Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression.
Bioinformatics (Oxford, England)
Polygenic risk modeling with latent trait-related genetic components.
European journal of human genetics : EJHG
MOTIVATION: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data.RESULTS: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank (Sudlow et al., 2015) dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. (2020).AVAILABILITY: https://github.com/rivas-lab/multisnpnet-Cox.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
View details for DOI 10.1093/bioinformatics/btab095
View details for PubMedID 33560296
Genetics of 35 blood and urine biomarkers in the UK Biobank.
Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.
View details for DOI 10.1038/s41431-021-00813-0
View details for PubMedID 33558700
Sex-specific genetic effects across biomarkers.
European journal of human genetics : EJHG
Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n=363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n=135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.
View details for DOI 10.1038/s41588-020-00757-z
View details for PubMedID 33462484
Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma.
2020; 16 (5): e1008682
Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian Mixture Model, Sex Effects Mixture Model(SEMM), to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 445 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist and hip circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.
View details for DOI 10.1038/s41431-020-00712-w
View details for PubMedID 32873964
A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank.
2020; 16 (10): e1009141
Protein-altering variants that are protective against human disease provide in vivo validation of therapeutic targets. Here we use genotyping data from UK Biobank (n = 337,151 unrelated White British individuals) and FinnGen (n = 176,899) to conduct a search for protein-altering variants conferring lower intraocular pressure (IOP) and protection against glaucoma. Through rare protein-altering variant association analysis, we find a missense variant in ANGPTL7 in UK Biobank (rs28991009, p.Gln175His, MAF = 0.8%, genotyped in 82,253 individuals with measured IOP and an independent set of 4,238 glaucoma patients and 250,660 controls) that significantly lowers IOP (beta = -0.53 and -0.67 mmHg for heterozygotes, -3.40 and -2.37 mmHg for homozygotes, P = 5.96 x 10-9 and 1.07 x 10-13 for corneal compensated and Goldman-correlated IOP, respectively) and is associated with 34% reduced risk of glaucoma (P = 0.0062). In FinnGen, we identify an ANGPTL7 missense variant at a greater than 50-fold increased frequency in Finland compared with other populations (rs147660927, p.Arg220Cys, MAF Finland = 4.3%), which was genotyped in 6,537 glaucoma patients and 170,362 controls and is associated with a 29% lower glaucoma risk (P = 1.9 x 10-12 for all glaucoma types and also protection against its subtypes including exfoliation, primary open-angle, and primary angle-closure). We further find three rarer variants in UK Biobank, including a protein-truncating variant, which confer a strong composite lowering of IOP (P = 0.0012 and 0.24 for Goldman-correlated and corneal compensated IOP, respectively), suggesting the protective mechanism likely resides in the loss of interaction or function. Our results support inhibition or down-regulation of ANGPTL7 as a therapeutic strategy for glaucoma.
View details for DOI 10.1371/journal.pgen.1008682
View details for PubMedID 32369491
Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.
American journal of human genetics
The UK Biobank is a very large, prospective population-based cohort study across the United Kingdom. It provides unprecedented opportunities for researchers to investigate the relationship between genotypic information and phenotypes of interest. Multiple regression methods, compared with genome-wide association studies (GWAS), have already been showed to greatly improve the prediction performance for a variety of phenotypes. In the high-dimensional settings, the lasso, since its first proposal in statistics, has been proved to be an effective method for simultaneous variable selection and estimation. However, the large-scale and ultrahigh dimension seen in the UK Biobank pose new challenges for applying the lasso method, as many existing algorithms and their implementations are not scalable to large applications. In this paper, we propose a computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size. We introduce snpnet, an R package that implements the proposed algorithm on top of glmnet and optimizes for single nucleotide polymorphism (SNP) datasets. It currently supports ?1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with ?1/?2 penalty. We demonstrate results on the UK Biobank dataset, where we achieve competitive predictive performance for all four phenotypes considered (height, body mass index, asthma, high cholesterol) using only a small fraction of the variants compared with other established polygenic risk score methods.
View details for DOI 10.1371/journal.pgen.1009141
View details for PubMedID 33095761
Cardiac Imaging of Aortic Valve Area from 34,287 UK Biobank Participants Reveal Novel Genetic Associations and Shared Genetic Comorbidity with Multiple Disease Phenotypes.
Circulation. Genomic and precision medicine
Population-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters, including genetic correlation, to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family history GWAS using cases ascertained on family history of disease agrees with combined hospital record and questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.
View details for DOI 10.1016/j.ajhg.2020.03.007
View details for PubMedID 32275883
Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank.
Biostatistics (Oxford, England)
Background - The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases. Methods - From a sample of 34,287 white British-ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac MRI sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening to identify genetic comorbidities. Results - A genome-wide association study of aortic valve area in these UK Biobank participants showed three significant associations, indexed by rs71190365 (chr13:50764607, DLEU1, p=1.8×10-9), rs35991305 (chr12:94191968, CRADD, p=3.4×10-8) and chr17:45013271:C:T (GOSR2, p=5.6×10-8). Replication on an independent set of 8,145 unrelated European-ancestry participants showed consistent effect sizes in all three loci, although rs35991305 did not meet nominal significance. We constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311,728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (Odds Ratio 1.14, p=2.3×10-6). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=308,683 individuals), phenome-wide association of >10,000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve area and birthweight along with other cardiovascular conditions. Conclusions - These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.
View details for DOI 10.1161/CIRCGEN.120.003014
View details for PubMedID 33125279
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.
We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox.
View details for DOI 10.1093/biostatistics/kxaa038
View details for PubMedID 32989444
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