A phenome-wide scan reveals convergence of common and rare variant associations.

BACKGROUND: Common and rare variants contribute to the etiology of complex traits. However, the extent to which the phenotypic effects of common and rare variants involve shared molecular mediators remains poorly understood. The question is essential to the basic and translational goals of the science of genomics, with critical basic-science, methodological, and clinical consequences. METHODS: Leveraging the latest release of whole-exome sequencing (WES, for rare variants) and genome-wide association study (GWAS, for common variants) data from the UK Biobank, we developed a metric, the COmmon variant and RAre variant Convergence (CORAC) signature, to quantify the convergence for a broad range of complex traits. We characterized the relationship between CORAC and effective sample size across phenome-wide association studies. RESULTS: We found that the signature is positively correlated with effective sample size (Spearman ρ = 0.594, P < 2.2e - 16), indicating increased functional convergence of trait-associated genetic variation, across the allele frequency spectrum, with increased power. Sensitivity analyses, including accounting for heteroskedasticity and varying the number of detected association signals, further strengthened the validity of the finding. In addition, consistent with empirical data, extensive simulations showed that negative selection, in line with enhancing polygenicity, has a dampening effect on the convergence signature. Methodologically, leveraging the convergence leads to enhanced association analysis. CONCLUSIONS: The presented framework for the convergence signature has important implications for fine-mapping strategies and drug discovery efforts. In addition, our study provides a blueprint for the expectation from future large-scale whole-genome sequencing (WGS)/WES and sheds methodological light on post-GWAS studies.

Examining the Association between Mitochondrial Genome Variation and Coronary Artery Disease.

Large-scale genome-wide association studies have identified hundreds of single-nucleotide variants (SNVs) significantly associated with coronary artery disease (CAD). However, collectively, these explain <20% of the heritability. Hypothesis: Here, we hypothesize that mitochondrial (MT)-SNVs might present one potential source of this "missing heritability". Methods: We analyzed 265 MT-SNVs in ~500,000 UK Biobank individuals, exploring two different CAD definitions: a more stringent (myocardial infarction and/or revascularization; HARD = 20,405), and a more inclusive (angina and chronic ischemic heart disease; SOFT = 34,782). Results: In HARD cases, the most significant (p < 0.05) associations were for m.295C>T (control region) and m.12612A>G (ND5), found more frequently in cases (OR = 1.05), potentially related to reduced cardiorespiratory fitness in response to exercise, as well as for m.12372G>A (ND5) and m.11467A>G (ND4), present more frequently in controls (OR = 0.97), previously associated with lower ROS production rate. In SOFT cases, four MT-SNVs survived multiple testing corrections (at FDR < 5%), all potentially conferring increased CAD risk. Of those, m.11251A>G (ND4) and m.15452C>A (CYB) have previously shown significant associations with body height. In line with this, we observed that CAD cases were slightly less physically active, and their average body height was ~2.00 cm lower compared to controls; both traits are known to be related to increased CAD risk. Gene-based tests identified CO2 associated with HARD/SOFT CAD, whereas ND3 and CYB associated with SOFT cases (p < 0.05), dysfunction of which has been related to MT oxidative stress, obesity/T2D (CO2), BMI (ND3), and angina/exercise intolerance (CYB). Finally, we observed that macro-haplogroup I was significantly (p < 0.05) more frequent in HARD cases vs. controls (3.35% vs. 3.08%), potentially associated with response to exercise. Conclusions: We found only spurious associations between MT genome variation and HARD/SOFT CAD and conclude that more MT-SNV data in even larger study cohorts may be needed to conclusively determine the role of MT DNA in CAD.

Population size influences the type of nucleotide variations in humans.

BACKGROUND: It is well known that the effective size of a population (Ne) is one of the major determinants of the amount of genetic variation within the population. However, it is unclear whether the types of genetic variations are also dictated by the effective population size. To examine this, we obtained whole genome data from over 100 populations of the world and investigated the patterns of mutational changes. RESULTS: Our results revealed that for low frequency variants, the ratio of AT→GC to GC→AT variants (ß) was similar across populations, suggesting the similarity of the pattern of mutation in various populations. However, for high frequency variants, ß showed a positive correlation with the effective population size of the populations. This suggests a much higher proportion of high frequency AT→GC variants in large populations (e.g. Africans) compared to those with small population sizes (e.g. Asians). These results imply that the substitution patterns vary significantly between populations. These findings could be explained by the effect of GC-biased gene conversion (gBGC), which favors the fixation of G/C over A/T variants in populations. In large population, gBGC causes high ß. However, in small populations, genetic drift reduces the effect of gBGC resulting in reduced ß. This was further confirmed by a positive relationship between Ne and ß for homozygous variants. CONCLUSIONS: Our results highlight the huge variation in the types of homozygous and high frequency polymorphisms between world populations. We observed the same pattern for deleterious variants, implying that the homozygous polymorphisms associated with recessive genetic diseases will be more enriched with G or C in populations with large Ne (e.g. Africans) than in populations with small Ne (e.g. Europeans).

The power of regional heritability analysis for rare and common variant detection: simulations and application to eye biometrical traits.

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits. However, they have explained relatively little trait heritability. Recently, we proposed a new analytical approach called regional heritability mapping (RHM) that captures more of the missing genetic variation. This method is applicable both to related and unrelated populations. Here, we demonstrate the power of RHM in comparison with single-SNP GWAS and gene-based association approaches under a wide range of scenarios with variable numbers of quantitative trait loci (QTL) with common and rare causal variants in a narrow genomic region. Simulations based on real genotype data were performed to assess power to capture QTL variance, and we demonstrate that RHM has greater power to detect rare variants and/or multiple alleles in a region than other approaches. In addition, we show that RHM can capture more accurately the QTL variance, when it is caused by multiple independent effects and/or rare variants. We applied RHM to analyze three biometrical eye traits for which single-SNP GWAS have been published or performed to evaluate the effectiveness of this method in real data analysis and detected some additional loci which were not detected by other GWAS methods. RHM has the potential to explain some of missing heritability by capturing variance caused by QTL with low MAF and multiple independent QTL in a region, not captured by other GWAS methods. RHM analyses can be implemented using the software REACTA (http://www.epcc.ed.ac.uk/projects-portfolio/reacta).