@article {114, title = {Genetics of 35 blood and urine biomarkers in the UK Biobank.}, journal = {Nat Genet}, volume = {53}, year = {2021}, month = {2021 02}, pages = {185-194}, abstract = {

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 {\textquoteright}multi-PRS{\textquoteright} 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}, issn = {1546-1718}, doi = {10.1038/s41588-020-00757-z}, author = {Sinnott-Armstrong, Nasa and Tanigawa, Yosuke and Amar, David and Mars, Nina and Benner, Christian and Aguirre, Matthew and Venkataraman, Guhan Ram and Wainberg, Michael and Ollila, Hanna M and Kiiskinen, Tuomo and Havulinna, Aki S and Pirruccello, James P and Qian, Junyang and Shcherbina, Anna and Rodriguez, Fatima and Assimes, Themistocles L and Agarwala, Vineeta and Tibshirani, Robert and Hastie, Trevor and Ripatti, Samuli and Pritchard, Jonathan K and Daly, Mark J and Rivas, Manuel A} } @article {91, title = {Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases.}, journal = {Am J Hum Genet}, volume = {106}, year = {2020}, month = {2020 05 07}, pages = {611-622}, abstract = {

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}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.03.007}, author = {DeBoever, Christopher and Tanigawa, Yosuke and Aguirre, Matthew and McInnes, Greg and Lavertu, Adam and Rivas, Manuel A} } @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 {128, title = {A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 11}, pages = {e1008802}, abstract = {

The clinical evaluation of a genetic syndrome relies upon recognition of a characteristic pattern of signs or symptoms to guide targeted genetic testing for confirmation of the diagnosis. However, individuals displaying a single phenotype of a complex syndrome may not meet criteria for clinical diagnosis or genetic testing. Here, we present a phenome-wide association study (PheWAS) approach to systematically explore the phenotypic expressivity of common and rare alleles in genes associated with four well-described syndromic diseases (Alagille (AS), Marfan (MS), DiGeorge (DS), and Noonan (NS) syndromes) in the general population. Using human phenotype ontology (HPO) terms, we systematically mapped 60 phenotypes related to AS, MS, DS and NS in 337,198 unrelated white British from the UK Biobank (UKBB) based on their hospital admission records, self-administrated questionnaires, and physiological measurements. We performed logistic regression adjusting for age, sex, and the first 5 genetic principal components, for each phenotype and each variant in the target genes (JAG1, NOTCH2 FBN1, PTPN1 and RAS-opathy genes, and genes in the 22q11.2 locus) and performed a gene burden test. Overall, we observed multiple phenotype-genotype correlations, such as the association between variation in JAG1, FBN1, PTPN11 and SOS2 with diastolic and systolic blood pressure; and pleiotropy among multiple variants in syndromic genes. For example, rs11066309 in PTPN11 was significantly associated with a lower body mass index, an increased risk of hypothyroidism and a smaller size for gestational age, all in concordance with NS-related phenotypes. Similarly, rs589668 in FBN1 was associated with an increase in body height and blood pressure, and a reduced body fat percentage as observed in Marfan syndrome. Our findings suggest that the spectrum of associations of common and rare variants in genes involved in syndromic diseases can be extended to individual phenotypes within the general population.

}, keywords = {Alagille Syndrome, Alleles, Biological Variation, Population, DiGeorge Syndrome, European Continental Ancestry Group, Female, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Testing, Genetic Variation, Genome-Wide Association Study, Humans, Male, Marfan Syndrome, Noonan Syndrome, Phenotype, Polymorphism, Single Nucleotide, United Kingdom}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008802}, author = {Tcheandjieu, Catherine and Aguirre, Matthew and Gustafsson, Stefan and Saha, Priyanka and Potiny, Praneetha and Haendel, Melissa and Ingelsson, Erik and Rivas, Manuel A and Priest, James R} } @article {102, title = {Race, socioeconomic deprivation, and hospitalization for COVID-19 in English participants of a national biobank.}, journal = {Int J Equity Health}, volume = {19}, year = {2020}, month = {2020 07 06}, pages = {114}, abstract = {

Preliminary reports suggest that the Coronavirus Disease 2019 (COVID- 19) pandemic has led to disproportionate morbidity and mortality among historically disadvantaged populations. We investigate the racial and socioeconomic associations of COVID- 19 hospitalization among 418,794 participants of the UK Biobank, of whom 549 (0.13\%) had been hospitalized. Both Black participants (odds ratio 3.7; 95\%CI 2.5-5.3) and Asian participants (odds ratio 2.2; 95\%CI 1.5-3.2) were at substantially increased risk as compared to White participants. We further observed a striking gradient in COVID- 19 hospitalization rates according to the Townsend Deprivation Index - a composite measure of socioeconomic deprivation - and household income. Adjusting for socioeconomic factors and cardiorespiratory comorbidities led to only modest attenuation of the increased risk in Black participants, adjusted odds ratio 2.4 (95\%CI 1.5-3.7). These observations confirm and extend earlier preliminary and lay press reports of higher morbidity in non-White individuals in the context of a large population of participants in a national biobank. The extent to which this increased risk relates to variation in pre-existing comorbidities, differences in testing or hospitalization patterns, or additional disparities in social determinants of health warrants further study.

}, keywords = {Adult, Aged, Biological Specimen Banks, Continental Population Groups, Coronavirus Infections, COVID-19, Female, Health Status Disparities, Hospitalization, Humans, Male, Middle Aged, Pandemics, Pneumonia, Viral, Prospective Studies, Socioeconomic Factors, United Kingdom}, issn = {1475-9276}, doi = {10.1186/s12939-020-01227-y}, author = {Patel, Aniruddh P and Paranjpe, Manish D and Kathiresan, Nina P and Rivas, Manuel A and Khera, Amit V} } @article {90, title = {Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 05}, pages = {e1008682}, abstract = {

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}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008682}, author = {Tanigawa, Yosuke and Wainberg, Michael and Karjalainen, Juha and Kiiskinen, Tuomo and Venkataraman, Guhan and Lemmel{\"a}, Susanna and Turunen, Joni A and Graham, Robert R and Havulinna, Aki S and Perola, Markus and Palotie, Aarno and Daly, Mark J and Rivas, Manuel A} } @article {48, title = {Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 Feb 15}, pages = {790}, abstract = {

Understanding the role of rare variants is important in elucidating the genetic basis of human disease. Negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence of SNP effect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1 - p)], where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α for 25 UK Biobank diseases and complex traits. All traits produce negative α estimates, with best-fit mean of -0.38 (s.e. 0.02) across traits. Despite larger rare variant effect sizes, rare variants (MAF < 1\%) explain less than 10\% of total SNP-heritability for most traits analyzed. Using evolutionary modeling and forward simulations, we validate the α model of MAF-dependent trait effects and assess plausible values of relevant evolutionary parameters.

}, keywords = {Algorithms, Alleles, Biological Specimen Banks, Gene Frequency, Genome-Wide Association Study, Genotype, Humans, Models, Genetic, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Selection, Genetic, United Kingdom}, issn = {2041-1723}, doi = {10.1038/s41467-019-08424-6}, author = {Schoech, Armin P and Jordan, Daniel M and Loh, Po-Ru and Gazal, Steven and O{\textquoteright}Connor, Luke J and Balick, Daniel J and Palamara, Pier F and Finucane, Hilary K and Sunyaev, Shamil R and Price, Alkes L} } @article {40, title = {Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations.}, journal = {Nat Genet}, volume = {50}, year = {2018}, month = {2018 Nov}, pages = {1600-1607}, abstract = {

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5\%<= minor allele frequency <5\%) and common (minor allele frequency >=5\%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 {\textpm} 1\% of low-frequency variant heritability ([Formula: see text]) versus 2.1 {\textpm} 0.2\% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 {\textpm} 12\% of [Formula: see text] versus 12 {\textpm} 2\% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5\%).

}, keywords = {Alleles, Biological Specimen Banks, Gene Frequency, Genetic Variation, Genetics, Population, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Molecular Sequence Annotation, Open Reading Frames, Polymorphism, Single Nucleotide, Selection, Genetic, United Kingdom, White People}, issn = {1546-1718}, doi = {10.1038/s41588-018-0231-8}, author = {Gazal, Steven and Loh, Po-Ru and Finucane, Hilary K and Ganna, Andrea and Schoech, Armin and Sunyaev, Shamil and Price, Alkes L} } @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} }