Systematic single-variant and gene-based association testing of 3,700 phenotypes in 281,850 UK Biobank exomes

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variation in human disease has not been explored at scale. Exome sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variation across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 3,700 phenotypes using single-variant and gene tests of 281,850 individuals in the UK Biobank with exome sequence data. We find that the discovery of genetic associations is tightly linked to frequency as well as correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside a browser framework for rapidly exploring rare variant association results.

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Fine-scale population structure in the UK Biobank: implications for genome-wide association studies

Human Molecular Genetics ◽

10.1093/hmg/ddaa157 ◽

2020 ◽

Vol 29 (16) ◽

pp. 2803-2811

Author(s):

James P Cook ◽

Anubha Mahajan ◽

Andrew P Morris

Keyword(s):

Population Structure ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Fine Scale ◽

Uk Biobank ◽

Genome Wide ◽

Scale Population ◽

The Uk ◽

The Impact

Abstract The UK Biobank is a prospective study of more than 500 000 participants, which has aggregated data from questionnaires, physical measures, biomarkers, imaging and follow-up for a wide range of health-related outcomes, together with genome-wide genotyping supplemented with high-density imputation. Previous studies have highlighted fine-scale population structure in the UK on a North-West to South-East cline, but the impact of unmeasured geographical confounding on genome-wide association studies (GWAS) of complex human traits in the UK Biobank has not been investigated. We considered 368 325 white British individuals from the UK Biobank and performed GWAS of their birth location. We demonstrate that widely used approaches to adjust for population structure, including principal component analysis and mixed modelling with a random effect for a genetic relationship matrix, cannot fully account for the fine-scale geographical confounding in the UK Biobank. We observe significant genetic correlation of birth location with a range of lifestyle-related traits, including body-mass index and fat mass, hypertension and lung function, even after adjustment for population structure. Variants driving associations with birth location are also strongly associated with many of these lifestyle-related traits after correction for population structure, indicating that there could be environmental factors that are confounded with geography that have not been adequately accounted for. Our findings highlight the need for caution in the interpretation of lifestyle-related trait GWAS in UK Biobank, particularly in loci demonstrating strong residual association with birth location.

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Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics

Bioinformatics ◽

10.1093/bioinformatics/bty999 ◽

2018 ◽

Vol 35 (14) ◽

pp. 2495-2497 ◽

Cited By ~ 27

Author(s):

Gregory McInnes ◽

Yosuke Tanigawa ◽

Chris DeBoever ◽

Adam Lavertu ◽

Julia Eve Olivieri ◽

...

Keyword(s):

Association Studies ◽

Genetic Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Uk Biobank ◽

Patient Privacy ◽

Web Based ◽

Genome Wide ◽

Wide Range ◽

The Uk

Abstract 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.

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Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics

10.1101/304188 ◽

2018 ◽

Cited By ~ 4

Author(s):

Gregory McInnes ◽

Yosuke Tanigawa ◽

Chris DeBoever ◽

Adam Lavertu ◽

Julia Eve Olivieri ◽

...

Keyword(s):

Association Studies ◽

Genetic Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Uk Biobank ◽

Patient Privacy ◽

Web Based ◽

Genome Wide ◽

Wide Range ◽

The Uk

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 the exploration of the relationship between genotype and phenotype in large 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. GBE currently hosts data from the UK Biobank and can be found freely available at biobankengine.stanford.edu.

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Genome-wide association study of liking of physical activity in the UK Biobank

10.1101/2021.10.13.21264969 ◽

2021 ◽

Author(s):

Yann C. Klimentidis ◽

Michelle Newell ◽

Matthijs D. van der Zee ◽

Victoria L. Bland ◽

Sebastian May-Wilson ◽

...

Keyword(s):

Physical Activity ◽

Genome Wide Association Study ◽

Association Studies ◽

Genome Wide Association ◽

Risk Scores ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genome Wide ◽

Wide Range ◽

The Uk

A lack of physical activity (PA) is one of the most pressing health issues facing society today. Our individual propensity for PA is partly influenced by genetic factors. Stated liking of various PA behaviors may capture additional dimensions of PA behavior that are not captured by other measures, and contribute to our understanding of the genetics of PA behavior. Here, in over 157,000 individuals from the UK Biobank, we sought to complement and extend previous findings on the genetics of PA behavior by performing genome-wide association studies of self-reported liking of several PA-related behaviors plus an additional derived trait of overall PA-liking. We identified a total of 19 unique genome-wide significant loci across all traits, only four of which overlap with loci previously identified for PA behavior. The PA-liking traits were genetically correlated with self-reported (rg: 0.38 to 0.80) and accelerometry-derived (rg: 0.26 to 0.49) PA measures, and with a wide range of health-related traits and dietary behaviors. Replication in the Netherlands Twin Register (NTR; n>7,300) and the TwinsUK (n>1,300) study revealed directionally consistent associations. Polygenic risk scores (PRS) were then trained in UKB for each PA-liking trait and for self-reported PA behavior. The PA-liking PRS significantly predicted the same liking trait in NTR. The PRS for liking of going to the gym predicted PA behavior in NTR (r2 = 0.40%) nearly as well as the one constructed based on self-reported PA behavior (r2 = 0.42%). Combining the two PRS into a single model increased the r2 to 0.59%, suggesting that although these PRS correlate with each other, they are also capturing distinct dimensions of PA behavior. In conclusion, we have identified the first loci associated with PA-liking, and extended and refined our understanding of the genetic basis of PA behavior.

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Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases

Genome Medicine ◽

10.1186/s13073-020-00797-4 ◽

2020 ◽

Vol 12 (1) ◽

Author(s):

Oliver S. Burren ◽

Guillermo Reales ◽

Limy Wong ◽

John Bowes ◽

James C. Lee ◽

...

Keyword(s):

Sample Size ◽

Genetic Risk ◽

Association Studies ◽

Evidence Base ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Immune Mediated ◽

The Uk ◽

Shared Risk

Abstract Background Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging. Methods Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension “basis” that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging. Results We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations. Conclusions Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.

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Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

10.1101/2021.12.24.21268381 ◽

2021 ◽

Author(s):

Abhishek Nag ◽

Lawrence Middleton ◽

Ryan S Dhindsa ◽

Dimitrios Vitsios ◽

Eleanor M Wigmore ◽

...

Keyword(s):

Gene Networks ◽

Rare Variants ◽

Association Studies ◽

Low Frequency ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Protein Coding ◽

The Uk ◽

Metabolic Biomarkers ◽

Coding Variants

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

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Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

10.1101/2020.06.24.20139576 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jack W. O’Sullivan ◽

John P. A. Ioannidis

Keyword(s):

Effect Size ◽

Association Studies ◽

Genome Wide Association ◽

P Value ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Single Nucleotide ◽

Genome Wide ◽

The Uk ◽

Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

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Insights into the genetic basis of retinal detachment

Human Molecular Genetics ◽

10.1093/hmg/ddz294 ◽

2019 ◽

Vol 29 (4) ◽

pp. 689-702 ◽

Cited By ~ 2

Author(s):

Thibaud S Boutin ◽

David G Charteris ◽

Aman Chandra ◽

Susan Campbell ◽

Caroline Hayward ◽

...

Keyword(s):

Retinal Detachment ◽

Association Studies ◽

Genetic Correlations ◽

Self Report ◽

Cataract Operation ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

Genetic Associations ◽

Data Set ◽

Genome Wide

Abstract Retinal detachment (RD) is a serious and common condition, but genetic studies to date have been hampered by the small size of the assembled cohorts. In the UK Biobank data set, where RD was ascertained by self-report or hospital records, genetic correlations between RD and high myopia or cataract operation were, respectively, 0.46 (SE = 0.08) and 0.44 (SE = 0.07). These correlations are consistent with known epidemiological associations. Through meta-analysis of genome-wide association studies using UK Biobank RD cases (N = 3 977) and two cohorts, each comprising ~1 000 clinically ascertained rhegmatogenous RD patients, we uncovered 11 genome-wide significant association signals. These are near or within ZC3H11B, BMP3, COL22A1, DLG5, PLCE1, EFEMP2, TYR, FAT3, TRIM29, COL2A1 and LOXL1. Replication in the 23andMe data set, where RD is self-reported by participants, firmly establishes six RD risk loci: FAT3, COL22A1, TYR, BMP3, ZC3H11B and PLCE1. Based on the genetic associations with eye traits described to date, the first two specifically impact risk of a RD, whereas the last four point to shared aetiologies with macular condition, myopia and glaucoma. Fine-mapping prioritized the lead common missense variant (TYR S192Y) as causal variant at the TYR locus and a small set of credible causal variants at the FAT3 locus. The larger study size presented here, enabled by resources linked to health records or self-report, provides novel insights into RD aetiology and underlying pathological pathways.

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LPA and APOE are associated with statin selection in the UK Biobank

10.1101/2020.08.28.272765 ◽

2020 ◽

Author(s):

Adam Lavertu ◽

Gregory McInnes ◽

Yosuke Tanigawa ◽

Russ B Altman ◽

Manuel A. Rivas

Keyword(s):

Statin Therapy ◽

High Intensity ◽

Drug Response ◽

Association Studies ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Treatment Decisions ◽

Genome Wide Association Studies ◽

Uk Biobank ◽

The Uk

AbstractGenetics plays a key role in drug response, affecting efficacy and toxicity. Pharmacogenomics aims to understand how genetic variation influences drug response and develop clinical guidelines to aid clinicians in personalized treatment decisions informed by genetics. Although pharmacogenomics has not been broadly adopted into clinical practice, genetics influences treatment decisions regardless. Physicians adjust patient care based on observed response to medication, which may occur as a result of genetic variants harbored by the patient. Here we seek to understand the genetics of drug selection in statin therapy, a class of drugs widely used for high cholesterol treatment. Genetics are known to play an important role in statin efficacy and toxicity, leading to significant changes in patient outcome. We performed genome-wide association studies (GWAS) on statin selection among 59,198 participants in the UK Biobank and found that variants known to influence statin efficacy are significantly associated with statin selection. Specifically, we find that carriers of variants in APOE and LPA that are known to decrease efficacy of treatment are more likely to be on atorvastatin, a stronger statin. Additionally, carriers of the APOE and LPA variants are more likely to be on a higher intensity dose (a dose that reduces low-density lipoprotein cholesterol by greater than 40%) of atorvastatin than non-carriers (APOE: p(high intensity) = 0.16, OR = 1.7, P = 1.64 × 10−4, LPA: p(high intensity) = 0.17, OR = 1.4, P = 1.14 × 10−2). These findings represent the largest genetic association study of statin selection and statin dose association to date and provide evidence for the role of LPA and APOE in statin response, furthering the possibility of personalized statin therapy.

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Genome-Wide Meta-Analysis of Late-Onset Alzheimer's Disease Using Rare Variant Imputation in 324,809 Subjects Identifies Novel Rare Variant Locus NCK2: The International Genomics of Alzheimer's Project (IGAP)

10.1101/2021.03.14.21253553 ◽

2021 ◽

Author(s):

Adam C. Naj ◽

Ganna Leonenko ◽

Xueqiu Jian ◽

Benjamin Grenier-Boley ◽

Maria Carolina Dalmasso ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Rare Variant ◽

Late Onset ◽

Sequence Data ◽

Association Studies ◽

Meta Analysis ◽

Genome Wide Association Studies ◽

Genome Wide ◽

The Uk

Risk for late-onset Alzheimer's disease (LOAD) is driven by multiple loci primarily identified by genome-wide association studies, many of which are common variants with minor allele frequencies (MAF)>0.01. To identify additional common and rare LOAD risk variants, we performed a GWAS on 25,170 LOAD subjects and 41,052 cognitively normal controls in 44 datasets from the International Genomics of Alzheimer's Project (IGAP). Existing genotype data were imputed using the dense, high-resolution Haplotype Reference Consortium (HRC) r1.1 reference panel. Stage 1 associations of P<10-5 were meta-analyzed with the European Alzheimer's Disease Biobank (EADB) (n=20,301 cases; 21,839 controls) (stage 2 combined IGAP and EADB). An expanded meta-analysis was performed using a GWAS of parental AD/dementia history in the UK Biobank (UKBB) (n=35,214 cases; 180,791 controls) (stage 3 combined IGAP, EADB, and UKBB). Common variant (MAF≥0.01) associations were identified for 29 loci in stage 2, including novel genome-wide significant associations at TSPAN14 (P=2.33×10-12), SHARPIN (P=1.56×10-9), and ATF5/SIGLEC11 (P=1.03[mult]10-8), and newly significant associations without using AD proxy cases in MTSS1L/IL34 (P=1.80×10-8), APH1B (P=2.10×10-13), and CLNK (P=2.24×10-10). Rare variant (MAF<0.01) associations with genome-wide significance in stage 2 included multiple variants in APOE and TREM2, and a novel association of a rare variant (rs143080277; MAF=0.0054; P=2.69×10-9) in NCK2, further strengthened with the inclusion of UKBB data in stage 3 (P=7.17×10-13). Single-nucleus sequence data shows that NCK2 is highly expressed in amyloid-responsive microglial cells, suggesting a role in LOAD pathology.

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