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Overlapping genetic risk in the spectrum of sudden death Troskie, Christine 2016

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	 OVERLAPPING GENETIC RISK IN THE SPECTRUM OF SUDDEN DEATH  by  Christine Troskie BHSc. (Hons), The University of Calgary, 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 (Pharmaceutical Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  December 2016 © Christine Troskie, 2016       ii		  Abstract  Sudden Infant Death Syndrome (SIDS) is a sudden death occurring during sleep in infants below 1 year of old, which devastates the impacted families. By nature, SIDS deaths are those where all alternative causes of death, like suffocation or strangulation are eliminated, leaving families with few answers. While SIDS impacts infants, a spectrum of Sudden Death disorders exists across all age ranges and with comorbid syndromes, many of which occur sudden and unexpectedly during sleep. There is genetic overlap in risk genes in these disorders, and most notably between SIDS, Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD). Known and suspected Sudden Death (SD) genes are alternatively spliced in the developing brain in age and region dependent patterns, which may explain the differential timing of sudden death disorders despite their shared molecular risk factors. The objective of my project was to identify genes associated with sudden death and determine the timing of their alternative splicing. A literature search followed by an integrated pathway analysis generated a Sudden Death (SD) candidate genes (N=248). Analysis of whole exome sequences for eight (8) sudden death probands (SIDS (N=4), SUDEP (N=3) and SUD (N=1) samples was performed and Variant Effector Predictor was used to annotate the variants. The overall number of variants in the exomes for the SIDS individuals ranged from 40869-69978, SUDEP ranged from 19162-63954, and SUD contained 63217. Within the SD candidate genes, the number of variants in SIDS (707-1160), SUDEP (379-1078), and SUD (1026) did not allow for distinguishing between cohorts. All probands regardless of age carried multiple pathogenic variants in genes associated with disorders with high incidences of sudden death such as Long QT Syndrome, Dilated Cardiomyopathy, iii		Dravet Syndrome, and Infantile Epileptic Encephalopathy many of which impacted one or more gene isoforms. The expression patterns of these genes of interest were evaluated using the Allen Brain Atlas for the Developing Mouse Brain to identify when, and what form each gene product is expressed. These patterns of personal genetic risk can be used to identify potential targets for molecular diagnostic screening and prevention.                  iv		Preface  This thesis is based on the work done by me in the lab of Dr. Tara L. Klassen. The sudden death blood samples came from Othon Mena at the San Diego Medical Examiner’s Office, Whole Exome Sequencing was performed by the lab of Dr. Corey Nislow, and the bioinformatic data processing was done by Patrick Boutet.                   v		Table of Contents Abstract ........................................................................................................................ ii	Preface .......................................................................................................................... iv	Table of Contents ......................................................................................................... v	List of Tables ............................................................................................................. vii	List of Figures .............................................................................................................. ix	List of Abbreviations .................................................................................................. xi	Acknowledgements .................................................................................................. xiii	Chapter 1: Background Knowledge and Scope of Thesis ........................................ 1	1.1.	 Sudden	Infant	Death	Syndrome	.................................................................................................	1	1.1.1	 Overlap	in	the	Spectrum	of	Sudden	Death	..................................................................	2	1.1.2	 SIDS	Triple	Risk	Hypothesis	..............................................................................................	5	1.1.3	 Environmental	Risk	Factors	and	Sudden	Death	.......................................................	6	1.1.4	 Physiological	Risk	Factors	and	Sudden	Death	...........................................................	6	1.1.5	 Role	of	Genetic	Variants	in	the	Predisposition	of	Sudden	Death	Spectrum	Disorders	........................................................................................................................................................	8	1.1.6	 Genetic	Overlap	in	Disorders	Predisposing	to	Sudden	Death	Nominates	Novel	SIDS	Genes	.....................................................................................................................................	14	1.2	 Project	Rationale	and	Scope	of	Thesis	.................................................................................	17	1.2.1									Hypothesis	..............................................................................................................................	17	1.2.2									Specific	Aims	..........................................................................................................................	18	Chapter 2: Materials and Methods .......................................................................... 22	2.1	 Compiling	the	Sudden	Death	Candidate	Gene	List	.........................................................	22	2.2	 DNA	Samples	....................................................................................................................................	23	2.3	 Whole	Exome	Sequencing	and	Processing	.........................................................................	23	2.4	 Bioinformatic	Analysis	................................................................................................................	27	2.5	 Allen	Brain	Atlas	and	Alternative	isoforms	.......................................................................	31	Chapter 3: Results...................................................................................................... 36						3.1.						Identification	of	Sudden	Death	Candidate	Genes	............................................................	36	3.1.1				Genes	Identified	in	the	Spectrum	of	Sudden	Death...................................................	36	3.1.2				Sudden	Death	Candidate	Genes	as	Disease	Genes	.....................................................	37	3.1.3				Sudden	Death	Candidate	Genes	Have	Different	Biological	Functions	..............	38	3.2.						Genetic	Variation	in	Sudden	Death	Exomes	.......................................................................	39	3.2.1.				Variation	Observed	in	SD	Whole	Exomes	.....................................................................	39	3.3.						Personal	Patterns	of	Variation	SD	Candidate	Genes	......................................................	39	3.3.1.				Variation	within	SD	Candidate	Genes	.............................................................................	39	3.3.2.				Genetic	Variation	in	SD	Candidate	Gene	Transcripts	..............................................	42	vi		3.4.				Sudden	Death	Gene	Variants	Are	Pathogenic	in	Multiple	Isoforms	..........................	43	3.5					Personal	Pathogenic	Variants	in	Sudden	Death	Probands	............................................	44	3.6					Expression	of	SD	Risk	Genes	in	the	Developing	Brain.....................................................	47	Chapter 4: Discussion and Conclusions ................................................................. 121	4.1					Challenges	in	Personalized	Risk	Prediction	.......................................................................121	4.2					Genetic	Variation	in	Sudden	Death	Probands	is	Pathogenic	......................................121	4.3					Limitations	and	Future	Directions..........................................................................................122	4.4					Summary	and	Conclusions	.........................................................................................................124	References ................................................................................................................. 125	                                     vii		List of Tables 		Table 1.1. Spectrum of Sudden Death by age and diagnosis……..............................  20  Table 2.1. Variant Annotation Classifications and Definitions……………………... 35  Table 3.1. Sudden death candidate genes with roles in muscle……………………... 63  Table 3.2. Sudden death candidate genes with roles in cardiac regulation………….. 65  Table 3.3. Sudden death candidate genes that encode for ion channels and associated proteins…….................................................................................................................70  Table 3.4. Sudden death candidate genes with roles in neuronal regulation………... 77  Table 3.5. Sudden death candidate genes with roles in the cytoskeleton…………….81  Table 3.6. Sudden death candidate genes with roles in general cell processes……… 82  Table 3.7. Sudden death candidate genes involved in serotonin……………………..89  Table 3.8. Sudden death candidate genes with roles in hypoxia…………………….. 90  Table 3.9. Sudden death candidate genes with roles in the immune system…………91  Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes……………….......... 93  Table 3.11. Overall number of Variants in Whole Exomes for Sudden Death Probands…………………………………………………………………………….104  Table 3.12. Total personal variation in SD Candidate Genes in the Whole Exomes for Sudden Death Probands……………………………………………………………. 105  Table 3.13. Number of Sudden Death candidate genes with variants, per category, in Whole Exomes for Sudden Death Probands………………………………………..106  Table 3.14. Number of transcripts affected by variants in Sudden Death candidate genes in Whole Exomes for Sudden Infant Death Syndrome (SIDS), Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD) probands…………………………………………………………………………….107  Table 3.15. Number of transcripts in SD candidate genes, per category, in Whole Exomes for Sudden Infant Death Syndrome (SIDS), Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD) probands…………..….108  Table 3.16. Number of transcripts affected by type of variant by impact in Whole Exomes for Sudden Infant Death Syndrome (SIDS), Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD) probands..................….110  viii		Table 3.17. Key Pathogenic Variants in Whole Exome for Sudden Infant Death Syndrome (SIDS) proband 2095…............................................................................112  Table 3.18. Key Pathogenic Variants in Whole Exome for Sudden Infant Death Syndrome (SIDS) proband 2098……………………………………………………113  Table 3.19. Key Pathogenic Variants in Whole Exome for Sudden Infant Death Syndrome (SIDS) proband 2477……………………………………………............114  Table 3.20. Key Pathogenic Variants in Whole Exome for Sudden Infant Death Syndrome (SIDS) proband 2475……………………………………………………115  Table 3.21. Key Pathogenic Variants in the Whole Exome for Sudden Unexpected Death in Epilepsy (SUDEP) proband 2069……………………................................116  Table 3.21. Key Pathogenic Variants in Sudden Unexpected Death in Epilepsy (SUDEP) proband 2231…………………………………….....................................117  Table 3.22. Key Pathogenic Variants in Whole Exome for Sudden Unexpected Death in Epilepsy (SUDEP) proband 2429………………………………………………..118  Table 3.23. Key Pathogenic Variants in Whole Exome for Sudden Unexpected Death (SUD) proband 2460……………………..................................................................119  Table 3.24. OBSCN Variants in the eight Whole Exomes for Sudden Death probands…………………………………………………………………………….120																							ix		List of Figures    Figure 1.2. The Spectrum of Sudden Death. Sudden Infant Death Syndrome (SIDS) occurs between the age of 0-1 year old, where all other possible causes of death are eliminated during autopsy…………………………………………………………… 21  Figure 2.1. Three Step Decision Tree for analysis of the 8 whole exome Sudden Death samples (4 SIDS, 3 SUDEP, 1 SUD)………………………………………………... 33  Figure 2.2. Scatter plot of the number of transcripts shown to be impacted by variants, as annotated by SnpEff and VEP. SnpEff retrieves transcript information from NCBI, whereas VEP retrieves transcript information from Ensembl………………………..34  Figure 3.1. Venn Diagram showing the overlap in a subcategory of the candidate genes implicated in SIDS, SUDEP, SUD, epilepsy and cardiac arrhythmias………..49  Figure 3.2. The percentage of the SD candidate genes, by functional category, that had variants present in Sudden Infant Death Syndrome (SIDS) probands………….. 50  Figure 3.3. The percentage of the SD candidate genes, by functional category, that had variants in Sudden Unexpected Death in Epilepsy (SUDEP) probands (N=3)…. 51  Figure 3.4. The percentage of the SD candidate genes, by functional category, that contained variants present in the single, Sudden Unexpected Death (SUD) proband. 52  Figure 3.5. The number of pathogenic variants in Sudden Death (SD) candidate genes in the Sudden Infant Death Syndrome (SIDS) probands (N=4)…………………….. 53  Figure 3.6. The number of transcripts impacted by pathogenic variants in Sudden Death (SD) candidate genes in Sudden Infant Death Syndrome (SIDS) probands (N=4)………………………………………………………………………………… 54  Figure 3.7. The number of transcripts impacted by pathogenic variants in the Sudden Death (SD) candidate genes in Sudden Unexpected Death in Epilepsy (SUDEP) probands (N=3)……………………………………………………………………… 55  Figure 3.8. The number of transcripts impacted by pathogenic variants in the Sudden Death (SD) candidate genes in Sudden Unexpected Death (SUD) proband (N=1)…. 56  Figure 3.9. The number of pathogenic variants in Sudden Death (SD) candidate genes in the Sudden Unexpected Death in Epilepsy (SUDEP) probands (N=3)…………... 57  Figure 3.10. The number of pathogenic variants in Sudden Death (SD) candidate genes in the Sudden Unexpected Death (SUD) proband (N=1)……………………... 58  Figure 3.11. Allen Brain Atlas for the Developing Mouse Brain In Situ Hybridization image for Mbp expression at embryonic day 15.5 (E15.5), equivalent to a roughly 4 month old infant……………………………………………………………………... 59 x		 Figure 3.12. Allen Brain Atlas for the Developing Mouse Brain In Situ Hybridization image of Mbp expression at postnatal day 4 (P4), equivalent to roughly a 1 year old child…………………………………………………………………………………. 60  Figure 3.13. Allen Brain Atlas for the Developing Mouse Brain In Situ Hybridization image of Hrc2c expression at embryonic day 15.5 (E15.5), equivalent to roughly a 4 month old infant……………………………………………………………………... 61  Figure 3.14. Allen Brain Atlas for the Developing Mouse Brain In Situ Hybridization image for Hrc2c expression at postnatal day 4 (P4), equivalent to roughly a 1 year old child………………………………………………………………………………….. 62                                      xi		List of Abbreviations 5-HT 5-Hydroxytryptamine  ARVC5 Arrhythmogenic Right Ventricular Cardiomyopathy Type 5 BQSR Base Quality Score Recalibration CPVT Catecholaminergic Polymorphic Ventricular Tachycardia  DCM Dilated Cardiomyopathy  DNA Deoxyribonucleic acid  EIEE Early Infantile Epileptic Encephalopathy  HCM Hypertrophic Cardiomyopathy  LQTS Long QT Syndrome  NCBI National Center for Biotechnology Information PhastCons Phylogenetic Analysis with Space/Time models Conservation SCD Sudden Cardiac Death  SD Sudden Death SIDS Sudden Infant Death Syndrome SUD Sudden Unexpected Death SUDC Sudden Unexpected Death in Childhood SUDEP Sudden Unexpected Death in Epilepsy SUDI Sudden Unexpected Death in Infancy SUDY Sudden Unexpected Death in the Young  SUND Sudden Unexpected Nocturnal Death VEP Variant Effect Predictor GATK – Genome Analysis Toolkit VQSR Variant Quality Score Recalibration WES Whole Exome Sequencing WGA Whole Genome Amplification xii		WGS Whole Genome sample                         xiii		Acknowledgements    My past two and a half years in Vancouver have been both incredibly challenging and rewarding. Firstly, I would like to thank my supervisor, Dr. Tara Klassen, for her patience, understanding, support and friendship. I would also like to thank Gemma Pinchin, Jen Brown, Arnab Ray, Dr. Alex Smith, Patrick Boutet, Marilyn Sun, and Ellery Lee for their friendship and daily support. You’ve become my family.  I would also like to thank Dr. Alica Goldman, Director of the S.T.O.P. SUDEP Biorepository Research at Baylor College of Medicine and member of the NIH Center for SUDEP, for providing the DNA samples for my SIDS, SUDEP, and SUD cohorts. In addition, I would like to thank Dr. Othon Mena, at the San Diego Medical Examiner’s Office, and Dr. Torbjorn Tomson at the Karolinska Institute for their assistance in acquiring the postmortem blood samples.  Thank you to Dr. Corey Nislow and the UBC Pharmaceutical Sciences Sequencing Center for performing the Whole Exome sequencing of the Sudden Death DNA samples and to Patrick Boutet for his knowledge and assistance in the bioinformatics processing of the exome data.  Lastly, I would like to thank my family and friends, in Canada and South Africa, for their constant love, guidance, and support.             	  1		Chapter 1: Background Knowledge and Scope of Thesis    1.1.   Sudden Infant Death Syndrome  In the Western world, the leading cause of death for infants before one year of age is Sudden Infant Death Syndrome (SIDS) (1). Epidemiological reveals that the peak age for a SIDS death occurs at roughly the 3rd month of life but can occur up to 12 months (2). These infants are predominantly found during sleep, face down in the prone position in an otherwise healthy infant. Like the spectrum of sudden death disorders, the overarching criteria for a SIDS diagnosis is that it eliminates all alternative causes of death, such as suffocation and infection and requires a full autopsy including neuropathological assessment (3). It has been postulated that cardiac regulation via vagal tone was found to be involved in the parasympathetic control of respiration and variations in heart rate influenced by sleeping position. The function of the cardiac pacemaker was also found to be influenced by the intrauterine environment, hormones, and postnatal maternal care (4). In 1992, the American Academy of Pediatrics’ recommendation to not place infants to sleep in the prone position (5), as well as the 1994 Back to Sleep campaign (6), resulted in a notable decrease in the number of infants that unexpectedly died from SIDS presumably by mitigating the environmental and physiological risk. While post-neonatal mortality has not decreased since this time, the way in which infant deaths are classified has evolved. Specifically, there has been an increase in the number of accidental deaths caused by strangulation and suffocation (7). It is now believed that many infant deaths were wrongly diagnosed with SIDS.  There is still a prevalent belief that a subpopulation of infants harbors an intrinsic risk of unexpected death. These include infants born prematurely who have an 2		increased risk of unexpected death, due to a compromised and underdeveloped nervous and respiratory system (8). Additional factors for SIDS in this subpopulation include African American race, tobacco or alcohol use using pregnancy, and male gender (8). In recent post-mortem studies a number of pathogenic gene variants in inflammatory and hypoxia-response genes, as well as genes involved in neural myelin sheath developmental pathways have been identified. However, voltage and ligand-gated ion channels continue to represent the largest category of causative risk genes due to their inherent role in establishing and regulating cellular excitability in heart, lung, brain and vagal nerve (3).  The brainstem houses a number of homeostatic regulatory nuclei, like the Dorsal Motor Nucleus of the Vagus, Raphe Nuclei (9), and Trigeminal Nerve, which act to communicate and control both cardiac and respiratory systems, perturbation of which can lead to death. Cellular apoptosis in the brainstem occurs more frequently in SIDS infants compared to children that survive infancy while dysfunction of the vagus nerve and aberrant neurocardiac signaling are known to be a cause of sudden death disorders (10). Intriguingly, the signal transduction properties of sympathetic and parasympathetic tracts originating in the brainstem, continue to mature exutero with changing excitability properties up to 3 months of age (11).   1.1.1 Overlap in the Spectrum of Sudden Death  Like SIDS (1), there are a number of clinical disorders where the cause of death is unexpected and unexplained even upon autopsy. These are classified by either known comorbid disorders, like Sudden Cardiac Death (SCD) (12) or Sudden Unexpected Death in Epilepsy (SUDEP) (13), the age of death as in SIDS, Sudden Unexpected Death in Children (SUDC) or Sudden Unexpected Death in the Young 3		(SUDY) (14) or by apparent cause of death, Sudden Unexpected Nocturnal Death (SUND) (15) or complete lack of other defining features as in Sudden Unexpected Death (SUD). The unifying definitions for the spectrum include autopsy-negative deaths in a range of population. (TABLE 1.1, FIGURE 1.1).  SCD occurs most frequently in individuals aged 1-35 but can occur across the entire age spectrum because of structural defects or electrical heart arrhythmias underlying mortality. The latter are frequently referred to as Sudden Arrhythmia Death Syndromes (SADS) where the most prevalent disorders are Long QT Syndrome (LQTS), Brugada Syndrome, Hypertrophic Cardiomyopathy (HCM), Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT), and Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) due to underlying genetic risk factors (12,16,17). Intriguingly, the risk factors across the sudden death spectrum appear to share features, including ventricular tachycardia and fibrillation, where young males have highest risk of SUND in males from Thailand, Cambodia, Japan and the Philippines (15).  Unlike SUD which accounts for 15-20% of all natural deaths (18), SUDEP is the single most common cause of death in those with epilepsy, accounting for 40-50% of deaths in epilepsy patients (13). These unexpected deaths are 40 times more likely in people suffering from epilepsy, when compared to the general population (13). The risk factors of SUDEP include being between 19-45 years of age, male, poor medication compliance, lack of seizure control even on multiple antiepileptic drugs, a history of tonic-clonic seizures, and prolonged seizure duration (19). Similar to SIDS, the majority of SUDEP cases occur during sleep. These SUDEP deaths occur with or without evidence of preceding seizure, where it is believed an environmental or physiological insult aggravates the underlying genetic predisposition resulting in the 4		fatality (20).  While relatively infrequent in pediatric epilepsies, SUDEP accounts for 30-50% of the deaths in severe early onset infantile epileptic encephalopathies, affecting between 1 in 500 and 1 in 1000 epilepsy patients yearly (21),where the seizure onset of occurs within the first 6 months of life where neuro-cardiac and/or neuro-respiratory dysfunction have both been shown as causative (19,20,22).   With the extensive genetic testing involved in two of the most prevalent sudden death disorders in the world, SUD and SUDEP, it has been revealed that they have overlapping etiology and risk patterns, which are shared with SIDS. In the US, prevalence of SUDEP is roughly 1.16 cases for every 1000 people with epilepsy per year (23). In comparison, a study in Denmark found that among 1-35 year old individuals, the incidence of SCD was 1.9 cases per 100 000 person-years (24), while 1 in 2000 infants in the Western world will die from SIDS within the first year of life (8). Indeed, the identification of shared genetic cause in a known Sudden Cardiac Death gene; KCNQ1 encoding KvLQT1 (Kv7.1), historically known as the cardiac delayed  rectifier potassium channel (25) is considered a cause of death in all three disorders; SIDS (26), and SUDEP (25) as well as SUD (27) and SUDY (28). Mutations in the KCNQ1 gene result in dysfunctional signaling in the protein product Kv7.1 (KvLQT1) which results in cardiac arrhythmia (Long QT Syndrome) and/or epilepsy and/or sudden death in both mouse and man (29–32). Retrospective and prospective analyses revealed a phenotypic overlap and prevalent misdiagnoses where 30% of patients with a cardiac arrhythmia also report a presumptive epilepsy phenotype which further confounds personal genetic risk prediction (33).  It has been proposed that due to the range of seizure manifestation in children and requirement for a diagnostic EEG, many children with undiagnosed epilepsy may die of SUDEP, which may be confused with SIDS, SCD  and SUDC resulting from 5		cardiac arrhythmia, hypoxemia or apnea with or without a seizure (34,35). Intriguingly, hippocampal malformations and morphologies are believed to play a role in sudden death in children even in the absence of epilepsy (36). Hallmarks of excitability defects in SIDS infants have been detected postmortem where granule cell dispersion and bilamination in the hippocampus were observed in 42% of 112 SIDS cases. This granule cell dispersion and bilamination within the hippocampus is also known to be a characteristic of temporal lobe epilepsy which is highly intractable and where patients are prone to cardiac dysfunction resulting in high rates of mortality (36). Volume loss within the autonomic region of the brainstem has also been seen in patients with temporal lobe epilepsy, in addition to SUDEP cases (37) Pediatric patients with SCN1A mutations causing Dravet syndrome also present with malformations in cortical brain development which persist as patients age (38). Thus, the potential that many SIDS cases are in fact misdiagnosed SUDEP, SUDC or even SCD cases is possible given the limited diagnostics performed in otherwise healthy infants prior to death demanding further improvements in preemptive genetic testing.     1.1.2 SIDS Triple Risk Hypothesis    Current understanding of SIDS risk and causation involves epidemiological and physiological risk factors, coined the Triple Risk Hypothesis (38). Simply, it is postulated that SIDS is caused by the summative effect of overlapping risk factors, working in concert, resulting in the sudden catastrophic and unpredictable death of the infant. The Triple Risk factors are; 1) environmental factors (such as sleeping position or infection), 2) genetic predisposition (e.g. cardiac ion channel gene mutation) and 3) a vulnerable developmental age (~2-4 months of age).   6		1.1.3 Environmental Risk Factors and Sudden Death  According to the Triple Risk Hypothesis for SIDS (39), the environment can play an important role in risk of death (40). Along the SD spectrum, environmental factors are also key players in controlling the risk of unexpected death. It has been long held that the principle risk of SIDS has been associated with an infant being placed in the prone sleeping position, where it appears infants are less able to regulate arousal and breathing (41). In 1994, the “Back to Sleep” campaign (42) in the United States promoted supine sleeping for infants and a 50% drop in the SIDS rate in the US was observed by 1999.  Following the reduction of SIDS deaths after the implementation of the “Back to Sleep” campaign, other risk predisposing factors became apparent as infants continued to die suddenly and unexpectedly despite this environmental intervention. These risk factors include being of African American descent, exposure to alcohol or tobacco during the prenatal period, premature birth and male gender (8). Additional environmental considerations such as temperature and control and regulation of respiratory drive were also investigated. Detailed analyses on key environmental and physiological factors (43) using birth certificates and infant death registries analyzed between 1990 and 2012, in Colorado, revealed that altitude is independently associated with SIDS risk. Infants born at higher altitudes have an increased risk of dying from SIDS  potentially due to decreased cerebral oxygenation and hypoxia (44).   1.1.4 Physiological Risk Factors and Sudden Death  More recently, the risk of SIDS has expanded to include challenge by a viral or bacterial infection, where it is believed that underlying physiological risk can be further exacerbated by a prone sleeping position during sleep. The brainstem, 7		particularly the medulla oblongata, regulates blood pressure, breathing, and heart rate during physiological arousal and awakening and can be influenced by local and systemic cytokine concentrations (45). Inflammatory cytokine release caused by infection, in combination with abnormal serotonin neurotransmitters in the brainstem, has been implicated as a cause of SIDS (45). More than one half of SIDS infants have abnormalities in the medullary serotonin (5-hydroxytryptamine (5-HT)) system, with decreased 5-HT receptor binding, while simultaneously showing elevated interleukin (IL)-6 cerebrospinal fluid levels in SIDS infants. In these infants, the IL-6 levels are the hallmark of infection with the medullary 5-HT system having significantly higher levels relative to controls. Here, the overexpression of IL-6 receptors on cells within the medulla, in some infants, leads to the increased risk of death (45,46).  Intriguingly, abnormal expression of 5-HT receptors in the brainstem has been observed in murine SUDEP models (47), while the use of Selective Serotonin Reuptake Inhibitors (SSRIs) in epilepsy patients has been shown to decrease SUDEP risk (48). Similarly, sleep position has been shown to impact cardiac rhythmicity, where shifting a newborn, male infant, born with a congenital heart defect, from the supine position to the prone position lengthened the QT interval significantly (49). SIDs infants have also been shown to have longer QT rhythms during sleep when recorded in the first days of life compared to normal infants which was also correlated with the observed autonomic instability observed in these same patients implicating the 5-HT innervation and regulation of the cardio-respiratory pathways (50). It has also been suggested that a similar campaign, where back sleeping is promoted, could decrease the risk of SUDEP (20). 	8		1.1.5 Role of Genetic Variants in the Predisposition of Sudden Death Spectrum Disorders   The spectrum of sudden death has shared genetic risk factors and genetic risk in SIDS is a component of the Triple Risk hypothesis (51)(39)(50) and encode proteins that have known roles in epilepsy, cardiac arrhythmia, muscle contraction, and respiratory regulation as well as general cellular processes and immune responses. This overlap implicates the same gene as causative in more than one syndrome which confounds the utility of preventative molecular diagnostics. If the same gene can cause multiple disorders, predicting what an individual may suffer from can be difficult.  Cardiac Genes: With the high incidence of inherited cardiac arrhythmia and fatal infantile inheritance (53), the role of abnormal cardiac conduction and heart development have been implicated in SIDS. More than 50% of SIDS infants have symptoms of cardiac abnormalities, such as shortness of breath, tachycardia, apnea,  and  records of  >1 lengthened Qtc interval on EKG during the first week of life (44). Of the known mechanisms of cardiac arrhythmia, SIDS has been principally linked with dysfunctional cardiac sodium currents regulating the depolarization of heart tissue (55). In the heart, sodium current is selectively conducted through the voltage gated sodium channel Nav1.5, encoded by the SCN5A gene, a known monogenic cause of Brugada syndrome and Short QT syndrome as well as SCD (56). In addition to the Nav1.5 pore forming alpha subunit, the sodium channel beta subunits Nav-4 (SCN1B-SCN4B genes) interact with and consequently modify ion conductance through the channel and regulate the conduction of sodium ions across cardiac cell membranes and in themselves are cardiac arrhythmia genes (55). Importantly, non-ion channel proteins involved in cardiac function and cellular processes like glycerol-3-phosphate dehydrogenase-like protein (GPD1L), alpha1-syntrophin (SNTA1), and 9		caveolin 3 (CAV3) have also been implicated in SIDS. A retrospective study performed on archived Danish neonatal blood spot genomic DNA for infants born between 2000 and 2006 revealed that 8/66 SIDS probands harbored a mutation in one of these candidate genes (53). Out of the 66 proband individuals, 6 mutations in SCN5A were identified as well as 1 mutation each in CAV3, GPD1L, and SCN3B. Intriguingly, one infant had bioinformatically nonpathogenic population mutations in both SCN5A and GPD1L when assessed independently, but it has been recognized that synergistic effects of compound and digenic mutations can result in excitability disorders and sudden death. Two SCN5A missense mutations, one on each allele, were found in an individual with Brugada Syndrome (57). When family members only presented with one of the mutations, no disease phenotype was seen (57). Most recently, a whole exome analysis revealed that >35% of Sudden Death in Infancy (SUDI) cases, which is a larger classification encompassing deaths due to SIDS, accidental asphyxia, infection, or cardiac causes (58), have a presumed pathogenic variant in a gene regulating cardiac function, and that 8/47 (17%) had an  ion channel gene mutation in a known cardiac arrhythmia gene (59). These findings are consistent with retrospective molecular autopsy studies in SUDEP where ~13% of cases had a deleterious variant in KCNH2, KCNQ1 or SCN5A (the three principle genes for cardiac arrhythmias) (60), and 18% had variants within HCN1-4, which regulate automaticity within the cardiac conduction system (61,62) . Respiratory Genes: The serotonin 5-hydroxytryptamine (5-HT) system originates in the medulla of the brainstem and is responsible for baseline respiratory regulation (13,63). This acts in concert with noradrenaline, also regulated by the brainstem, to regulate respiratory rhythm. The dynamic balance between the sympathetic and parasympathetic  systems have been implicated in an increased SIDS 10		risk (63). Monoamine oxidase A (MAOA) is responsible for regulating both presynaptic levels of serotonin and noradrenaline, yet despite this central role, genetic risk in MOA in SIDS is controversial. Two cohorts of  >200 SIDS infants had differential genetic risk of common polymorphisms within the promoter of MAOA leading to a low and normal expression of the enzyme (63,64). In the smaller cohort (N=213), the male SIDS cases had a significantly increase in the low expression allelic form, which is hypothesized to reduce the MAOA enzyme in the brain stem and in turn increases the serotonin and noradrenaline levels in SIDS infants. However, this is in direct contrast to the current pathophysiological theory that SIDS risk is increased in infants with reduced brain serotonin levels, and thus continues to be debated as a genetic cause (64). Like SIDS, the 5-HT system is also associated with SUDEP risk, where low levels of serotonin have been found in patients who have died from SUDEP believed to result from reduced respiratory drive and response to resulting hypoxia (19,47). DBA mice, an animal model of SUDEP with dysfunction in 5-HT regulation, exhibit seizure-induced respiratory arrest (S-IRA), which leads to cardiac arrest following an audiogenic, sound-cause seizure (65). SSRIs have also been shown to prevent S-IRA and fatal downstream cardiac arrest in these mice, such that increasing synaptic levels of serotonin is protective effect against seizure induced neurorespiratory dysfunction (47). In mice, the 5-HT neurons have also been shown to switch their function from tonic respiratory drive during the postnatal period, needed to provide the baseline respiratory level, to chemoreceptive respiratory control, stimulating above baseline levels, as the mice mature (65). This suggests that This is similar to the recorded human SUDEP cases where cerebral silencing and respiratory depression precede fatal cardiac arrest leading to death (19).  In addition to these central regulatory pathways other genes have been shown 11		to have altered gene expression during respiratory challenge. These include Heme oxygenase-1 (HO-1 encoded by HMOX1)) which is down regulated in alpine climbers with a corresponding constitutively high heme levels in the same individuals (66).  Similarly, the roles of Fanconi anemia (FA) protein FANCD2 involved in the DNA repair pathway, have also been revealed to have modified expression and transcriptional repression in hypoxic conditions (53). Serotonin: 5-hydroxytryptamine (HT) neurons have been shown to regulate baseline respiratory control during the postnatal period in mice and respiratory arrest caused by seizures are an established mechanism in SUDEP (47)(65). Within the brainstems of the DBA mouse model, the serotonin receptors are under expressed where administering fluoxetine, a serotonin reuptake inhibitor, to these mice was able to reduce the incidence of sudden death, presumably by increasing synaptic serotonin levels (47). DBA mice also have abnormal, both increased and decreased, expression of the HTR2B, HTR1A, HTR1B, HTR1D, HTR3D, HTR3E, HTR2C, HTR5A, HTR2A, HTR7 genes, out of the total 14 known serotonin receptors (47,67,68). Mutations in the serotonin transporter SLC6A4 have shown to be implicated in neurodevelopmental abnormalities in infants, including decreased wakefulness and increased irritability (69,70).  Immune Genes: Infants between 2-4 months may be more susceptible to SIDS resulting from a developing immune system with corresponding low levels of systemic antibodies and thus, are at an  increased  risk of infection by common bacteria and viruses (71). IL-6 and IL-10 levels during an immune response is highly regulated by genetic factors where up to 50% of a response is due to personal genotype (72). Specifically,  infants with low expression levels of interleukin-10 (IL10) are at an increased risk of SIDS due to an impaired ability to inhibit the 12		proinflammatory response that occurs due to an infection leading ultimately inducing a prolonged inflammatory response (73). Analysis of cerebrospinal fluid in SIDS infants contains elevated levels of, interleukin-6 (IL6)  compared to other infant deaths as well as Vascular Endothelial Growth Factor (VEGF) (46,74). VEGF involved in the development of the respiratory system such that abnormal levels may impair infantile breath regulation during vulnerable developmental periods. It is important to note that while limited in scope and yet to be done on a systematic scale, small cohort studies have shown that immune genes, and more specifically variants, at the population level, involved in the regulatory regions of inflammatory genes are involved in SIDS risk (75). Neuronal Regulation: Known SIDS risk genes encode ion channels and other proteins that are involved in neuronal regulation and signaling in the infantile brain. Genes encoding for subunits of the neuronal nicotinic acetylcholine receptors, CHRNA2 and CHRNA4, have been implicated in SIDS but also have a role in in autosomal dominant nocturnal frontal lobe epilepsy (76).  Several gene non-ion channel genes associated with myelin sheath development and synaptic transmission, including GAP43, MBP, TPPP, SLC1A3, SLC25A4, PHOX2B, SNAP25, and VAMP2 have also been shown to contribute to SIDS risk (77,78).  Muscle Regulation: Other genes associated with SIDS risk ,  such as MYOM1, that express proteins involved in muscle cell regulation have a role in cardiac death(79). MYOM1 encodes for a protein expressed in the myofibraller M band of muscle cells (80). Similarly, a SPTAN1 gene mutation, which encodes for plasma membrane-stabilizing spectrin proteins (80), implicated in Structural Focal Epilepsy was found in an individual who died from SUDEP. This supports nominating genes involved in abnormal muscle regulation and response in heart, diaphragm or 13		vasculature response as potential Sudden Death (SD) risk genes (81). Cytoskeleton & General Cell Processes:	  Genetic variation in the TMEM43 gene, encoding for a protein involved in maintaining the structure of the nuclear envelope has been identified in Brugada patients (82). Like other SIDS genes, this protein has also been implicated in Arrhythmogenic Right Ventricular Cardiomyopathy Type 5 (ARVC5) a non-ion channel cause of SCD and SUD (83). LGI1, SMC4, and COL6A3 also involved in various aspects of cellular regulation and maintenance have been shown to be significantly associated with SUDEP when compared to disease controls (84). LGI1 stabilizes synapses and regulates voltage-gated potassium ion channels, while SMC4 encodes for a chromosomal structural maintenance protein and COL6A3 encodes for collagen, a cell-binding protein (80). Thus, dysfunction of these proteins are presumed to have ubiquitous regulatory effects on cell functions in all tissues in the body, including the brain, heart and lungs.  Ion Channels & Associated Proteins: Ion channels are believed to play a major role in SUDEP, SIDS, and SUD. Inherited channelopathies play a role in cardiac diseases, such as Long QT Syndrome and Brugada Syndrome, and sudden cardiac death (85). In addition, SCN1A and KNCA1 have been shown to be implicated in both epilepsy and SUDEP (3). KCNQ1 loss-of-function mutations lead to Long QT Syndrome 1 (LQT1), LQT2 is caused by mutations in potassium channel gene KCNH2, LQT3 is caused by sodium channel gene SCN5A mutations, LQT7 is caused by potassium channel gene KCNJ2 mutations, and LQT8 is caused by calcium channel gene CACNA1C mutations (85).  14		1.1.6 Genetic Overlap in Disorders Predisposing to Sudden Death Nominates Novel SIDS Genes  Despite the previously identified candidate genes in SIDS, the overlap in the predisposing comorbid or pathophysiological-related conditions nominates a range of other known genetic causes, of which the majority are related to excitability disorders in brain (epilepsy) and heart (cardiac arrhythmias). These genes include the most common causes of SCD in young adults; Hypertrophic Cardiomyopathy (HCM), Dilated Cardiomyopathy (DCM), and Familial Atrial Fibrillation (AF) where impaired cardiac function is the principle cause of mortality (86). Even within these disorders there is considerable overlap in causative genes which underscores the complexity of risk prediction in the spectrum of SD. Importantly, while most disease genes encode ion channels, there are others that encode protein kinases, scaffolding proteins, as well as myosin and actin cardiac muscle filaments. Similarly, SUDEP risk is not equal across all patients and in addition to the idiopathic generalized genetic epilepsies (IE/GGE) it occurs most frequently in Early Infantile Epileptic Encephalopathies (EIEE), and patients with Dravet Syndrome (DS), carrying between 5.7-10% and being responsible for approximately 60% of fatalities (87). Cardiac arrhythmias have been seen during focal or tonic-clonic seizures in both mouse and man which has been established as a cause of SUDEP  (13,34,88).   Hypertrophic Cardiomyopathy (HCM) - HCM can lead to sudden cardiac death due to thickening of heart septum and ventricle walls, thus restricting blood flow through the heart (89). Prevalence is roughly 1/500 and mortality is 0.7-1% per year for patients (90). Additionally, it has been shown that the 15 year cumulative incidence of SCD was 6% (86). Genes associated with HCM include CAV3, OBSCN, LAMP2, MYL2, MYL3, MYOZ2, PRKAG2, TNNI3, CSRP3, ACTN2, TPM1, PLN, 15		MYBPC3, TNNC1, TNNT2, ACTC1, and MYH7 (79,81,89,91,92,61).  Dilated Cardiomyopathy (DCM) –DCM leads to reduced blood flow due to thinning of the cardiac walls and enlargement of the cardiac chambers (89). Similar to HCM, the 15 year cumulative incidence of SCD was 5% (86). Genes associated include MYH6, MYH7, TNNT1, TNNT2, TNNC1, BAG3, RBM20, CTF1, DES, and EMB, TAZ, CSRP3, TPM1, ACTC1, MYBPC3 (89,93–95). Familial Atrial Fibrillation (AF) –AF is an inherited condition that leads to abnormal heart rhythms due to improper electrical activity in the atria of the heart. A meta-analysis of 7 studies looking at the incidence of SDC in 6061 atrial fibrillation patients found a pooled relative risk of 1.88 and an absolute risk increase of 0.6/1000 participant years (96). No studies have been conducted to look at the risk of SCD for patients newly diagnosed with atrial fibrillation (97). Overall, atrial fibrillation is diagnosed in roughly 5% of adults over the age of 65, with risk factors including age, high blood pressure, diabetes, and heart disease (98). However, there is syndromic overlap and comorbidities. In inherited atrial arrhythmias, a risk factor for SADS, both atrial fibrillation (AF) and atrial flutter, are commonly seen in individuals with Brugada Syndrome and Long QT Syndrome. In these individuals the initial occurrence of AF is during nighttime (99,100). The prevalence of AF increases with age, where among adults younger than 55 years old was 0.1%, and the prevalence for adults older than 80 was 9% (101). Genes with causative variants for AF include LMNA, GJA5, KCNA5, and KCNE1 the latter being a known regulatory gene for the epilepsy and cardiac channelopathy gene KCNQ1 which causes SCD, SUD and SUDEP (81,102–104). Early Infantile Epileptic Encephalopathy (EIEE) – Commonly known as Ohtahara Syndrome, this severe infantile epilepsy syndrome is diagnosed prior to 3 16		months of age, with the first seizures typically occurring in utero or during the first 10 days of life (76). Up to 75% of the infants have onset of symptoms within a month of birth with high rates of morbidity and mortality with ~ 25%  of patients dying  before the age of 2 (105). Surviving children have severe physical and developmental disabilities. These may progress to further diagnoses of West Syndrome and Lenox-Gastaut Syndrome, both seizure disorders found in older children (106). West Syndrome (commonly referred to as Infantile Spasms) and Lenox-Gastaut Syndrome are both currently diagnosed etiolgically based on seizure type and disease progression, with the former characterized by full body stiffness-type seizures compared to those with Lenox-Gastaut Syndrome suffering from a variety of different types of seizures (107). Similar to EIEE, the incidence of Ohtahara Syndrome is roughly 1/50 000 in the UK and 1/100 000 in Japan (104). Genes associated with Ohtahara Syndrome include CDKL5, SLC12A5, STXBP1, SPTAN1, PCDH19, SCN2A, SCN8A, and TBC1D24 , where PDCH19, SCN2A and SCN8A, overlap with other infantile epilepsies like Dravet Syndrome (104,108,109).  Early Myoclonic Encephalopathy (EME) – Similar to EIEE, EME also has an early, infantile onset. Seizures are observed within 3 months of life, with most beginning within the first few weeks. The principal differential diagnostic is that EME involves partial, myoclonic seizures, whereas EIEE involves tonic spasms (110). Similar to EIEE, the prognosis for children with EME is poor, with roughly half of children passing away before the age of 2 (110). Many of the genes implicated in EIEE overlap with EME due to their role in neuronal excitability and neuronal development (105).  Severe Myoclonic Epilepsy of Infancy  (SMEI) and Dravet Syndrome (DS) – While the syndromic overlap between DS and SMEI remains contentious the unifying 17		features of these syndromes is the onset prior to the age of 1, with the incidence ranging from 1/20 000 and 1/40 000 live births (105,111). Febrile seizures are a common instigator; however afebrile patients are also observed due to the underlying genetics. Over 80% of patients with a SMEI/DS clinical phenotypes have a molecular diagnosis with a gene mutation in SCN1A while other causative genes include SCN2A, SCN1B, GABRAG2 and PCDH19 (112). The rate of SUDEP within Dravet Syndrome is the highest in the pediatric population, with an incidence ranging from 5.7-10% and accounting for roughly 60% of fatal epilepsy deaths (13). Cardiac arrhythmias have been seen during focal or tonic-clonic seizures in Dravet patients, implicating neuro-cardiac dysfunction as a cause of SUDEP (13,34).   1.2   Project Rationale and Scope of Thesis Personalized medicine has marked a new milestone in clinical practice as the demand for genetic diagnosis is rising steeply, including pre-emptive screening, familial and population genetic profiling, and molecular autopsy for medico-legal purposes. Although pathogenic mutations have been identified in the Spectrum of Sudden Death, they lack the ability to accurately predict clinical phenotype. Risk assessment is further complicated by the overlap of causative genes and shared mechanisms of neuro-cardiac and neuro-respiratory dysfunction observed in these patients. There is an urgent priority to identify and translate genetic predictors of Sudden Death into clinical practice for risk stratification and prevention.  1.2.1   Hypothesis  I hypothesize that life-stage dependent expression patterns of alternatively spliced isoforms of key candidate genes in the brainstem cause differential timing of sudden death syndromes across age groups. Here, personal patterns of genetic 18		variation within my Sudden Death Candidate genes will be analyzed in the context of transcript variability and pathogenicity to determine if there is a role in an individual’s person genetic risk.  1.2.2 Specific Aims  Specific Aim 1: Identification of Sudden Death Candidate Genes: A list of candidate genes that play a known or potential role in the spectrum of sudden death disorders with a focus on SIDS, SUDEP, and SUD was compiled from an in-depth analysis of the published literature. This produced a tiered list of known and suspected “Sudden Death Candidate Genes” (SD genes). Using the preliminary seed terms related to Sudden Death and pathophysiological mechanisms such as 1) respiratory distress; 2) immune dysfunction; 3) hypoxia; 4) cellular apoptosis 5) inflammation, the literature nominated additional genes implicated as causative or contributory in other diseases. This initial gene list was then analyzed for pathway-network analysis using the webserver GeneMANIA (113) to expand the list to biologically related genes using functional co-expression or co-localization as criteria.   Specific	Aim	2:	Genetic	Variation	in	Sudden	Death	Exomes:	Due to the relative rarity of Sudden Death disorders and the inability to predict who will die when, both retrospective DNA and at time of death blood samples were collected on Guthrie cards (Neonatal) from individuals who have died from SIDS, SUDEP, and SUD. Whole genomic DNA was extracted and subjected to Whole Genome Amplification (WGA) Library preparation was performed using the Nextera Rapid Capture Exome Kit (Illumina, San Diego) followed by data collection on the Illumina HiSeq2500. WES for a total of eight deidentified and anonymized probands was obtained. This includes four SIDS cases (two male; two female), three SUDEP (three male) and one 19		SCD (one male) cases for variant analysis. An integrated bioinformatics pipeline including transcript annotation, and multiple pathogenicity algorithms was employed. The number, nature and functional consequences of variants within the SD genes were compared across SD disorders and by individual to identify the potential cause of death.  	Specific Aim 3: Spatio‐temporal	expression	patterns	of	SD	Candidate	genes	 :	In this aim, the central hypothesis is tested in that the expression of key SD genes that were predicted to be deleterious and pathogenic in SD exomes from Aim 2 , will be further analyzed using the resources available as part of the Allen Brain Atlas (114). The Allen Brain Atlas for the Developing Mouse Brain was used to identify relative expression levels of the SD genes within the brain and specifically the brainstem through development. A total of 8 stages are available; Embryological day (E)11.5, E13.5, E15.5, E18.5, Postnatal day (P)4, P14, P28, P56. According to Translating Time, P15.5 corresponds to a 4 month old infant and P28 is roughly a 1 year old infant.  According to Translating Time (115), 35 day post-conception mice (PC) is equivalent to 379 days PC in humans and 45 day PC mice is equivalent to 621 days PC in humans. Building in this biological context, the pathogenic variants in SD genes were mapped to their transcription patterns in the developing and mature brain to evaluate if their dysfunction influences the brainstem in an age dependent manner. This provides further stratification of risk patterns in the overlapping risk genes observed in the eight Sudden Death samples.	    20		Table 1.1: Spectrum of Sudden Death by age and diagnosis  SIDS SUDEP SUD/SCD Age  0-1 year old Lifelong  >18  Diagnosis Autopsy-negative death. Usually found lying face down in bed. Autopsy-negative death. Usually found lying face down in bed.  Autopsy-negative death. SCD when cardiac cause is believed.                    21		                                         Figure 1.1. The Spectrum of Sudden Death. Sudden Infant Death Syndrome (SIDS) occurs between the age of 0-1 year old, where all other possible causes of death are eliminated during autopsy. Children who die from Sudden Unexpected Death in Children (SUDC) are usually between the ages of 1 and 18 years old, whereas Sudden Unexpected Death in the Young affects young adults (18 to age 40) and is similar to both SIDS and SUDC because individuals are also generally found lying face down. SUDY by definition is a sudden unexpected death, however historically there has been an implication of cardiac involvement as an underlying mechanism. This is similar to Sudden Unexpected Death (SUD) which impacts individuals over the age of 18 and can be specified as Sudden Cardiac Death (SCD) if cardiac regulation is thought to play a role in the death. Sudden Unexpected Death (SUDEP) can occur at any age in an individual with epilepsy however pediatric SUDEP deaths are rare with the exception of the severe Infantile Epileptic Encephalopathy disorders like Dravet syndrome where up to 30% of patients die of SUDEP.  22		Chapter 2: Materials and Methods 	2.1 Compiling the Sudden Death Candidate Gene List  A detailed literature review of SIDS, SUDC, SUDEP, and SCD on PubMed was done to establish a primary list of the genes known to be causative or contributory in multiple patients via pathophysiological mechanism in these diseases. All literature up to and including January 31, 2016 was reviewed using a systematic key word search to first identify the syndrome (e.g. SIDS) and then a review of the results for genes or key molecules was performed for the top 200 papers returned. The results from the top 50 genes were used to populate a candidate gene table for each disorder. A subsequent search of syndrome and the terms; i) genes; 2) genomics; 3) genetics; 4) exome; 5) sequencing; 6) inheritance; 7) autopsy; 8) diagnostics; 9) polymorphism; 10) testing (e.g. SIDS genes; SIDS exome; SIDS genetics); 11) mutations; 12) variants was performed to further expand the candidate list. This formed the basis of gene based searches for diseases and disorders associated with the SD genes and their known or presumed pathophysiological mechanisms.  To better understand the mechanistic and functional overlap across the SD genes, a bioinformatics pathways analysis was performed to stratify and categorize the candidate genes. The original genes acquired from the PubMed literature search (N=169) were entered into GeneMANIA Webserver (113). Here, the underlying database and association algorithm of GeneMANIA uses available biological and disease datasets to identify additional genes involved in the same pathways, DNA-protein interactions, or protein-protein interactions based on the input gene list. In addition, GeneMANIA provides filtering functions enabling a tiered pathway and network analysis of H. sapiens associated datasets specifically. This increases the 23		likelihood that risk genes identified within specific pathways relate to the function, coexpression or colocalization of different combinations of SD genes. GeneMANIA allowed for the inclusion of an additional 79 genes, bringing the total number of SD candidate genes to 248.    2.2  DNA Samples  In collaboration with Dr. Alica Goldman (BCM Center for SUDEP Research Director NIH funded S.T.O.P SUDEP Biorepository), Dr. Torbjorn Tomson (Karolinska) and Dr. Othon Mena (San Diego Coroner) we have performed the first in depth review of SUDY cohort (ages 0-35 years old). This review of 13,050 deaths registered between 2008 and 2013 in the San Diego Medical Examiner’s Office identified 122 definite SUDY cases of which 48%, 28%, and 23% fulfilled clinical diagnosis of SUDEP, SIDS, and SCD cases, respectively (unpublished). Of these samples, we used 4 SIDS (2 male, 2 female), 3 SUDEP (3 male), and 1 SUD (1 male) for this project, where we were blinded for cause of death until all variant annotation was complete. Using the novel innovations supported by the CURE Grant (PI-Goldman, Co-Is Klassen, Tomson) we have piloted WGS from trace amounts of blood card derived genomic DNA (gDNA). gDNA was extracted with the QiaAmp Mini Spin Kit (Qiagen, Hilda, Germany) amplified with the Repli-g Ultrafast Whole Genome Amplification (WGA) mini kit (Qiagen) and the Genomeplex WGA kit (Sigma-Aldrich, St. Louis, MO) was used to fragment the template DNA (116).   2.3 Whole Exome Sequencing and Processing 	Whole genome amplification (WGA) was performed on the DNA samples from the blood cards to bring up the total amount of DNA to the minimum of 50 24		ng/uL. For the 8 samples, the concentration of DNA measured by the 2200 TapeStation Instrument (Agilent Technologies, Santa Clara, USA) ranged from 53 ng/μL to 408 ng/μL. Library preparation was performed by the UBC Pharmaceutical Sciences Sequencing Center for 8 Sudden Death individuals using a single sequencing library from the 8 samples, using Nextera Rapid Capture Exome Kit (Illumina, San Diego, USA). Per sample, 50 ng of DNA was required for the library preparation step. The initial part of the library preparation fragmented the DNA and tagged each sample with a unique index pair. The samples were then pooled based on DNA concentrations, assessed by fluorimetry, and hybridized to exome-specific capture probes. The final library went through minor PCR amplification and cleanup, prior to quality control and sequencing. Importantly, this exome dataset will only reveal variants in the transcript encoding portion of the genome, generally referred to as the protein coding region of the genome. Whole exome sequencing was chosen to keep costs down for the pilot project, in addition to allowing us to only look at variants found within the exons of genes. While this limits the amount of variation available for analysis compared to Whole Genome Sequencing, this approach provides the high-quality reads required to address the principle hypothesis of a singular variant having pathogenic effects on multiple transcripts within the same gene of interest.  Following library preparation, the samples were read using the Illumina Hi-Seq 2500 (Illumina, San Diego, USA) and the paired end FASTQ files containing the raw sequencing data were produced for each individual. The FASTQ files were run through FastQC as a quality assurance step. Trim_galore (117) was then used to remove Nextera adapter sequences. Following this step, SolexaQA++ (118,119) was used for more read trimming and selection.  Following the current standard in personalized epilepsy genomics, the Broad 25		Institute’s GATK 3.4 Best Practices workflow (120) was implemented to process and annotate the WES variants. Here, data collection was obtained by runs across 2 lanes resulting in 2 distinct paired end files that were merged prior to downstream processing. As described above, the FASTQ files were pre-processed into paired end files with library linkers were removed, the resulting WES FASTQ file was subsequently aligned to the Hg19 reference genome and the base quality score is generated using the Burrows-Wheeler Aligner (121), resulting in the output of a SAM file. Picard tools (122) was used to convert the SAM file to a BAM file, in addition to being used to mark duplicates. Removal of duplicate artifacts where the same DNA fragments may be sequenced several times, limits the evidence for calling a putative variant, enabling the GATK pipeline to ignore these duplicates using an internal read filter algorithm.  The GATK pipeline performed and refined local sequence alignments around insertion/deletion (indel) events. Principally, this step refines the initial mapping step which produces artifacts, such as reads flank indel events which are, in the absence of refinement, mapped with mismatching bases, that are called as variants but are in fact mapping artifacts. The realignment process employed here identifies the most consistent placement of the reads relative to indels, to minimize abnormal calls which improves the accuracy of the base recalibration. Base Quality Score Recalibration (BQSR) was then used to correct sequencing errors and other experimental artifacts through the employment of a quality score to improve variant calls within each sequencing read. These quality scores were per-base estimates of error from the sequencing runs as generated by the Illumina HiSeq 2500. BQSR was a process in which machine learning is used to model these errors empirically from the input data and adjust the quality scores per the algorithm, improving the accuracy of the variant 26		calls. Here, base recalibration used a model of covariation, based on the data and a set of known variants, and adjusted the base quality scores in the data based on the model (120). This was followed by a second round of marking duplicates and realignment in the absence of low quality base calling, as recommended by the GATK Best Practices.    The second phase of analysis was coined Variant Discovery, which identifies those bases or regions of the individual exome data that are different from the reference genome. These variant regions are identified and given a quality score where both false positives, also known as specificity, and false negatives, also known as sensitivity, based on high stringency filtering were minimized to capture the amount and extent of variation.  The whole genome amplification (WGA) required to bring the DNA concentration up to 50 ng/μL prior to library preparation can introduce more errors as compared to normal, whole DNA preparations. This step converts the BAM file into a Variant Call Format file (VCF). During this conversion, a Variant Quality Score Recalibration (VQSR) is generated, which allows variants filtering and prioritization based on the probability that they are true genetic variants instead of sequencing or data processing artifacts. In addition, this step includes the layering of functional annotations (such as gene structure) onto the individual genetic variants, allowing for predictions of the protein level implication effects of the variants. The final resulting VCF file is a text file that captures nucleotide insertions/deletions (indels), single nucleotide polymorphisms (SNPs) also known as single nucleotide variants (SNVs), and structural variation calls which include multiple nucleotide, duplication, deletion and repeat expansions. Importantly, as per GATK Best Practices for small (N=8) datasets, we included the analysis of 22 whole exome raw BAM files retrieved from phase 1 of the 1000 Genome Project in our data analysis pipeline to 27		bring the total number of samples up to the recommended 30 (123,124). To minimize variant call errors, ethnic and gender matched controls were selected where 6 samples were Europeans of which 4 were female and 2 were male, while, 16 samples were retrieved from the North American region, of which 9 were male and 7 were female. This reflects the white Caucasian/white Hispanic nature of the SD samples collected by the San Diego Coroner’s office. The resulting VCF file was then used for Bioinformatics analysis and functional annotation.  2.4 Bioinformatic Analysis The WES VCF file for the 8 probands was subjected to variant annotation. This annotation was done employing both Variant Effector Predictor (VEP)  (125,126), providing Ensembl (127) transcript annotation, and SnpEff variant annotation software (version 4.11, build 2015-10-03 by Pablo Cingolani) (128,129), providing the National Centre for Biotechnology Information (NCBI) (130) transcript annotation. From the literature, it appeared that the Ensembl set of transcripts was larger than the NCBI set (131). In addition, VEP was able to retrieve more transcript information for our probands (FIGURE 2.2) compared to SnpEff. Thus, we chose to only focus on the transcript data from VEP. Data for analysis included the 1) chromosome and 2) nucleotide position, 3) SNP ID (dbSNP rs accession) if known, 4) reference allele at the position of interest as reported in Hg19, and 4) the alternative allele encoded by the variant in that individual. In addition to being mapped to all known transcripts in the Ensmbl (VEP) and NCBI (SnpEff) databases, the gene variants were also annotated as to transcript (e.g. intronic, missense, nonsense, splice site) and functional consequence (e.g. potentially or probably pathogenic, benign, deleterious) or consequence score (e.g. amino acid substitution matrix) using 28		bioinformatics tools. These include 1) CADD (Combined Annotation Dependent Depletion) Phred, 2) PolyPhen 2 HDIV Pred, 3) SIFT Pred, and 4) PhastCons 100Way Vertebrate (132,133). These annotations used the most current Ensembl VEP and the dbNSFP (version 2.9) (134) annotation databases to determine the effects of the variants (TABLE 2.1). The physiochemical consequences of amino acid substitution were calculated as a Grantham score using a Perl script graciously provided by Stephane Flibotte, in the Department of Zoology at the University of British Columbia  (135,136).  Initially, transcript information was collected from both the NCBI and Ensembl databases, using SnpEff and VEP respectively. As an example, for proband 2069 who died from SUDEP, FIGURE 2.1 represents the variants found in the candidate gene list, along arbitrary positions on the chromosomes. Generally, Ensembl provided information on more transcripts when compared to NCBI, thus we chose to focus on the data from VEP annotations.  Because of the diversity of transcripts to which the resulting WES data were mapped, the functional annotations were difficult to categorize using the annotation categories. Thus, variants were binned into 3’ Untranslated Region (UTR), 5’ UTR, Synonymous, Nonsynonymous, Splice Site, Splice Region, Nonsense, Intronic, Regulatory, and Insertion/Deletion. These classes were arbitrary but related to the presumed functional and/or biological consequence resulting from the variant effect on one or more transcripts. UTR Variant- A mutation located in the flanking Untranslated Regions (UTR) regions of a gene  3’ variants are located downstream of the gene, whereas 5’ UTR variants are located upstream of a gene (137) based on which is the encoding strand. Thus, a 5’ UGT variant on the sense strand of DNA would be complimented by the 3’ 29		location on the antisense strand. Variants in these regions are known to XYZ on functional impact. Splice Site Variant bin included variants from both splice acceptor and splice donor variants. Splice acceptor variants are mutations that changes the two bases of 3’ end of an intron, whereas a splice donor changes the two bases of the 5’ end of an intron.  Splice Region Variant – Single nucleotide substitutions that will change either between the 3rd and 8th base from the start or end of an intron, or within 3 bases of an exon. This bin included variants that were also contained within the splice region but also overlapped with intron variants, synonymous variants, and nonsense-mediated decay (NMD) variants depending on the isoform sequence they were annotated with. An NMD variant is a sequence of DNA that is the target of nonsense-mediated decay resulting in no translation of the mRNA into protein equating to a complete loss of function at the protein level.  Intronic Variant - intron variants, noncoding exon variants, and noncoding transcript variants. Variants that were labelled as intron variants by SnpEff (128,129) and VEP (125) were located within the introns of a gene but not within 8 bases of its ends. Non-coding exon transcript variants were variants that changed non-coding exons in non-coding transcripts. Ensembl defines the impact of these variants as “modifiers” due to the difficulties in predicting the impact of non-coding variants. Since this is similar to the effect than an intron variant would cause, due to not being present in the final protein, these were grouped them together. A noncoding transcript variant is a variant located within the noncoding RNA gene. Synonymous variant bin included variants that resulted in no change to the encoded amino acid but do occur within the amino acid coding portion of the exon. 30		Due to the redundancy of the genetic code, multiple codons can code for the same amino acids, this is frequently referred to as the “wobble hypothesis” (138). Variants result in a different triplet codon however it continues to code for the same amino acid and thus are presumed to be less pathogenic than other types of variants. Within the synonymous variant bin, we included synonymous variants that were also labelled as targets for nonsense-mediate decay (NMD).  Nonsynonymous variant bin included missense variants and stop lost variants. This category is presumed to be the most pathogenic under molecular diagnostic testing as missense variants involve changing the codon such that a different amino acid is expressed. Stop lost variants involve the changing of one of the bases in the stop codon of a gene, resulting in the loss of the termination signal at the end of translation. Premature Stop Codon/Lost Start Codon variant bin traditionally considered to be a nonsynonomous variant because it involves the coding of a premature stop codon or the loss of a start codon, here these are binned separately due to their known pathogenic role in excitability disorders. These mutations result in premature termination of translation during protein synthesis. The resulting truncated protein formed leads to a complete loss of function.  Insertion/Deletion bin contained any variants where the alleles for an insertion or deletion of 1 or more bases were included. This includes expansions, repeats and duplications or deletions impacting the structural nature of the underlying gene in the chromosome. Many of these variants were annotated based on their presumed impact on protein coding however they were binned separately due to the change in underlying chromosomal organization rather than impact on mRNA or protein. Regulatory variant bin includes upstream, downstream and intergenic 31		variants. Upstream variants are located at the 5’ end of a gene, within the regulatory region of whereas a downstream variant is located at the 3’ end of a gene, also within the regulatory region. Intergenic variants are variants located in the regions between genes and may also overlap with regulatory regions of DNA. Variants within these regions are of importance due to the possibility of affecting gene promotor regions or other areas that may play a role in regulating gene expression.  Spanning Deletion bin was used variants where the allele was missing in that sample, due to the presence of an upstream deletion resulting in a frameshift mutation (139). These are overlapping deletions that are so large that they cover multiple variant sites and are captured in the VCF v4.3 specifications using a “*” (140). Three decision trees were generated to provide a focus for analyzing the exome data from the SD cohorts (FIGURE 2.1). The first decision tree follows the analysis of the complete whole exome data for each proband, comparing the total number of variants between cohorts, as well as between probands. Following filtering the exome data to only include the SD candidate genes, the second decision tree follows the logic of determining the differences in number of variants within the SD candidate genes. If the resulting data demonstrated that the number of SD candidate genes impacted by variants differed among probands and did not allow for prediction of cause of death.  The third decision tree looks specifically at the number of transcripts that are impacted by variants within the SD candidate genes. Once the variability in the number of impacted transcripts is determined, the spatio-temporal expression of genes containing pathogenic variants can be determined.   2.5 Allen Brain Atlas and Alternative isoforms Using our compiled gene list, we have begun to query the NCBI, Ensembl and 32		UCSC gene repositories to identify and compare the alternatively spliced isoforms reported for our candidate genes. Importantly, there are a number of key risk genes with a large number of splice forms, such as CACNA1C encoding the Cav2.1 voltage-gated calcium channel, which has 32 alternative protein forms. These results were also used during the variation annotation phase of Aim #2.  We then evaluated the expression patterns of each SD gene using the Allen Brain Atlas. Based on ISH analysis, the molecular probes in Allen Brain are limited to the principle (i.e. longest) transcript and/or the isoform that was successfully hybridized in subsequent rounds of optimization. While many of the candidate genes were present within the adult mouse brain atlas, there were fewer represented across the developing murine brain. Where possible, the location and relative abundance of the SD gene expression patterns were noted. Genes with high levels of expression within the brainstem were noted and will be compared against what is known about tissue expression patterns in cardiac and pulmonary tissues as well as the developing and mature vagal and phrenic nerves. The Allen Brain Atlas (114) for the Mouse Brain and the Developing Mouse Brain was used to determine the presence of the longest form of each candidate gene within areas of the brain, with key interest in the brainstem. Thus, if evidence exists for the expression of a gene with a potentially deleterious variant in one of our sudden death samples, this could be used as supporting evidence for cause of death.       33		                                                       Figure 2.1: Three Step Decision Pathway for analysis of the 8 whole exome Sudden Death samples (4 SIDS, 3 SUDEP, 1 SUD). Initially beginning with the raw data files for our 8 exomes, Step 1 was to evaluate total amounts of variation in the cohort and individual cases at the level of variant and gene. Step 2 was to evaluate the amount of variation in the Sudden Death genes as a candidate gene set and further subdivided into functional classes while Step 3 placed an emphasis on variant pathogenicity and transcript involvement.   34		 Figure 2.2. Scatter plot of the number of transcripts shown to be impacted by variants, as annotated by SnpEff and VEP. SnpEff retrieves transcript information from NCBI, whereas VEP retrieves transcript information from Ensembl.     0510152025303540450 100 200 300 400 500Number of TranscriptsArbitrary PositionProband 2069: Raw Number of Transcripts in Candidate Genes SnpEff (NCBI)VEP (Ensembl)35		Table 2.1: Variant Annotation Classifications and Definitions Variant Annotation Scoring/Function CADD Phred  CADD was acquired from dbNSFP (version 2.9 for hg19) (134). Scores are determined using a matrix to create a score between 0 and 99. A score of >10-20 means potentially deleterious, meaning that they likely reduced the overall fitness of the organism (141,142). PolyPhen2 HDIV Phred  Acquired from dbNSFP (version 2.9 for hg19) (134). Determines if a mutation will be benign (B), possibly damaging (P), or damaging (D) by looking at the amino acid substitution that occurs (143).  SIFT Pred  Acquired from dbNSFP (version 2.9 for hg19) (134). Determines if a mutation will lead to a change in the overall charge of the protein. If a change occurs, then the variant is deemed deleterious (D). If a change in the net charge does not occur, the variant is deemed tolerated (T) (143). phastCons 100 Way Vertebrate  Acquired from dbNSFP (version 2.9 for hg19) (134). Using multiple alignments of 100 vertebrate genomes, including the human genome to determine the probability (between 0 and 1.0) that the site belongs to a highly evolutionarily conserved sequence. If the site is highly conserved, it likely has an important function. The larger the score, the more conserved (143). A cutoff of 0.84 for conservation has previously been used (144). Grantham  Score is used to determine the physicochemical nature of an amino acid substitution. A higher score means a greater difference in chemical properties between two amino acids, in terms of polarity and molecule volume. Scores range from 0 to 215. A score of 0-50 is Conservative, 51-100 is Moderately Conservative, 101-150 is Moderately Radical, and >151 is Radical. (135,136,145). dbSNP Acquired from SnpSift (132,133) and provides the rsID, showing if the variant is present in the dbSNP database.  36		Chapter 3: Results 	3.1.  Identification of Sudden Death Candidate Genes  3.1.1 Genes Identified in the Spectrum of Sudden Death The compilation of SD candidate gene list using previously published literature on SIDS, SUDEP and SUD resulted in 248 genes (TABLE 3.1-3.9). While it is recognized that a pathophysiological and mechanistic overlap exists across the sudden death spectrum, which includes key risk genes central to cardiorespiratory function, the full extent of risk across the entire age and syndrome spectrum has yet to be evaluated in a concerted way. It is generally assumed that sudden death is induced by neurological dysfunction, as in epilepsy, heart dysfunction, and cardiology. However, SIDS research also implicates environmental and immune triggers as potential causes. Importantly, respiratory infections and the subsequent inflammatory response may play a role in triggering sudden death in infants during a vulnerable age. Here, known causative and contributory genes were entered into GeneMANIA (113) and those genes implicated through co-expression and co-localization pathway and interaction analysis were included in the SD candidate gene list (TABLE 3.1-3.9). The original sudden death genes collected from the literature (N=169), prior to expansion using GeneMANIA, encode proteins involved in various cellular response roles, including ion channels, sarcomeric regulation in muscle cells, and cytoskeletal regulation. As expected by pathway analysis, the additional GeneMANIA acquired genes are involved in the same regulatory roles within cells and are found in key tissues implicated in the presumed disease mechanism. In addition to genes implicated in SIDS, SUD and SUDEP, the full 248 candidate genes evaluated for involvement in other diseases including those 37		considered high risk for sudden death, such as epilepsy and cardiac arrhythmias (TABLE 3.10). These genes may or may not be genes undergoing molecular diagnostic or laboratory hematological diagnostic testing in these conditions. 70% of SD genes (N=174) are implicated in disease, the majority lead to cardiac arrhythmias including Long QT Syndrome and Brugada Syndrome where the causative genes have a high rate of mutations identified in post-mortem analysis of SUDY, SUD or SCD. Notably, there are a number of genes which are involved in more than one disease further complicating risk prediction (TABLE 3.10). In addition to excitability disorders, genes found in the development of cancer were also found to be implicated in SIDS and nominated via pathway analysis (N=10). Specifically, these were genes involved in the inflammatory and or immune response, such as TBX21, or cell death, such as APPBP2 which would support the environmental component of the Triple Risk Hypothesis for SIDS.    3.1.2    Sudden Death Candidate Genes as Disease Genes The genetic overlap between genes implicated in SIDS, SUDEP, SUD, cardiac arrhythmias, and epilepsy was observed within a subset (N=174) candidate genes (FIGURE 3.1). Of these a total of 19 genes were only implicated in SUDEP, 26 genes were only implicated in SIDS, 2 genes were only implicated in SUD. Similarly, 26 genes were only implicated in epilepsy, and 31 genes were only implicated in cardiac arrhythmias, while 17 genes were involved in both SUD and cardiac arrhythmias, and 4 genes were involved in both epilepsy and cardiac arrhythmias. Importantly, analysis of the SD genes revealed that KCNQ1 (N=1) was identified in all five (5) categories of sudden death (SIDS, SUD, SUDEP) and causative excitability disorders (cardiac arrhythmia and epilepsy). 38		  3.1.3   Sudden Death Candidate Genes Have Different Biological Functions   The SD candidate genes were categorized based function (80). The nine functional categories were: muscle regulation, cardiac regulation, neuronal regulation, cytoskeletal role, hypoxia, inflammation, serotonin receptors and transporters, general cellular processes, and ion channels and associated proteins. Genes that encoded proteins associated with the proper function of muscle cells within the body were binned into the muscle regulation category (N=16) (TABLE 3.1). Genes that were specifically involved in homeostasis within the heart were binned into the cardiac regulation category (N=45) (TABLE 3.2). However, ion channels, regardless of their role in different tissues, were binned into the ion channel category (N=47) (TABLE 3.3). Genes, excluding ion channels, that play a role in the brain were binned into the neuronal regulation category (N=30) (TABLE 3.4). Any of the SD candidate genes with a role in the cellular cytoskeleton were binned into the cytoskeletal role category (N=14) (TABLE 3.5), whereas genes that had an overall role in cellular homeostasis were placed in the general cellular processes category (N=65) (TABLE 3.6). Serotonin receptors and transporters were categorized separately (N=11) (TABLE 3.7). The genes that have been shown to specifically play a role in hypoxia within the body were binned together (N=10) (TABLE 3.8), and genes with a role in the immune system and the inflammatory response were categorized together (N=10) (TABLE 3.9).    39		3.2. Genetic Variation in Sudden Death Exomes   3.2.1. Variation Observed in SD Whole Exomes   Using the established decision tree, the whole exomes for the SD probands were analyzed. Initially, the total number of variants in the whole exome were counted and compared to a literature value for the average number of variants in a healthy individual who on average had a range of 25 000-55 000 variants observed in whole exome data depending on capture kit, coverage and platform used. Whole exome data from SIDS proband blood spot gDNA following WGA had a range of 40869-69978 variants (N=4) (TABLE 3.11) where the average number of variants is 55257 (SEM ± 6098) which is similar to results obtained from whole blood analysis of those with Amyotrophic Lateral Sclerosis (ALS) (146).  The range of variants observed for the SUDEP probands, was 19162 to 63954, variants were present in the whole exomes while the single SUD proband, a total of 63217 variants were present in the whole exome data (TABLE 3.11). These variants were distributed across the entire genome and included both known and novel variants and polymorphisms, similar to results observed previously in epilepsy, cardiac arrhythmia and other sudden death cases.  There were no discernable differences between SD categories, such that the total number of variants in the whole exome did not distinguish SIDS from those in SUDEP or SUD cohorts, despite the large range observed in SUDEP cases.  3.3. Personal Patterns of Variation SD Candidate Genes   3.3.1. Variation within SD Candidate Genes  	 The	total	amount	of	whole	exome	variation	was	subsequently	filtered	to	consider	 only	 the	 nominated	 SD	 genes	 (N=248)	 to	 evaluate	 the	 overlap	 or	40		independent	patterns	of	variation	within	the	probands.	As	observed	in	the	whole	exome,	the	amount	of	variation	was	extensive	and	encompassed	many	different	candidate	 genes,	 including	 those	 with	 established	 overlap.	 There	 were	 no	distinguishing	 features	 across	 the	 amount	 of	 variation	 by	 cohort,	 where	 SIDS	probands	(N=4),	had	an	average	of	1014.5	(SEM	±125)	variants	present	within	the	 exomes	 compared	 to	 the	 SUDEP	 probands	 (N=3),	 an	 average	 of	 787	 (SEM	±209)	variants	were	present	within	 the	exomes.	For	 the	single	SUD	proband,	a	total	of	1026	variants	were	present	within	the	exomes.			 When	the	SD	groups	are	compared,	the	personal	variation	observed	in	the	SD	candidate	genes	is	extensive	when	only	the	totals	are	considered,	even	among	the	 same	 cause	 of	 death	 (TABLE	 3.12).	 For	 the	 SIDS	 group,	 the	 amount	 of	variation	ranged	from	774	to	1160	variants	while	the	SUDEP	group	ranged	from	379	 to	 1078	 variants.	 The	 single	 SUD	 proband	 had	 1026	 variants	 in	 the	 SD	candidate	 genes.	 Thus,	 even	 when	 only	 the	 presumed	 mechanistic	 genes	 are	considered,	 the	amount	of	personal	variation	 is	 incapable	of	distinguishing	 the	cause	 of	 death	 and	 cannot	 discriminate	 between	 the	 individuals	 within	 each	category	 suggesting	 patterns	 rather	 than	 total	 gene	 variation	 is	 important	 for	diagnostic	purposes.	SD	 candidate	 gene	 variants	 were	 further	 evaluated	 by	 considering	 the	total	number	of	genes	containing	variants	by	functional	category	(TABLE	3.13).		Out	of	the	4	SIDS	probands,	94%	(15/16)	of	the	SD	candidate	genes	involved	in	muscle	 regulation	 (FIGURE	 3.2),	 all	 of	 the	 43	 genes	 involved	 in	 cardiac	regulation,	 79%	 (23/29)	 of	 the	 genes	 involved	 in	 neuronal	 regulation,	 93%	(13/14)	 of	 the	 genes	 involved	 in	 the	 cellular	 cytoskeleton,	 50%	 (9/18)	 of	 the	41		genes	 with	 roles	 in	 hypoxia,	 83%	 (5/6)	 of	 the	 genes	 involved	 in	 the	 immune	response,	 82%	 (9/11)	 of	 the	 serotonin	 receptor	 and	 transporter	 genes,	 77%	(49/64)	of	the	genes	involved	in	general	cellular	processes,	98%	(46/47)	of	the	genes	encoding	ion	channels	and	their	associated	proteins	contained	variants.		 The	 SUDEP	 probands	 (N=3)	 had	 the	 majority	 of	 genes	 located	 in	 the	muscle	 regulation	 (88%	 (14/16))	 and	 cardiac	 regulation	 categories	 (88%	(38/43)	(FIGURE	3.3).	Variation	in	cellular	cytoskeleton	(71%	(6/18)),	hypoxia	(56%	(11/18)),	 serotonin	 receptor	 (55%	 (6/11))	 and	 ion	 channel	 genes	 (89%	(42/47)	were	similar	to	SIDS	probands	however	there	were	slight	differences	in	which	 genes	 contained	 variants.	 Interestingly,	 each	 of	 the	 immune	 response	genes	(N=6)	had	one	or	more	variants	in	at	least	one	proband.	The	numbers	and	patterns	 of	 variants	 observed	 in	 the	 single	 SUD	 proband	 did	 not	 differ	significantly	from	those	in	the	SIDS	or	SUDEP	patients,	including	having	a	variant	in	4	out	of	the	6	immune	response	genes	(FIGURE	3.4).	These	personal	patterns	of	 variation	 by	 category	 are	 indistinguishable	 across	 the	 SD	 cohorts	 further	suggest	 the	genetic	and	mechanistic	overlap	underlying	risk	 in	 the	spectrum	of	sudden	death.		To	further	evaluate	personal	risk	in	SD	disorders,	the	individual	probands	were	compared	within	each	cohort	 to	 further	 stratify	 risk	prediction.	First,	 the	total	variants	by	 individual	within	 the	SD	candidate	genes	were	compared.	For	SIDS	 probands,	 the	 total	 genes	 containing	 a	 variant	 ranged	 from	 109	 to	 213	while	 SUDEP	probands	 had	 93	 to	 159	 genes	 impacted	while	 the	 SUD	proband	had	168	genes	impacted.	The	second	analyses	further	subdivided	the	number	of	SD	genes	containing	variants	into	their	functional	categories,	which	regardless	of	42		proband	showed	the	same	distributions	as	observed	at	the	cohort	level.	The	SIDS	probands	had	equal	distribution	across	General	Cellular	Processes	(29:46:32:40	genes),	 Cardiac	 Regulation	 (24:49:37:38),	 and	 Ion	 Channel	 (25:51:29:37)	categories	(TABLE	3.13).	 	This	equitable	distribution,	and	in	fact	the	number	of	genes	 impacted	were	 similar	 in	 the	 SUDEP	probands	where	24,	 33	and	34	 ion	channel	genes	contained	variants.			3.3.2. Genetic Variation in SD Candidate Gene Transcripts  The WES SD gene variants were annotated to all possible isoform transcripts reported in Ensembl using the VEP algorithm. This revealed that individual variants can impact multiple alternate splice variants depending upon which coding exon the variant is contained in, and how frequently that exon is used in a transcript. In SIDS probands (N=4), the genetic variation in the SD gene impacted 3226-9388 transcripts with an average of 5836 (SEM ±1333) transcripts (TABLE 3.14). This range was similar to the 1671-7046 SUDEP probands (mean=4415 SEM ±1552; N=3) and the SUD proband who had 7269 variant-affected transcripts within the SD candidate genes. The large variation in affected transcripts within SD cohorts and similar ranges between cohorts means that it is not possible to use the number of affected transcripts to determine cause of death.  For	 the	 three	 SD	 groups,	 number	 of	 SD	 candidate	 genes	 containing	variants	 were	 all	 within	 similar	 ranges	 where	 there	 was	 no	 substantial	 or	distinguishing	 differences	 in	 the	 number	 of	 affected	 genes	 between	 the	 three	groups.	 For	 the	 SIDS	 probands,	 a	 large	 proportion	 were	 in	 the	 Cardiac	Regulation,	 General	 Cell	 Processes,	 and	 Ion	 Channel	 categories	 (TABLE	 3.15).	43		There	was	some	variation	between	the	SIDS	probands,	where	proband	2098	had	more	impacted	SD	candidate	genes	in	all	categories	when	compared	to	the	other	three	 probands.	 Variation	 between	 the	 three	 SUDEP	 probands	 was	 also	 seen,	with	 several	 impacted	 genes	 also	 belonging	 to	 the	 Cardiac	Regulation,	General	Cell	Processes,	and	Ion	Channel	categories.	Lastly,	the	single	SUD	probands	also	has	numerous	variant‐containing	candidate	genes	within	the	Cardiac	Regulation,	General	 Cell	 Processes,	 and	 Ion	 Channel	 categories.	 Since	 the	 same	 three	categories	contained	 the	majority	of	 impacted	SD	candidate	genes	 for	all	 three	SD	 groups,	 it	 is	 not	 possible	 to	 predict	 the	 cause	 of	 death	 simply	 by	 the	 SD	categories	that	contain	the	most	variants.		For	 both	 the	 SIDS	 and	 SUDEP	 groups,	 most	 impacted	 transcripts	 were	located	 within	 SD	 candidate	 genes	 in	 the	 Cardiac	 Regulation	 and	 Muscle	Regulation	categories.	For	the	single	SUD	proband,	most	transcripts	located	were	in	SD	candidate	genes	in	the	Cardiac	Regulation	and	Ion	Channel	categories.			3.4. Sudden Death Gene Variants Are Pathogenic in Multiple Isoforms 	 Because variant containing exons can be spliced in alternate patterns, including their absence in the protein coding isoform, individual variants may have different functional effects. When this variant impact is considered as part of the analysis, it is observed that the SIDS probands (N=4) have multiple variants impacting untranslated regions with both 5’ and 3’ Untranslated Regions (UTR) having between 36-102 and 35-103 transcripts respectively. Potentially pathogenic variants impacting the splice region (53-153 transcripts) and splice sites (0-8 transcripts) of transcripts were also observed. Unexpectedly, the SIDS probands had a number of potentially pathogenic variants impacting multiple splice variants including 44		nonsynonomous variants encoding amino acid substitutions (259-692 transcripts) as well as those encoding premature stop or lost start codons (0-783 transcripts) and large insertions and deletions (301-699 transcripts) (TABLE 3.16).  Within the SUDEP probands (N=3), multiple variants impact transcripts in the 3’ and 5’ UTR regions, where 18-136 and 7-75 transcript are impacted, respectfully. The splice region (43-100) and splice site (0-3) are also impacted, in addition to the nonsynonymous variants (163-528) and variants leading to a premature stop codon or lost start codon (0-1). When looking specifically at large insertions or deletions, 199-693 transcripts were affected (TABLE 3.16).   The single SUD proband had 130 and 77 transcripts affected in the 3’ UTR and 5’ UTR regions, respectfully, by variants in the SD candidate genes. Within he transcripts, the splice regions (134 transcripts) and splice sites (3 transcripts) were also impacted by the variants. Additionally, 853 transcripts were affected by synonymous variants and 504 were affected by nonsynonymous. Premature stop codons and lost start codons affected no transcripts, whereas large insertions and deletions affected 631 transcripts (TABLE 3.16). The SD cohorts cannot be distinguished using the number of transcripts similarly impacted by variants due to the high similarity in ranges.   3.5 Personal Pathogenic Variants in Sudden Death Probands Overall, the majority of variants deemed pathogenic using bioinformatics variant annotation cutoffs impacted an array of SD candidate genes in the SIDS proband. The principle impacted functional categories impact were the Ion Channel, General Cellular processes, Cardiac Regulation, and Muscle Regulation categories (FIGURE 3.5). Most of the pathogenic variants within the transcripts were found to 45		be nonsynonymous in nature, or located within intronic or regulatory regions (FIGURE 3.6, 3.7, 3.8). These patterns of personal variation were also observed in the SUDEP and SUD probands (FIGURE 3.9, FIGURE 3.10).  For SIDS proband 2095, key pathogenic variants were identified in SCN2A, RYR3, DAG1, IL10RA, TTN, and TRPM6 (TABLE 3.17). All of these genes have been previously associated with excitability and sudden death disorders, including SIDS, SUDEP, Early Infantile Epileptic Encephalopathy, Epilepsy, and HCM. Variants within three of these genes; RYR3, DAG1, and IL10RA have previously been reported in dbNSP, however variants in SCN2A, TTN and TRPM6 are novel to this proband. Depending on the specific gene the number of transcripts containing these six key variants is from 2 to 21.  DSP, KCNJ5, KCNH2, SCN1B, OBSCN and ABCC8 were observed to contain pathogenic variants in SIDS proband 2098 (TABLE 3.18). Not only have these genes been previously associated with disease states such as HCM, Long QT Syndrome, Dravet Syndrome, SUD, as well as Rapid Onset Dystonia-Parkinsonism, and Hyperinsulinemic Hypoglycemia of Infancy. Like the other SIDS proband, variants in DSP, KCNJ5, KCNH2, and OBSCN are known. These variants impact between 2 to 7 transcripts depending on gene isoform.  The final SIDS probands had pathogenic variants in some of the same genes (TABLE 3.19) as the above where proband 2475 had a variant in SCN2A while proband 2477 had a variant in SCN1B (TABLE 3.20). Interestingly, not only did these probands have overlap with the other SIDS probands, but also each other where SCN8A also contained a variant in each proband. The variants in these probands were also a combination of novel and previously reported variants. These SD candidate genes are associated with SIDS, Neonatal Bartter Syndrome, Early Infantile Epileptic 46		Encephalopathy, HCM and Cancer. The range of affected transcripts for these variants is from 2 to 21 and 3-110 transcripts in proband 2475 and 2477 respectively.  As predicted, the personal variation in the SUDEP probands shared similar pattersn of risk genes impacting multiple functional categories. SUDEP proband 2231 has key pathogenic variants located in HTR3E, KCNH2, RYR3, OBSCN, ERBB2, and TTN (TABLE 3.21). These genes have been previously associated with include SUDEP, SIDS, Epilepsy, Cardiac Arrhythmias, Long QT Syndrome, HCM, and Cancer. All 6 variants have previously been reported in dbSNP. There is overlap between SUDEP proband 2429 and both the SUDEP and SIDS probands where pathogenic variants are observed in SCN1B, KCNJ5, KCNH2 and OBSCN genes, all of which have been identified to cause excitability defects in heart or brain and are associated with the spectrum of sudden death disorders (TABLE 3.22). Intriguingly, SUDEP proband 2069 had a different risk gene profile with compound variants in ATP1A3, as well as variants in TMEM214, LMNA, TRDN and TTN (TABLE 3.23).  The single SUD proband, 2460, contained key pathogenic variants in GOT2, OBSCN, RBM20, SCN5A, TTN, and DAG1 (TABLE 3.24). All 6 variants have been seen previously in the and dbSNP and impact 1 to 21 transcripts. The genes that contain the variants are associated with SUD, SUDEP, SIDS, DCM, HCM, Epilepsy, Long QT Syndrome, and Brugada Syndrome.  Collectively, several genes, including OBSCN (TABLE 3.25), was observed to contain pathogenic variants in all 8 probands. However, the number of transcripts impacted by the personal variants was not the same ranging from5-12 transcripts. Each proband’s unique set of pathogenic variants, as well as the number of transcripts impacted by the variant, can be used to distinguish between the SD cohorts. Specifically, the SIDS probands all had variants that impacted genes associated 47		seizure disorders with an infantile onset epilepsy. As expected SUDEP proband 2429 also contained a pathogenic variant variant in SCN1B causative for Severe Myoclonic Epilepsy of Infancy however it cannot be determined if this is a cause of epilepsy, sudden death or represents the previously reported overlap in risk in this patient population. Thus, looking at the specific timing of expression of different transcripts within the brainstem is an integral consideration in predicting the timing of sudden death and the potential for undiagnosed seizure disorders as a potential contributing cause.   3.6 Expression of SD Risk Genes in the Developing Brain  The In Situ Hybridization (ISH) data from the Allen Brain Atlas for the Developing Mouse Brain was used to detect expression of the SD candidate genes containing pathogenic variants within the brainstem of the mouse at different stages within development. Through the exome data, high priority genes were identified with pathogenic variants. However, due to limited data available from the Allen Brain Atlas for the Developing Mouse Brain, there was limited ability to identify and compare the expression of the genes of interest within the brain across developmental stages. It is also important to recognize that an RNA probe for only one mRNA isoform per gene was used to detect the presence of the protein. The Allen Brain for the Developing Mouse Brain using ISH showed low expression of Mbp in the brainstem region of the mouse at embryonic day 15.5 (E15.5), which is roughly equivalent to a 4 month old infant (FIGURE 3.11). The human equivalent of this gene, MBP, was found to contain pathogenic variants in some of the SUDEP probands but none of the SIDS probands. Expression of Mbp was shown to increase at postnatal day 4 (P4), roughly equivalent to a 1 year old child in humans (FIGURE 48		3.12) In comparison, Hrc2c had undetectable to low expression in the brainstem at E15.5 (FIGURE 3.13), which increased slightly at P4. The human version of this gene, HRC2C, contained pathogenic variants in the SIDS probands but none in the SUDEP or SUD probands (FIGURE 3.14). Variability in expression within the brainstem of genes containing pathogenic variants was seen across mouse brain developmental stages but did not allow identification or comparison of specific isoforms.                 49		 Figure 3.1. Venn Diagram showing the overlap in a subcategory of the candidate genes implicated in SIDS, SUDEP, SUD, epilepsy and cardiac arrhythmias. No genes were implicated in all five disorders (N=0), 26 were only implicated in SIDS, 19 were only implicated in SUDEP, 2 were only implicated in SUD, 26 were only implicated in Epilepsy, and 31 were only implicated in cardiac arrhythmias. Venn diagram was generated using Bioinformatics Evolutionary Genomics tool (147).                                   50		 Figure 3.2. The percentage of the SD candidate genes, by functional category, that had variants present in Sudden Infant Death Syndrome (SIDS) probands.                     94 10079935083 82 77980102030405060708090100%	of	Total	GenesCategory51		 Figure 3.3. The percentage of the SD candidate genes, by functional category, that had variants in Sudden Unexpected Death in Epilepsy (SUDEP) probands (N=3).             88 88597156100557089020406080100120%	of	Total	GenesCategory52		 Figure 3.4. The percentage of the SD candidate genes, by functional category, that contained variants present in the single, Sudden Unexpected Death (SUD) proband.                        	               81 864173 67 647350760102030405060708090100%	if	Ttotal	GenesCategory53		    Figure 3.5. The number of pathogenic variants in Sudden Death (SD) candidate genes in the Sudden Infant Death Syndrome (SIDS) probands (N=4). Pathogenicity was defined as scoring pathogenic for at least one of the five variant annotation methods; CADD Phred (>10), PolyPhen2 HDIV Phred (Possibly damaging or Damaging), SIFT Pred (Deleterious), PhastCons 100 Way Vertebrate (>0.84), and Grantham (>100).                       MuscleRegulationCardiacRegulationNeuronalRegulationCytoskeletal	Role HypoxiaImmuneRoleSerotoninGeneralcellprocessesIonChannels	&Associated	Genes2095 34 12 4 4 1 1 3 11 102098 51 19 0 5 1 3 5 18 122475 32 21 2 2 0 2 4 19 132477 30 23 7 7 2 2 5 18 160102030405060Number	of	VariantsCategory2095 2098 2475 247754		 Figure 3.6. The number of transcripts impacted by pathogenic variants in Sudden Death (SD) candidate genes in Sudden Infant Death Syndrome (SIDS) probands (N=4). Pathogenicity was defined as scoring pathogenic for at least one of the five variant annotation methods; CADD Phred (>10), PolyPhen2 HDIV Phred (Possibly damaging or Damaging), SIFT Pred (Deleterious), PhastCons 100 Way Vertebrate (>0.84), and Grantham (>100).                                   3'	UTR 5'	UTR SpliceRegionSpliceSiteRegulatorySynonymousNonsynonymousPrematureStopCodon/LostStartCodonIntronicInsertions/DeletionsSpanningDeletion2095 2 12 5 0 139 45 332 1 216 0 02098 15 16 0 1 168 24 428 0 374 0 02231 20 15 0 0 109 0 355 5 262 0 02429 11 19 0 0 126 15 428 6 174 0 0050100150200250300350400450500Number	of	TranscriptsImpact2095 2098 2231 242955		   Figure 3.7. The number of transcripts impacted by pathogenic variants in the Sudden Death (SD) candidate genes in Sudden Unexpected Death in Epilepsy (SUDEP) probands (N=3). Pathogenicity was defined as scoring pathogenic for at least one of the five variant annotation methods; CADD Phred (>10), PolyPhen2 HDIV Phred (Possibly damaging or Damaging), SIFT Pred (Deleterious), PhastCons 100 Way Vertebrate (>0.84), and Grantham (>100).                                 3'	UTR 5'	UTR SpliceRegionSpliceSiteRegulatorySynonymousNonsynonymousPrematureStopCodon/LostStartCodonIntronicInsertions/DeletionsSpanningDeletion2069 9 3 0 0 95 41 232 1 156 0 02231 29 24 0 0 194 63 443 0 303 0 02429 15 12 0 1 166 36 404 1 396 0 0050100150200250300350400450500Number	of	TranscriptsCategory2069 2231 242956		  Figure 3.8. The number of transcripts impacted by pathogenic variants in the Sudden Death (SD) candidate genes in Sudden Unexpected Death (SUD) proband (N=1). Pathogenicity was defined as scoring pathogenic for at least one of the five variant annotation methods; CADD Phred (>10), PolyPhen2 HDIV Phred (Possibly damaging or Damaging), SIFT Pred (Deleterious), PhastCons 100 Way Vertebrate (>0.84), and Grantham (>100).                    3'UTR5'UTRSpliceRegionSpliceSiteRegulatorySynonymousNonsynonymousPrematureStopCodon/Lost	StartCodonIntronicInsertion/DeletionSpanningDeletion2460 15 13 0 0 138 20 369 0 230 0 0050100150200250300350400450500Number	of	TranscriptsImpact57				Figure 3.9. The number of pathogenic variants in Sudden Death (SD) candidate genes in the Sudden Unexpected Death in Epilepsy (SUDEP) probands (N=3). Pathogenicity was defined as scoring pathogenic for at least one of the five variant annotation methods; CADD Phred (>10), PolyPhen2 HDIV Phred (Possibly damaging or Damaging), SIFT Pred (Deleterious), PhastCons 100 Way Vertebrate (>0.84), and Grantham (>100). 													MuscleRegulationCardiacRegulationNeuronalRegulationCytoskeletal	Role HypoxiaImmuneRoleSerotoninGeneralCellularProcessesIonchannels&Associated	Genes2069 26 9 0 1 0 0 4 13 82231 38 26 3 3 2 3 4 17 132429 39 29 0 4 2 4 5 19 130102030405060Number	of	VariantsCategory2069 2231 242958		 Figure 3.10. The number of pathogenic variants in Sudden Death (SD) candidate genes in the Sudden Unexpected Death (SUD) proband (N=1). Pathogenicity was defined as scoring pathogenic for at least one of the five variant annotation methods; CADD Phred (>10), PolyPhen2 HDIV Phred (Possibly damaging or Damaging), SIFT Pred (Deleterious), PhastCons 100 Way Vertebrate (>0.84), and Grantham (>100).                    MuscleRegulationCardiacRegulationNeuronalRegulationCytoskeletal	Role HypoxiaImmuneRoleSerotoninGeneralCellularProcessesIonchannels&Associated	Genes2460 36 22 2 3 1 3 9 17 110510152025303540Number	of	VariantsCategory59		  Figure 3.11. Allen Brain Atlas for the Developing Mouse Brain In Situ Hybridization image for Mbp expression at embryonic day 15.5 (E15.5), equivalent to a roughly 4 month old infant. Pathogenic variants found in MBP within the SUDEP probands and lacking from the SIDS probands.                  60		  Figure 3.12. Allen Brain Atlas for the Developing Mouse Brain In Situ Hybridization image of Mbp expression at postnatal day 4 (P4), equivalent to roughly a 1 year old child. Pathogenic variants found in MBP within the SUDEP probands and lacking from the SIDS probands. An RNA antisense probe was used to detect expression levels.  												 61		 Figure 3.13. Allen Brain Atlas for the Developing Mouse Brain In Situ Hybridization image of Htr2c expression at embryonic day 15.5 (E15.5), equivalent to roughly a 4 month old infant. Pathogenic variants in HTR2C found within the SIDS probands and lacking from the SUDEP and SUD. An RNA antisense probe was used to detect expression levels.                    62		 Figure 3.14. Allen Brain Atlas for the Developing Mouse Brain In Situ Hybridization image for Htr2c expression at postnatal day 4 (P4), equivalent to roughly a 1 year old child. Pathogenic variants in HTR2C were found within the SIDS probands and lacking from the SUDEP and SUD probands. An RNA antisense probe was used to detect expression levels.                63		Table 3.1. Sudden death candidate genes with roles in muscle GENE LOCATION Function Reference TCAP chr17:37,821,599-37,822,807 Mediates the antiparallel assembly of titin on the sarcomeric Z disk. Implicated in cancer. GeneMANIA (113) MYOM1  chr18:3,066,805-3,220,106 Component of the myofibrallar M band in muscle cells. Implicated in DCM.Marston 2015 (79) NEB chr2:152,341,853-152,591,001 Encodes for Nebulin, part of the cytoskeletal matrix in the sarcomeres of skeletal muscle. GeneMANIA (113) SGCG chr13:23,755,060-23,899,304 Encodes for Gamma-sarcogylcan, a sarcolemmal transmembrane glycoprotein, that interacts with dystrophin. GeneMANIA (113) MYL7 chr7:44,178,463-44,180,916 Encodes for Myosin Light Chain 7, a motor protein found in all eukaryotic cells. GeneMANIA (113) TNNI2  chr11:1861432-1862910 Encodes for a fast-twitch skeletal muscle protein, which is responsible for calcium-dependent regulation of striated muscle contraction. GeneMANIA (113) SPTAN1  chr9:131,314,837-131,395,941 Encodes for Spectrins, which are filamentous cytoskeletal proteins that function as scaffolds to stabilize the cell membrane and organelles. Bagnall 2016 (81)  SLMAP chr3:57,743,174-57,914,894 Component of conserved striatin-interacting phosphatase and kinase complex. Ishikawa 2012 (148) TTN chr2:179,390,717-179,672,150 Structural protein found in striated muscle. Campuzano 2014 (73) LMNA chr1:156,104,904-156,107,058 A component of the nucleoplasmic side of the inner nuclear membrane. Guo 2015 (94) PKP2 chr12:32,943,680-33,049,780 Links cadherins to intermediate filaments in the cytoskeleton. Campuzano 2014 (73) HRC  chr19:49,654,456-49,658,681 Sarcoplasmic reticulum protein that binds LDL. GeneMANIA (113) TRDN chr6:123,537,484-123,958,238 Regulates Ca2+ release via RYR1 and RYR2, the calcium release channels in the sarcoplasmic reticulum. Wilde and Behr 2013 (85)  SMCHD1  chr18:2,655,886-2,805,015 A component of the SMC (Structural Maintanence of Chromosomes) protein. Helps maintain X inactivation in females. GeneMANIA (113) 64		Table 3.1. Sudden death candidate genes with roles in muscle GENE LOCATION Function Reference CKMT2  chr5:80,529,139-80,562,217 Encodes for mitochondrial creatine kinase, which transports high energy phosphate from the mitochondria to creatine. GeneMANIA (113) CHRNA1 chr2:175,612,320-175,629,200 Alpha subunit of the muscle acetylcholine receptor. Hantai 2004 (149)    65		 Table 3.2. Sudden death candidate genes with roles in cardiac regulation GENE	 LOCATION	 Function ReferenceMYL2 chr12:111,348,624-111,358,404 Encodes for the regulatory light chain that is associated with the cardiac myosin beta (or slow) heavy chain. Plays a role in HCM. Noseworthy 2008 (92) MYL3 chr3:46,899,357-46,904,973 Encodes for the myosin light chain 3, an alkali light chain. Plays a role in HCM. Noseworthy 2008 (92) GLA chrX:100,652,779-100,663,001 Encodes for a homodimeric glycoprotein that hydrolyses the terminal alpha-galactosyl moieties in glycolipids and glycoproteins. Plays a role in HCM. Marston 2015 (79) LAMP2 chrX:119,560,003-119,603,204 Encodes for membrane glycoproteins. Plays a role in HCM. Miani 2012 (91)  MYOZ2 chr4:120,056,939-120,108,944 Encodes for a sarcomeric protein that binds to calcineurin, a phosphatase involved in calcium-dependent signal transduction. Plays a role in HCM. Noseworthy 2008 (92) PRKAG2 chr7:151,253,201-151,574,316 Encodes for a component of AMP-activated protein kinase. Plays a role in HCM. Bagnall 2014 (150) TNNI3 chr19:55,663,136-55,668,957 Encodes for a subunit of the troponin complex of the thin filaments in striated muscle. Plays a role in HCM. Bagnall 2014 (150) CSRP3 chr11:19,203,577-19,232,118 Encodes for a protein involved in regulatory processes, as part of cellular differentiation and development. Plays a role in HCM & DCM. Bagnall 2014 (150) ACTN2 chr1:236,849,770-236,927,558 Encodes for a protein that ancors the myofibrillar actin filaments in skeletal Bagnall 2014 (150) 66		Table 3.2. Sudden death candidate genes with roles in cardiac regulation GENE	 LOCATION	 Function Referencemuscle cells. Plays a role in HCM.TPM1 chr15:63,334,838-63,364,113 Encodes for an actin-binding protein that plays a role in contraction within smooth and striated muscle cells. Plays a role in HCM & DCM. Bagnall 2014 (150) PLN chr6:118,869,442-118,881,587 Encodes a substrate for cAMP-dependent protein kinase in cardiac muscle cells. Plays a role in HCM. Raghow 2016 (89) MYBPC3 chr11:47,353,396-47,374,253 Encodes for the cardiac myosin-binding protein C. It is only expressed in heart muscle. Plays a role in HCM & DCM.Bagnall 2014 (150) TNNC1 chr3:52,485,107-52,488,057 Encodes for a troponin subunit, found on the actin filament in muscle cells. Plays a role in HCM & DCM. Bagnall 2014 (150) ACTC1 chr15:35,080,297-35,087,927 Encodes for actin, expressed in cardiac muscle cells. Plays a role in HCM, DCM & LVNC. Campuzano 2015 (61) MYH7 chr14:23,881,947-23,904,870 Encodes for the heavy chain of cardiac muscle myosin. Plays a role in HCM, DCM & LVNC. Bagnall 2014 (150) SNTA1 chr20:31,995,763-32,031,698 Encodes for a cytoplasmic peripheral membrane scaffold protein. Plays a role in Long QT syndrome.   Adler 2015 (53) MYH6 chr14:23,851,199-23,877,486 Encodes for the heavy chain of cardiac muscle myosin. Bagnall 2014 (150) TNNT1 chr19:55,644,161-55,660,606 Encodes for a subunit of troponin, located on the thin filament of the sarcomere. Plays a role in DCM. Bagnall 2014 (150) TNNT2 chr1:201,328,136-201,346,890 Encodes for a tropomyosin-binding subunit of the troponin complex, located on the Bagnall 2014 (150) 67		Table 3.2. Sudden death candidate genes with roles in cardiac regulation GENE	 LOCATION	 Function Referencethin filament of striated muscle. Plays a role in HCM and DCM.  BAG3 chr10:121,410,882-121,437,329 Bind to the Hsc70/Hsp70 ATPase domain. Plays a role in DCM. Knezevic 2015 (93) RBM20 chr10:112,404,155-112,599,227 Encodes a protein that is able to regulate splicing and binds to RNA. Plays a role in DCM. Guo 2012 (94) CTF1 chr16:30,907,928-30,914,881 Encodes for Cardiotropin 1, which binds to ILST/gp130 receptors. Plays a role in DCM. Walsh 2016 (95) DES chr2:220,283,099-220,291,461 Encodes for a muscle specific intermediate filament protein. Plays a role in DCM. Bagnall 2014 (150) EMB chr5:49,692,031-49,737,234 Encodes for a transmembrane glycoprotein involved in cell growth. Plays a role in DCM. GeneMANIA (113) TAZ chrX:153,639,877-153,650,063 Encodes for a protein expressed in cardiac and skeletal muscle. Plays a role in DCM & VCN. Bagnall 2014 (150) NPPA chr1:11,905,767-11,907,840 Encodes for Cardiodilatin-related peptide, involved in regulating extracellular fluid volume. Plays a role in DCM. GeneMANIA (113) GJA5 chr1:147,228,332-147,232,714 Encodes for connexin, which is a part of gap functions. Plays a role in Familial AF.  Bagnall 2014 (150) CAV3 chr3:8,775,486-8,788,451 Encodes for caveolin, which are scaffolding proteins. Plays a role in HCM.  Adler 2015 (53) MIA3 chr1:222,791,444-222,841,351 Encodes a protein required for collagen release. Implicated in heart disease. GeneMANIA (113) 68		Table 3.2. Sudden death candidate genes with roles in cardiac regulation GENE	 LOCATION	 Function ReferenceANKRD1 chr10:92,671,857-92,681,032 Found in the endothelium and is induced by IL1 and TNF-alpha. Plays a role in DCM. Bogomolovas 2014 (151) PDE3A chr12:20,522,179-20,837,041 Encodes a protein involved in cardiovascular function, by regulating smooth muscle relaxation and contraction. Implicated in cancer.  GeneMANIA (113) POPDC2 chr3:119,360,908-119,379,404 Transmembrane protein in skeletal and cardiac muscle. GeneMANIA (113) TNNI3K chr1:74,663,947-75,010,112 MAP Kinase involved in cardiac physiology.Theis 2014 (152) RYR2 chr1:237205702-237997288 Calcium channel found in cardiac muscle cells. Causes the release of calcium ions that triggers cardiac muscle contraction.   Wilde and Behr 2013 (85)  TIE1 chr1:43,776,664-43,788,779 Tyrosine kinase that plays a role in angiogenesis and blood vessel stability.   Leu 2015 (84) GPD1L chr3:32,148,003-32,210,207 Encode a protein that binds to sodium ion channel SCN5A. Implicated in Brugada syndrome.  Adler 2016 (53) OBSCN chr1:228,395,831-228,566,575 Sarcomeric signaling protein implicated in HCM. Marston 2015 (79) DAG1 chr3:49,507,565-49,573,051 Encodes for the Dystroglycan protein. Plays a role in HCM.  GeneMANIA (113)  CASQ2 chr1:116,242,626-116,311,426 Calsequestrin, located in the sarcoplasmic reticulum of cardiac cells Wilde and Behr 2013 (85) VCL chr10:75,757,872-75,879,914 Encodes for a cytoskeletal protein associated with cell-matrix and cell-cell junctions. Plays a role in HCM & DCM.  Campuzano 2014 (73) DSP chr6:7,541,870-7,586,946 Encodes a protein that Bagnall 2014 69		Table 3.2. Sudden death candidate genes with roles in cardiac regulation GENE	 LOCATION	 Function Referenceconnects intermediate filaments to desmosomal plaques forms a component of functional desmosomes. Plays a role in HCM.  (150) RYR3 chr15:33,603,177-34,158,303 Encodes for a ryanodine receptor, functions to release calcium from intracellular storage. Implicated in arrhythmias. Klassen 2014 (3) AKAP9 chr7:91,570,189-91,739,987 Encodes a protein that binds to the regulatory subunit of protein kinase A. Plays a role in arrhythmias, Brugada syndrome, and Long QT syndrome.  Allegue 2015 (82)  NPPB chr1:11917521-11918992 Encodes for Natiuretic Peptide B. High levels of this protein indicate heart failure.  GeneMANIA (113) LDB3 chr10:88,428,426-88,495,824 Encodes a PDZ domain-containing protein, which interacts with other proteins in cytoskeleton assembly. Plays a role in LVNC.  Hata 2016 (153)         70		Table 3.3. Sudden death candidate genes that encode for ion channels and associated proteins GENE LOCATION Function Reference HCN4 chr15:73,612,200-73,661,605 Encodes for a voltage-gated potassium channel. Implicated in Brugada Syndrome and SUDEP. Campuzano 2014 (62,73) CACNA1C chr12:2,080,229-2,080,366 Encodes for a voltage-gated calcium channel. Implicated in cardiac arrhythmias, epilepsy, and Sudden Cardiac Death.  Wilde and Behr 2013 (85) CACNA2D1 chr7:81,579,418-82,073,031 Encodes for a voltage-gated calcium channel. Implicated in short QT syndrome, HCM, and Brugada syndrome.  Wilde and Behr 2013 (85) CACNB2 chr10:18,429,606-18,830,688 Encodes for a voltage-gated calcium channel. Implicated in Brugada syndrome, cardiac arrhythmias, Sudden Cardiac Death and HCM.  Wilde and Behr 2013 (85) KCND3 chr1:112,318,454-112,531,777 Encodes for a voltage-gated potassium channel. Implicated in Brugada syndrome, cardiac arrhythmias, and Sudden Cardiac Death.  Wilde and Behr  2013 (85) KCNIP1 chr5:169,780,881-170,163,636 Encodes for a voltage-gated potassium channel. Implicated in DCM. Horn 2006 (154) KCNIP3 chr2:95,963,072-96,051,825 Encodes for a voltage-gated potassium channel.  GeneMANIA (113) KCNE3 chr11:74,165,886-74,178,600 Encodes for a voltage-gated potassium channel. Implicated in Brugada syndrome, Wilde and Behr 2013 (85) 71		Table 3.3. Sudden death candidate genes that encode for ion channels and associated proteins GENE LOCATION Function Reference cardiac arrhythmias, and sudden cardiac death.  KCNE4 chr2:223,916,648-223,920,357 Encodes for a voltage-gated potassium channel.  GeneMANIA (113) KCNE1 chr21:35,818,986-35,828,107 Encodes for a voltage-gated potassium channel. Implicated in Long QT syndrome, Epilepsy, and Sudden Cardiac Death.  Wilde and Behr 2013 (85) KCNE2 chr21:35,736,323-35,743,440 Encodes for a voltage-gated potassium channel. Implicated in Long QT syndrome, epilepsy, Sudden Cardiac Death and cardiac arrhythmias.   Wilde and Behr 2013 (85), Andreason 2013 (155) KCNH2 chr7:150,642,044-150,675,402 Encodes for a voltage-gated potassium channel. Implicated in Long QT syndrome, epilepsy, Sudden Cardiac Death and cardiac arrhythmias. Tu 2011 (60) KCNH7 chr2:163,227,917-163,695,257 Encodes for a voltage-gated potassium channel.  GeneMANIA (113) KCNJ2 chr17:68,165,676-68,176,183 Encodes for a voltage-gated potassium channel. Implicated in Short QT syndrome, cardiac arrhythmias, and Sudden Cardiac Death.  Wilde and Behr 2013 (85) KCNJ3 chr2:155,555,093-155,714,864 Encodes for a voltage-gated potassium channel. Also involved in hypoxia (check ref). Neary 2013 (1) KCNJ5 chr11:128,761,313-128,787,951 Encodes for a voltage-gated potassium channel. Wilde and Behr 2013 (85) 72		Table 3.3. Sudden death candidate genes that encode for ion channels and associated proteins GENE LOCATION Function Reference Involved in Long QT Syndrome, Sudden Cardiac Death, and cardiac arrhythmias.  KCNJ8 chr12:21,917,889-21,927,755 Encodes for a voltage-gated potassium channel.  Wilde and Behr  2013 (85)  KCNQ1 chr11:2,466,221-2,870,340 Encodes for a voltage-gated potassium channel. Implicated in Long QT, SIDS, Sudden Cardiac Death and SUDEP. Tu 2011 (60) SCN1B chr19:35,521,592-35,525,174 Encodes for one of the beta subunit of the voltage-gated sodium channel.  Wilde and Behr 2013 (85) SCN1A chr2:166,845,670-166,930,180 Encodes for one of the alpha subunit of the voltage-gated sodium channel.  Wilde and Behr 2013 (85), Klassen 2013 (116), Kalume 2013 (13), Ferrari 2015 (156) SCN2A chr2:166,152,283-166,248,820 Encodes for one of the alpha subunit of the voltage-gated sodium channel. Implicated in early infantile epileptic encephalopathy.  Lemke 2012 (157), Howell 2015 (158) SCN2B chr11:118,033,519-118,047,337 Encodes for one of the beta subunits of the voltage-gated sodium channel. Implicated in atrial fibrillation. Winkel 2015 (55) SCN3B chr11:123,499,895-123,525,315 Encodes for one of the beta subunits of the voltage-gated sodium channels. Implicated in Brugada syndrome, cardiac arrhythmias, and Sudden Cardiac Death.  Adler 2016 (53), Wilde and Behr 2013 (85) SCN4B chr11:118,004,092-118,023,630 Encodes for one of the beta subunits of the voltage-gated sodium channels. Adler 2016 (53), Wilde and Behr 2013 (85) 73		Table 3.3. Sudden death candidate genes that encode for ion channels and associated proteins GENE LOCATION Function Reference Implicated in Long QT Syndrome, cardiac arrhythmias, and Sudden Cardiac Death.  SCN5A chr3:38,589,553-38,674,850 Encodes for one of the alpha subunits of the voltage-gated sodium channels. Implicated in Long QT Syndrome, Brugada Syndrome, Epilepsy, Sudden Unexpected Death in Epilepsy, cardiac arrhythmias, and Sudden Cardiac Death. Tu 2011 (60), Wilde and Behr 2013 (85) SCN7A chr2:167,260,083-167,343,481 Encodes for an alpha subunit of the voltage-gated sodium channels. Involved in hypoxia (ref) and neonatal epilepsy.Okumura 2011 (159) SCN8A chr12:51,985,020-52,206,648 Encodes for an alpha subunit of the voltage-gated sodium channels.  Veeramah 2012 (160) , Wagnon 2014 SCN9A chr2:167,051,697-167,232,497 Encodes for an alpha subunit of the voltage-gated sodium channels. Implicated in neuropathic pain. Li 2015  (161) TRPM4 chr19:49,661,016-49,715,098 Encodes for a calcium-activated nonselective ion channel, able to move monovalent cations across the membrane. Implicated in Brugada Syndrome.  Wilde and Behr 2013 (85) KCNJ11 chr11:17,406,796-17,410,206 Encodes for a voltage gated, inward rectifying potassium channel. Implicated in Permanent Neonatal Diabetes Mellitus. Martins 2015 (162) 74		Table 3.3. Sudden death candidate genes that encode for ion channels and associated proteins GENE LOCATION Function Reference KCNJ1 chr11:128,707,909-128,712,429 Encodes for a voltage-gated, inward rectifying potassium channel. Implicated in Neonatal Bartter Syndrome. GeneMANIA (113) HCN1 chr5:45,254,950-45,696,498 Encodes for a hyperpolarized-activated cyclic nucleotide potassium channel. Implicated in Early Infantile Epileptic Encephalopathy and cardiac arrhythmias.  Wilde and Behr 2013 (85) HCN2 chr19:589,893-617,159 Encodes for a hyperpolarized-activated cyclic nucleotide potassium channel. Implicated in Sinoatrial Node Disease, epilepsy and cardiac arrhythmias.  Wilde and Behr 2013 (85) KCNA1 chr12:5,019,071-5,040,527 Encodes for a voltage-gated potassium channel. Implicated in Epilepsy, SUDEP, and cardiac arrhythmias. Glasscock 2010 (88), Klassen 2013 (116) KCNA5 chr12:5,153,085-5,155,954 Encodes for a voltage-gated potassium channel. Implicated in Familial atrial fibrillation (AF).  Suzuki 2012 (102) KCNEIL (also known as KCNE5) chrX:108,866,929-108,868,393 Encodes for a voltage-gated potassium channel. Implicated in Brugada Syndrome.  Skinner 2005 (103) CALM1 chr14:90,863,327-90,874,619 Encodes for Calmodulin 1, which is a EF-hand calcium-binding protein.   Wilde and Behr 2013 (85) RANGRF chr17:8,191,969-8,193,409 Encodes for a protein that Wilde and Behr 2013 (85) 75		Table 3.3. Sudden death candidate genes that encode for ion channels and associated proteins GENE LOCATION Function Reference functions to regulation the function of Nav1.5 cardiac sodium channel.  RYR1 chr19:38,924,340-39,078,204 Calcium release channel in sarcoplasmic reticulum of skeletal muscle.  Lanner 2012 (163) ATP1A2 chr1:160,085,549-160,113,381 Encodes for a subunit of the Na/K transport ATPase. Implicated in Familial Hemiplegic Migraines.  Ferrari 2015 (156) KCNT1 chr9:138,594,031-138,684,992 Encodes for a voltage-gated potassium channel. Implicated in epilepsy.  EuroEPINOMICS 2014 (164), Moller 2015 (109) KCNIP2 chr10:103,585,731-103,603,677 Encodes for a voltage-gated potassium channel.  GeneMANIA (113) NIPA2 chr15:23,004,684-23,034,427 Encodes for a Selective Mg2+ transporter. Implicated in Angelman syndrome.   GeneMANIA (113) TRPM6 chr9:77,337,411-77,503,010 Encodes a protein that contains both a protein kinase and an ion channel domain. Implicated in atrial fibrillation. Zhang 2015 (165) SCNM1 chr1:151,129,140-157,142,773 Encodes for a zinc finger, which modifies expression of SCN8A. GeneMANIA (113) SLC8A1 chr2:40,339,286-40,657,444 Encodes for a solute carrier protein, that exchanges Na and Ca across the membrane. GeneMANIA (113) ATP1A3 chr19:42,470,734-42,498,428 Encodes for a subunit of the Na/K transport ATPase. Paciorkowski 2015 (166) 76		Table 3.3. Sudden death candidate genes that encode for ion channels and associated proteins GENE LOCATION Function Reference Implicated in early life epilepsy, episodic prolonged apnea, and postnatal microcephaly.      77		Table 3.4. Sudden death candidate genes with roles in neuronal regulation GENE LOCATION Function Reference CALM2 chr2:47,387,221-47,403,740 Encodes calmodulin, involved in binding to calcium. Plays a role in Long QT, epilepsy, and catecholaminergic polymorphic ventricular tachycardia (CPVT).  Wilde and Behr 2013 (85) GAP43 chr3:115,342,151-115,440,334 Growth associated protein 43, expressed at high levels during neuronal development.  Salomonis 2014 (77) MBP chr18:74,690,789-74,844,774 Myelin sheath development and glial cell differentiationSalomonis 2014 (77) PLP1 chrX:103,031,754-103,047,547 Myelin sheath development and glial cell differentiation GeneMANIA (113) TPPP chr5:659,977-693,510 Myelin sheath development and glial cell differentiation Salomonis 2014 (77) TUBB4A chr19:6,494,330-6,502,330 Encodes for a member of the beta tubulin family, eventually assembling to form microtubules. Associated with Spastic Paraplegia. Kumar 2015 (167) GJA1 chr6:121,756,745-121,770,873 Encodes for connexin and forms a component of gap junctions, allowing small molecules to pass between cells.  Andreason 2013 (155) MAOA chrX:43,514,155-43,606,071 Encodes mitochondrial enzymes that cause oxidative deamination of neurotransmitters, such as dopamine, serotonin, and norepinephrine.  Grob 2014 (63) PHOX2B chr4:41,746,099-41,750,987 Encodes a protein that acts as a transcription factor, involved in the development of neurons and Liebrechts-Akkerman 2014 (78), Salomonis 2014 (77) 78		Table 3.4. Sudden death candidate genes with roles in neuronal regulation GENE LOCATION Function Reference neurotransmitters.  NFATC1 chr18:77,160,326-77,289,323 Encodes for a component of the nuclear factor of activated T cells. GeneMANIA (113) NXT2 chrX:108,779,010-108,787,927 Encodes a protein that contains a nuclear transport factor 2 (NTF2) domain. Plays an important role in transporting molecules between the cytoplasm and nucleus.   GeneMANIA (113) SLC1A3 chr5:36,606,457-36,688,436 Encodes for a high affinity glutamate transporter.  Salomonis 2014 (77) SLC25A4 chr4:186,064,417-186,071,538 Encodes for a gated pore that translocates ADP from the cytoplasm to the mitochondrial matrix. Also transports ATP from the mitochondrial matrix to the cytoplasm.  Salomonis 2014 (77) SNAP25 chr20:10,199,477-10,288,066 Encodes for a presynaptic plasma membrane protein involved in regulation of neurotransmitter release.  Salomonis 2014 (77) VAMP2 chr17:8,062,465-8,066,293 Encodes for a vesicle-associated membrane protein and regulates synaptic transmission. Implicated in familial infantile myasthenia (respiratory depression).  Salomonis 2014 (77) NOS1AP chr1:162,069,774-162,370,475 Cytosolic protein that binds neuronal NOS. Implicated in Long QT syndrome.  Allegue 2015 (82) CHD2 chr15:93,426,526- Involved in chromatin EuroEPINOMICS 79		Table 3.4. Sudden death candidate genes with roles in neuronal regulation GENE LOCATION Function Reference 93,571,237 remodeling.   2014 (164) TBC1D24 chr16:2,525,147-2,555,735 Involved in membrane trafficking in the brain. Implicated in familial infantile myoclonic epilepsy.  Falace 2010 (168) STXBP1 chr9:130,374,544-130,457,460 Role in neurotransmitter release via regulation of syntaxin, a transmembrane attached protein receptor. Implicated in Dravet Syndrome.  EuroEPINOMICS 2014 (164), Carvill 2014 (108) GABRB3 chr15:26,543,546-26,939,539 Subunit of a ligand-gated chloride channel in the brain. Implicated in absence epilepsy.  DeLorey 1999 (169) CHRNA7 chr15:32,322,691-32,464,722 Encodes for a subunit of the brain nicotinic acetylcholine receptor.  Machaalani 2011 (170)  CHRNA4 chr20:61,974,575-62,009,753 Encodes for a subunit of the brain nicotinic acetylcholine receptorChen 2015 (76) CHRNA2 chr8:27,317,279-27,337,400 Encodes for a subunit of the brain nicotinic acetylcholine receptor.  Chen 2015 (76) SLC12A5 chr20:44,650,329-44,688,784 Neuronal K-Cl transporter that lowers intracellular chloride concentrations.  Stodberg 2015 (171) PAFAH1B1 chr17:2,496,504-2,588,909 Encodes for a protein that is required during brain development and neuronal proliferation.  Bagnall 2014 (150) EN1 chr2:119,599,747-119,605,759 Encodes a protein involved in pattern formation during development of nervous system  Campuzano 2014 (73) APH1A chr1:150,237,799-150,241,609 Encodes a component of the gamma secretase complex, which cleaves GeneMANIA (113) 80		Table 3.4. Sudden death candidate genes with roles in neuronal regulation GENE LOCATION Function Reference integral membrane proteins. Has been implicated in Altzeimers.  SLC1A4 chr2:65,216,495-65,251,000 Encodes for a Glutamate/Neutral Amino Acid Transporter. Implicated in Infantile Spasms.  Conroy 2016 (172) CDKL5 chrX:18,425,583-18,653,629 Encodes for a Ser/Thr protein kinase. Implicated in Early Infantile Epileptic Encephalopathy and X-linked Infantile Spasms Syndrome. EuroEPINOMICS 2014 (164) SLC2A1 chr1:43,391,052-43,424,530 Encodes a glucose transporter in the blood brain barrier.   GeneMANIA (113)                81		Table 3.5. Sudden death candidate genes with roles in the cytoskeleton GENE LOCATION Function Reference ANK2 chr4:113,970,785-114,304,896 Encodes an ankyrin protein, which links integral proteins to the cytoskeleton. Wilde and Behr 2013 (85) KRT2 chr12:53,038,342-53,045,959 Encodes a keratin protein, which is expressed in epithelial cells. Salomonis 2014 (77) KRT9 chr17:39,722,094-39,728,310 Encodes a keratin protein, which is expressed in epithelial cells. Salomonis 2014 (77) KRT17 chr17:39,775,692-39,780,882 Encodes a keratin protein, which is expressed in epithelial cells. GeneMANIA (113) LAMC3 chr9:133,884,469-133,969,860 Encodes for Laminins, extracellular glycoproteins. GeneMANIA (113) PDLIM2 chr8:22,436,254-22,451,810 Encodes a protein involved in cell migration and adhesion. Implicated in cancer. GeneMANIA (113) MAPT chr17:43,971,748-44,105,699 Encodes for microtubule-associated protein tau, expressed in the nervous system. Alternative isoforms are expressed at different stages, depending on neuronal maturation and neuron type. Salomonis 2014 (77) DSG2 chr18:29,078,027-29,128,814 Encodes a desmoglein protein, which is involved in cell-cell junctions. Plays a role in arrhythmias. GeneMANIA (113) JUP chr17:39,910,859-39,942,964 Encodes a protein, found in the cytoplasm, which is a component of desmosomes and intermediate junctions. Forleo 2015  (173) VSIG1 chrX:107,288,200-107,322,414 Encodes a junctional adhesion molecule. Found to be expressed in cancer cells. GeneMANIA (113) DSC1 chr18:28709214-28742819 Encodes a calcium-dependent glycoprotein, expressed in epithelial cells and involved in cell-cell adhesion. GeneMANIA (113) CXADR chr21:18,884,700-18,965,897 Encodes a group B coxsackievirus and subgroup C adenovirus receptor. Implicated in cancer. GeneMANIA (113) PCDH19 chrX:99,546,642-99,665,271 Encodes a calcium-dependent cell adhesion protein expressed in the brain. Redies 2012 (174), Kwong 2012 (175) ERMAP chr1:43,282,795-43,310,660 Encodes for a transmembrane protein. GeneMANIA (113) 82		Table 3.6. Sudden death candidate genes with roles in general cell processes GENE LOCATION Function Reference PCNT chr21:47,744,036-47,865,682 Encodes a protein involved in cell adhesion. GeneMANIA (113) CCAR1 chr10:70,480,971-70,551,309 Encodes a protein involved in transcription regulation.  GeneMANIA (113) TGFB3 chr14:76,424,442-76,448,092 Encodes a protein involved in embryogenesis and cell differentiation. Implicated in arrhythmias.  Dashash 2006 (71) TMEM43 chr3:14,166,440-14,185,180 Encodes a protein involved in maintaining the nuclear envelope structure. Implicated in familial arrhythmagenic right ventricular dysplasia type 5 (ARVD5).  Siragam 2014 (83) FAM213A chr10:82,167,585-82,192,753 Encodes a protein involved in redox regulation of the cell. Implicated in cancer. GeneMANIA (85) LGI1 chr10:95,517,566-95,557,916 Encodes a protein involved in voltage-gated potassium channel regulation. The protein may also be involved in neuronal growth regulation and cell survival.  Leu 2015 (84), Klein 2016 (176) SMC4 chr3:160,117,062-160,152,750 Encoded protein is involved in structural maintenance of chromosomes. Leu 2015 (84) COL6A3 chr2:238,232,646-238,323,018 Encodes for collagen, which is a cell-binding protein. Implicated in muscular dystrophy and myopathy.  Leu 2015 (84) ALG13 chrX:110,909,043-111,003,877 Encodes a subunit of the bipartite UDP-acetylglucosamine transferase enzyme. Implicated in X-linked intellectual EuroEPINOMICS 2014 (164) 83		Table 3.6. Sudden death candidate genes with roles in general cell processes GENE LOCATION Function Reference disability and Congenital Disorders of Glycosylation.  FASN chr17:80,036,214-80,056,208 Encodes for Fatty Acid Synthetase.  EuroEPINOMICS 2014 (164) DNM1 chr9:130,965,658-131,017,527 Encodes for GTP-binding protein.  EuroEPINOMICS 2014 (164) APPBP2 chr17:58,520,520-58,603,580 Encodes for a protein involved in intracellular protein transport and regulated cell death. Implicated in cancer and Alzheimers.  Coppola 2013 (177) PTRH2 chr17:57,751,997-57,784,987 Promotes caspase-independent apoptosis. Involved in infantile-onset multisystem neurological, endocrine, and pancreatic disease.  Coppola 2013 (177) CLTC chr17:57.697,219-57,773,671 Encodes Clathrin, a protein that is present on the cytoplasmic side of organelles. Coppola 2013 (177) TUBD1 chr17:57,936,851-57,970,304 Encodes for tubulin, which make up Microtubules. Coppola 2013 (177) CSTB chr21:45,192,393-45,196,326 Encodes an intracellular thiol proteinase inhibitor. Implicated in epilepsy.  Striana 2010 (178) CHD9 chr16:53,088,945-53,361,414 Encodes for Chromodomain helicase DNA brinding protein 9, which is a transcriptional coactivator of PPARA.   GeneMANIA (113) CSTF2T chr10:53,455,246-53,459,355 Encodes a protein involved in cleaving the 3’ end of RNA in preparation for adding on a PolyA tail.  GeneMANIA (113) COPZ2 chr17:46,103,533- Encodes a subunit of GeneMANIA (113) 84		Table 3.6. Sudden death candidate genes with roles in general cell processes GENE LOCATION Function Reference 46,115,152 the coatomer protein complex 2, involved in cellular vesicle formation.  AP2B1 chr17:33,914,282-34,053,436 Encodes a protein that binds clathrins to vesicles.  Pignatelli 2012 (179), GeneMANIA (113) AKR1C3 chr10:5,090,958-5,149,878 Encodes a Aldo-keto Reductase enzyme. Involved in cell growth regulation and reduction of prostaglandins.  GeneMANIA (113) APAF1 chr12:99,039,078-99,129,211 Encodes a protein involved in initiating apoptosis.  GeneMANIA (113) GOT1 chr10:101,156,627-101,190,530 Encodes an enzyme involved in amino acid metabolism.   Salomonis 2014 (77) GOT2 chr16:58,741,035-58,768,246 Encodes an enzyme involved in amino acid metabolism.   Salomonis 2014 (77) BAX chr19:49,458,117-49,464,519 Encodes BCL2-associated X protein, which accelerates programmed cell death.  Tehranian 2008 (180) CHD7 chr8:61,591,324-61,780,586 Encodes a protein involved in rRNA synthesis. Implicated in CHARGE syndrome, which involves heart defects and abnormal growth and development.   Fujita 2009 (181) CPLX1 chr4:778,745-819,945 Encodes a protein involved in synaptic vesicle exocytosis. Implicated in Wolf-Hirschhorn Syndrome, which involves seizures and abnormal growth and development.  Glynn 2007 (182) LACTB2 chr8:71,549,501-71,581,447 Encodes for Lactamase, beta 2. GeneMANIA (113) MANEA chr6:96,025,373-96,057,328 Encodes an enzyme which is able to cleave oligosaccharides.  GeneMANIA (113) GAPDHS chr19:36,024,314-36,036,218 Encodes an enzyme involved in Salomonis 2014 (77) 85		Table 3.6. Sudden death candidate genes with roles in general cell processes GENE LOCATION Function Reference carbohydrate metabolism.  SGPP1 chr14:64,150,935-64,194,756 Encodes a protein involved in sphingolipid metabolism.  GeneMANIA (113) ARID4B chr1:235,330,210-235,491,532 Encodes a protein involved in cellular proliferation, apoptosis and differentiation. GeneMANIA (113) SMARCA5 chr4:144,434,616-144,478,642 Encodes a gene involved in chromatin remodeling. Implicated in cancer.  GeneMANIA (113) TNKS2 chr10:93,558,151-93,625,232 Encodes a protein involved in telomere lengthening and apoptosis. Implicated in cancer GeneMANIA (113) TMEM123 chr11:102,267,056-102,323,775 Encodes a protein involved in oncosis.  GeneMANIA (113) ERBB2 chr17:37,856,254-37,884,915 Encodes for erythoblastic leukemia viral oncogene homolog 2. Implicated in cancer.  GeneMANIA (113) VIT chr2:36,923,833-37,041,937 Encodes for Vitrin, involved in cell adhesion.  GeneMANIA (113) ZNF709 chr19:12,571,998-12,595,632 Encodes for Zinc finger protein 209, involved in transcription regulation. Implicated in cancer. GeneMANIA (113) ZZZ3 chr1:78,030,190-78,148,343 Encodes for Zinc finger, ZZ-type containing 3, involved in transcription regulation.  GeneMANIA (113) ZNF44 chr19:12,382,625-12,405,714 Encodes for Zinc-finger, protein 44, involved in transcription regulation.Bassuk 2013 (183) BAD chr11:64,037,302-64,052,176 Encodes a protein involved in programmed cell death. GeneMANIA (113) NPRL3 chr16:84,271-138,860 Encodes a protein Ricos 2015 (184) 86		Table 3.6. Sudden death candidate genes with roles in general cell processes GENE LOCATION Function Reference involved in increased GTP hydrolysis in the TORC1 pathway. FAM71A chr1:212,797,789-212,800,120 Encodes a protein of unknown function, known as Family with Sequence Similarity 71A.  GeneMANIA (113) C15orf41 chr15:36871812-37102449 Implicated in Congenital Dyserythropoietic Anemia Type 1. Encodes an Open Reading Frame GeneMANIA (113) BAZ2B chr2:160,175,490-160,473,203 Encodes a Zinc Finger, involved in transcription regulation.  GeneMANIA (113) SLC30A3 chr2:27,476,552-27,498,685 Encodes a Solute carrier family 30 member 3, involved in transporting zinc to the extracellular space.  GeneMANIA (113) DEPDC5 chr22:32,149,944-32,303,012 Encodes the DEP Domain Containing 5, involved in inhibition of the amino acid-sensing brance of the mTORC1 pathway. Implicated in familial focal epilepsy.  Ricos 2015 (184) VPS13A chr9:779,792,269-80,036,457 Encodes a gene involved in controlling the steps in the cycling of proteins through the trans-Golgi network to the endosomes, lysosomes, and the plasma membrane. Implicated in epilepsy.  Connolly 2014 (185) HTRA1 chr10:124,221,041-124,274,424 Encodes a Serine Protease, which regulates insulin-like growth factors.  Feng 2015 (47) S1PR1 chr1:101,702,305-101,707,076 Encodes a sphingosine receptor, involved in cell-cell GeneMANIA (113) 87		Table 3.6. Sudden death candidate genes with roles in general cell processes GENE LOCATION Function Reference adhesion. Implicated in cancers. ABCC8 chr11:17,414,432-17,498,449 Encodes for ATP-binding cassette, which transports proteins across membranes. Involved in hyperinsulinemic hypoglycemia of infancy Takagi 2013 (186) GPR26 chr10:125,425,871-125,456,913 Encodes G-Coupled Protein Receptor 26.  GeneMANIA (113) TMEM214 chr2:27,255,778-27,264,563 Encodes for a protein that works with CAPS4 in ER-stress induced apoptosis.  GeneMANIA (113) RORB chr9:77,112,281-77,308,093 Encodes for a protein DNA-binding protein that is able to bind to hormone response elements upstream of genes to enhance their expression. Implicated in epilepsy. Baqlietto 2014 (187) GOLGA2 chr9: 131,018,108-131,038,274 Encodes for a protein located in the Golgi apparatus.  Shamseldin 2016 (188) GOLGA1 chr9: 127,640,646-127,710,771 Encodes for a protein located in the Golgi apparatus.  GeneMANIA (113) MAPKAP1 chr9: 128,199,672-128,469,513 Encodes a subunit of mTORC2, which is involved in cell growth regulation.  GeneMANIA (113) TSC1 chr9: 135,766,735-135,820,020 Encodes a protein that negatively regulates the mTORC1 pathway.  GeneMANIA (113) BTAF1 chr10: 93,683,526-93,790,082 Encodes for a TATA box-binding protein-associated factor, involved in DNA transcription.  GeneMANIA (113) TUBGCP5 chr15: 22,833,395-22,873,892 Encodes a protein involved in microtubule binding. GeneMANIA (113) ALDOA chr16: 30,064,411-30,081,778 Encodes a protein involved in glycolysis.  Sorensen 2015 (189) POLR3K chr16: 96,407-103,628 Encodes a subunit of RNA Polymerase 3.  GeneMANIA (113) 88		Table 3.6. Sudden death candidate genes with roles in general cell processes GENE LOCATION Function Reference NACA2 chr17: 59,667,794-59,668,563 Encodes a protein that prevents polypeptides from binding to the ER in cells.  GeneMANIA (113) METTL2A chr17: 60,501,228-60,527,454 Encodes a methyltransferase enzyme.  GeneMANIA (113) EIF4ENIF1 chr22: 31,832,963-31,892,094 Encodes a transport protein involved in signaling between the cytoplasm and nucleus.   GeneMANIA (113)                   89		TABLE 3.7. Sudden death candidate genes involved in serotonin GENE LOCATION Function Reference HTR2B chr2:231,972,950-231,989,824 Encodes a serotonin receptor. Feng 2015 (47) HTR1A chr5:63,255,875-63,258,119 Encodes a serotonin receptor.  Feng 2015 (47) HTR1B chr6:78,171,948-78,173,120 Encodes a serotonin receptor. Feng 2015 (47) HTR1D chr1:23,518,388-23,521,222 Encodes a serotonin receptor. Feng 2015 (47) HTR2A chr13:47,405,677-47,471,211Encodes a serotonin receptor. Feng 2015 (47) HTR2C chrX:113,818,551-114,144,624 Encodes a serotonin receptor. Feng 2015 (47) HTR5A chr7:154,862,034-154,879,102 Encodes a serotonin receptor. Feng 2015 (47) HTR3E chr3:183,817,967-183,824,783 Encodes a serotonin receptor.  Feng 2015 (47) HTR3D chr3:183,750,619-183,757,157 Encodes a serotonin receptor. Feng 2015 (47) HTR7 chr10:92,500,576-92,617,671 Encodes a serotonin receptor. Feng 2015 (47) SLC6A4 chr17:28,521,337-28,562,986 Encodes a serotonin transporter, involved in neuronal signaling. Implicated in Obsessive Compulsive Disorder. Blair 2016 (69)            90		TABLE 3.8. Sudden death candidate genes with roles in hypoxia GENE LOCATION Function Reference GAPDH chr12:6,643,585-6,647,537 Encodes glyceraldehyde-3-phosphate dehydrogenase.  Implicated in cancer.GeneMANIA (113) HSP90B1 chr12:104,324,112-104,341,708 Encodes a heat shock protein. Implicated in tumor formation. Salomonis 2014 (77) SPTBN1 chr2:54,683,454-54,898,583 Encodes spectrin, which is an actin crosslinking and scaffolding protein. GeneMANIA (113) TF chr3:133,464,800-133,497,850 Encodes a glycoprotein that is able to transport iron from the intestines, liver and immune system to all other cells in the body.  GeneMANIA (113) YWHAG chr7:75,956,108-75,988,342 Encodes a 14-3-3 protein that binds to phosphoserine-containing proteins, in order to signal transduction.  Salomonis 2014 (77)  HMOX1 chr22:35,777,060-35,790,207 Encodes for Heme Oxygenase, which involved in converting heme to bilirubin.  Miura 2012 (65) ALDOC chr17:26,900,133-26,903,951 Encodes a gene involved in glycolysis, Expressed in the hippocampus and Purkinje cells of the brain.  Wang 2007  (190) HBA1 chr16:226,679-227,520 Encodes a subunit of hemoglobin.  GeneMANIA (113) MB chr22:36,002,811-36,013,384 Encodes myoglobin, which is expressed in skeletal and cardiac muscle cells and is involved in oxygen storage and diffusion. GeneMANIA (113) FANCD2 chr3:10,068,113-10,141,344 Encodes a protein involved in chromosomal stability. Adler 2016 (53)  91		TABLE 3.9. Sudden death candidate genes with roles in the immune system GENE LOCATION Function Reference IL6 chr7:22,766,766-22,771,621 Encodes a cytokine, which is released by cells in response to infection to signal an inflammatory response. Rognum 2009 (45) IL10R chr11:117,857,106-117,872,199 Encodes a cytokine, which is released by cells in response to infection to signal an inflammatory response.  Opdal 2004 (191) TNF chr6:31,543,344-31,546,113 Encodes a cytokine secreted by macrophages and involved in the inflammatory response.  Bonny 2011 (192) AKNA chr9:117,096,433-117,156,685 Encodes a transcription factor that activates expression of the CD40 receptor, which is expressed on the surface of lymphocytes.  Ma 2011 (193) ATG5 chr6:106,632,352-106,773,695 Encodes a gene involved in autophagy vesicle formation and inflammatory cell differentiation. Kuma 2004 (194) ERVW-1 chr7:92,098,079-92,099,695 Encodes a human endogenous provirus envelope protein, expressed in the placenta.  Ruebner 2013 (195) PAQR3 chr4:79,839,094-79,860,582 Encodes a progestin and adipoQ receptor. GeneMANIA (113)  PRKD3 chr2:37,477,646-37,544,222 Encodes for protein kinase D3. GeneMANIA (113) TBX21 chr17:45,810,610-45,823,485 Encodes T-box 21, which is a transcription factor involved in immune system development. Implicated in cancer. GeneMANIA (113) 92		TABLE 3.9. Sudden death candidate genes with roles in the immune system NPRL2 chr3:50,384,761-50,388,522 Encodes a protein that has tumor suppressive actions. Ricos 2016 (184)                        93		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association TCAP Muscle    Cancer  MYOM1  Muscle     DCM SPTAN1  Muscle  X   SLMAP Muscle    Brugada Syndrome TTN Muscle X    LMNA Muscle    DCM PKP2 Muscle X    TRDN Muscle    Cardiac arrhythmias CHRNA1 Muscle    Congenital Myasthenic Syndrome MYL2 Cardiac regulation   X HCM MYL3 Cardiac regulation   X HCM GLA Cardiac regulation    HCM LAMP2 Cardiac regulation    HCM MYOZ2 Cardiac regulation   X HCM PRKAG2 Cardiac regulation  X  HCM TNNI3 Cardiac regulation  X  HCM CSRP3 Cardiac regulation  X  HCM & DCM ACTN2 Cardiac regulation  X  HCM TPM1 Cardiac regulation  X  HCM & DCM PLN Cardiac regulation   X HCM MYBPC3 Cardiac regulation  X  HCM & DCM TNNC1 Cardiac regulation  X  HCM & DCM  TNNT2 Cardiac regulation  X  HCM & DCM ACTC1 Cardiac regulation   X HCM, DCM, & LVNC MYH7 Cardiac regulation  X  HCM, DCM, & LVNC 94		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association SNTA1 Cardiac regulation    Long QT Syndrome, Brugada Syndrome  MYH6 Cardiac regulation  X   TNNT1 Cardiac regulation  X  DCM BAG3 Cardiac regulation    DCM, Heart failure  RBM20 Cardiac regulation    DCM CTF1 Cardiac regulation    DCM DES Cardiac regulation  X  DCM EMB Cardiac regulation    DCM TAZ Cardiac regulation  X  DCM & VCN NPPA Cardiac regulation    DCM GJA5 Cardiac regulation  X  Familial AF CAV3 Cardiac regulation    Brugada Syndrome, HCMMIA3 Cardiac regulation    Heart Disease  ANKRD1 Cardiac regulation    DCM, Heart failure PDE3A Cardiac regulation    Cancer  TNNI3K Cardiac regulation    Familial AF, DCM RYR2 Cardiac regulation   X Epilepsy, Cardiac arrhythmias TIE1 Cardiac regulation  X   GPD1L Cardiac regulation    Brugada Syndrome OBSCN Cardiac regulation   X HCM DAG1 Cardiac regulation    HCM 95		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association CASQ2 Cardiac regulation  X  Cardiac arrythmias VCL Cardiac regulation    HCM & DCM DSP Cardiac regulation    HCM RYR3 Cardiac regulation  X   AKAP9 Cardiac regulation    Cardiac arrythmias, Brugada Syndrome, and Long QT Syndrome LDB3  Cardiac regulation    LVNC CALM2 Neuronal Regulation    Long QT, Epilepsy, Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT) GAP43 Neuronal Regulation X    MBP Neuronal Regulation X    TPPP Neuronal Regulation X    TUBB4A Neuronal Regulation    Spastic Paraplegia  GJA1 Neuronal Regulation X    MAOA Neuronal Regulation X    PHOX2B Neuronal Regulation X    SLC1A3 Neuronal Regulation X    SLC25A4 Neuronal Regulation X    SNAP25 Neuronal Regulation X    VAMP2 Neuronal Regulation X   Familial Infantile 96		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association Myasthenia (respiratory distress) NOS1AP Neuronal Regulation    Long QT Syndrome, Brugada Syndrome CHD2 Neuronal Regulation    Epilepsy TBC1D24 Neuronal Regulation    Familial Infantile Myoclonic Epilepsy  STXBP1 Neuronal Regulation    Epilepsy GABRB3 Neuronal Regulation    Absence epilepsy CHRNA7 Neuronal Regulation X    CHRNA4 Neuronal Regulation    Epilepsy CHRNA2 Neuronal Regulation    Epilepsy SLC12A5 Neuronal Regulation    Epilepsy of Infancy with Migrating Focal Seizures  PAFAH1B1 Neuronal Regulation  X   EN1 Neuronal Regulation X    APH1A Neuronal Regulation    Alzheimer’s Disease SLC1A4 Neuronal Regulation    Infantile Spasms CDKL5 Neuronal Regulation    Early Infantile Epileptic Encephalopathy, X-linked Infantile Spasms Syndrome ANK2 Cytoskeleton   X Cardiac arrhythmias KRT2 Cytoskeleton X    97		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association KRT9 Cytoskeleton X    PDLIM2 Cytoskeleton    Cancer MAPT Cytoskeleton X    DSG2 Cytoskeleton    Cardiac arrhythmias  JUP Cytoskeleton    Cardiac arrhythmias  VSIG1 Cytoskeleton    Cancer CXADR Cytoskeleton    Cancer PCDH19 Cytoskeleton    Dravet Syndrome GAPDH Hypoxia    Cancer  Hsp90b1 Hypoxia X   Tumor formation Ywhag Hypoxia X    IL6 Inflammation X    IL10R Inflammation X    TNF Inflammation    Brugada Syndrome AKNA Inflammation X    ATG5 Inflammation    Autophagy in neonates  ERVW-1 Inflammation    Pathological pregnancies (preeclampsia, intraeuterine growth restrictions, and high elevated liver and low platelets syndrome)  TBX21 Inflammation    Cancer NPRL2 Inflammation    Epilepsy HTR2B Serotonin  X   98		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association HTR1A Serotonin  X   HTR1B Serotonin  X   HTR1D Serotonin  X   HTR2A Serotonin  X   HTR2C Serotonin  X   HTR5A Serotonin  X   HTR3E Serotonin  X   HTR3D Serotonin  X   HTR7 Serotonin  X   SLC6A4 Serotonin X   Obsessive Compulsive Disorder  TGFB3 General cell processes    Cardiac arhythmias TMEM43 General cell processes    Familial Arrhythmagenic Right Ventricular Dysplasia Type 5 (ARVD5) FANCD2 General cell processes    Brugada Syndrome FAM213A General cell processes    Cancer LGI1 General cell processes  X   SMC4 General cell processes   X   COL6A3 General cell processes   X  Muscuar Dystrophy, Myopathy ALG13 General cell processes    Epilepsy, X-linked Intellectual Disability, Congenital Disorders of Glycosylation FASN General cell processes    Epilepsy DNM1 General cell processes    Epilepsy APPBP2 General cell processes    Epilepsy, Cancer, Alzheimers 99		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association PTRH2 General cell processes    Epilepsy, Infantile-onset Multisystem Neurological, Endocrine, and Pancreatic Disease  CLTC General cell processes    Epilepsy TUBD1 General cell processes    Epilepsy CSTB General cell processes  X  Epilepsy  GOT1 General cell processes X    GOT2 General cell processes X    BAX General cell processes     Cognitive impairment after traumatic brain injuries  CHD7 General cell processes    CHARGE Syndrome, involving sight problems, deafness, heart defects, slow growth, and urinary tract problems.  CPLX1 General cell processes    Wolf-Hirchhorn Syndrome, involving developmental disabilities and seizures. RORB General cell processes    Epilepsy GOLGA2 General cell processes    Epilepsy  ALDOA General cell processes    Cancer HCN4 Ion channels & related genes    Brugada Syndrome, SUDEP 100		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association CACNA1C Ion channels & related genes   X Epilepsy, cardiac arrhythmias, Centrally Mediated Ventilation CACNA2D1 Ion channels & related genes   X Cardiac arrhythmias, Short QT Syndrome, HCM, and Brugada Syndrome CACNB2 Ion channels & related genes   X Cardiac arrhythmias, Brugada Syndrome, HCMKCND3 Ion channels & related genes   X Cardiac arrhythmias, Brugada Syndrome KCNIP1 Ion channels & related genes    DCM KCNE3 Ion channels & related genes   X Cardiac arrhythmias, Brugada Syndrome KCNE1 Ion channels & related genes   X Epilepsy, cardiac arrhythmias, Long QT Syndrome KCNE2 Ion channels & related genes   X Epilepsy, cardiac arrhythmias, Long QT Syndrome KCNH2 Ion channels & related genes   X Epilepsy, cardiac arrhythmias, Long QT Syndrome KCNJ2 Ion channels & related genes   X Cardiac arrhythmias, Short QT Syndrome KCNJ3 Ion channels &    Long QT Syndrome, 101		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association related genes Epilepsy KCNJ5 Ion channels & related genes   X Cardiac arrhythmias, Long QT Syndrome KCNJ8 Ion channels & related genes X    KCNQ1 Ion channels & related genes X X X Long QT Syndrome,  SCN1B Ion channels & related genes  X X Epilepsy, Sudden Cardiac Death, cardiac arrhythmias SCN1A Ion channels & related genes  X X Epilepsy, cardiac arrhythmias, Familial Hemiplegic Migraines  SCN2A Ion channels & related genes    Early Infantile Epileptic Encephalopathy  SCN2B Ion channels & related genes X   Atrial Fibrillation SCN3B Ion channels & related genes   X Cardiac arrhythmias, Brugada Syndrome SCN4B Ion channels & related genes   X Cardiac arrhythmias, Long QT Syndrome SCN5A Ion channels & related genes  X X Epilepsy, Long QT Syndrome, Brugada Syndrome SCN7A Ion channels & related    Neonatal Epilepsy 102		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association genes SCN8A Ion channels & related genes   X Epilepsy SCN9A Ion channels & related genes    Neuropathic pain  TRPM4 Ion channels & related genes   X Cardiac arrhythmias, Brugada Syndrome KCNJ11 Ion channels & related genes    Transient Neonatal Diabetes Mellitus KCNJ1     Neonatal Bartter Syndrome HCN1 Ion channels & related genes    Early Infantile Epileptic Encephalopathy, Cardiac arrhythmias  HCN2 Ion channels & related genes    Sinoatrial Node Disease, Epilepsy, and Cardiac arrhythmias  KCNA1 Ion channels & related genes  X  Epilepsy, and cardiac arrhythmias KCNA5 Ion channels & related genes    Familial Atrial Fibrillation, DCM KNCE5 Ion channels & related genes  X  Brugada Syndrome, cardiac arrhythmias CALM1 Ion channels & related genes    Cardiac arrhythmias RANGRF Ion   X Cardiac 103		Table 3.10. Overlapping SIDS, SUDEP, and SUD Risk Genes  GENE Subgroup Association with SIDS Association with SUDEP Association with SUD Other Disease Association channels & related genes arrhythmias RYR1 Ion channels & related genes   X  ATP1A2 Ion channels & related genes    Familial Hemiplegic Migraine KCNT1 Ion channels & related genes    Epilepsy NIPA2 Ion channels & related genes    Angelman Syndrome ATP1A3 Ion channels & related genes     Early life epilepsy, episodic prolonged apnea, and postnatal microcephaly.             104		Table 3.11. Overall number of Variants in Whole Exomes for Sudden Death Probands obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification Individual  Number of Variants Proband 2095 (SIDS) 51,737 Proband 2098 (SIDS) 69,978  Proband 2475 (SIDS) 40,869 Proband 2477 (SIDS) 58,445 Proband 2069 (SUDEP) 19,162 Proband 2231 (SUDEP) 63,954 Proband 2429 (SUDEP) 50,226 Proband 2460 (SUD) 63,217                105		Table 3.12. Total personal variation in SD Candidate Genes in the Whole Exomes for Sudden Death Probands, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification. Sample  Number of Variants Proband 2095 (SIDS) 765 Proband 2098 (SIDS) 1121 Proband 2475 (SIDS) 697 Proband 2477 (SIDS) 958 Proband 2069 (SUDEP) 379 Proband 2231 (SUDEP) 1062 Proband 2429 (SUDEP) 921 Proband 2460 (SUD) 1001                106		Table 3.13. Number of Sudden Death candidate genes with variants, per category, in Whole Exomes for Sudden Death Probands, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification Category Muscle Regulation Cardiac Regulation Neuronal Regulation Cytoskeletal Role Hypoxia Immune Role Serotonin Receptors & Transporters General Cellular Processes  Ion Channels & Associated Proteins Total  Proband 2095 (SIDS) 4 24 6 8 5 2 3 29 25 109 Proband 2098 (eererSIDS) 17 49 20 8 8 7 7 46 51 213 Proband 2475 (SIDS) 9 37 11 8 11 4 8 32 29 151 Proband 2477 (SIDS) 14 38 12 11 8 5 8 40 37 173 Proband 2069 (SUDEP) 7 16 6 7 3 3 3 24 24 93 Proband 2231 (SUDEP) 12 32 12 10 7 4 4 45 33 159 Proband 2429 (SUDEP) 9 36 13 12 10 5 3 37 34 159 Proband 2460 (SUD) 13 38 12 8 8 4 8 41 36 168    			107		Table 3.14. Number of transcripts affected by variants in Sudden Death candidate genes in Whole Exomes for Sudden Infant Death Syndrome (SIDS), Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD) probands, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification.  SIDS (N=4) SUDEP (N=3) SUD (N=1) 3226 1671 7269 9388 7046  4437 4527  6293   																			108		Table 3.15. Number of transcripts in SD candidate genes, per category, in Whole Exomes for Sudden Infant Death Syndrome (SIDS), Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD) probands, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification.  SIDS (N=4) SUDEP (N=3) SUD (N=1) Muscle Regulation 990 2322 1069 1233 226 1741 14 1306 Cardiac Regulation 1086 2640 1029 2278 333 1913 1510 1757 Neuronal Regulation 70 342 180 325 96 247 196 305 Cytoskeletal Role  190 389 202 283 71 213 280 378 Hypoxia  428 547 433 721 249 591 489 369 Immune Role 64 185 85 151 30 104 141 935 Serotonin 43 26 70 109		Table 3.15. Number of transcripts in SD candidate genes, per category, in Whole Exomes for Sudden Infant Death Syndrome (SIDS), Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD) probands, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification.  SIDS (N=4) SUDEP (N=3) SUD (N=1) 79 52 72 76 42 General Cellular Processes  631 1086 654 1071 314 997 836 925 Ion Channels & Associated Proteins 323 1798 733 1093 326 1164 1019 1224 												110		Table 3.16. Number of transcripts affected by type of variant by impact in Whole Exomes for Sudden Infant Death Syndrome (SIDS), Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD) probands, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification.   SIDS (N=4) SUDEP (N=3) SUD 3’ Untranslated Region 36 102 76 98 18 136 70 130 5’ Untranslated Region 35 103 50 76 7 70 75 77 Splice Region 53 153 78 126 43 100 63 134 Splice Site  0 8 1 1 1 0 3 3 Regulatory Region  529  1315  729  1001 304 1085 814 1279 Synonymous  328  369  544  643  152  744  561 853 111		Table 3.16. Number of transcripts affected by type of variant by impact in Whole Exomes for Sudden Infant Death Syndrome (SIDS), Sudden Unexpected Death in Epilepsy (SUDEP), and Sudden Unexpected Death (SUD) probands, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification.   SIDS (N=4) SUDEP (N=3) SUD Nonsynonymous  250 692 426 503 163 528 294 504 Premature Stop Codon 0 1 8 0 1 0 1  0 Intronic  1693 5074 2233 3146 783 3788 2149  3653 Insertion/Deletion 301 967 472 699 199 693 497 631 Spanning Deletion 1 4 0 0 0 2 0  5 				112					Table 3.17. Key Pathogenic Variants in Whole Exome for Sudden Infant Death Syndrome (SIDS) proband 2095, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification. Gene SCN2A RYR3 DAG1 IL10RA TTN TRPM6 CADD Phred 29.6 17.02 21.6 14.51 14.29 28 PolyPhen - Potentially damaging, Damaging Benign Benign Benign Damaging SIFT Phred  - Tolerated, Deleterious, Deleterious Tolerated Tolerated Deleterious Deleterious PhastCons 1.0 0.491 1.0 0.013 1.0 0.99 Grantham - 180 177 215 107 160 Amino Acid Substitution (NCBI Transcripts) - p.Arg1641Cys p.Ser14Trp p.Cys4Trp p.Glu8144Ala p.Asp1126Tyr Number of Transcripts Affected  5 2 21 10 11 7 rsID from dbNSP - rs4780144 rs2131107  - rs16866465  - Disease Early Infantile Epileptic Encephalopathy, Early Myoclonic Encephalopathy, Dravet Syndrome, Severe Myoclonic Epilepsy of Infancy SUDEP HCM SIDS SIDS Epilepsy 			113					Table 3.18. Key Pathogenic Variants in Whole Exome for Sudden Infant Death Syndrome (SIDS) proband 2098, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification. Gene DSP KCNJ5 KCNH2 SCN1B OBSCN ABCC8 CADD Phred 18.35 17.19 16.87 9.129 11.49 26.6 PolyPhen Damaging Benign Potentially damaging, Damaging,  Benign Benign Damaging SIFT Phred  Deleterious Tolerated Tolerated Deleterious Tolerated Deleterious, Tolerated PhastCons 0.859 1.0 1.0 0.41 1.0 0.003 Grantham 180 29 78 110 152 125 Amino Acid Substitution (NCBI Transcripts) p.Arg1537Cys p.Gln282Glu p.Lys897Thr p.Ser248Arg p.Val1600Asp p.Gly111Arg Number of Transcripts Affected  2 3 7 6 6 4 rsID from dbNSP rs28763967  rs7102584  rs1805123   rs7532342  - Disease Rapid Onset Dystonia-Parkinsonism (RDP), Alternating Hemiplegia of Childhood (AHC) involving neonatal seizures SUD, Long QT Syndrome SUD, Epilepsy, Long QT Syndrome  Dravet Syndrome, Severe Myoclonic Epilepsy of Infancy HCM Hyperinsulinemic hypoglycemia of infancy 				114		Table 3.19. Key Pathogenic Variants in Whole Exome for Sudden Infant Death Syndrome (SIDS) proband 2477, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification. Gene SCN8A HSP90B1 SLC1A4 SCN1B SCN1B GOT2 CADD Phred 25.9 14.19 32 9.129 2.198 21.8 PolyPhen Damaging Damaging Damaging  Benign Benign Benign SIFT Phred  Deleterious Deleterious Deleterious Deleterious Deleterious Tolerated PhastCons 1.0 1.0 1.0 0.41 0.034 1.0 Grantham 56 98 109 110 71 109 Amino Acid Substitution (NCBI Transcripts) p.Glu1223Lys p.Pro321Leu p.Gly361Val p.Ser248Arg p.Arg250Thr p.Val346Gly Number of Transcripts Affected  4 5 5 110 71 3 rsID from dbNSP - rs116891695  - rs67701503  rs67486287  rs30842  Disease SUD, Epilepsy SIDS Infantile Spasms Severe Myoclonic Epilepsy of Infancy, Dravet Syndrome  Severe Myoclonic Epilepsy of Infancy, Dravet Syndrome SIDS 											115		Table 3.20. Key Pathogenic Variants in Whole Exome for Sudden Infant Death Syndrome (SIDS) proband 2475, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification. Gene KCNJ1 PKP2 SCN8A SCN2A SMACA5 DAG1 CADD Phred 32 28.6 34 29.4 32 21.6 PolyPhen Potentially damaging Damaging Damaging Damaging Damaging  Benign  SIFT Phred  Deleterious Deleterious Deleterious Deleterious Deleterious Tolerated  PhastCons 1.0 1.0 1.0 1.0 1.0 1.0 Grantham 56 155 64 89 125 177 Amino Acid Substitution (NCBI Transcripts) p.Glu258Lys p.Phe424Ser p.Ala1575Val  p.Arg1163Cys p.Arg620Gly p.Ser14Trp Number of Transcripts Affected  7 2 3 5 2 21 rsID from dbNSP - -  rs182326351  rs17183814  - rs2131107   Disease Neonatal Bartter Syndrome SIDS Early Infantile Epileptic Encephalopathy, Early Myoclonic Encephalopathy  Early Infantile Epileptic Encephalopathy, Ealry Myoclonic Encephalopathy, Dravet Syndrome Cancer  HCM 								116		Table 3.21. Key Pathogenic Variants in the Whole Exome for Sudden Unexpected Death in Epilepsy (SUDEP) proband 2069, obtained from Dried Blood Spots (DBS) gDNA and subjected to Whole Genome Amplification. Gene ATP1A3 ATP1A3 TMEM214 LMNA TRDN TTN CADD Phred 4.325 28.9 22.7 17.50 13.72 18.53 PolyPhen Benign Deleterious Deleterious Benign, Potentially damaging Benign Benign  SIFT Phred  Deleterious Deleterious Deleterious Deleterious Tolerated Tolerated PhastCons 1.0 1.0 1.0 1.0 1.0 1.0 Grantham - 159 21 29 142 142 Amino Acid Substitution (NCBI Transcripts) - p.Gly633Cys p.Val351Met p.Arg401His p.Ile438Ser p.Ser1295Leu Number of Transcripts Affected  5 5 13 22 2 11 rsID from dbNSP rs919390 - rs1124649 rs141490569  rs2873479  rs1552280  Disease Rapid Onset Dystonia-Parkinsonism (RDP), Alternating Hemiplegia of Childhood (AHC) involving neonatal seizures Rapid Onset Dystonia-Parkinsonism (RDP), Alternating Hemiplegia of Childhood (AHC) involving neonatal seizures Seizures DCM Cardiac Arrhythmias SIDS 				117		Table 3.21. Key Pathogenic Variants in Sudden Unexpected Death in Epilepsy (SUDEP) proband 2231, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification.   Gene HTR3E KCNH2 RYR3 OBSCN ERBB2 TTN CADD Phred 17.31 18.92 17.02 15.77 22.1 20.6 PolyPhen Benign Benign  Potentially damaging, damaging  Damaging Potentially damaging  Damaging SIFT Phred  Tolerated Tolerated Tolerated, Deleterious, Deleterious Deleterious Deleterious Deleterious, Tolerated PhastCons 0.995 0 0.491 1.0 1.0 1.0 Grantham 58 102 180 125 27 109 Amino Acid Substitution (NCBI Transcripts) p.Ala86Thr p.Arg1047Leu p.Arg1641Cys p.Gly4209Arg p.Pro1170Ala p.Gly34278Val Number of Transcripts Affected  7 7 2 5 20 41 rsID from dbNSP rs7627615  rs36210421  rs4780144  rs56218706  rs1058808  rs3731752  Disease SUDEP Epilepsy, Cardiac Arrhythmias, Long QT Syndrome  SUDEP SUD, HCM Encodes for erythroblastic leukemia viral oncogene homolog 2. Involved in cancer. SIDS  					118		Table 3.22. Key Pathogenic Variants in Whole Exome for Sudden Unexpected Death in Epilepsy (SUDEP) proband 2429, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification. Gene OBSCN TTN SCN1B HTR3D KCNJ5 TRDN CADD Phred 18.33 20.6 9.129 21.8 17.19 13.72 PolyPhen Benign  Damaging Benign Potentially damaging, benign  Benign Benign  SIFT Phred  Deleterious  Deleterious, Tolerated  Deleterious Tolerated  Tolerated Tolerated  PhastCons 0 1.0 0.41 0.024 1.0 1.0 Grantham 180 109 110 29 20 142 Amino Acid Substitution (NCBI Transcripts) p.Arg5619Cys p.Gly3427Val p.Ser248Arg,. p.Arg435His p.Gln282Glu p.Ile438Ser Number of Transcripts Affected  5 41 6 4 3 2 rsID from dbNSP rs3795800  rs3731752  rs67701503 rs6789754 rs7102584 rs2873479  Disease HCM SIDS Dravet Syndrome, Severe Myoclonic Epilepsy of Infancy SUDEP SUD, Cardiac Arrhythmias, Long QT Syndrome  Cardiac Arrhythmias 									119		Table 3.23. Key Pathogenic Variants in Whole Exome for Sudden Unexpected Death (SUD) proband 2460, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification. Gene GOT2 RBM20 OBSCN SCN5A TTN DAG1 CADD Phred 21.8 25.7 11.49 22.4 22.6 21.6 PolyPhen Benign Damaging Benign  Potentially damaging, Damaging Damaging Benign  SIFT Phred  Tolerated  Deleterious Tolerated Deleterious Deleterious Tolerated PhastCons 1.0 1.0 1.0 1.0 1.0 1.0 Grantham 109 94 152 144 160 177 Amino Acid Substitution (NCBI Transcripts) p.Val346Gly p.Gly232Asp p.Val1600Asp p.Ser524Tyr p.Asp2243Tyr p.Ser14Trp Number of Transcripts Affected  3 1 6 10 10 21 rsID from dbNSP rs30842  rs61735268  rs7532342  rs41313691 rs138787974 rs2131107  Disease SIDS DCM SUD, HCM SUDEP, SUD, Epilepsy, Long QT Syndrome, Brugada Syndrome SIDS HCM 									120		Table 3.24. OBSCN Variants in the eight Whole Exomes for Sudden Death probands, obtained from Dried Blood Spot (DBS) gDNA and subjected to Whole Genome Amplification. Proband Number of Variants Number of Transcripts Affected 2095(SIDS) 7 9, 6, 12, 12, 9, 5, 4 2098 (SIDS) 9 5, 6, 12, 12, 5, 5, 6, 6, 4 2475 (SIDS) 5 5, 6, 12, 12, 4 2477 (SIDS) 2 9, 5  2069 (SUDEP) 2 6, 5  2231 (SUDEP) 12 5, 6, 12, 12, 9, 5, 5, 6, 6, 6, 4  2429 (SUDEP) 10 12, 12, 12, 11, 5, 5, 5, 7, 4, 4 2460 (SUD) 11 9, 6, 12, 12, 11, 9, 5, 6, 6, 4, 4 			                 121		Chapter 4: Discussion and Conclusions 	4.1 Challenges in Personalized Risk Prediction  To	achieve	 truly	personalized	 risk	prediction,	 the	 clear	defined	decision	 tree	can	provide	appropriate	questions	to	be	evaluated	when	profiling	or	predicting	potential	 risk	 of	 sudden	 death	 even	 in	 small	 cohorts.	 Here,	 the	 inability	 to	differentiate	 among	 the	 SD	 probands	 regardless	 of	 population,	 cohort,	 or	personal	comparison	underscores	the	challenges	of	molecular	diagnostics	where	risk	prediction,	and	potentially	therapeutic	intervention.	The	amount	and	nature	of	 personal	 genetic	 variation	 and	 the	 biological	 integration	 of	 spatio‐temporal	expression	 patters	 during	 development	 requires	 a	 new	 analytical	 paradigm	where	statistical	predictions	are	superseded	by	integrated	risk	analysis.		  4.2 Genetic Variation in Sudden Death Probands is Pathogenic  	Currently,	 the	 Triple	 Risk	 Hypothesis	 which	 include	 environmental	factors,	a	vulnerable	age,	and	genetic	predisposition	 is	employed	to	encompass	all	 presumed	 contributors	 to	 risk.	 Here,	 the	 comparison	 of	 Sudden	 Death	probands	 representing	 infants	 (SIDS),	 syndromes	 (SUDEP)	 and	 adult	 sudden	death	(SUD)	allowed	for	the	consideration	of	genetic	variation	and	pathogenicity	in	 alternatively	 spliced	 transcripts	 in	 a	 subset	 of	 SD	 candidate	 genes.	Not	 only	was	 the	 overall	 exomic	 variation	 extensive,	 but	 the	 distribution,	 relative	contribution	 and	 functional	 classes	 affected	 by	 variants	 was	 similar	 across	 all	probands	 regardless	 of	 cause	 of	 death.	 Even	 when	 transcript	 data	 was	considered	 and	 pathogenic	 consequences	 calculated,	 the	 individual	 patterns	122		were	complex	and	indistinguishable	between	individuals	or	cause	of	death.	This	unequal	 valence	 and	 contribution	 underscores	 the	 challenge	 molecular	diagnostic	 risk	 prediction	 has	 clinically,	 and	 required	 further	 consideration	 of	select	 candidate	genes	 in	 the	developing	brain	where	 spatio‐temporal	patterns	were	 noted.	 Specifically,	 the	 development	 of	 the	 brainstem	 and	 regulatory	cardio‐respiratory	nuclei	 had	 changing	patterns	of	 expression	of	 the	 candidate	genes	across	age	ranges,	including	pre	and	postnatally.	Individual	 risk	 genes	 were	 observed	 to	 contain	 potentially	 pathogenic	variants	 using	 one	 or	more	 of	 the	 bioinformatics	 variant	 annotation	methods.	Thus,	simply	having	a	pathogenic	mutation	within	this	gene	is	not	predictive	of	the	type	of	death	that	will	occur.	 Instead,	 it	 is	highly	 likely,	 that	as	proposed	in	the	Triple	Risk	Hypothesis	for	SIDS,	risk	factors,	in	this	instance	genetic	variation	combines	 to	 increase	 risk	 such	 that	 the	 cause	 of	 death	 in	 each	 of	 these	individuals	 is	 likely	 a	 result	 of	 the	 compounded	 functional	 affect	 of	 multiple	pathogenic	variants	within	their	exome.		 4.3 Limitations and Future Directions 	As	 seen	 in	 the	decision	 trees	 (Figure	2.1),	 it	was	not	possible	 to	predict	the	 cause	 of	 death	 in	 the	 probands	 by	 the	 number	 of	 variants	 in	 all	 the	 genes	within	 the	whole	 exomes	 or	 in	 the	 subsection	 of	 the	 SD	 candidate	 genes.	 The	eight	 probands	 did	 have	 more	 variants	 on	 average	 than	 controls	 from	 the	literature	(146,196)	where	roughly	55,727	variants	were	found	in	an	individual.	It	 is	possible	 that	 these	additional	variants	 contribute	 to	a	 compounding	affect	that	 leads	to	sudden	death.	Large	variability	 in	the	number	of	genes	containing	123		variants,	the	number	of	 impacted	transcripts,	and	the	specific	number	and	type	of	 pathogenic	 variants	 between	 different	 probands	 within	 the	 same	 cause	 of	death	group.		For	all	three	group;	SIDS,	SUDEP,	and	SUD,	numerous	affected	transcripts	fell	 into	 the	 Intronic	Variant	bin	 (Table	3.16),	which	 included	non‐coding	exon	variants,	 noncoding	 transcript	 exon	 variants,	 and	 intron	 variants.	While	 these	variants	do	not	modify	protein	structure,	it	does	not	diminish	possible	biological	impact	as	many	variants	of	this	type	impact	distal	regulatory	elements,	even	up	to	100	kb	away	 from	the	target	region	(197).	The	alteration	of	gene	regulation	frequently	impact	expression	levels	leading	to	an	increase	or	decrease	in	protein	expression.	 Similarly,	 many	 variants	 coded	 directly	 to	 the	 regulatory	 regions	which	 can	 modify	 transcription	 factor	 or	 DNA	 polymerase	 binding	 also	modifying	gene	expression	patterns.	Variants	with	established	pathogenic	effects	like	 Nonsynonymous,	 and	 Indels	 were	 also	 present	 in	 large	 numbers	 in	 all	individuals.	 Missense	 mutations	 encode	 amino	 acid	 substitutions	 that	 impact	protein	 packing,	 alter	 protein	 function	 or	 protein	 biogenesis.	 The	 larger	insertions	and	deletions	would	result	in	large	frameshifts	in	the	coding	sequence,	altering	 the	 codons	 required	 to	 attract	 the	 proper	 amino	 acids	 during	 protein	translation.		For	some	genes,	multiple	variants	were	located	within	the	same	gene.	The	number	 of	 transcripts	 affected	 by	 the	 variant	 was	 counted	 separately	 and	compound	 mutations	 were	 not	 taken	 into	 consideration.	 Therefore,	 the	 true	overall	 number	 of	 transcripts	 affected	 in	 each	 of	 the	 probands	 is	 less	 than	presented	 because	 some	 transcripts	 were	 impacted	 by	 multiple	 variants	 and	thus,	counted	multiple	times.		124		The	conclusions	that	can	be	drawn	from	our	results	are	limited,	due	to	the	small	 number	 of	 samples	 that	 we	 have.	 However,	 the	 exact	 cause	 of	 death	appears	to	be	different	even	within	the	three	sudden	death	categories	due	to	the	large	 differences	 between	 individuals	 in	 terms	 of	 the	 genes	 affected	 and	 the	number	of	variants	present	within	these	genes.	However,	 it	was	 intriguing	that	all	of	 the	SIDS	probands	had	pathogenic	variants	 in	genes	 involved	 in	epilepsy,	such	 as	 Early	 Infantile	 Epileptic	 Encephalopathy,	 also	 known	 as	 Ohtahara	Syndrome.	It	is	possible	that	the	infants	had	mild,	undetectable	seizures	or	that	the	seizures	only	occurred	when	the	parents	of	these	infants	were	not	present.		It	also	underscores	the	role	of	the	brain	in	the	regulation	of	cardiac	and	respiratory	function	 such	 that	 abnormal	 signalling	 via	 the	 brainstem	 is	 a	 viable	pathophysiological	mechanism	 consistent	with	 the	 Triple	 Risk	 Hypothesis	 and	matches	the	etiology	of	death	observed	in	both	SUDEP	and	SIDS.		 4.4 Summary and Conclusions 	Small cohorts, oligogenic heterogeneity, and large number of rare private variants in Sudden Death cases require novel approaches in variant prioritization and functional impact prediction such as incorporating spatio-temporal expression patterns for genetic variant pathogenicity predictions. At the moment, due to the role of the same genes in a variety of disorders across the SD spectrum, it is difficult to predict the developmental timing of risk. 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