Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics
2019; 35 (14): 2495–97
Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide.
Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology.
2019; 10 (1): 4064
Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n=157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP=0.035, p=7.12*10-4) and the clinically predicted phenotype from VUMC (h2SNP=0.046, p=1.51*10-2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t=4.02, p=5.75*10-5) and genetic correlation (rg=1.073, SE=0.36, p=0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg=0.34-0.81) as well as several psychiatric disorders (rg=0.26-0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.
View details for PubMedID 30610202
SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2019; 24: 184–95
Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply 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 identify 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 the human phenome will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.
View details for DOI 10.1038/s41467-019-11953-9
View details for PubMedID 31492854
Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study
2018; 9: 1612
Genetic variations of the human genome are linked to many disease phenotypes. While whole-genome sequencing and genome-wide association studies (GWAS) have uncovered a number of genotype-phenotype associations, their functional interpretation remains challenging given most single nucleotide polymorphisms (SNPs) fall into the non-coding region of the genome. Advances in chromatin immunoprecipitation sequencing (ChIP-seq) have made large-scale repositories of epigenetic data available, allowing investigation of coordinated mechanisms of epigenetic markers and transcriptional regulation and their influence on biological function. To address this, we propose SNPs2ChIP, a method to infer biological functions of non-coding variants through unsupervised statistical learning methods applied to publicly-available epigenetic datasets. We systematically characterized latent factors by applying singular value decomposition to ChIP-seq tracks of lymphoblastoid cell lines, and annotated the biological function of each latent factor using the genomic region enrichment analysis tool. Using these annotated latent factors as reference, we developed SNPs2ChIP, a pipeline that takes genomic region(s) as an input, identifies the relevant latent factors with quantitative scores, and returns them along with their inferred functions. As a case study, we focused on systemic lupus erythematosus and demonstrated our method's ability to infer relevant biological function. We systematically applied SNPs2ChIP on publicly available datasets, including known GWAS associations from the GWAS catalogue and ChIP-seq peaks from a previously published study. Our approach to leverage latent patterns across genome-wide epigenetic datasets to infer the biological function will advance understanding of the genetics of human diseases by accelerating the interpretation of non-coding genomes.
View details for PubMedID 30864321
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. Here, we characterize the effect of 18,228 protein-truncating variants across 135 phenotypes from the UK Biobank and find 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We perform phenome-wide analyses and directly measure the effect in homozygous carriers, commonly referred to as "human knockouts," across medical phenotypes for genes implicated as being protective against disease or associated with at least one phenotype in our study. We find several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.
View details for PubMedID 29691392