A cross-population atlas of genetic associations for 220 human phenotypes. - Sakaue, Saori; Kanai, Masahiro; Tanigawa, Yosuke; Karjalainen, Juha; Kurki, Mitja; Koshiba, Seizo; Narita, Akira; Konuma, Takahiro; Yamamoto, Kenichi; Akiyama, Masato; Ishigaki, Kazuyoshi; Suzuki, Akari; et al. (Nat Genet, 2021)
Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen (n = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics.
Keywords: ABO Blood-Group System; Biological Specimen Banks; Genetic Association Studies; Genetic Loci; Genetic Pleiotropy; Genetic Predisposition to Disease; Genome-Wide Association Study; Humans; Major Histocompatibility Complex; Meta-Analysis as Topic; Mutation; Phenotype

Genetics of 35 blood and urine biomarkers in the UK Biobank. - Sinnott-Armstrong, Nasa; Tanigawa, Yosuke; Amar, David; Mars, Nina; Benner, Christian; Aguirre, Matthew; Venkataraman, Guhan Ram; Wainberg, Michael; Ollila, Hanna M; Kiiskinen, Tuomo; Havulinna, Aki S; Pirruccello, James P; et al. (Nat Genet, 2021)
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 (>0.1 s.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.
Keywords: Biological Specimen Banks; Biomarkers; Cardiovascular Diseases; Diabetes Mellitus, Type 2; DNA Copy Number Variations; Genetic Pleiotropy; HLA Antigens; Humans; Linkage Disequilibrium; Liver-Specific Organic Anion Transporter 1; Mendelian Randomization Analysis; Polymorphism, Single Nucleotide; Proteins; Renal Insufficiency, Chronic; Serine Endopeptidases; United Kingdom

Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases. - DeBoever, Christopher; Tanigawa, Yosuke; Aguirre, Matthew; McInnes, Greg; Lavertu, Adam; Rivas, Manuel A (Am J Hum Genet, 2020)
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.
Keywords: Asthma; Databases, Factual; Disease; Female; Genetics, Medical; Genome-Wide Association Study; Genotype; Humans; Male; Neoplasms; Phenotype; United Kingdom

A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. - Qian, Junyang; Tanigawa, Yosuke; Du, Wenfei; Aguirre, Matthew; Chang, Chris; Tibshirani, Robert; Rivas, Manuel A; Hastie, Trevor (PLoS Genet, 2020)
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

Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma. - Tanigawa, Yosuke; Wainberg, Michael; Karjalainen, Juha; Kiiskinen, Tuomo; Venkataraman, Guhan; Lemmelä, Susanna; Turunen, Joni A; Graham, Robert R; Havulinna, Aki S; Perola, Markus; Palotie, Aarno; Daly, Mark J; et al. (PLoS Genet, 2020)
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 (β = -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.
Keywords: Adult; Aged; Aged, 80 and over; Angiopoietin-like Proteins; Biological Specimen Banks; Case-Control Studies; Cohort Studies; Female; Finland; Gene Frequency; Genetic Predisposition to Disease; Genetics, Population; Genome-Wide Association Study; Glaucoma; Humans; Intraocular Pressure; Loss of Function Mutation; Male; Middle Aged; Mutation, Missense; Polymorphism, Single Nucleotide; United Kingdom

Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics. - McInnes, Gregory; Tanigawa, Yosuke; DeBoever, Chris; Lavertu, Adam; Olivieri, Julia Eve; Aguirre, Matthew; Rivas, Manuel A (Bioinformatics, 2019)
SUMMARY: 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 exploration of the relationship between genotype and phenotype in 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. AVAILABILITY AND IMPLEMENTATION: GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.
Keywords: Biological Specimen Banks; Genome-Wide Association Study; Genotype; Humans; Phenomics; Phenotype

Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. - DeBoever, Christopher; Tanigawa, Yosuke; Lindholm, Malene E; McInnes, Greg; Lavertu, Adam; Ingelsson, Erik; Chang, Chris; Ashley, Euan A; Bustamante, Carlos D; Daly, Mark J; Rivas, Manuel A (Nat Commun, 2018)
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