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Targeted high-throughput sequencing of candidate genes for chronic obstructive pulmonary disease Matsson, Hans; Söderhäll, Cilla; Einarsdottir, Elisabet; Lamontagne, Maxime; Gudmundsson, Sanna; Backman, Helena; Lindberg, Anne; Rönmark, Eva; Kere, Juha; Sin, Don; Postma, Dirkje S; Bossé, Yohan; Lundbäck, Bo; Klar, Joakim Nov 11, 2016

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RESEARCH ARTICLE Open AccessTargeted high-throughput sequencing ofcandidate genes for chronic obstructivepulmonary diseaseHans Matsson1,2* , Cilla Söderhäll1,2, Elisabet Einarsdottir1,3, Maxime Lamontagne4, Sanna Gudmundsson5,Helena Backman6, Anne Lindberg7, Eva Rönmark6, Juha Kere1,3, Don Sin8, Dirkje S. Postma9, Yohan Bossé4,10,Bo Lundbäck11 and Joakim Klar5AbstractBackground: Reduced lung function in patients with chronic obstructive pulmonary disease (COPD) is likely due toboth environmental and genetic factors. We report here a targeted high-throughput DNA sequencing approach toidentify new and previously known genetic variants in a set of candidate genes for COPD.Methods: Exons in 22 genes implicated in lung development as well as 61 genes and 10 genomic regionspreviously associated with COPD were sequenced using individual DNA samples from 68 cases with moderate orsevere COPD and 66 controls matched for age, gender and smoking. Cases and controls were selected from theObstructive Lung Disease in Northern Sweden (OLIN) studies.Results: In total, 37 genetic variants showed association with COPD (p < 0.05, uncorrected). Several variantspreviously discovered to be associated with COPD from genetic genome-wide analysis studies were replicatedusing our sample. Two high-risk variants were followed-up for functional characterization in a large eQTL mappingstudy of 1,111 human lung specimens. The C allele of a synonymous variant, rs8040868, predicting a p.(S45=) in thegene for cholinergic receptor nicotinic alpha 3 (CHRNA3) was associated with COPD (p = 8.8 x 10−3). This associationremained (p = 0.003 and OR = 1.4, 95 % CI 1.1-1.7) when analysing all available cases and controls in OLIN (n = 1,534).The rs8040868 variant is in linkage disequilibrium with rs16969968 previously associated with COPD and alteredexpression of the CHRNA5 gene. A follow-up analysis for detection of expression quantitative trait loci revealed thatrs8040868-C was found to be significantly associated with a decreased expression of the nearby gene cholinergicreceptor, nicotinic, alpha 5 (CHRNA5) in lung tissue.Conclusion: Our data replicate previous result suggesting CHRNA5 as a candidate gene for COPD and rs8040868 as arisk variant for the development of COPD in the Swedish population.Keywords: COPD, Sequencing, eQTL, Association, Lung development, CHRNA5BackgroundChronic obstructive pulmonary disease (COPD), charac-terised by a persistent airflow obstruction [1], is a life-threatening disease accounting for 6 % of all deathsglobally in 2012 [2]. The development of the disease is in-fluenced by environmental determinants, most commonlycigarette smoking, genetic risk factors and possible geneticprotective factors [3]. Candidate gene association studieshave suggested several potential COPD susceptibilitygenes, and genome-wide association studies (GWAS) haveidentified multiple COPD susceptibility loci [4]. However,genetic mapping in families with high penetrance for adisease gene variant can be helpful in pinpointing newsusceptibility genes even for multifactorial traits. Recently,we reported mutations in the gene for fibroblast growthfactor 10 (FGF10) involved in lung development, as a pos-sible cause of COPD in families from Sweden [5]. Hence,* Correspondence: hans.matsson@ki.se1Department of Biosciences and Nutrition, Karolinska Institutet, 7-9, SE-141 83Huddinge, Sweden2Department of Women’s and Children’s Health, Karolinska Institutet,Stockholm, SwedenFull list of author information is available at the end of the article© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Matsson et al. BMC Pulmonary Medicine  (2016) 16:146 DOI 10.1186/s12890-016-0309-ya monogenic form of COPD could result from muta-tions in FGF10. To date, the only other known mono-genic form of COPD is alpha 1-antitrypsin deficiencycaused by disruption of the alpha-1-antiproteinase(SERPINA1) gene [6].Typically in GWAS, common polymorphisms aretested for association. In this study, we provide an alter-native approach with the aim to perform an in-depthanalysis of exons of candidate genes for COPD by usinghigh-throughput sequencing. This allowed us to detectthe full spectrum of single nucleotide variation at anyfrequency in selected genomic regions and to also cap-ture variants with a potential functional effect on geneexpression levels. We show here that targeted highthroughput sequencing using a well-defined population-based case–control sample can i) assess the impact ofcommon variants in genes important for lung develop-ment, and ii) test genetic variants in a large set of candi-date genes and genomic regions for association withCOPD. To accomplish this we captured and sequenced22 genes implicated in lung development as well as 61genes and 10 genomic regions previously associatedwith COPD. The sample used here is comprised ofcases and controls from The Obstructive Lung Diseasein Northern Sweden (OLIN) studies. The population innorthern Sweden, an admixture of three different eth-nic groups (Swedes, Finns and Saami), showed a dra-matic growth of population size since the 18th centuryfrom a relatively small founder population [7]. This re-sulted in founder effects that significantly reduced theheterogeneity of this population, making it suitable forgenetic association studies of multifactorial phenotypes,such as COPD [8].This study assessed Swedish COPD cases and controlsand assessed detected variants in candidate genes forassociation with COPD. We replicated a previous de-scribed association signal in CHRNA3, which also asso-ciated with lower CHRNA5 gene expression. The DNAcapture design and targeted sequencing used here showpotential to detect known single nucleotide variants inassociation with COPD with the additional potential toalso detect low-frequent variants. The result presentedhere using the relative limited sample size could be rep-licated using our targeted capture design in larger sam-ples from different populations.MethodsPatient material and ethics statementThe OLIN studies are an on-going research program fo-cused on asthma, allergy and COPD. It started 30 yearsago [9] and now involves more than 50,000 subjectsfrom northern Sweden. Within OLIN, a COPD-cohortwas identified at re-examination of several cohorts in2002–2004 [10]. At recruitment, COPD (n = 993) wasdefined using the fixed ratio of FEV1 / FVC < 0.70(forced expiratory volume in 1 s / forced vital capacity).When calculating the ratio FEV1 / FVC, the highestvalues of FEV1 and the highest value of forced vital cap-acity (FVC) or slow vital capacity (SVC) were used. Thishas support in the GOLD documents [1] and is ac-knowledged in the recent ERS task force guidelines forepidemiological studies on COPD [11]. An age and gen-der matched control population (n = 993) without ob-structive lung function impairment was also recruited[10]. Since 2005 the OLIN COPD cohort with corre-sponding controls is followed up annually with a basicprogram including spirometry and interviews regardingsymptoms and morbidity [12]. We initially selected 96COPD cases (18 non-smokers, 43 former smokers and35 smokers) from those who had an FEV1 < 80 % of pre-dicted value in 2005 and either FEV1 / FVC < LLN(lower limit of normal) in 2010 or were rapid declinerswith an annual FEV1 decline of ≥ 60 ml between 2005and 2010. We also identified a set of 96 age- andgender-matched controls (33 smokers and 63 formersmokers) with normal lung function. These 96 cases and96 controls are henceforth termed the OLIN discoverysample (Table 1). Furthermore, we defined an OLIN rep-lication sample consisting of individuals from the OLINCOPD study for which DNA was available (n = 1,534).From this group we classified individuals as cases whenFEV1 / FVC was lower than LLN in 2010, or if they hada yearly FEV1 decline from 2005 to 2010 of at least60 ml (n = 256). The remaining individuals were used asTable 1 Pulmonary function in patients and controlsAverage FVC (cm3) FVC LLN (cm3) FVC pred (cm3) FEV1 (cm3) FEV1 LLN (cm3) FEV1 pred (cm3) FEV1%pred (%) FEV1 / FVC FEV1 / FVCLLNFEV / FVCpredCases 2.69 (0.95) 2.87 (0.81) 3.79 (0.81) 1.50 (0.59) 2.01 (0.55) 2.78 (0.61) 53 (14) 0.56 (0.11) 0.62 (0.03) 0.73 (0.03)Controls 3.96 (0.90) 2.77 (0.77) 3.83 (0.77) 3.11 (0.69) 2.05 (0.55) 2.82 (0.59) 110 (9) 0.79 (0.05) 0.62 (0.03) 0.74 (0.03)P value 6.3 × 10−18 0.42 0.75 4.0 × 10−41 0.62 0.58 2.5 × 10−81 2.5 × 10−45 0.24 0.23Values in parenthesis denote standard deviations. FVC Forced vital capacity, LLN Lower limit of normal, FVC pred, predicted FVC based on age, gender and lengthin the population. FEV1 Forced expiratory volume in 1 s, FEV1 Predicted FEV1 based on age, gender and length in the population, FEV1%pred, FEV1 Divided bypredicted FEV1 in the population, FEV1 / FVC ratio when investigated the fifth year of the evaluations. The lower limit of normal (LLN) values where calculated bysubtracting 1.645 × RSD (residual standard deviation) from the predicted value (pred). FEV1%pred is the measured FEV1 value divided by the predicted value(i.e., FEV1 / FEV1pred). P values are calculated using two-sided Student’s t-test assuming equal variance. SD Standard deviationMatsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 2 of 10a reference group (n = 1,278). The physiological pa-rameters for the OLIN replication sample includedaverage age (cases 64 years, SD 11; controls 66 years,SD 11; P = 0.0012), gender (cases 123 females, 133males; controls 569 females, 709 males; P = 0.30),smoking habits (cases 11 pack/year, SD 15; controls 23pack/year, SD 17; P = 4.2 × 10−9), weight (cases 73 kg,SD 15; controls 77 kg, SD 14; P = 1.2 × 10−4) and height(cases 168 cm, SD 9; controls 168 cm, SD 10; P = 0.8). Thephenotype description of this sample included measuresof FVC (cases 3.29, SD 1.01; controls 3.50, SD 1. 03;P = 0.0031), FEV1 (cases 1.90, SD 0.67; controls 2.64,SD 0.81; P = 9.5 × 10−43) and FEV1% of predictedvalues (cases 67 %, SD 17 %; controls 95 % SD 16 %;P = 5.3 × 10−73), as well as the FEV1/FVC ratio (cases0.57, SD 0.08; controls 0.75, SD 0.07; P = 2.0 × 10−108)when investigated the fifth year of the evaluations.For description of individual reference values, seeAdditional file 1.P values for differences in parameters between casesand controls were calculated using two-sided Stu-dent’s t-tests, assuming equal variance. The ethicsboard of Umeå University (Dnr 04-045 M, supple-ment approved 2005-06-13) approved the use of indi-vidual phenotypic data and DNA samples for geneticresearch.Sequencing and quality controlsIn total 22 genes implicated in lung development, 61 genesand 10 genomic regions previously associated with COPD(Additional files 1 and 2), were investigated using targetedsequencing of captured genomic regions (HaloPlex Proto-col Version A, Agilent Technologies, Santa Clara, CA). Re-gions of 1.5 kb of genomic sequence, including specificintergenic polymorphisms, was also included in the design.The regions of interest (ROI) were designed to target allknown exons of major/known transcripts and at least 20base pairs (bp) of intronic sequences flanking each exon.The sequence capture design included 953 target regionsspanning 204.384 bp with 95.9 % (196.066 bp) coverage anaverage. Captured genomic regions were subjected to highthroughput paired-end (100 bp read module) sequencing(HiSeq2000, Illumina, San Diego, CA) at the Science forLife Laboratory in Uppsala, Sweden. Sequence reads werealigned to the hg19 reference genome and single nucleo-tide variants (SNVs) were called using GATK UnifiedGenotyper (GATK bundle v.2.2) [13]. Next, we enrichedfor high quality SNVs by removing SNVs with low confi-dence (QD< 1.5), Phred scaled quality score (<50) andSNVs within SNV clusters. These high quality variants arehenceforth referred to as ‘variants’. We also removed indi-vidual cases and controls with sequencing read depth con-sistently < 10 reads. The strategy for filtering and qualitycontrols are illustrated in Fig. 1.Statistical analysesTest for genetic association and genetic effect was per-formed for each predicted variant separately using thediscovery sample. In addition, rs8040868 and rs11728716were tested for association using the available OLINsample (replication sample) as, according to RegulomeDB,these two markers present with potential functional effectson gene regulation (Table 2). Tests for allelic associationof individual variants with COPD were performed usingthe Fisher’s exact test. Results were considered statisticallysignificant when p < 0.05. No adjustment for multipletesting was performed in these analyses. Effect size wasmeasured using odds-ratios (OR) with 95 % confidenceintervals (CI).Visualization SNPs associated with COPD located inthe CHRNA3/5 region was made using LocusZoom v1.1[14] available on http://locuszoom.sph.umich.edu/locuszoom/. RefSeq gene/transcript case–control tests for ag-gregation of genetic variants in the targeted genomic re-gions were performed using PLINK/SEQ v0.1 [15]. Testswere divided into an analysis of rare variants with minorallele frequency (MAF) < 5 % or common variants(MAF ≥ 5 %). The UNIQ test, which identifies uniquerisk alleles, was utilized using default parameters tocount the total number of alleles found only in cases(risk variants). Similarly, the SKAT burden tests,which assesses excess of rare alleles in cases comparedto controls, was also utilized. Since both UNIQ andSKAT burden are 1-sided tests, we also swapped thephenotype information and analyzed the effects inboth directions (excess of alleles in cases or controls)separately to capture evidence of both risk and pro-tective alleles. Due to the matched design of the caseand control groups no covariate adjustments (age, sex,pack-years) were performed in analysis using the dis-covery sample.Fig. 1 Flow chart of the strategy for variant calling, quality controland association tests. QC = quality controlMatsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 3 of 10Linkage disequilibrium information of associated vari-ants was extracted from genotypes from the sequencinganalysis using Haploview 4.2 [16].Functional analysis of associated variantsTo study the possible effect of associated variants on geneexpression, we used information from RegulomeDB, aTable 2 Associated genetic variants in the discovery sample set (n = 96 cases and n = 96 controls)Chr Pos (bp) SNP rs ID Alt Ref F c (%) F p (%) OR (95 % CI) P value Gene Coding RG score ExAc EurfrequencyaCADD_PHREDb1 110230569 72989301 G A 10.0 22.1 2.5 (1.0-6.2) 0.038 GSTM1 4 0.24 NA1 110233057 111436983 C T 11.6 26.7 2.8 (1.2-6.2) 0.013 GSTM1 7 0.24 NA2 216991935 12694384 A C 29.4 17.9 0.5 (0.3-0.9) 0.04 XRCC5 5 NA NA2 218669225 61741262 C T 13.6 0 NA 0.027 TNS1 p.(Asp1722Ser) 4 0.13 12.492 218746990 2303381 T A 7.7 1.5 0.2 (0.0-0.9) 0.019 TNS1 6 NA NA3 55520778 566926 T G 39.2 25.0 0.5 (0.3-0.9) 0.017 WNT5A 3a NA NA4 106638697 72671840 G A 2.3 11.4 5.3 (1.5-18.9) 5.8 × 10−3 GSTCD 6 NA NA4 106647679 72671858 T C 3.0 10.5 3.7 (1.2-11.7) 0.026 GSTCD 7 NA NA4 106755996 11728716 A G 3.4 21.0 7.4 (2.5-22.5) 6.5 × 10−5 GSTCD 1f NA NA5 58284208 3805557 C T 22.0 11.9 0.5 (0.2-0.9) 0.034 PDE4D 7 NA NA5 58284283 3805556 G A 22.0 11.8 0.5 (0.2-0.9) 0.033 PDE4D 6 0.84 NA5 58286625 1553114 C T 22.0 11.8 0.5 (0.2-0.9) 0.033 PDE4D 7 0.84 NA5 141993867 17223611 T C 10.6 3.0 0.3 (0.1-0.8) 0.025 FGF1 5 NA NA6 142703137 2143390 T C 2.9 19.2 7.9 (1.6-37.6) 4.5 × 10−3 GPR126 p.(Asp373=) 7 0.12 NA6 151197501 9322290 C T 17.4 9.0 0.5 (0.2-1.0) 0.047 MTHFD1L 5 NA NA6 151206894 147872265 T C 7.6 2.2 0.3 (0.1-1.0) 0.049 MTHFD1L 7 0.002 NA6 151263456 803451 A G 41.8 55.7 1.8 (1.1-2.9) 0.04 MTHFD1L 7 NA NA6 151264132 803448 T C 37.9 50.7 1.7 (1.0-2.7) 0.037 MTHFD1L 6 NA NA6 152183551 1643821 A G 25.8 39.0 1.8 (1.1-3.1) 0.026 ESR1 6 NA NA8 42552530 41272375 C G 1.5 6.9 4.8 (1.0-22.8) 0.034 CHRNB3 5 NA NA9 98239503 3780573 A G 18.3 8.7 0.4 (0.2-0.9) 0.041 PTCH1 5 NA NA10 81706281 6413520 G A 0.8 5.9 8.2 (1.0-66.4) 0.036 SFTPD p.(Ser45=) 5 0.07 NA10 123358096 41301039 G C 25.0 2.8 0.1 (0.0-0.7) 0.017 FGFR2 4 NA NA11 102738499 632009 T C 29.6 50.8 2.5 (1.5-4.1) 6.7 × 10−4 MMP12 7 NA NA12 23737566 11046992 A G 21.2 33.1 1.8 (1.1-3.2) 0.039 SOX5 6 NA NA12 110224916 60258652 T C 2.4 10.5 4.7 (1.0-22.9) 0.05 TRPV4 5 NA NA12 110224922 1861810 A C 36.6 53.8 2.0 (1.1-3.8) 0.04 TRPV4 5 NA NA12 110232032 59870578 A G 7.8 1.6 0.2 (0.0-0.9) 0.034 TRPV4 4 NA NA12 110232034 59940634 T G 7.8 1.6 0.2 (0.0-0.9) 0.034 TRPV4 4 NA NA15 71434029 2004101 A T 0.8 5.6 7.5 (0.9-61.6) 0.035 THSD4 7 NA NA15 78790189 2292115 G A 4.8 0 NA 0.03 IREB2 7 NA NA15 78911181 8040868 C T 31.8 47.8 2.0 (1.2-3.2) 8.8 × 10−3 CHRNA3 p.(Val53=) 1f 0.41 NA16 16130514 903880 A C 15.9 27.2 2.0 (1.1-3.6) 0.027 ABCC1 4 NA NA16 16205741 9673292 C G 4.5 0 NA 0.013 ABCC1 6 NA NA16 16230290 212087 A G 38.6 52.2 1.7 (1.1-2.8) 0.028 ABCC1 5 0.44 NA16 16235366 113328089 A G 6.9 1.5 0.2 (0.0-1.0) 0.034 ABCC1 5 NA NA20 15967390 41275442 T C 9.1 17.7 2.1 (1.0-4.5) 0.049 MACROD2 p.(Thr100Met) 4 0.18 3.98Alt, non-reference allele. Ref, reference allele, OR Odds ratio with 95 % confidence intervals, F c Frequency in controls, F p Frequency in cases (patients), RG Score,RegulomeDB score, NA Not availableaNon-Finnish European allele frequency extracted from ExAc v.0.3.1bThe “PHRED-scaled” CADD score based on ranks of all SNV in the hg19 genome referenceMatsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 4 of 10database that combines ENCODE data sets (chromatinimmunoprecipitation sequencing (ChIP-seq) peaks,DNase I hypersensitivity peaks, DNase I footprints) withadditional data sources (ChIP-seq data from the NCBI Se-quence Read Archive, conserved motifs, expression quan-titative trait loci (eQTL), and experimentally validatedfunctional variants) [17]. A scoring system is based on theconfidence of the functionality of variants, a lower scorecorresponding to stronger confidence. Subcategories areused to denote additional functional annotations. Com-bined Annotation Dependent Depletion (CADD) scoreswere used to assess potential structural and functional ef-fect of associated nonsynonymous variants [18].Lung expression quantitative trait loci analysesThe existence of expression quantitative trait loci(eQTLs) was investigated as previously described usinggenotyping and gene expression data from 1,111 patientswho underwent lung surgery at one of three sites, LavalUniversity (discovery sample), University of BritishColumbia, and University of Groningen (replicationsample sets) (referred to as Laval, UBC, and Groningen)[19, 20]. The eQTL data is derived from non-tumourlung parenchymal samples and expression data wereadjusted for age, gender, and smoking status. EstimatedP-values for each region were Bonferroni-corrected formultiple testing based on the number of SNPs and probesets (number of SNPs x number of probe sets) and wereconsidered significant if corrected p < 0.05.SNP genotyping for validation of rs11728716 andrs8040868Individuals from the OLIN replication sample (n = 1,534)were genotyped for the rs11728716 and rs8040868 vari-ants (99.2 % and 99.8 % success rate, respectively) at theUppsala Genome Center (Uppsala, Sweden) using com-mercially available TaqMan assays (Life Technologies,Carlsbad, CA). Assay conditions were according to manu-facturer’s recommendations. Effect size was estimated bycomparing ORs with 95 % CI between cases and controls.Furthermore, to assess smoking dependence, we measuredassociation and effect sizes also between the groups ‘non-smokers’ and ‘ever smokers’, and between ‘currentsmokers’ and ‘former smokers’.ResultsSelection of cases and controls for the discovery sampleThe characteristics of each sample are listed in this sec-tion as value ± standard deviation. Cases and controlswere matched for age (cases: 68 ± 10 years; controls:66 ± 11 years; p = 0.15), gender (cases: 35 females, 61males; controls: 31 females, 65 males; p = 0.65) and smok-ing habits (cases: 26 ± 19 pack/year; controls: 28 ± 12pack/year; p = 0.53). Both groups were also closelymatched for weight (cases: 76 ± 15 kg; controls: 77 ±15 kg; p = 0.85) and height (cases: 169 ± 9 cm; controls:169 ± 9 cm; p = 0.7). No non-smokers were included inthe control group to avoid false negative results. The casespresented a significant reduction in lung function consist-ent with moderate or severe COPD. This is illustrated bya reduced FVC (cases: 2.69 ± 0.95 L; controls: 3.96 ±0.90 L; p = 6.3 × 10−18), FEV1 (cases: 1.50 ± 0.59 L; con-trols: 3.11 ± 0.69 L; p = 4.0 × 10−41) and FEV1% of pre-dicted values (cases: 53 ± 14 %; controls: 110 ± 9 %;p = 2.5 × 10−81), as well as the FEV1/FVC ratio (cases:0.56 ± 0.11; controls: 0.79 ± 0.05; p = 2.5 × 10−45) wheninvestigated the fifth year of the evaluations (Table 1).Test for association between genetic variants and COPDWe identified 2,151 SNVs after analysis of the sequencedtarget regions. After variant and sample quality controlprocedures, 1588 SNVs and 68 cases and 66 controlswere retained in the downstream analysis (Fig. 1). Out ofthe 1588 variants, we identified 37 variants with signifi-cantly different allele frequencies in cases and controls(henceforth referred to as ‘associated variants’) (Table 2).We initially detected two novel variants in the discoverysample: GRCh37.p13, 5:g.157002804C >G in the ADAM19gene and GRCh37.p13, 7:g.73477874C >A in ELN. How-ever, using Sanger sequencing of the same sample weexcluded both variants, as they were monomorphic.Three of the associated variants were shown to beunique to controls including missense variant rs61741262(p.Asn1722Ser) in TNS1. The most significantly associatedvariants were all intronic (GSTCD, rs11728716, p = 6.5 ×10−5, OR = 7.4 (2.5-22.5) and MMP12, rs632009, p = 6.7 ×10−4, OR = 2.5 (1.5-4.1).Although the majority of the associated variants wereintronic (or intergenic), five were protein-coding(Table 2). Of these, two variants predicted amino-acidsubstitutions (missense variants): p.(Thr100Met) inMACROD2 and p.(Asn1722Ser) in TNS1 respectively.The p.(Asn1722Ser) variant could be potential damagingbased on a relative high CADD score or 12.49. Of thecoding variants, we found that rs2143390, predicting ap.(D373=) in GPR126 (p = 0.005, OR = 7.9 (1.6-37.6)),rs6413520 in SFTPD (p.(Ser45=), p = 0.036, OR = 8.2(1.0-66.4)), rs8040868 in CHRNA3 (p.(Val53=), p = 8.8 ×10−3, OR = 2.0 (1.2-3.2)) and rs41275442 in MACROD2(p.(Thr100Met), p = 0.049, OR = 2.1 (1.0-4.5)) conferredmoderate to high risk for COPD (Table 2).We tested the presence of variants uniquely found incases or controls as well as gene burden tests in casesagainst controls as specified in PLINK/SEQ v0.1 usingstandard settings. We noted that the ADAM19, WNT2,CHRNA5, NOS3 and PTCH1 genes all harbor rare vari-ants (MAF < 5 %) uniquely found in cases (Additionalfile 3). Conversely, the FGF8, CTNNB1 and HHIP genesMatsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 5 of 10contain rare variants uniquely found in the controlsample (Additional file 3). Neither gene burden ana-lysis (SKAT) or analysis of rare alleles (MAF < 5 %)yielded significant results. However, by performing ajoint analysis with only common alleles (MAF ≥ 5 %)in target regions using SKAT, we showed a significantgene burden for the genes GSTCD, FGF1, ELN andESR1 (Additional file 4).Haplotypes and linkage disequilibriumWe identified five regions with associated variants inpairwise LD (r2 > 0.7, D´ = 1.0). The regions were lo-cated at the GSTM1 gene locus on chromosome 1(rs72989301-rs111436983), GSTCD on chromosome 4(rs72671840-rs72671858), PDE4D on chromosome 5(rs3805557- rs3805556- rs1553114), MTHFDIL onchromosome 6 (rs803451- rs803448) and TRPV4 onchromosome 12 (rs59870578-rs59940634) (Additionalfile 5). Furthermore, the variant rs8040868 on chromo-some 15q21.1 is in pairwise LD (r2 = 0.76, D’ = 1.0) withrs16969968, a nonsynonymous variant previously asso-ciated with expression of the CHRNA5 gene [21]. Thers16969968 variant was included in our capture designbut it did not reach significant association in the OLINdiscovery sample (OR 1.6; p = 0.07) (Additional file 6).In silico analysis of predicted functions of associatedvariantsAccording to RegulomeDB, all 37 associated variantswere located within known and predicted regulatory ele-ments in intergenic regions (Table 2). We noted that avariant in CHRNA3 (rs8040868) and a variant in GSTCD(rs11728716) each showed a RegulomeDB score of “1f”,denoting the presence of transcription binding site orDNAse peak.Lung eQTL resultsAccording to RegulomeDB, the rs8040868 (CHRNA3)and rs11728716 (GSTCD) variants could present withpotential functional effects on gene regulation. To deter-mine if these variant could represent eQTL, we analysedthe genotypes and gene expression data in the discoverysample (Laval) as well as replication samples (UBC andGroningen). One of these variants, rs8040868:C > T, wasconfirmed to be significantly associated with gene ex-pression of the nearby gene CHRNA5 in all three datasets, with the C allele (minor allele) associated withlower CHRNA5 expression (Fig. 2 and Additional file 7).Interestingly, we could also see a high correlation be-tween rs8040868 and expression of an anti-sense tran-script (AF147302) of unknown function from theadjacent IREB2 gene region (data not shown). AF147302is likely a result of strong bi-directional promoter activ-ity in this region [22].The rs8040868 variant is associated with COPD in theOLIN replication sampleWe also investigated pulmonary data from the replica-tion sample (n = 1,534; cases = 256, controls = 1278).Analysis using RegulomeDB predicted both rs11728716and rs8040868 variants as being functional (score 1f forboth variants). We therefore selected these two variantsfor genotyping in all available OLIN samples (n = 1,534).The frequency of the rs8040868-C allele was 35 % inthe reference group (n = 1,278) and 42 % in the cases(n = 256) resulting in a significant association (p = 0.003)and an OR of 1.4 (95 % CI 1.1-1.7) for COPD. SweGenvariant frequency database reports a 39 % frequency ofrs8040868 in 1000 whole genomes representing a cross-section of the Swedish population (https://swefreq.nbis.se). The frequency of the homozygous rs8040868-CCFig. 2 CHRNA5 gene expression levels in the lungs according to genotype groups for rs8040868. The left y-axis represents standardised geneexpression levels in the lung with heterozygous genotype group set to zero. The x-axis represents the three genotyping groups, TT, CT and CC(risk allele C), for the variant in (a) the discovery set (Laval) n = 408; P = 4.2 × 10−10, and the replication sets (b) UBC, n = 287; P = 2.31 × 10−7, and(c) Groningen, n = 342; P = 1.5 × 10−6. The number of subjects per genotype group is indicated in parenthesis. The right y-axis shows the proportionof the gene expression variance explained by the variant (black bar)Matsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 6 of 10genotype was 12 % in the reference group and significantlyhigher (18 %) in the cases (p = 0.018). When comparingsmoking status using all genotyped individuals, nosignificant difference in allele frequency between nei-ther the groups ‘non-smokers’ (n = 589) and ‘eversmokers’ (n = 943) (OR 1.1 95 % CI 1.0-1.7; p = 0.09)nor between the groups ‘current smokers’ (n = 312)and ‘former smokers’ (n = 631) (OR 1.2 95 % CI 1.0-1.4; p = 0.11) was seen. The latter test was used to as-sess nicotine dependency and aptitude for smokingcessation under the assumption that a genetic variantassociated with these traits would be underrepresentedin a former smoking group as compared to a group ofcurrent smokers, i.e., harder to quit smoking. The testsfor association with smoking must however be takenwith caution as the confidence intervals are wide and alarger sample size would be needed for replication.Analysis of rs11728716 using the OLIN replicationsample (n = 1,534) revealed no association with COPD(p = 0.07; OR = 2.2). In order to test if rs11728716 is as-sociated with severe COPD, we stratified the availableCOPD cases based on severity and selected cases withFEV1%pred < 40 % and FEV1/FVC < LLN. Our resultsshow a significant association (p = 0.017) betweenrs11728716 and the group of severe COPD (n = 14).The allele frequency of rs11728716-A was 10 % amongcases with severe COPD and 4 % in the controls.Discussion and conclusionGenetic variants influencing lung function in childrenand adults may ultimately lead to the development ofCOPD [23]. Since limited disease-specific therapy forCOPD is available, an improved knowledge of geneticvariants modulating the pathogenic mechanisms under-lying COPD is greatly needed. We aimed here to identifygenetic variants within, or close to, the coding regions ofgenes and loci previously associated with COPD, or ingenes involved in lung development. We opted for aqualitative rather than a quantitative approach with theselection of cases with moderate or severe COPD andprogressive decline in lung function. Furthermore, con-trols were all smokers without COPD that, in our studydesign, can aid the identification of potential protectivegenetic variants and aid detection of genetic variants as-sociated with severe COPD. When applying a Bonferronicorrection for the total number of variants detected, novariants showed statistically significant association. Wedid, however, identify several variants with a likely bio-logical significance, as indicated by high effect sizes(odds ratio), that we believe warrants further investiga-tion in a larger sample. Furthermore, potential func-tional effects of variants were investigated using datafrom a large number of lung samples and we describehere a COPD lung eQTL.When comparing our association data with the lungeQTL data (discovery data set from Laval University), wecould identify a variant associated with COPD that was alsoassociated with level of gene expression (Fig. 2). This vari-ant, synonymous variant (rs8040868) in CHRNA3 onchromosome 15, confers a risk for the development ofCOPD in both our OLIN discovery sample with moderateor severe COPD and our OLIN replication sample includ-ing all available COPD cases and controls in OLIN (OR 1.4,p = 0.003). In the lung eQTL data, we could see a correl-ation of the C allele of rs8040868 with lower expressionlevels of CHRNA5 (Fig. 2), and, to a lesser extent, alsoCHRNA3 and PSMA4, which are located in close proximityto CHRNA5. The α-nicotinic receptor (CHRNA3/5) genelocus on chromosome 15q25.1 is associated with COPD,lung cancer and peripheral arterial disease, as well as othersmoking related conditions [24, 25] and nicotine addiction[26, 27]. Recently, the CHRNA3/5 locus was implicated inall-cause mortality among smokers in a Finnish cohort [2].The rs8040868-C allele associates with both reduced pul-monary function and lung cancer [24, 25, 28, 29] and af-fects DNA-methylation and transcription of CHRNA5 [30].Furthermore, rs8040868 is also in LD with a nearby variant(rs16969968) previously reported to be associated with ex-pression levels of CHRNA5 in the lung [21]. The directionof effect is the same for both SNPs, with the minor allelesassociated with reduced expression of CHRNA5. Also re-cently, rs16969968 was found to be the most significantlyassociated variant in an exome array analysis in a study in-cluding more than 6,100 COPD cases and 6,000 controlsubjects across five cohorts [31].Several genetic variants showed association withCOPD in our population, but did not correlate with geneexpression levels in the lung, including previously identi-fied variants in the genes glutathione S-transferase, c-terminal domain containing (GSTCD), surfactant proteinD (SFTPD) and matrix metalloproteinase-12 (MMP12)[32–36]. We identified a haplotype consisting of threerisk-conferring variants, rs72671840, rs72671858 andrs11728716 (G-T-A haplotype), at the GSTCD genelocus on chromosome 4q24. The variant rs11728716 haspreviously been associated with lung function [32–34]and is likely to affect the transcription of GSTCD. Weshow here that rs11728716 was associated with severeCOPD using the OLIN replication sample. Although intri-guing, due to the limited number of severe COPD casesused in this study, this result needs further verification ina larger sample. The other two variants (rs72671840 andrs72671858) are of unknown function [37]. GSTCD en-codes a glutathione S-transferase C-terminal domain pro-tein involved in detoxification by catalysing conjugation ofglutathione to products of oxidative stress. We found as-sociation between COPD and rs6413520, a synonymousvariant, p.(Ser45=), within SFTPD on chromosomeMatsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 7 of 1010q22.3. This variant conferred a high risk (OR = 8.2) forCOPD in our study and has previously been reported tobe associated with COPD susceptibility [36]. SFTPD en-codes surfactant protein D, of importance for the regula-tion of oxidant production, inflammatory responses, andapoptotic cell clearance in the lung [38]. We also identi-fied rs632009, in the MMP12 gene on chromosome11q22.3, to confer moderate risk. Matrix metalloprotein-ases (MMPs) are involved in both tissue remodelling andrepair and several members of the MMP family have beenimplicated in COPD pathology [35, 39, 40].In this study, we also found association (uncorrected)with novel susceptibility variants. Several variants in theG-protein-coupled receptor 126 (GPR126) gene onchromosome 6q24.1 have previously been associatedwith FEV1 / FVC ratio [32]. GPR126 belongs to a super-family of G protein-coupled receptors and is involved incell signalling and adhesion. Studies in mice show aninduction of Gpr126 expression between embryonicday 7 and 11 with expression in the developing heartand face as well as a high expression in the adult lung[41]. We found significant association between a syn-onymous variant in the GPR126 gene (rs2143390,p.(Asp373=)) and COPD. The alternative T allele ishighly overrepresented in cases compared to controls(p = 4.5 × 10−3, OR = 7.9).We also focused our attention to the chromosome4q31 locus upstream of HHIP, previously shown to beassociated with expression of the gene [20, 42]. TheHHIP upstream region belongs to one of the so farstrongest COPD association signals [43], but no associ-ation could be seen in our case–control groups for anyupstream variants.The sequencing approach allowed us to detect rare al-leles in both cases and controls. We therefore performedgene burden tests to find evidence of overrepresented rareor common variants in individual genes or transcripts inthe cases or controls, respectively. Interestingly, we foundthat the genes ADAM19, WNT2, CHRNA5, NOS3 andPTCH1 all contain rare variants (MAF < 5 %) uniquelyfound in cases of the OLIN discovery sample. These vari-ants, and especially the coding variants with predictedfunctional effect, could be followed up in a larger case–control sample for verification and further genetic andfunctional analysis.We assessed 83 genes and 10 genomic regions of 1.5 kbsize for variants associated with COPD in a sample fromNorthern Sweden. Still, one limitation of our study is thatthe targeted capture design may exclude yet unknown gen-omic regions that can harbour genetic variation influencingCOPD. Also, the two novel variants detected after sequen-cing were monomorphic and an assessment of the false dis-covery rate using HaloPlex with subsequent Illuminasequencing would be helpful in order to evaluate our set ofcandidate genes as a gene panel for COPD. Furthermore,we cannot rule out that some findings are influenced bypopulation substructure and replication of our result in dif-ferent populations is essential. It is also possible that somerisk variants were not identified due to the limited numberof cases and controls used for sequencing. Using a con-servative Bonferroni correction based on the 1588 variantsdetected resulted in no variants reached significant associ-ation with COPD. However, we believe there is no definiteconsensus regarding the type of multiple testing proceduresto use in targeted sequencing based approaches. Further-more, many parameters such as variant quality checks,genotyping success rate and sequencing depth limit will in-fluence the number of variants found, and consequently,multiple testing adjustments. Also, in addition to includegenes including variants previously associated with COPDor asthma, we explored if a set of genes involved in lung de-velopment would harbour variants in association withCOPD in the Swedish discovery sample. Therefore, as thestudy is exploratory with a mixed hypothesis the p valuesfor association testing in this study are not corrected formultiple testing.Despite the limited size of the discovery sample usedhere, we identified several high-risk genetic variants forCOPD and we replicated several previous GWAS results.In particular, our results support the CHRNA5 gene as alikely candidate gene for COPD where the rs8040868-Callele confers a risk for the disease in the Swedish popu-lation. Furthermore, we indicate the advantage of usingless heterogeneous populations in the studies of complexdisorders.Additional filesAdditional file 1: Supplementary methods. Detailed methodsdescription regarding classification of COPD, sequencing protocol andlinkage disequilibrium analysis. (DOCX 123 kb)Additional file 2: Targeted genes and variants for targeted highthroughput sequencing. The table lists the selected genes and variantsincluded in the study. (DOCX 79 kb)Additional file 3: Number of rare variants found uniquely in cases andcontrols. The table present data of the UNIQ test of rare variants in casesversus controls. (DOCX 78 kb)Additional file 4: Gene burden analysis of common variants. The tablepresent the SKAT analysis of gene burden of common variants. (DOCX 86 kb)Additional file 5: Pairwise linkage disequilibrium (LD) of associatedvariants. A list of detected genetic variants found to be in LD. (DOCX 46 kb)Additional file 6 A plot containing genomic positions and p-values ofvariants in the CHRNA3/CHRNA5 gene locus with rs8040869 andrs16969968 highlighted. (PDF 144 kb)Additional file 7 Probe sets replicated in both replication sets (UBC andGroningen) in the lung eQTL analyses. A table of replicated probe sets inthe lung eQTL analysis. (DOCX 38 kb)AbbreviationsChIP-seq: Chromatin immunoprecipitation sequencing; COPD: ChronicObstructive Pulmonary Disease; eQTLs: Expression quantitative trait loci;Matsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 8 of 10FEV1: Expiratory volume in 1 s FVC: forced vital capacity; GWAS: Genome-wide association studies; LLN: Lower limit of normal; OLIN: The ObstructiveLung Disease in Northern Sweden studies; SNVs: Single nucleotide variantsforced; SVC: Slow vital capacity;AcknowledgmentsThe authors would like to especially thank the participants and staff in theOLIN studies. The authors would further like to thank the staff at theRespiratory Health Network Tissue Bank of the FRQS for their valuableassistance with the lung eQTL dataset at Laval University.FundingJ. Klar is funded by Svenska Sällskapet för Medicinsk Forskning (SSMF)and Magnus Bergvalls Stiftelse (014–00163). The lung eQTL study at LavalUniversity was supported by the Chaire de pneumologie de la FondationJD Bégin de l’Université Laval, the Fondation de l’Institut universitaire decardiologie et de pneumologie de Québec, the Respiratory Health Networkof the FRQS, the Canadian Institutes of Health Research (MOP - 123369),and the Cancer Research Society and Read for the Cure. M. Lamontagneis the recipient of a doctoral studentship from the Fonds de rechercheQuébec - Santé (FRQS). Y. Bossé holds a Canada Research Chair inGenomics of Heart and Lung Diseases.Availability of data and materialsThe datasets generated and/or analysed during the current study areavailable in the European Nucleotide Archive repository as study accessionnumber PRJEB13652, http://www.ebi.ac.uk/ena/data/view/PRJEB13652.Author contributionsHM, JK and BL designed the study. HM, CS and JK drafted the manuscript. HMand JK performed DNA capture experiments and COPD association analysis. EEperformed gene burden analysis. SG performed Sanger sequencing validation.BL, HB, AL and ER contributed to sample selection and phenotypecharacterisation. ML, YB and DS conducted eQTL analyses. All authorsrevised the manuscript and approved the final version to be published.Competing interestsThe authors declare that they have no competing of interests.Ethics approval and consent to participateInformed consent of research use of spirometry data and DNA samples wereobtained from the participants in the OLIN studies that include all participants inour study presented here. The ethics board of Umeå University (Dnr 04-045 M,supplement 2005-06-13) approved the use of phenotypic and genetic data forresearch purposes. Results in this study are presented as groups withoutpersonal identifiers.Author details1Department of Biosciences and Nutrition, Karolinska Institutet, 7-9, SE-141 83Huddinge, Sweden. 2Department of Women’s and Children’s Health,Karolinska Institutet, Stockholm, Sweden. 3Molecular Neurology ResearchProgram, University of Helsinki and Folkhälsan Institute of Genetics, Helsinki,Finland. 4Institut universitaire de cardiologie et de pneumologie de Québec,Québec, Canada. 5Department of Immunology, Genetics and Pathology,Science for Life Laboratory, Uppsala University, Uppsala, Sweden.6Department of Public Health and Clinical Medicine, Division of Occupationaland Environmental Medicine, Umeå University, Umeå, Sweden. 7Departmentof Public Health and Clinical Medicine, Division of Medicine, Umeå University,Umeå, Sweden. 8The University of British Columbia Center for Heart LungInnovation, St-Paul’s Hospital, Vancouver, Canada. 9Center Groningen, GRIACresearch institute, University of Groningen, Groningen, The Netherlands.10Department of Molecular Medicine, Laval University, Québec, Canada.11Krefting Research Centre, Institute of Medicine, University of Gothenburg,Gothenburg, Sweden.Received: 9 June 2016 Accepted: 6 November 2016References1. GOLD. From the Global Strategy for the Diagnosis, Management andPrevention of COPD, Global Initiative for Chronic Obstructive Lung Disease(GOLD). 2015. http://www.goldcopd.org/. Accessed date 26 Apr 2016.2. WHO. Chronic obstructive pulmonary disease (COPD). 2015. Fact sheet N°315. http://www.who.int/en/. Accessed 27 Apr 2016.3. Eisner MD, Anthonisen N, Coultas D, Kuenzli N, Perez-Padilla R, Postma D, etal. An official American Thoracic Society public policy statement: Novel riskfactors and the global burden of chronic obstructive pulmonary disease.Am J Respir Crit Care Med. 2010;182:693–718.4. Bosse Y. Updates on the COPD gene list. Int J Chron Obstruct Pulmon Dis.2012;7:607–31.5. Klar J, Blomstrand P, Brunmark C, Badhai J, Hakansson HF, Brange CS, et al.Fibroblast growth factor 10 haploinsufficiency causes chronic obstructivepulmonary disease. J Med Genet. 2011;48:705–9.6. Silverman EK, Speizer FE. Risk factors for the development of chronicobstructive pulmonary disease. Med Clin North Am. 1996;80:501–22.7. Einarsdottir E, Egerbladh I, Beckman L, Holmberg D, Escher SA. The geneticpopulation structure of northern Sweden and its implications for mappinggenetic diseases. Hereditas. 2007;144:171–80.8. Kristiansson K, Naukkarinen J, Peltonen L. Isolated populations and complexdisease gene identification. Genome Biol. 2008;9:109.9. Lundback B, Nystrom L, Rosenhall L, Stjernberg N. Obstructive lung diseasein northern Sweden: respiratory symptoms assessed in a postal survey.Eur Respir J. 1991;4:257–66.10. Lindberg A, Lundback B. The Obstructive Lung Disease in Northern SwedenChronic Obstructive Pulmonary Disease Study: design, the first yearparticipation and mortality. Clin Respir J. 2008;2 Suppl 1:64–71.11. Bakke PS, Ronmark E, Eagan T, Pistelli F, Annesi-Maesano I, Maly M, et al.Recommendations for epidemiological studies on COPD. Eur Respir J. 2011;38:1261–77.12. Stridsman C, Mullerova H, Skar L, Lindberg A. Fatigue in COPD and theimpact of respiratory symptoms and heart disease–a population-basedstudy. COPD. 2013;10:125–32.13. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al.The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.14. Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, et al.LocusZoom: regional visualization of genome-wide association scan results.Bioinformatics. 2010;26:2336–7.15. PLINK/SEQ. O. source. 2014. https://atgu.mgh.harvard.edu/plinkseq/start-pseq.shtml. Accessed 20 Oct 2015.16. Haploview 4.2. B. Institute. 2009. http://www.broadinstitute.org/haploview/haploview. Accessed 23 Oct 2015.17. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al.Annotation of functional variation in personal genomes using RegulomeDB.Genome Res. 2012;22:1790–7.18. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A generalframework for estimating the relative pathogenicity of human geneticvariants. Nat Genet. 2014;46:310–5.19. Hao K, Bosse Y, Nickle DC, Pare PD, Postma DS, Laviolette M, et al. LungeQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet.2012;8:e1003029.20. Lamontagne M, Couture C, Postma DS, Timens W, Sin DD, Pare PD, et al.Refining susceptibility loci of chronic obstructive pulmonary disease withlung eqtls. PLoS One. 2013;8:e70220.21. Nguyen JD, Lamontagne M, Couture C, Conti M, Pare PD, Sin DD, et al.Susceptibility loci for lung cancer are associated with mRNA levels ofnearby genes in the lung. Carcinogenesis. 2014;35:2653–9.22. Uesaka M, Nishimura O, Go Y, Nakashima K, Agata K, Imamura T.Bidirectional promoters are the major source of gene activation-associatednon-coding RNAs in mammals. BMC Genomics. 2014;15:35.23. Desai TJ, Cardoso WV. Growth factors in lung development and disease:friends or foe? Respir Res. 2002;3:2.24. Pillai SG, Ge D, Zhu G, Kong X, Shianna KV, Need AC, et al. Agenome-wide association study in chronic obstructive pulmonarydisease (COPD): identification of two major susceptibility loci. PLoSGenet. 2009;5:e1000421.25. Soler Artigas M, Loth DW, Wain LV, Gharib SA, Obeidat M, Tang W, et al.Genome-wide association and large-scale follow up identifies 16 new lociinfluencing lung function. Nat Genet. 2011;43:1082–90.Matsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 9 of 1026. Cho MH, Boutaoui N, Klanderman BJ, Sylvia JS, Ziniti JP, Hersh CP, et al.Variants in FAM13A are associated with chronic obstructive pulmonarydisease. Nat Genet. 2010;42:200–2.27. Cho MH, McDonald ML, Zhou X, Mattheisen M, Castaldi PJ, Hersh CP, et al.Risk loci for chronic obstructive pulmonary disease: a genome-wideassociation study and meta-analysis. Lancet Respir Med. 2014;2:214–25.28. Hancock DB, Artigas MS, Gharib SA, Henry A, Manichaikul A, Ramasamy A, etal. Genome-wide joint meta-analysis of SNP and SNP-by-smokinginteraction identifies novel loci for pulmonary function. PLoS Genet.2012;8:e1003098.29. Scherf DB, Sarkisyan N, Jacobsson H, Claus R, Bermejo JL, Peil B, et al.Epigenetic screen identifies genotype-specific promoter DNA methylationand oncogenic potential of CHRNB4. Oncogene. 2013;32:3329–38.30. Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, Castagne R, et al.Genetics and beyond–the transcriptome of human monocytes anddisease susceptibility. PLoS One. 2010;5:e10693.31. Hobbs BD, Parker MM, Chen H, Lao T, Hardin M, Qiao D, et al. Exome ArrayAnalysis Identifies a Common Variant in IL27 Associated with ChronicObstructive Pulmonary Disease. Am J Respir Crit Care Med. 2016;194:48–57.32. Hancock DB, Eijgelsheim M, Wilk JB, Gharib SA, Loehr LR, Marciante KD, etal. Meta-analyses of genome-wide association studies identify multiple lociassociated with pulmonary function. Nat Genet. 2010;42:45–52.33. Repapi E, Sayers I, Wain LV, Burton PR, Johnson T, Obeidat M, et al.Genome-wide association study identifies five loci associated with lungfunction. Nat Genet. 2010;42:36–44.34. Soler Artigas M, Wain LV, Repapi E, Obeidat M, Sayers I, Burton PR, et al.Effect of five genetic variants associated with lung function on the risk ofchronic obstructive lung disease, and their joint effects on lung function.Am J Respir Crit Care Med. 2011;184:786–95.35. Haq I, Chappell S, Johnson SR, Lotya J, Daly L, Morgan K, et al. Associationof MMP - 12 polymorphisms with severe and very severe COPD: A casecontrol study of MMPs - 1, 9 and 12 in a European population. BMC MedGenet. 2010;11:7.36. Foreman MG, Kong X, DeMeo DL, Pillai SG, Hersh CP, Bakke P, et al.Polymorphisms in surfactant protein-D are associated with chronicobstructive pulmonary disease. Am J Respir Cell Mol Biol. 2011;44:316–22.37. Myers AJ, Gibbs JR, Webster JA, Rohrer K, Zhao A, Marlowe L, et al. A surveyof genetic human cortical gene expression. Nat Genet. 2007;39:1494–9.38. Pastva AM, Wright JR, Williams KL. Immunomodulatory roles of surfactantproteins A and D: implications in lung disease. Proc Am Thorac Soc.2007;4:252–7.39. Hunninghake GM, Cho MH, Tesfaigzi Y, Soto-Quiros ME, Avila L, Lasky-Su J,et al. MMP12, lung function, and COPD in high-risk populations. N Engl JMed. 2009;361:2599–608.40. Wallace AM, Sandford AJ. Genetic polymorphisms of matrixmetalloproteinases: functional importance in the development of chronicobstructive pulmonary disease? Am J Pharmacogenomics. 2002;2:167–75.41. Moriguchi T, Haraguchi K, Ueda N, Okada M, Furuya T, Akiyama T. DREG, adevelopmentally regulated G protein-coupled receptor containing twoconserved proteolytic cleavage sites. Genes Cells. 2004;9:549–60.42. Zhou X, Baron RM, Hardin M, Cho MH, Zielinski J, Hawrylkiewicz I, et al.Identification of a chronic obstructive pulmonary disease geneticdeterminant that regulates HHIP. Hum Mol Genet. 2012;21:1325–35.43. Wilk JB, Chen TH, Gottlieb DJ, Walter RE, Nagle MW, Brandler BJ, et al.A genome-wide association study of pulmonary function measures in theFramingham Heart Study. PLoS Genet. 2009;5:e1000429.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Matsson et al. BMC Pulmonary Medicine  (2016) 16:146 Page 10 of 10

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