Seyerle, Amanda A.

Background: Risk for venous thromboembolism has a strong genetic component. Whole genome sequencing from the TOPMed program (Trans-Omics for Precision Medicine) allowed us to look for new associations, particularly rare variants missed by standard genome-wide association studies. Methods: The 3793 cases and 7834 controls (11.6% of cases were individuals of African, Hispanic/Latino, or Asian ancestry) were analyzed using a single variant approach and an aggregate gene-based approach using our primary filter (included only loss-of-function and missense variants predicted to be deleterious) and our secondary filter (included all missense variants). Results: Single variant analyses identified associations at 5 known loci. Aggregate gene-based analyses identified only PROC (odds ratio, 6.2 for carriers of rare variants; P =7.4×10 −14 ) when using our primary filter. Employing our secondary variant filter led to a smaller effect size at PROC (odds ratio, 3.8; P =1.6×10 −14 ), while excluding variants found only in rare isoforms led to a larger one (odds ratio, 7.5). Different filtering strategies improved the signal for 2 other known genes: PROS1 became significant (minimum P =1.8×10 −6 with the secondary filter), while SERPINC1 did not (minimum P =4.4×10 −5 with minor allele frequency <0.0005). Results were largely the same when restricting the analyses to include only unprovoked cases; however, one novel gene, MS4A1 , became significant ( P =4.4×10 −7 using all missense variants with minor allele frequency <0.0005). Conclusions: Here, we have demonstrated the importance of using multiple variant filtering strategies, as we detected additional genes when filtering variants based on their predicted deleteriousness, frequency, and presence on the most expressed isoforms. Our primary analyses did not identify new candidate loci; thus larger follow-up studies are needed to replicate the novel MS4A1 locus and to identify additional rare variation associated with venous thromboembolism.

PR interval (PR) prolongation is a predictor of atrial fibrillation, a common cardiac arrhythmia, and is an established risk factor for pacemaker implantation, heart failure, stroke, and all-cause mortality. Previous genome-wide association studies in European (EU) and African (AA) descent populations have identified multiple loci associated with PR. However, it is unclear whether these loci are relevant in other racial groups, including Hispanic/Latinos (HL). Extending studies to include admixed populations such as HL also provides the potential to narrow regions associated with PR and identify population specific variants. Therefore, we evaluated 1,135 SNPs from four previously identified PR loci ( SCN5A, SCN10A, CAV1-CAV2, and TBX5-TBX3 ), fine-mapped on the Illumina Metabochip for association with PR. Associations were examined using linear regression assuming an additive genetic model and adjusting for global ancestry and clinical covariates (age, sex, RR interval, body mass index, height, systolic blood pressure) in 11,800 HL participants from participating studies of the Population Architecture using Genomics and Epidemiology (PAGE) consortia. Three of the four loci ( SCN5A, SCN10A, and CAV1-CAV2 ) generalized to HL populations ( P <7.1E-4 based on the number of SNPs correlated at r 2 >0.2 with EU-identified index SNPs). At each of these loci, we identified SNPs with stronger evidence of association with PR than the EU-identified index SNP when examined in the HL population ( SCN5A : rs7374540; SCN10A : rs6801957; CAV-CAV2 : rs717957). Conditional analyses also identified two additional independent SNPs at each of the three generalized loci ( SCN5A : rs3796387, rs9861242; SCN10A : rs10428132, rs7433723; CAV1-CAV2 : rs3801995, rs3807994), representing potential secondary signals. Furthermore, linkage disequilibrium (LD) patterns in Hispanics enabled the narrowing of the regions flanking the EU-identified index SNPs. For example, at the CAV1-CAV2 locus, HL LD patterns indicated that seven SNPs were in LD with rs3807989, the EU-identified index SNP, compared to 17 SNPs that were in LD with rs3807989 when applying EU LD patterns, reducing the size of the region by 71 kilobases. Finally, we examined evidence for population specific signals outside of the specific regions identified in EU populations which were associated with PR in Hispanics by focusing on SNPs uncorrelated with the EU index signals. At all four loci, multiple SNPs had evidence of association independent of the signals examined based on EU results. In conclusion, the same genetic loci that influence PR in European descent populations are also important in Hispanic populations, although we detected evidence for population specific SNPs. These results emphasize the importance of examining genetic associations in diverse populations in order to narrow the genomic interval for future functional interrogation.

Background: The Trans-Omics for Precision Medicine (TOPMed) program is performing whole-genome sequencing across multiple studies, including longitudinal cohorts, case-control, and case-only sample sets. Detecting the effects of low frequency variants requires large sample sizes, which can only be achieved by combining data across diverse study designs, including matching of case-only sample sets with controls from other studies. Here we present a strategy for combined analysis of venous thromboembolism (VTE) case status in 3 longitudinal cohorts and 3 case-only sample sets, in the context of whole-genome association studies. Methods: For each cohort study, we sampled ‘pure’ controls (without replacement) from the risk set of each incident case, within strata defined by sex, ancestry and birth-cohort. Pure controls had no event throughout their period of observation. A case’s risk set was defined as controls with no prior VTE history and under observation through an age at least as old as the case. For case-only sample sets, controls for each case were sampled from a cohort study, using the same risk set definition. Because of limited overlap in birth years between the cohort studies and the case-only sample sets, this matching was done within strata defined only by sex and ancestry group. Mixed model logistic regression will be used to account for relatedness as a random effect. Although conditional logistic regression is not practical for whole-genome association studies, case-control matching is implicitly recognized by a fixed effect for age-at-event for each matched set (1 case and >=1 matched controls). Additional fixed effects will include sample set and sex. We will also adjust for variations in case-control ratio among the matched sets. Results: In two cohort studies, we matched 1,231 cases to 4,820 controls (overall ratio = 1:3.9); only 500 controls and 5 cases could not be matched. For the case-only sample sets we matched 2,141 cases to 2,780 controls from one cohort study (overall ratio = 1:1.3); zero controls and only 14 cases could not be matched. Conclusions: We were successfully able to match nearly all cases to controls, and more than 90% of controls were also matched. By matching controls to cases based on age at event, we can account for the different risk of VTE by age clusters. Although this strategy does not provide an asymptotically unbiased estimated of the hazard ratio, compared to classical risk set sampling, it uses a large portion of the available data, it provides odds ratio estimates yielding the correct sign of association, and it reduces the potential influence of resampling subjects with rare variants. This strategy also enables combined analysis of multiple studies with different designs.