Background

Genotypes generated in next generation sequencing studies contain errors which can significantly impact the power to detect signals in common and rare variant association tests. These genotyping errors are not explicitly filtered by the standard GATK Variant Quality Score Recalibration (VQSR) tool and thus remain a source of errors in whole exome sequencing (WES) projects that follow GATK's recommended best practices. Therefore, additional data filtering methods are required to effectively remove these errors before performing association analyses with complex phenotypes. Here we empirically derive thresholds for genotype and variant filters that, when used in conjunction with the VQSR tool, achieve higher data quality than when using VQSR alone.

Results

The detailed filtering strategies improve the concordance of sequenced genotypes with array genotypes from 99.33% to 99.77%; improve the percent of discordant genotypes removed from 10.5% to 69.5%; and improve the Ti/Tv ratio from 2.63 to 2.75. We also demonstrate that managing batch effects by separating samples based on different target capture and sequencing chemistry protocols results in a final data set containing 40.9% more high-quality variants. In addition, imputation is an important component of WES studies and is used to estimate common variant genotypes to generate additional markers for association analyses. As such, we demonstrate filtering methods for imputed data that improve genotype concordance from 79.3% to 99.8% while removing 99.5% of discordant genotypes.

Conclusions

The described filtering methods are advantageous for large population-based WES studies designed to identify common and rare variation associated with complex diseases. Compared to data processed through standard practices, these strategies result in substantially higher quality data for common and rare association analyses.

Background

Genetic variation can be used to study causal relationships between biomarkers and diseases. Here, we identify new common and rare genetic variants associated with cardiovascular-related protein levels (protein quantitative trait loci [pQTLs]). We functionally annotate these pQTLs, predict and experimentally confirm a novel molecular interaction, and determine which pQTLs are associated with diseases and physiological phenotypes.

Methods and results

As part of a larger case-control study of venous thromboembolism, serum levels of 51 proteins implicated in cardiovascular diseases were measured in 330 individuals from the Tromsø Study. Exonic genetic variation near each protein's respective gene (cis) was identified using sequencing and arrays. Using single site and gene-based tests, we identified 27 genetic associations between pQTLs and the serum levels of 20 proteins: 14 associated with common variation in cis, of which 6 are novel (ie, not previously reported); 7 associations with rare variants in cis, of which 4 are novel; and 6 associations in trans. Of the 20 proteins, 15 were associated with single sites and 7 with rare variants. cis-pQTLs for kallikrein and F12 also show trans associations for proteins (uPAR, kininogen) known to be cleaved by kallikrein and with NTproBNP. We experimentally demonstrate that kallikrein can cleave proBNP (NTproBNP precursor) in vitro. Nine of the pQTLs have previously identified associations with 17 disease and physiological phenotypes.

Conclusions

We have identified cis and trans genetic variation associated with the serum levels of 20 proteins and utilized these pQTLs to study molecular mechanisms underlying disease and physiological phenotypes.

Venous thromboembolism (VTE) is a frequent complication in patients with cancer. Homozygous carriers of the fibrinogen gamma gene (FGG) rs2066865 have a moderately increased risk of VTE, but the effect of the FGG variant in cancer is unknown. We aimed to investigate the effect of the FGG variant and active cancer on the risk of VTE. Cases with incident VTE (n=640) and a randomly selected age-weighted sub-cohort (n=3,734) were derived from a population-based cohort (the Tromsø study). Cox-regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for VTE according to categories of cancer and FGG In those without cancer, homozygosity at the FGG variant was associated with a 70% (HR 1.7, 95% CI: 1.2-2.3) increased risk of VTE compared to non-carriers. Cancer patients homozygous for the FGG variant had a two-fold (HR 2.0, 95% CI: 1.1-3.6) higher risk of VTE than cancer patients without the variant. Moreover, the six-months cumulative incidence of VTE among cancer patients was 6.4% (95% CI: 3.5-11.6) in homozygous carriers of FGG and 3.1% (95% CI: 2.3-4.7) in those without risk alleles. A synergistic effect was observed between rs2066865 and active cancer on the risk of VTE (synergy index: 1.81, 95% CI: 1.02-3.21, attributable proportion: 0.43, 95% CI: 0.11-0.74). In conclusion, homozygosity at the FGG variant and active cancer yielded a synergistic effect on the risk of VTE.

Venous thromboembolism occurs frequently in cancer patients. Two variants in the factor 5 gene (F5), rs6025 encoding for the factor V Leiden mutation R506Q, and rs4524 encoding K858R, have been found to be associated with venous thromboembolism. We assessed the joint effect of active cancer and these two F5 variants on venous thromboembolism risk in a case-cohort study. Cases with a first venous thromboembolism (n=609) and a randomly selected age-weighted cohort (n=1,691) were sampled from the general population in Tromsø, Norway. Venous thromboembolism was classified as cancer-related if it occurred in the period 6 months before to 2 years after a diagnosis of cancer. Active cancer was associated with an 8.9-fold higher risk of venous thromboembolism (95% CI 7.2-10.9). The risk of cancer-related venous thromboembolism was 16.7-fold (95% CI 9.9-28.0) higher in subjects heterozygous for rs6025 compared with non-carriers of this variant without active cancer. In subjects with active cancer the risk of venous thromboembolism was 15.9-fold higher (95% CI 9.1-27.9) in those with one risk allele at rs4524, and 21.1-fold (95% CI 12.4-35.8) higher in those with two risk alleles compared with non-carriers without active cancer. A synergistic interaction was observed between active cancer and factor V Leiden (relative excess risk due to interaction 7.0; 95% CI 0.5-14.4) and rs4524 (relative excess risk due to interaction 15.0; 95% CI 7.5-29.2). The incidence of venous thromboembolism during the initial 6 months following a diagnosis of cancer was particularly high in subjects with risk alleles at these loci. This implies that the combination of cancer and F5 variants synergistically increases venous thromboembolism risk.

Background

Much controversy surrounds the association of traditional cardiovascular disease risk factors with venous thromboembolism (VTE).

Methods

We performed an individual level random-effect meta-analysis including 9 prospective studies with measured baseline cardiovascular disease risk factors and validated VTE events. Definitions were harmonized across studies. Traditional cardiovascular disease risk factors were modeled categorically and continuously using restricted cubic splines. Estimates were obtained for overall VTE, provoked VTE (ie, VTE occurring in the presence of 1 or more established VTE risk factors), and unprovoked VTE, pulmonary embolism, and deep-vein thrombosis.

Results

The studies included 244 865 participants with 4910 VTE events occurring during a mean follow-up of 4.7 to 19.7 years per study. Age, sex, and body mass index-adjusted hazard ratios for overall VTE were 0.98 (95% confidence interval [CI]: 0.89-1.07) for hypertension, 0.97 (95% CI: 0.88-1.08) for hyperlipidemia, 1.01 (95% CI: 0.89-1.15) for diabetes mellitus, and 1.19 (95% CI: 1.08-1.32) for current smoking. After full adjustment, these estimates were numerically similar. When modeled continuously, an inverse association was observed for systolic blood pressure (hazard ratio=0.79 [95% CI: 0.68-0.92] at systolic blood pressure 160 vs 110 mm Hg) but not for diastolic blood pressure or lipid measures with VTE. An important finding from VTE subtype analyses was that cigarette smoking was associated with provoked but not unprovoked VTE. Fully adjusted hazard ratios for the associations of current smoking with provoked and unprovoked VTE were 1.36 (95% CI: 1.22-1.52) and 1.08 (95% CI: 0.90-1.29), respectively.

Conclusions

Except for the association between cigarette smoking and provoked VTE, which is potentially mediated through comorbid conditions such as cancer, the modifiable traditional cardiovascular disease risk factors are not associated with increased VTE risk. Higher systolic blood pressure showed an inverse association with VTE.

Although recent Genome-Wide Association Studies have identified novel associations for common variants, there has been no comprehensive exome-wide search for low-frequency variants that affect the risk of venous thromboembolism (VTE). We conducted a meta-analysis of 11 studies comprising 8,332 cases and 16,087 controls of European ancestry and 382 cases and 1,476 controls of African American ancestry genotyped with the Illumina HumanExome BeadChip. We used the seqMeta package in R to conduct single variant and gene-based rare variant tests. In the single variant analysis, we limited our analysis to the 64,794 variants with at least 40 minor alleles across studies (minor allele frequency [MAF] ~0.08%). We confirmed associations with previously identified VTE loci, including ABO, F5, F11, and FGA. After adjusting for multiple testing, we observed no novel significant findings in single variant or gene-based analysis. Given our sample size, we had greater than 80% power to detect minimum odds ratios greater than 1.5 and 1.8 for a single variant with MAF of 0.01 and 0.005, respectively. Larger studies and sequence data may be needed to identify novel low-frequency and rare variants associated with VTE risk.

Background

Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes.

Objectives

To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events.

Methods

Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10^-9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27).

Results

Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes.

Conclusions

The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.