3 2 BUB1 2 CDH1 3BUB3 2 * CIN2 2 CHD1 3 * BUB3 2 * CIN8 3 **CHD1 2 ** CIN8 3 CIN8 2 ** CDC40 2 * CLB2 3CHL1 2 * CLB2 2 *** CLB2 3 * CHL1 2 * CTF4 2CIN2 2 *** DOA1 2 * DDC1 2 * CIN2 2 * CTK1 3 **CIN8 3 *** DOC1 2 DOA1 3 * CIN8 3 DBP7 3CLB2 2 *** GIM4 2 EXO1 3 *** CLB2 3 DOA1 2CSM3 2 ** HST3 2 FKH2 2 * CLN3 2 * DOC1 3 **CTF4 2 ** KAR3 3 * FUN30 2 CMK1 2 * HOS2 2 *CTK1 2 ** MCK1 2 * GIM4 2 CSM3 2 HTZ1 3 *DOC1 2 ** MMS22 2 HOS2 2 ** CST9 2 * KAR3 3 *GIM4 3 *** PAN3 2 * HST1 2 ** CTF4 2 * KEM1 2 **HST3 2 ** PHR1 2 HST3 2 * CTF8 2 ** MAD2 2HTZ1 2 *** RAD61 2 * HTZ1 2 *** CTI6 2 MRC1 3KAR3 3 ** RAD9 2 KAR3 2 * CTK1 3 ** PHR1 2 ***LSM6 2 * RDH54 2 * MAD3 2 DBP7 2 RAD61 3MMS1 2 ** REV1 3 MCK1 3 DCS1 2 * RPB9 2MRC1 2 ** REV3 3 * MEC3 2 ** DDC1 2 RPN10 3PHR1 2 RPB9 2 * MIH1 2 ** DOA1 3 RTS1 2 *RAD61 3 ** RTT107 2 ** MMS1 2 ** DOC1 3 * SAP155 3RPL2B 2 SAP155 2 * MMS22 2 DST1 2 * SGO1 3 *SAP155 2 *** SHP1 2 MMS4 2 DUN1 2 * SHP1 2 *SHP1 2 ** SRS2 3 MRC1 2 * ELC1 2 *** SIT4 3 *SRS2 2 *** SSN8 2 * MSI1 2 ** ELG1 2 * SRV2 2 *SSN8 2 TEL1 3 * PER1 2 ESC2 2 ** SWR1 2TOF1 3 ** TOF1 2 RAD17 3 * ETR1 2 TOF1 3TSA1 2 * RAD24 3 FKH2 3 UBR1 2 *UBR1 2 *** RAD55 2 FUN30 2 YTA7 2 ***RAD9 3 *** GIM4 3 *RDH54 2 * GRR1 2 *RPD3 2 GSH1 2RTS1 2 HOS2 2RTT107 2 * HST3 3 **SAE2 2 HTZ1 2 *SAP155 2 *** KAR3 3SGF73 2 * KEM1 2 *SHP1 2 ** MAD1 2SHU1 2 *** MAD2 2 *SIN3 2 * MCK1 3 *SKY1 2 MEC3 2SOD1 2 ** MMS1 3SRS2 3 * MMS2 2 *SSZ1 3 ** MSH4 2 **TDP1 2 MSI1 2 *TEL1 3 MUS81 2 **TOF1 2 NAM7 2UBC12 2 * NCS6 2 *UBR1 2 * NUP84 2PER1 3PHR1 2 *POL32 2 *PPH3 2PSO2 2PSY2 3PSY3 2 **RAD17 2RAD18 2RAD24 2 **RAD5 3 **RAD51 2RAD54 2RAD55 3 **RAD57 2 *RAD61 2 *RAD9 2 *RDH54 2 **RGA2 2RPB9 3 **RPN10 2 ***RTC6 2 *RTS1 2 *RTT101 2RTT107 2SAP155 3 *SAP4 2 **SAS2 2 *SEM1 3 **SGF73 3SGO1 2SLX8 2SNF4 2 *SNF6 2 *SOD1 2 *SRS2 3 *SSN3 2SSZ1 2 **SYF2 2 *TDP1 2TEL1 2 ***TOF1 3 *TRF5 2 *TSA1 2 *UBR1 3VPS34 2 *ND MMS CPT Bleomycin BenomylGeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequency GeneNumber of shared screensSignificance frequencyASF1 2 * APN1 3 ** ASF1 2 *** ASF1 3 BIK1 2 *BIM1 2 * BUB1 2 * BIM1 2 BIM1 2 *** BIM1 3 **BUB1 2 ** BUB3 2 * BUB3 2 BUB1 2 CDH1 3BUB3 2 * CIN2 2 CHD1 3 * BUB3 2 * CIN8 3 **CHD1 2 ** CIN8 3 CIN8 2 ** CDC40 2 * CLB2 3CHL1 2 * CLB2 2 *** CLB2 3 * CHL1 2 * CTF4 2CIN2 2 *** DOA1 2 * DDC1 2 * CIN2 2 * CTK1 3 **CIN8 3 *** DOC1 2 DOA1 3 * CIN8 3 DBP7 3CLB2 2 *** GIM4 2 EXO1 3 *** CLB2 3 DOA1 2CSM3 2 ** HST3 2 FKH2 2 * CLN3 2 * DOC1 3 **CTF4 2 ** KAR3 3 * FUN30 2 CMK1 2 * HOS2 2 *CTK1 2 ** MCK1 2 * GIM4 2 CSM3 2 HTZ1 3 *DOC1 2 ** MMS22 2 HOS2 2 ** CST9 2 * KAR3 3 *GIM4 3 *** PAN3 2 * HST1 2 ** CTF4 2 * KEM1 2 **HST3 2 ** PHR1 2 HST3 2 * CTF8 2 ** MAD2 2HTZ1 2 *** RAD61 2 * HTZ1 2 *** CTI6 2 MRC1 3KAR3 3 ** RAD9 2 KAR3 2 * CTK1 3 ** PHR1 2 ***LSM6 2 * RDH54 2 * MAD3 2 DBP7 2 RAD61 3MMS1 2 ** REV1 3 MCK1 3 DCS1 2 * RPB9 2MRC1 2 ** REV3 3 * MEC3 2 ** DDC1 2 RPN10 3PHR1 2 RPB9 2 * MIH1 2 ** DOA1 3 RTS1 2 *RAD61 3 ** RTT107 2 ** MMS1 2 ** DOC1 3 * SAP155 3RPL2B 2 SAP155 2 * MMS22 2 DST1 2 * SGO1 3 *SAP155 2 *** SHP1 2 MMS4 2 DUN1 2 * SHP1 2 *SHP1 2 ** SRS2 3 MRC1 2 * ELC1 2 *** SIT4 3 *SRS2 2 *** SSN8 2 * MSI1 2 ** ELG1 2 * SRV2 2 *SSN8 2 TEL1 3 * PER1 2 ESC2 2 ** SWR1 2TOF1 3 ** TOF1 2 RAD17 3 * ETR1 2 TOF1 3TSA1 2 * RAD24 3 FKH2 3 UBR1 2 *UBR1 2 *** RAD55 2 FUN30 2 YTA7 2 ***RAD9 3 *** GIM4 3 *RDH54 2 * GRR1 2 *RPD3 2 GSH1 2RTS1 2 HOS2 2RTT107 2 * HST3 3 **SAE2 2 HTZ1 2 *SAP155 2 *** KAR3 3SGF73 2 * KEM1 2 *SHP1 2 ** MAD1 2SHU1 2 *** MAD2 2 *SIN3 2 * MCK1 3 *SKY1 2 MEC3 2SOD1 2 ** MMS1 3SRS2 3 * MMS2 2 *SSZ1 3 ** MSH4 2 **TDP1 2 MSI1 2 *TEL1 3 MUS81 2 **TOF1 2 NAM7 2UBC12 2 * NCS6 2 *UBR1 2 * NUP84 2PER1 3PHR1 2 *POL32 2 *PPH3 2PSO2 2PSY2 3PSY3 2 *RAD17 2RAD18 2RAD24 2 *RAD5 3 *RAD51 2RAD54 2RAD55 3 *RAD57 2 *RAD 1 2 *RAD9 2 *RDH54 2 **RGA2 2RPB9 3 **RPN10 2 ***RTC6 2 *RT 1 2 *RTT101 2RTT107 2SAP155 3 *SAP4 2 *SAS2 2 *SEM1 3 **SGF7 3S O1 2SLX8 2SNF4 2 *SNF6 2 *SOD1 2 *SRS2 3 *SSN3 2SSZ1 2 **SYF2 2 *TDP1 2TEL1 2 ***TOF1 3 *TRF5 2 *TSA1 2 *UBR1 3VPS34 2 *ND MMS CPT Bleomycin Benomyl 122 A.3 Mathematical explanation of genetic interaction formula   Figure ‎4-1. The calculation of genetic interaction in an SGA.  The fitness of a mutant strain is determined relative to the wild-type (WT) strain under the same condition. A genetic interaction between two mutated genes is determined relative to the expected combined fitness of the two single mutants, thus taking into account the growth of the WT, the observed growth of the double mutant and the growth of each single mutant, in the same environment/condition. The fitness of the query can change between conditions, therefore, the E-C calculation does not allow for comparing genetic interactions of the double mutant between conditions. The (E-C)/C calculation, which allows to account for slow growing or sensitive strains, also enables to compare double mutants between conditions, as we do not have to take into account the fitness of the query.   123 A.4 Final lists of validated genetic interactions with scc1 query mutation  Gene Hit Type of predicted GI  ScanLag score Condition  GIM4 SL -0.32 ND RAD61 SL -0.21 ND DOC1 SL -0.3 ND CSM3 SL -0.32 ND MMS1 SL -0.33 ND SRS2 SL -0.38 ND HTZ1 SL -0.26 ND SAP155 SL -0.25 ND LSM6 SL -0.2 ND CIN2 SL -0.27 ND MRC1 SL -0.36 ND CTK1 SL -0.34 ND REV1 SC (mms) -0.29 MMS REV3 SC (mms) -0.18 MMS APN1 SC (mms) -0.28 MMS TEL1 SC (mms, cpt) -0.29 MMS TEL1 SC (mms, cpt) -0.37 CPT MCK1 SC (cpt, bleo) -0.25 CPT RAD24 SC (cpt) -0.14 CPT RAD9 SC (cpt) -0.12 CPT DOA1 SC (cpt, bleo) -0.26 CPT EXO1 SC (cpt) -0.47 CPT SIT4 SC (beno) -0.33 Benomyl CDH1 SC (beno) -0.22 Benomyl SGO1 SC (beno) -0.32 Benomyl SWC5 PS (ND, mms, cpt, bleo) 0.54 ND, MMS,  124 Gene Hit Type of predicted GI  ScanLag score Condition  CPT CLN2 PS (all 5 conditions) 0.26 ND Table ‎4-3. Validated genetic interactions based on SGA data.    Gene Hit Type of predicted GI  ScanLag score Condition  DOA1 SC (cpt, bleo) -0.33 Benomyl RAD24 SC (cpt) -0.17 Benomyl APN1 SC (mms) -0.26 CPT CDH1 SC (beno) -0.37 CPT DBP7 SC (beno) -0.36 CPT FKH2 SC (bleo) -0.21 CPT PSY2 SC (bleo) -0.25 CPT RAD5 SC (bleo) -0.16 CPT REV3 SC (mms) -0.15 CPT RPN10 SC (beno) -0.23 CPT SGO1 SC (beno) -0.4 CPT SIT4 SC (beno) -0.37 CPT CDH1 SC (beno) -0.17 MMS DBP7 SC (beno) -0.22 MMS DOA1 SC (cpt, bleo) -0.25 MMS EXO1 SC (cpt) -0.45 MMS FKH2 SC (bleo) -0.21 MMS MCK1 SC (cpt, bleo) -0.38 MMS PER1 SC (bleo) -0.11 MMS PSY2 SC (bleo) -0.11 MMS RAD24 SC (cpt) -0.16 MMS SGF73 SC (bleo) -0.11 MMS  125 Gene Hit Type of predicted GI  ScanLag score Condition  SGO1 SC (beno) -0.38 MMS SIT4 SC (beno) -0.15 MMS UBR1 SC (bleo) -0.19 MMS DBP7 SC (beno) -0.24 ND DBP7 SC (beno) -0.24 ND DOA1 SC (cpt, bleo) -0.26 ND EXO1 SC (cpt) -0.39 ND FKH2 SC (bleo) -0.29 ND FKH2 SC (bleo) -0.29 ND MCK1 SC (cpt, bleo) -0.26 ND RAD5 SC (bleo) -0.2 ND RAD5 SC (bleo) -0.2 ND RPN10 SC (beno) -0.25 ND RPN10 SC (beno) -0.25 ND SGO1 SC (beno) -0.29 ND SGO1 SC (beno) -0.29 ND SIT4 SC (beno) -0.27 ND SIT4 SC (beno) -0.27 ND TEL1 SC (mms, cpt) -0.37 ND UBR1 SC (bleo) -0.28 ND UBR1 SC (bleo) -0.28 ND APN1 SC (mms) -0.3 ND  Table ‎4-4. New negative genetic interactions.    

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