Medical relevance of protein-truncating variants across 337,208 individuals in the UK Biobank study

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. We characterized the effect of 18,228 proteintruncating variants across 135 phenotypes from the UK Biobank and found 27 associations between medical phenotypes and protein-truncating variants in genes outside the major histocompatibility complex. We performed phenome-wide analyses and directly measured the effect of homozygous carriers, commonly referred to as "human knockouts," across medical phenotypes for genes implicated to be protective against disease or associated with at least one phenotype in our study and found several genes with strong pleiotropic or non-additive effects. Our results illustrate the importance of protein-truncating variants in a variety of diseases.Protein-truncating variants (PTVs), genetic variants predicted to shorten the coding sequence of genes, are a promising set of variants for drug discovery since identification of PTVs that protect against human disease provides in vivo validation of therapeutic targets 1,2,3,4 . Although tens of thousands of standing germline PTVs have been identified 5,6 , their medical relevance across a broad range of phenotypes has not been characterized. Because most PTVs are present at low frequency, assessing the effects of PTVs requires genotype data from a large number of individuals with linked phenotype data for a variety of diseases and physiological measurements. The recent release of genotype and linked clinical and questionnaire data for 488,377 individuals in the UK Biobank provides an unprecedented opportunity to assess the clinical impact of truncating protein-coding genes at a resolution not previously possible.. CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/179762 doi: bioRxiv preprint first posted online Aug. 23, 2017; ResultsTo assess the clinical relevance of PTVs, we cataloged predicted PTVs present in the Affymetrix UK Biobank array and their effects on medical phenotypes from 337,208 unrelated individuals in the UK Biobank study 7,8 . We defined PTVs as single-nucleotide variants (SNVs) predicted to introduce a premature stop codon or to disrupt a splice site or small insertions or deletions (indels) predicted to disrupt a transcript's reading frame 5 . We identified 18,228 predicted PTVs in the UK Biobank array that were polymorphic across 8,750 genes after filtering (Methods, Figure S1). Each participant had 95 predicted PTVs with minor allele frequency (MAF) less than 1% on average, and 778 genes were predicted to be homozygous or compound heterozygous for PTVs with MAF less than 1% in at least one individual. The observed number of PTVs per individual is consistent with the ~100 loss-of-function variants observed in the 1000 Genomes project 9 . In contrast...

Identification and Replication of Six Loci Associated With Gallstone Disease

“…The development team was granted access to data on the full UK Biobank cohort. Participants who satisfied the following criteria were included for the genetic analyses: (1) self‐reported white British ancestry, (2) not used to compute principal components, (3) not outliers for heterozygosity and missingness, (4) no evidence of sex chromosome aneuploidy, and (5) 10 or fewer putative third‐degree relatives in the cohort . In total, 151,169 individuals who did not meet these criteria were removed.…”

“…Either phenotypes were retained from the original data fields or data fields were computationally combined and manually revised to create phenotypes. The data fields from the UK Biobank used for phenotypes included hospital inpatient health‐related outcomes summary information data, computational grouping of phenotypes with cancer registry, death registry data, family history, and verbal questionnaire data . The association analysis with phenotypes was performed using logistic regression with Firth‐fallback in PLINK 2.00, adjusted for sex, age, array type, and the first four genetic principal components …”

“…The HC188 code was created by combining UK Biobank participants with in‐hospital ICD, Ninth Revision (ICD‐9) code K80 (cholelithiasis) in UK Biobank data field 41202 and participants with self‐reported “cholelithiasis/gallstones” from data field 20002. There were 14,940 cases of HC188 (cholelithiasis/gallstones) in GBE, with ICD‐defined cases comprising ~80% and self‐reported cases comprising ~20% . As controls, 322,268 participants who had neither of the diagnoses were used.…”

Identification And Replication Of Six Loci Associated With Gallstone Disease

“…Participants who satisfied the following criteria were included for the genetic analyses: (1) self-reported white British ancestry, (2) not used to compute principal components, (3) not outliers for heterozygosity and missingness, (4) no evidence of sex chromosome aneuploidy, and (5) 10 or fewer putative third-degree relatives in the cohort. (15) In total, 151,169 individuals who did not meet these criteria were removed. Genetic associations were calculated with UK Biobank data from the remaining 337,208 participants.…”

“…The data fields from the UK Biobank used for phenotypes included hospital inpatient health-related outcomes summary information data, computational grouping of phenotypes with cancer registry, death registry data, family history, and verbal questionnaire data. (15) The association analysis with phenotypes was performed using logistic regression with Firth-fallback in PLINK 2.00, adjusted for sex, age, array type, and the first four genetic principal components. (15) The GWAS tool in GBE displays information on chromosomal position (GRCh37), reference and risk allele, rs number, associated gene, effect on amino acid sequence, minor allele frequency (MAF), and odds ratio (OR)/beta-coefficients and P values from regression analyses with the trait or disease of interest.…”