Supplementary Information for Variants in FAM13A are associated with chronic obstructive pulmonary disease

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1 Supplementary Information for Variants in FAM13A are associated with chronic obstructive pulmonary disease Michael H. Cho 1,2, Nadia Boutaoui 1, Barbara J. Klanderman 1, Jody S. Sylvia 1, John P. Ziniti 1, Craig P. Hersh 1,2, Dawn L. DeMeo 1,2, Gary M. Hunninghake 1,2, Augusto A. Litonjua 1,2, David Sparrow 3, Christoph Lange 4, Sungho Won 4, James R. Murphy 5, Terri H. Beaty 6, Elizabeth A. Regan 5, Barry J. Make 5, John E. Hokanson 7, James D. Crapo 5, Xiangyang Kong 8, Wayne H. Anderson 9, Ruth Tal-Singer 8, David A. Lomas 10, Per Bakke 11, Amund Gulsvik 11, Sreekumar G. Pillai 9,12, Edwin K. Silverman 1,2 1 Channing Laboratory and 2 Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women s Hospital and Harvard Medical School, Boston, MA, USA; 3 Veterans Administration Boston Healthcare System and Boston University Schools of Public Health and Medicine, Boston, MA, USA; 4 Harvard School of Public Health, Harvard University, Boston, MA, USA; 5 National Jewish Medical and Research Center, Denver, CO, USA; 6 Johns Hopkins School of Public Health, Baltimore, MD, USA; 7 Colorado School of Public Health, University of Colorado Denver, Aurora, CO, USA; GlaxoSmithKline Research and Development, 8 King of Prussia, PA, and 9 Research Triangle Park, NC, USA; 10 Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK; 11 Haukeland University Hospital and Institute of Medicine, University of Bergen, Bergen, Norway; 12 Current affiliation: Hoffman La Roche, Nutley, NJ. 1

2 Supplementary Methods Study Populations Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE; SCO104960, NCT ): Details of the ECLIPSE study have been described previously 1. Briefly, ECLIPSE is a 3-year longitudinal study of COPD. Cases were diagnosed by post-bronchodilator spirometry 2 as GOLD Stage II or greater (forced expiratory volume in 1 second (FEV 1 ) < 80% predicted and FEV 1 / forced vital capacity (FVC) < 0.7) and without severe alpha-1 antitrypsin deficiency; smoking controls had normal lung function (postbronchodilator FEV 1 >85% predicted and FEV 1 /FVC > 0.7). Both cases and controls were self-reported white ethnicity, between ages and were required to have at least a 10 pack-year smoking history. As the study was designed to evaluate COPD-related endpoints, recruitment was weighted towards cases; a total of 1839 cases and 196 smoking controls were genotyped. National Emphysema Treatment Trial (NETT): Details of the National Emphysema Treatment Trial, a clinical trial to evaluate lung volume reduction surgery, have been published previously 3. NETT subjects were enrolled at 17 clinical centers based on severe airflow obstruction by spirometry 2 (FEV 1 <45% predicted 4 ) and emphysema on computed tomographic (CT) imaging of the chest. The NETT Genetics Ancillary Study contains a subset of the original cohort with blood available for genotyping. After providing written informed consent, NETT participants provided a blood sample for DNA extraction for genetic studies of COPD. The study was approved by the institutional review boards at participating NETT centers. A total of 382 selfreported white subjects without severe alpha-1 antitrypsin deficiency were included in this study. Normative Aging Study (NAS): The Normative Aging Study is an ongoing longitudinal study of healthy men established in 1963 and conducted by the Veterans Administration (VA) 5. Briefly, men from the greater Boston area, ages 21 to 80 years, enrolled in the study after an initial health screening determined that they were free of known chronic medical conditions. Since enrollment, the participants have undergone comprehensive clinical examinations at 5-year intervals for those < 52 years old and at 3-year intervals for those > 52 years old. Selection of controls for COPD genetic studies from this population has been described previously 6 ; self-reported white control subjects had DNA available for genotyping, a history of at least 10 pack-years of cigarette smoking and no evidence for airflow obstruction by spirometry 2 at their most recent visit (FEV 1 > 80% predicted 7 and FEV 1 /FVC > 90% predicted 4. Anonymized data were used, as approved by the institutional review boards of Partners Healthcare System and the Boston VA. A total of 453 subjects meeting enrollment criteria were included in this study. Norway: Details on the Bergen, Norway case-control study have been described previously 8. Subjects were recruited from Bergen, Norway, and in contrast to the NAS and ECLIPSE studies, were required to have > 2.5 pack years of smoking history. GOLD Stage II or greater COPD cases were diagnosed by post-bronchodilator spirometry 2 (FEV 1 < 80% predicted and FEV 1 /FVC < 0.7), while controls had normal spirometry (FEV 1 > 80% predicted and FEV 1 /FVC > 0.7). Subjects with alpha-1 antitrypsin genotypes PI ZZ, ZNull, Null-Null or SZ were excluded. All subjects gave informed consent, and the appropriate institutional review boards approved the study. A total of 933 cases and 919 controls of self-identified white ethnicity were included in this study. COPDGene: COPDGene ( is an ongoing 21-center observational study to identify the genetic risk factors of COPD. The primary inclusion criteria are self-identified racial/ethnic category as either non-hispanic white or African-American between ages of 45 and 80 years. Cases are diagnosed by postbronchodilator spirometry 2,9 as GOLD Stage II or greater (FEV 1 < 80% predicted and FEV 1 /FVC < 0.7) ; smoking controls have normal spirometry. Both cases and controls are between ages 45 and 80 and have at least 10 pack-years of smoking history, and can be current or former smokers. The study protocol includes pre- and post-bronchodilator spirometry performed using a standardized protocol and spirometer (ndd EasyOne Spirometer, Zurich, Switzerland), a modified American Thoracic Society (ATS) Respiratory Epidemiology Questionnaire, and a blood sample for DNA. This study was approved by the appropriate institutional review boards. For our replication analysis, a subset of 1006 Non-Hispanic white subjects (502 cases and 504 controls) was chosen. 2

3 International COPD Genetics Network (ICGN): Subjects in the family-based ICGN study have been described previously 8. Briefly, subjects with known COPD were recruited as probands, and siblings and available parents were ascertained through the probands. Inclusion criteria for probands were airflow limitation (postbronchodilator FEV 1 < 60% predicted and FEV 1 /VC (vital capacity) < 90% predicted) at a relatively early age (45 to 65 years), smoking history > 5 pack-years, and at least one eligible sibling (with > 5 pack-year smoking history), without severe alpha-1 antitrypsin deficiency. COPD in siblings was defined by a post-bronchodilator FEV 1 < 80% predicted and FEV1/VC< 90% predicted. After removing subjects that overlapped with the ECLIPSE study, a total of 983 probands and 1876 siblings were available for analysis from this study. Boston Early-Onset COPD Study (EOCOPD): Details of the Boston Early-Onset COPD Study have been published previously 10. Briefly, EOCOPD is an extended-pedigree study of genetic susceptibility to COPD; probands were ascertained on the basis of the presence of severe COPD, defined as FEV 1 < 40% predicted before age 53 in the absence of severe alpha-1 antitrypsin deficiency. All family members were invited to participate, resulting in 949 total subjects included from 127 families. For this analysis, COPD was defined as GOLD Stage II or greater based on post-bronchodilator spirometry 2 (FEV 1 < 80% predicted and FEV 1 /FVC < 0.7). Genotyping and Quality Control For the NETT/NAS cohort, genotyping was performed on the Illumina Quad 610 (Illumina, Inc; San Diego, CA) at the Channing Laboratory, Brigham and Women s Hospital. Cases and controls were randomly allocated in batches, which included at least one replicate sample. Genotyping for the Norway cohort was performed on the HumanHap 550 (V1, V3, and Duo) and for ECLIPSE on the HumanHap 550 V3, both at outside institutions. Two channel intensities from each cohort were brought into Beadstudio workspaces, and reclustering was performed using project samples. Subjects with a call rate of < 95% and SNPs with a call frequency < 95% were a priori removed. The remaining markers were then cleaned following Illumina guidelines ( SNPs with a cluster separation below 0.3 were manually inspected, with a threshold set for each workspace below which no SNPs were deemed unambiguous. We additionally examined SNPs with a low ABR mean, ABT mean, and heterozygote excess. Finally, X, Y, and mitochondrial SNPs were manually inspected for inappropriate heterozygous calls. Manual SNP reviews and reclustering were performed by the same four operators, with one operator (NB) reviewing all manual calls from all cohorts. Manual reviews were also performed for the cluster plots from the top association results reported in this manuscript. Subject and Marker Cleaning BeadStudio workspaces were exported to ped file format and further quality control assessment was performed using Python ( and R ( scripts in conjunction with PLINK (1.05) 11. Quality control of genotyping data was assessed by subject and by marker. Subject data were excluded after examining missingness, reproducibility and discordances, relatedness, sex, and inbreeding. All subjects with a genotype call rate of < 95% were removed. For the Norway cohort, in which different workspaces were used, this call rate was calculated based on the shared set of markers. Subjects with discordance > 1% in replicate genotyping were discarded. For the NETT and NAS cohorts, for whom we had prior genotyping data on multiple platforms, those samples with discordances of > 5% compared with previous genotyping were discarded. Relatedness was examined using rggrr 12 and estimated IBD in PLINK, using a cutoff of Within each set of related subjects, unrelated individual(s) were chosen based on phenotypic criteria. In addition to testing for relatedness within each cohort, a test for relatedness between cohorts was performed prior to construction of the primary (merged) dataset. Sex assignment was based on X homozygosity estimates, with those > 0.8 as male, and < 0.2 as female. Discordant samples were removed. Inbreeding coefficients > 0.2 were removed. Finally, among samples run in replicate, the sample with the higher passing genotype rate was chosen. Markers were excluded based on missingness, minor allele frequency, reproducibility, and Hardy- Weinberg equilibrium. Markers with missingness > 5% were excluded. A strict minor allele frequency cutoff was not specified; instead, markers that were monoallelic or singleton in each dataset were excluded. Within 3

4 replicates, SNPs showing significant discordance (examining the distribution of discordances) were excluded. Markers with extreme Hardy-Weinberg deviation P value less than 10-8 were also excluded. Differential missingness between cases and controls was assessed, but not used to exclude markers at this stage. A summary of the quality control measures is shown in Supplementary Table 1 and Supplementary Figure 1. Population Stratification An unadjusted association analysis for case-control status on the primary dataset revealed a genomic inflation factor of 1.24, consistent with a modest degree of population stratification. We generated a pruned SNP dataset using a r 2 of 0.1, a window size of 1500 and step size of 150, as implemented in PLINK 11. We included only autosomal SNPs with a minor allele frequency of at least 0.01 and Hardy-Weinberg equilibrium P value > In addition, we excluded regions of long-range LD 13. We generated principal components on our cohort and HapMap Phase 3 subjects, and confirmed that the vast majority of subjects matched with the CEU population. We subsequently generated principal components for our cohort and confirmed these and other outliers were removed, using default settings in EIGENSTRAT (EIGENSOFT Version 2.0) 14 ; 6 iterations of deviations of 6 sigma and above, along the top 10 principal components). We used the Tracy-Widom statistic 15 and changes in SNP P value rank with the addition of principal components as guides to determine the number of principal components to include. We examined SNP loadings using Q-Q plots and plots by distance to examine for significant deviations from the norm, or contiguous SNPs with high loadings, respectively. Deviations in either of these plots were examined in relation to an expanded range of long-range LD 16 potentially removed, and re-analyzed. The same procedure for generation of principal components and outlier removal was applied to the primary (merged) dataset, and separately for each cohort, prior to association analysis. For the primary analysis, a total of 81 subjects (29 subjects from ECLIPSE, 28 from NETT/NAS, and 24 from Norway) were excluded as principal component outliers, leaving a total of 4320 (98% of the original set) subjects in the primary analysis. Based on the Tracy-Widom statistic and change in ranks, a total of 16 principal components were retained for analysis. Statistical Analysis Genome-wide association analyses were performed in PLINK 11 version 1.05 (pngu.mgh.harvard.edu/~purcell/plink/), using trend tests for unadjusted analysis, and logistic regression under an additive model with models adjusted for age, pack-years of cigarette smoking, and population-based principal components. For the stratified analysis, results from case-control studies were combined using Z- scores for weighting by the inverse variance. Statistical heterogeneity and overall effect size were assessed using I 2 17 and fixed effects models in the meta and rmeta packages in R ( Linkage disequilibrium was assessed using Haploview 4.1 ( 18. To investigate the relationship of other smoking-related covariates with the SNP of interest, logistic regression was also performed adjusting for average number of cigarettes smoked per day (as a measurement of addiction) or current cigarette smoking (excluding the NETT cohort, which did not include current smokers) in addition to principal components for genetic ancestry and age. Association of SNPs with pack-years of cigarette smoking was assessed using a linear regression, adjusting for age and principal components, in cases and in controls. The odds ratio for pack-years of smoking was obtained from a logistic regression model of case-control status without adjustment for other covariates. To test for an interaction between pack-years and the genotype, we added a cross-product term to the regression model. Replication SNPs for replication in COPDGene were chosen from among the top P values in the primary analysis, excluding rs (r 2 with rs13180, 0.99) and rs (r 2 with rs , 0.99). In the family-based cohorts, only rs and rs were genotyped. SNPs were genotyped in all three replication populations via the 5 to 3 exonuclease TaqMan (Applied Biosystems, Foster City, CA) method using ABI Pre-Designed SNP Genotyping Assays as per standard protocol. Genotype completion rates were > 97.5% across all loci, and there were no discordances. For the COPDGene case-control analysis, association of SNPs with COPD affection status was assessed using logistic regression under an additive model adjusting for 4

5 age and pack-years of cigarette smoking. To assess for evidence of population stratification across the COPDGene cohort, a set of 75 ancestry informative markers was genotyped. Across this set of markers, there was no evidence of population stratification (λ = , χ 2 = 0.36) 20. Family-based analyses were performed using PBAT , adjusted for age and pack-years of smoking in the COPD affection status analysis, and for age, pack-years, sex, and height in the analysis of FEV 1. P values reported for replication cohorts are onesided; overall P values combining the case-control and family-based studies were calculated via a weighted Z- score method, using total sample size or number of informative families, and alternatively, using Fisher s method 22,23. 5

6 Supplementary Note Supplementary Results Further examination of the role of FAM13A SNPs and cigarette smoking showed that the effect of rs was similar when adjusting for other smoking-related covariates of average number of cigarettes smoked per day or current cigarette smoking status (excluding the NETT cohort) (P value=4.28x10-8 and 1.12x10-10, respectively) as in the analysis with adjustment for pack-years of smoking. The relationship of the FAM13A rs SNP with pack-years of cigarette smoking was not significant in cases or controls in the primary analysis (P=0.29 and P=0.32 respectively) or the COPDGene replication cohort (P=0.96 and P=0.57). A test for interaction of pack-years of smoking and rs was also not statistically significant in the primary analysis (P=0.14) or the COPDGene, EOCOPD, or ICGN replication cohorts (P=0.47, 0.09, 0.11, respectively). Tests for statistical heterogeneity in the stratified analysis revealed that, in contrast to the SNPs in FAM13A, there was evidence of heterogeneity in the HHIP locus SNPs, and for rs and rs at the CHRNA3/CHRNA5/IREB2 locus (I 2 = 0.30 to 0.54). Specifically, in the HHIP locus, there was evidence for a stronger effect in the NETT/NAS cohort and a weaker effect in the Norway cohort; for rs and rs , there was no association in the ECLIPSE cohort (Supplementary Table 4b). Supplementary Acknowledgements The National Emphysema Treatment Trial was supported by the National Heart, Lung, and Blood Institute contracts N01HR76101, N01HR76102, N01HR76103, N01HR76104, N01HR76105, N01HR76106, N01HR76107, N01HR76108, N01HR76109, N01HR76110, N01HR76111, N01HR76112, N01HR76113, N01HR76114, N01HR76115, N01HR76116, N01HR76118, and N01HR International COPD Genetics Network (ICGN): In addition to Drs Lomas, Make, and Silverman, ICGN investigators included: Alvar G. Agusti, Hospital Universitari Son Dureta, Fundación Caubet-Cimera and Ciber Enfermedades Respiratorias, Spain. Peter M.A. Calverley, University of Liverpool, U.K; Claudio F. Donner, Division of Pulmonary Disease, S. Maugeri Foundation, Veruno (NO), Italy; Robert D. Levy, University of British Columbia, Vancouver, Canada; Peter D. Paré, University of British Columbia, Vancouver, Canada; Stephen Rennard, Section of Pulmonary & Critical Care, University of Nebraska Medical Center, Omaha, NE, United States; Jørgen Vestbo, Department of Cardiology and Respiratory Medicine, Hvidovre Hospital, Copenhagen, Denmark; Emiel F.M. Wouters, University Hospital Maastricht, Netherlands. We acknowledge the co-investigators in the NETT Genetics Ancillary Study including Joshua Benditt, Gerard Criner, Malcolm DeCamp, Philip Diaz, Mark Ginsburg, Larry Kaiser, Marcia Katz, Mark Krasna, Neil MacIntyre, Barry Make, Rob McKenna, Fernando Martinez, Zab Mosenifar, John Reilly, Andrew Ries, Paul Scanlon, Frank Sciurba, and James Utz. We thank Inga-Cecilie Sørheim and Emily S. Wan for their assistance in genotyping quality control. 6

7 Supplementary References 1. Vestbo, J. et al. Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). Eur Respir J 31, (2008). 2. Standardization of Spirometry, 1994 Update. American Thoracic Society. Am J Respir Crit Care Med 152, (1995). 3. Fishman, A. et al. A randomized trial comparing lung-volume-reduction surgery with medical therapy for severe emphysema. N Engl J Med 348, (2003). 4. Crapo, R.O. & Morris, A.H. Standardized single breath normal values for carbon monoxide diffusing capacity. Am Rev Respir Dis 123, (1981). 5. Bell, B., Rose, C. & Damon, H. The Normative Aging Study: an interdisciplinary and longitudinal study of health and aging.. Aging Hum Dev 3, 5-17 (1972). 6. Hersh, C.P. et al. Attempted replication of reported chronic obstructive pulmonary disease candidate gene associations. Am J Respir Cell Mol Biol 33, 71-8 (2005). 7. O'Connor, G.T., Sparrow, D. & Weiss, S.T. A prospective longitudinal study of methacholine airway responsiveness as a predictor of pulmonary-function decline: the Normative Aging Study. Am J Respir Crit Care Med 152, (1995). 8. Zhu, G. et al. The SERPINE2 gene is associated with chronic obstructive pulmonary disease in two large populations. Am J Respir Crit Care Med 176, (2007). 9. Miller, M.R. et al. Standardisation of spirometry. Eur Respir J 26, (2005). 10. Silverman, E.K. et al. Genetic epidemiology of severe, early-onset chronic obstructive pulmonary disease. Risk to relatives for airflow obstruction and chronic bronchitis. Am J Respir Crit Care Med 157, (1998). 11. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, (2007). 12. Abecasis, G.R., Cherny, S.S., Cookson, W.O. & Cardon, L.R. GRR: graphical representation of relationship errors. Bioinformatics 17, (2001). 13. Fellay, J. et al. A whole-genome association study of major determinants for host control of HIV-1. Science 317, (2007). 14. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, (2006). 15. Patterson, N., Price, A.L. & Reich, D. Population structure and eigenanalysis. PLoS Genet 2, e190 (2006). 16. Price, A.L. et al. Long-range LD can confound genome scans in admixed populations. Am J Hum Genet 83, 132-5; author reply (2008). 17. Higgins, J.P., Thompson, S.G., Deeks, J.J. & Altman, D.G. Measuring inconsistency in meta-analyses. BMJ 327, (2003). 18. Barrett, J.C., Fry, B., Maller, J. & Daly, M.J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21, (2005). 19. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, (1999). 20. Pritchard, J.K. & Rosenberg, N.A. Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 65, (1999). 21. Lange, C., DeMeo, D., Silverman, E.K., Weiss, S.T. & Laird, N.M. PBAT: tools for family-based association studies. Am J Hum Genet 74, (2004). 22. van Zwet, W.R. & Oosterhoff, J. On the Combination of Independent Test Statistics. The Annals of Mathematical Statistics 38, (1967). 23. Won, S., Morris, N., Lu, Q. & Elston, R.C. Choosing an optimal method to combine P-values. Stat Med 28, (2009). 7

8 Supplementary Table 1: Summary of quality control results for each of the three datasets ECLIPSE NETT/NAS Norway Illumina Platform Human Hap550v3 Quad610 Human Hap550 V1,V3,DUO Genotyped Subjects Concordance rate in 99.9% / >99.9% >99.9% / >99.9% 99.8% / 99.9% replicates (mean / median) Missingness > 5% Discordance Relatedness Gender mismatch Passing subjects Genotyped markers Beadstudio QC / Missingness > 5% Minor allele frequency Reproducibility HWE Passing markers

9 Supplementary Table 2: Baseline Characteristics Supplementary Table 2a: Baseline characteristics for subjects passing quality control and principal components outlier removal in the primary analysis. Means for age, pack-years and FEV 1 percent predicted (post-bronchodilator for all cohorts except for the NAS controls), and FEV 1 /FVC ratio are shown. ECLIPSE NETT NAS Norway Primary (Merged) Cases Controls Cases Controls Cases Controls Cases Controls N Sex (# male) Age Pack-years FEV 1, % predicted FEV 1 /FVC Supplementary Table 2b: Baseline characteristics for subjects used in replication. Means for age, pack-years and post-bronchodilator FEV 1 percent predicted. COPDGene EOCOPD ICGN Cases Controls Probands Relatives Probands Relatives N Sex (# male) Age Pack-years FEV 1, % predicted FEV 1 /FVC

10 Supplementary Table 3: Individual and stratified analysis in each of the three GWAS cohorts for SNPs in FAM13A. Locus SNP ECLIPSE NETT-NAS Norway Stratified MAF OR (95% CI) P MAF OR (95% CI) P MAF OR (95% CI) P OR (95% CI) P FAM13A rs ( ) 9.06x ( ) 1.36 x ( ) 4.31x ( ) 7.74x10-8 rs ( ) 7.75 x ( ) 2.69 x ( ) 7.94 x ( ) 4.11x10-8 rs ( ) 4.43 x ( ) 7.60 x ( ) 4.67 x ( ) 1.80x

11 Supplementary Table 4: Additional Top Loci Supplementary Table 4a. Additional top loci for the genome-wide association study in the primary analysis, with results in the COPDGene replication cohort. Locus Primary COPD Gene Primary + COPDGene SNP Location Minor allele MAF OR (95% CI) P value Rank MAF OR (95% CI) P value OR (95% CI) P value CHRNA3/ CHRNA5/IREB2 rs : C ( ) 4.81x rs : C ( ) 5.01x ( ) ( ) 2.01x10-8 rs : C ( ) 5.37x ( ) ( ) 8.32x10-8 HHIP rs : A ( ) 2.80x ( ) ( ) 3.99x10-7 rs : G ( ) 8.38x ( ) ( ) 6.68x10-7 rs : G ( ) 9.24x ( ) ( ) 5.03x10-7 rs : C ( ) 9.87x Supplementary Table 4b: Additional top loci in primary analysis shown as individual analyses in each of the three studies, as well as combined P values for the stratified analyses. Locus SNP ECLIPSE NETT-NAS Norway ECLIPSE NETT-NAS Norway ECLIPSE NETT-NAS Norway COPDGene MAF OR (95% CI) P MAF OR (95% CI) P MAF OR (95% CI) P OR P OR (95% CI) P CHRNA3/ CHRNA5/IREB2 rs ( ) ( ) ( ) 9.95x ( ) 3.42x10-5 rs ( ) ( ) ( ) 7.90x ( ) 4.29x ( ) 1.63x10-6 rs ( ) ( ) 8.69x ( ) 1.51x ( ) 3.88x ( ) 5.31x10-6 rs ( ) ( ) ( ) 4.32x ( ) 1.69x ( ) 2.14x10-5 HHIP rs ( ) 8.33x ( ) 6.70x ( ) 2.44x ( ) 6.61x ( ) 1.18x10-7 rs ( ) ( ) 6.94x ( ) 2.32x ( ) 8.44x ( ) 9.30x10-8 rs ( ) ( ) 6.05x ( ) 2.46x ( ) 7.52x

12 Supplementary Figure 1: Summary of Quality Control Workflow 12

13 Supplementary Figure 2: Q-Q plot of the primary analysis, after adjusting for population stratification, pack-years of smoking, and age. 13

14 Supplementary Figure 3: Manhattan plot of P values for the primary analysis, showing the top 2 SNPs at each locus. 14