Biology subjects

DeBoever, C.

Publications and source records attributed to DeBoever, C..

5 recordsLinked to original sources

bioRxiv2018

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight novel adipocyte biology

To characterize latent components of genetic associations, we applied truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identified key components of genetic associations and the contributions of variants, genes, and phenotypes to each component. As an illustration of the utility of the approach to inform downstream experiments, we report putative loss of function variants, rs114285050 (GPR151) and rs150090666 (PDE3B), that substantially contribute to obesity-related traits, and experimentally demonstrate the role of these genes in adipocyte biology. Our approach to dissect components of genetic associations across human phenotypes will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.

genetics↗

bioRxiv2018

Allele-specific NKX2-5 binding underlies multiple genetic associations with human EKG traits

Genetic variation affecting the binding of transcription factors (TFs) has been proposed as a major mechanism underlying susceptibility to common disease. NKX2-5, a key cardiac development TF, has been associated with electrocardiographic (EKG) traits through GWAS, but the extent to which differential binding of NKX2-5 contributes to these traits has not yet been studied. Here, we analyzed transcriptomic and epigenomic data generated from iPSC-derived cardiomyocyte lines (iPSC-CMs) from seven whole-genome sequenced individuals in a three-generational family. We identified ~2,000 single nucleotide variants (SNVs) associated with allele-specific effects (ASE) on NKX2-5 binding. These ASE-SNVs were enriched for altered TF motifs (both cognate and other cardiac TFs), and were positively correlated with changes in H3K27ac in iPSC-CMs, suggesting they impact cardiac enhancer activity. We found that NKX2-ASE-SNVs were significantly enriched for being heart-specific eQTLs and EKG GWAS variants, suggesting that altered NKX2-5 binding at multiple sites across the genome influences EKG traits. We used a fine-mapping approach to integrate iPSC-CM molecular phenotype data with a GWAS for heart rate, and determined that NKX2-5 ASE variants are likely causal for numerous known, as well as previously unidentified, heart rate loci. Analyzing Hi-C and gene expression data from iPSC-CMs at these heart rate loci, we identified several genes likely to be causally involved in heart rate variability. Our study demonstrates that differential binding of NKX2-5 is a common mechanism underlying genetic association with EKG traits, and shows that characterizing variants associated with differential binding of development TFs in iPSC-derived cell lines can identify novel loci and mechanisms influencing complex traits.

genetics↗

bioRxiv2018

Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics

Large biobanks linking phenotype to genotype have led to an explosion of genetic association studies across a wide range of phenotypes. Sharing the knowledge generated by these resources with the scientific community remains a challenge due to patient privacy and the vast amount of data. Here we present Global Biobank Engine (GBE), a web-based tool that enables the exploration of the relationship between genotype and phenotype in large biobank cohorts, such as the UK Biobank. GBE supports browsing for results from genome-wide association studies, phenome-wide association studies, gene-based tests, and genetic correlation between phenotypes. We envision GBE as a platform that facilitates the dissemination of summary statistics from biobanks to the scientific and clinical communities. GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

bioinformatics↗

bioRxiv2018

Bayesian model comparison for rare variant association studies of multiple phenotypes

Whole genome sequencing studies applied to large populations or biobanks with extensive phenotyping raise new analytic challenges. The need to consider many variants at a locus or group of genes simultaneously and the potential to study many correlated phenotypes with shared genetic architecture provide opportunities for discovery and inference that are not addressed by the traditional one variant, one phenotype association study. Here, we introduce a Bayesian model comparison approach that we refer to as MRP (Multiple Rare-variants and Phenotypes) for rare-variant association studies that considers correlation, scale, and direction of genetic effects across a group of genetic variants, phenotypes, and studies. The approach requires only summary statistic data. To demonstrate the efficacy of MRP, we apply our method to exome sequencing data (N = 184,698) across 2,019 traits from the UK Biobank, aggregating signals in genes. MRP demonstrates an ability to recover previously-verified signals such as associations between PCSK9 and LDL cholesterol levels. We additionally find MRP effective in conducting meta-analyses in exome data. Notable non-biomarker findings include associations between MC1R and red hair color and skin color, IL17RA and monocyte count, IQGAP2 and mean platelet volume, and JAK2 and platelet count and crit (mass). Finally, we apply MRP in a multi-phenotype setting; after clustering the 35 biomarker phenotypes based on genetic correlation estimates into four clusters, we find that joint analysis of these phenotypes results in substantial power gains for gene-trait associations, such as in TNFRSF13B in one of the clusters containing diabetes and lipid-related traits. Overall, we show that the MRP model comparison approach is able to improve upon useful features from widely-used meta-analysis approaches for rare variant association analyses and prioritize protective modifiers of disease risk.

genetics↗

bioRxiv2017

Medical relevance of protein-truncating variants across 337,208 individuals in the UK Biobank study

Protein-truncating variants can have profound effects on gene function and are critical for clinical genome interpretation and generating therapeutic hypotheses, but their relevance to medical phenotypes has not been systematically assessed. We characterized the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and found 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We performed phenome-wide analyses and directly measured the effect of homozygous carriers, commonly referred to as \"human knockouts,\" across medical phenotypes for genes implicated to be protective against disease or associated with at least one phenotype in our study and found several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.

genetics↗