@article {121, title = {A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 10}, pages = {e1009141}, abstract = {

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 l1-penalized linear model, logistic regression, Cox model, and also extends to the elastic net with l1/l2 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.

}, keywords = {Algorithms, Asthma, Biological Specimen Banks, Body Height, Body Mass Index, Cholesterol, Cohort Studies, Genetics, Population, Genome-Wide Association Study, Genotype, Humans, Logistic Models, Phenotype, Polymorphism, Single Nucleotide, Proportional Hazards Models, United Kingdom}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1009141}, author = {Qian, Junyang and Tanigawa, Yosuke and Du, Wenfei and Aguirre, Matthew and Chang, Chris and Tibshirani, Robert and Rivas, Manuel A and Hastie, Trevor} } @article {66, title = {Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Sep 06}, pages = {4064}, abstract = {

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.

}, issn = {2041-1723}, doi = {10.1038/s41467-019-11953-9}, author = {Tanigawa, Yosuke and Li, Jiehan and Justesen, Johanne M and Horn, Heiko and Aguirre, Matthew and DeBoever, Christopher and Chang, Chris and Narasimhan, Balasubramanian and Lage, Kasper and Hastie, Trevor and Park, Chong Y and Bejerano, Gill and Ingelsson, Erik and Rivas, Manuel A} } @article {28, title = {Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Apr 24}, pages = {1612}, abstract = {

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.

}, keywords = {Databases, Nucleic Acid, Genome-Wide Association Study, Humans, Phenotype, Proteins, Sequence Deletion, United Kingdom}, issn = {2041-1723}, doi = {10.1038/s41467-018-03910-9}, author = {DeBoever, Christopher and Tanigawa, Yosuke and Lindholm, Malene E and McInnes, Greg and Lavertu, Adam and Ingelsson, Erik and Chang, Chris and Ashley, Euan A and Bustamante, Carlos D and Daly, Mark J and Rivas, Manuel A} }