Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses.
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ABSTRACT:
SUBMITTER: Barton AR
PROVIDER: S-EPMC8349845 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In biobank data analysis, most binary phenotypes have unbalanced case-control ratios, and this can cause inflation of type I error rates. Recently, a saddle point approximation (SPA) based single-variant test has been developed to provide an accurate and scalable method to test for associations of such phenotypes. For gene- or region-based multiple-variant tests, a few methods exist that can adjust for unbalanced case-control ratios; however, these methods are either less accurate when case-control ratios are extremely unbalanced or not scalable for large data analyses. To address these problems, we propose SKAT- and SKAT-O- type region-based tests; in these tests, the single-variant score statistic is calibrated based on SPA and efficient resampling (ER). Through simulation studies, we show that the proposed method provides well-calibrated p values. In contrast, when the case-control ratio is 1:99, the unadjusted approach has greatly inflated type I error rates (90 times that of exome-wide sequencing α = 2.5 × 10-6). Additionally, the proposed method has similar computation time to the unadjusted approaches and is scalable for large sample data. In our application, the UK Biobank whole-exome sequence data analysis of 45,596 unrelated European samples and 791 PheCode phenotypes identified 10 rare-variant associations with p value < 10-7, including the associations between JAK2 and myeloproliferative disease, HOXB13 and cancer of prostate, and F11 and congenital coagulation defects. All analysis summary results are publicly available through a web-based visual server, and this availability can help facilitate the identification of the genetic basis of complex diseases.
| S-EPMC7042481 | biostudies-literature
Project description: Not available
| S-EPMC10632123 | biostudies-literature
Project description:Genome-wide association studies have uncovered thousands of common variants associated with human disease, but the contribution of rare variants to common disease remains relatively unexplored. The UK Biobank contains detailed phenotypic data linked to medical records for approximately 500,000 participants, offering an unprecedented opportunity to evaluate the effect of rare variation on a broad collection of traits1,2. Here we study the relationships between rare protein-coding variants and 17,361 binary and 1,419 quantitative phenotypes using exome sequencing data from 269,171 UK Biobank participants of European ancestry. Gene-based collapsing analyses revealed 1,703 statistically significant gene-phenotype associations for binary traits, with a median odds ratio of 12.4. Furthermore, 83% of these associations were undetectable via single-variant association tests, emphasizing the power of gene-based collapsing analysis in the setting of high allelic heterogeneity. Gene-phenotype associations were also significantly enriched for loss-of-function-mediated traits and approved drug targets. Finally, we performed ancestry-specific and pan-ancestry collapsing analyses using exome sequencing data from 11,933 UK Biobank participants of African, East Asian or South Asian ancestry. Our results highlight a significant contribution of rare variants to common disease. Summary statistics are publicly available through an interactive portal ( http://azphewas.com/ ).
| S-EPMC8458098 | biostudies-literature
Project description:Genome-wide association studies have discovered hundreds of associations between common genotypes and kidney function but cannot comprehensively investigate rare coding variants. Here, we apply a genotype imputation approach to whole exome sequencing data from the UK Biobank to increase sample size from 166,891 to 408,511. We detect 158 rare variants and 105 genes significantly associated with one or more of five kidney function traits, including genes not previously linked to kidney disease in humans. The imputation-powered findings derive support from clinical record-based kidney disease information, such as for a previously unreported splice allele in PKD2, and from functional studies of a previously unreported frameshift allele in CLDN10. This cost-efficient approach boosts statistical power to detect and characterize both known and novel disease susceptibility variants and genes, can be generalized to larger future studies, and generates a comprehensive resource ( https://ckdgen-ukbb.gm.eurac.edu/ ) to direct experimental and clinical studies of kidney disease.
| S-EPMC9995463 | biostudies-literature
Project description:BackgroundRare diseases collectively affect up to 10% of the population, but often lack effective treatment, and typically little is known about their pathophysiology. Major challenges include suboptimal phenotype mapping and limited statistical power. Population biobanks, such as the UK Biobank, recruit many individuals who can be affected by rare diseases; however, investigation into their utility for rare disease research remains limited. We hypothesized the UK Biobank can be used as a unique population assay for rare diseases in the general population.MethodsWe constructed a consensus mapping between ICD-10 codes and ORPHA codes for rare diseases, then identified individuals with each rare condition in the UK Biobank, and investigated their age at recruitment, sex bias, and comorbidity distributions. Using exome sequencing data from 167,246 individuals of European ancestry, we performed genetic association controlling for case/control imbalance (SAIGE) to identify potential rare pathogenic variants for each disease.ResultsUsing our mapping approach, we identified and characterized 420 rare diseases affecting 23,575 individuals in the UK Biobank. Significant genetic associations included JAK2 V617F for immune thrombocytopenic purpura (p = 1.24 × 10-13) and a novel CALR loss of function variant for essential thrombocythemia (p = 1.59 × 10-13). We constructed an interactive resource highlighting demographic information ( http://www-personal.umich.edu/~mattpat/rareDiseases.html ) and demonstrate transferability by applying our mapping to a medical claims database.ConclusionsEnhanced disease mapping and increased power from population biobanks can elucidate the demographics and genetic associations for rare diseases.
| S-EPMC9364550 | biostudies-literature
Project description:IntroductionA previous study of 200,000 exome-sequenced UK Biobank participants to test for association of rare coding variants with hypertension implicated two genes at exome-wide significance, DNMT3A and FES. A total of 42 genes had an uncorrected p value <0.001. These results were followed up in a larger sample of 470,000 exome-sequenced participants.MethodsWeighted burden analysis of rare coding variants in a new sample of 97,050 cases and 172,263 controls was carried out for these 42 genes. Those showing evidence for association were then analysed in the combined sample of 167,127 cases and 302,691 controls.ResultsThe association of DNMT3A and FES with hypertension was replicated in the new sample and they and the previously implicated gene NPR1, which codes for a membrane-bound guanylate cyclase, were all exome-wide significant in the combined sample. Also exome-wide significant as risk genes for hypertension were GUCY1A1, ASXL1, and SMAD6, while GUCY1B1 had a nominal p value of <0.0001. GUCY1A1 and GUCY1B1 code for subunits of a soluble guanylate cyclase. For two genes, DBH, which codes for dopamine beta hydroxylase, and INPPL1, rare coding variants predicted to impair gene function were protective against hypertension, again with exome-wide significance.ConclusionThe findings offer new insights into biological risk factors for hypertension which could be the subject of further investigation. In particular, genetic variants predicted to impair the function of either membrane-bound guanylate cyclase, activated by natriuretic peptides, or soluble guanylate cyclase, activated by nitric oxide, increase risk of hypertension. Conversely, variants impairing the function of dopamine beta hydroxylase, responsible for the synthesis of norepinephrine, reduce hypertension risk.
| S-EPMC10712968 | biostudies-literature
Project description:A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10-11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene-trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.
| S-EPMC8596853 | biostudies-literature
Project description:Genotype imputation has been the pillar of the success of genome-wide association studies (GWAS) for identifying common variants associated with common diseases. However, most GWAS have been run using only 60 HapMap samples as reference for imputation, meaning less frequent and rare variants not being comprehensively scrutinized. Next-generation arrays ensuring sufficient coverage together with new reference panels, as the 1000 Genomes panel, are emerging to facilitate imputation of low frequent single-nucleotide polymorphisms (minor allele frequency (MAF) <5%). In this study, we present a two-step imputation approach improving the quality of the 1000 Genomes imputation by genotyping only a subset of samples to create a local reference population on a dense array with many low-frequency markers. In this approach, the study sample, genotyped with a first generation array, is imputed first to the local reference sample genotyped on a dense array and hereafter to the 1000 Genomes reference panel. We show that mean imputation quality, measured by the r(2) using this approach, increases by 28% for variants with a MAF between 1 and 5% as compared with direct imputation to 1000 Genomes reference. Similarly, the concordance rate between calls of imputed and true genotypes was found to be significantly higher for heterozygotes (P<1e-15) and rare homozygote calls (P<1e-15) in this low frequency range. The two-step approach in our setting improves imputation quality compared with traditional direct imputation noteworthy in the low-frequency spectrum and is a cost-effective strategy in large epidemiological studies.
| S-EPMC4326719 | biostudies-literature
Project description:Coronary artery disease (CAD) genome-wide association studies typically focus on single nucleotide variants (SNVs), and many potentially associated SNVs fail to reach the GWAS significance threshold. We performed gene and pathway-based association (GBA) tests on publicly available Coronary ARtery DIsease Genome wide Replication and Meta-analysis consortium Exome (n = 120,575) and multi ancestry pan UK Biobank study (n = 442,574) summary data using versatile gene-based association study (VEGAS2) and Multi-marker analysis of genomic annotation (MAGMA) to identify novel genes and pathways associated with CAD. We included only exonic SNVs and excluded regulatory regions. VEGAS2 and MAGMA ranked genes and pathways based on aggregated SNV test statistics. We used Bonferroni corrected gene and pathway significance threshold at 3.0 × 10-6 and 1.0 × 10-5, respectively. We also report the top one percent of ranked genes and pathways. We identified 17 top enriched genes with four genes (PCSK9, FAM177, LPL, ARGEF26), reaching statistical significance (p ≤ 3.0 × 10-6) using both GBA tests in two GWAS studies. In addition, our analyses identified ten genes (DUSP13, KCNJ11, CD300LF/RAB37, SLCO1B1, LRRFIP1, QSER1, UBR2, MOB3C, MST1R, and ABCC8) with previously unreported associations with CAD, although none of the single SNV associations within the genes were genome-wide significant. Among the top 1% non-lipid pathways, we detected pathways regulating coagulation, inflammation, neuronal aging, and wound healing.
| S-EPMC8361107 | biostudies-literature
Project description:AimsThe study aimed to identify specific genes and functional genetic variants affecting susceptibility to two alcohol-related phenotypes: heavy drinking and problem drinking.MethodsPhenotypic and exome sequence data were downloaded from the UK Biobank. Reported drinks in the last 24 hours were used to define heavy drinking, while responses to a mental health questionnaire defined problem drinking. Gene-wise weighted burden analysis was applied, with genetic variants which were rarer and/or had a more severe functional effect being weighted more highly. Additionally, previously reported variants of interest were analysed inidividually.ResultsOf exome sequenced subjects, for heavy drinking, there were 8166 cases and 84,461 controls, while for problem drinking, there were 7811 cases and 59,606 controls. No gene was formally significant after correction for multiple testing, but three genes possibly related to autism were significant at P < 0.001, FOXP1, ARHGAP33 and CDH9, along with VGF which may also be of psychiatric interest. Well established associations with rs1229984 in ADH1B and rs671 in ALDH2 were confirmed, but previously reported variants in ALDH1B1 and GRM3 were not associated with either phenotype.ConclusionsThis large study fails to conclusively implicate any novel genes or variants. It is possible that more definitive results will be obtained when sequence data for the remaining UK Biobank participants become available and/or if data can be obtained for a more extreme phenotype such as alcohol dependence disorder. This research has been conducted using the UK Biobank Resource.
| S-EPMC9270990 | biostudies-literature