Citations

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Abstract Summary: The development of the Infinium HumanMethylation450 BeadChip enables epigenome-wide association studies at a reduced cost. One observation of the 450K data is that many CpG sites the beadchip interrogates have very large measurement errors. Including these noisy CpGs will decrease the statistical power of detecting relevant associations due to multiple testing correction. We propose to use intra-class correlation coefficient (ICC), which characterizes the relative contribution of the biological variability to the total variability, to filter CpGs when technical replicates are available. We estimate the ICC based on a linear mixed effects model by pooling all the samples instead of using the technical replicates only. An ultra-fast algorithm has been developed to address the computational complexity and CpG filtering can be completed in minutes on a desktop computer for a 450K data set of over 1000 samples. Our method is very flexible and can accommodate any replicate design. Simulations and a real data application demonstrate that our whole-sample ICC method performs better than replicate-sample ICC or variance-based method. Availability and implementation: CpGFilter is implemented in R and publicly available under CRAN via the R package ‘CpGFilter’. Contact: chen.jun2@mayo.edu or xlin@hsph.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Objective— Matrix gla protein (MGP) inhibits arterial and cartilaginous calcification. A threonine to alanine (Thr83Ala) polymorphism (codon 83) in MGP is associated with myocardial infarction and femoral artery calcification. We examined the association of the MGP Thr83Ala polymorphism with quantity and progression of coronary artery calcification (CAC), a noninvasive measure of subclinical coronary atherosclerosis. Methods and Results— In 605 participants of the Epidemiology of Coronary Artery Calcification Study, generalized linear mixed models were fit to determine the association of MGP Thr83Ala with CAC quantity and progression. There was a significant additive relation between MGP Thr83Ala and CAC progression ( P =0.001). In the fully adjusted model, every 1 Ala83 allele increase was associated with an estimated 1.9% (95% confidence interval, 0.7%–3.0%) per year since baseline larger increase in CAC quantity. A proxy single nucleotide polymorphism for MGP Thr83Ala (rs6488724) was similarly associated with CAC progression in an independent cohort from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. Conclusion— Increased risk of myocardial infarction associated with MGP ThrAla83 genotype observed elsewhere may be related to faster progression of subclinical coronary atherosclerosis. MGP genotype could be a potential candidate for identifying individuals at increased risk of atherosclerotic disease who would benefit from aggressive primary prevention strategies.

Background— Coronary artery calcification (CAC), a marker of coronary artery atherosclerosis, can be measured accurately and noninvasively with the use of electron beam computed tomography. Serial measures of CAC quantify progression of calcified coronary artery plaque. Little is known about the role of genetic factors in progression of CAC quantity. Methods and Results— We quantified the relative contributions of measured risk factors and unmeasured genes to CAC progression measured by 2 electron beam computed tomography examinations an average of 7.3 years apart in 877 asymptomatic white adults (46% men) from 625 families in a community-based sample. After adjustment for baseline risk factors and CAC quantity, the estimated heritability of CAC progression was 0.40 ( P <0.001). Baseline risk factors and CAC quantity explained 64% of the variation in CAC progression. Thus, genetic factors explained 14% of the variation [(100−64)×(0.40)] in CAC progression. After adjustment for risk factors, the estimated genetic correlation (pleiotropy) between baseline CAC quantity and CAC progression was 0.80 and was significantly different than 0 ( P <0.001) and 1 ( P =0.037). The environmental correlation between baseline CAC quantity and CAC progression was 0.42 and was significantly different than 0 ( P =0.006). Conclusions— Evidence was found that many but not all genetic factors influencing baseline CAC quantity also influence CAC progression. The identification of common and unique genetic influences on these traits will provide important insights into the genetic architecture of coronary artery atherosclerosis.

Background— Obesity is associated with coronary artery calcification (CAC), a marker of the presence and extent of subclinical coronary atherosclerosis. Obesity adds incremental information in identifying those at higher risk of coronary heart disease to traditional risk factor assessment. The present study examined associations between obesity measures and progression of CAC in those at higher (≥10%) and lower (<10%) 10-year coronary heart disease risk according to the Framingham risk equation. Methods and Results— In this study, 443 asymptomatic white individuals >30 years of age (243 men) had baseline and follow-up CAC measurements an average of 8.9 years apart. Multivariable linear regression models were fit to determine associations of obesity measures at baseline with progression of CAC defined as log e of the difference between follow-up and baseline CAC area plus 1 divided by time (in years) between examinations, adjusting for baseline CAC quantity, age, sex, baseline hypertension status, and baseline cholesterol level. Among 329 participants (74.3%) in the lower-risk group, waist circumference ( P =0.024), waist-to-hip ratio ( P <0.001), body mass index ( P =0.036), and being overweight compared with being underweight or of normal weight ( P =0.008) were each significantly positively associated with progression of CAC. Among those at higher coronary heart disease risk, no baseline obesity measures were associated with CAC progression. Conclusions— Various measures of obesity were associated with increased progression of CAC in those at lower risk of coronary heart disease. Future studies examining the effectiveness of weight reduction strategies in reducing CAC progression among those with an otherwise favorable risk factor profile may be warranted.