Whole Genome Analysis of Venous Thromboembolism: the Trans-Omics for Precision Medicine Program

Abstract

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.

Keywords: cardiovascular diseases; genetics; genome-wide association study; single nucleotide polymorphisms; venous thromboembolism; whole genome sequencing.

Figure 1.

Manhattan Plot of Single Variant Tests in Whole Genome Sequence Analysis of VTE in TOPMed Participants The horizontal axis represents the build 38 genomic position by chromosome of each single nucleotide variant and the vertical axis records the −log10(p-value) from the association analysis. The dashed horizontal line indicates the genome-wide significance threshold p = 1×10⁻⁸. Only variants with a minor allele count (MAC) greater than 20 were included. Variants with a call rate < 90% (samples with read depth less than 10 were set to missing) in all of TOPMed freeze8 samples were excluded. Labels indicate associated gene loci.

Figure 2.

Manhattan Plots for SMMAT Rare Variant Analyses of VTE in TOPMed Participants Using Primary Filter (A) and Secondary Filter (B) The horizontal axis represents the build 38 genomic position by chromosome of each aggregate unit and the vertical axis records the -log10(p-value) from the aggregate association analysis using the SMMAT test with Wu weights. Aggregate units with a cumulative minor allele count less than 5 are not included. (A) The dashed horizontal line indicates the genome-wide Bonferroni significance threshold p = 2.6×10⁻⁶. The primary filter included high confidence loss-of-function variants based on Loss-of-Function Transcript Effect Estimator [https://github.com/konradjk/loftee]; missense variants that were predicted to be deleterious by either SIFT4G, Polyphen2_HDIV, Polyphen2_HVAR, or LRT; and synonymous variants, inframe insertions, or inframe deletions predicted to be deleterious by fathmm_XF_coding_score. (B) The dashed horizontal line indicates the genome-wide Bonferroni significance threshold p = 2.3×10⁻⁶. The secondary filter included variants that were predicted to cause frameshift, stop gain, stop lost, start lost, or that changed the splice donor or acceptor sites by Ensembl Variant effect predictor (VEP) and variants predicted to be missense by VEP.

Figure 2.

Manhattan Plots for SMMAT Rare Variant Analyses of VTE in TOPMed Participants Using Primary Filter (A) and Secondary Filter (B) The horizontal axis represents the build 38 genomic position by chromosome of each aggregate unit and the vertical axis records the -log10(p-value) from the aggregate association analysis using the SMMAT test with Wu weights. Aggregate units with a cumulative minor allele count less than 5 are not included. (A) The dashed horizontal line indicates the genome-wide Bonferroni significance threshold p = 2.6×10⁻⁶. The primary filter included high confidence loss-of-function variants based on Loss-of-Function Transcript Effect Estimator [https://github.com/konradjk/loftee]; missense variants that were predicted to be deleterious by either SIFT4G, Polyphen2_HDIV, Polyphen2_HVAR, or LRT; and synonymous variants, inframe insertions, or inframe deletions predicted to be deleterious by fathmm_XF_coding_score. (B) The dashed horizontal line indicates the genome-wide Bonferroni significance threshold p = 2.3×10⁻⁶. The secondary filter included variants that were predicted to cause frameshift, stop gain, stop lost, start lost, or that changed the splice donor or acceptor sites by Ensembl Variant effect predictor (VEP) and variants predicted to be missense by VEP.