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Protein-altering variants at copy number-variable regions influence diverse human phenotypes.

ABSTRACT: Copy number variants (CNVs) are among the largest genetic variants, yet CNVs have not been effectively ascertained in most genetic association studies. Here we ascertained protein-altering CNVs from UK Biobank whole-exome sequencing data (n = 468,570) using haplotype-informed methods capable of detecting subexonic CNVs and variation within segmental duplications. Incorporating CNVs into analyses of rare variants predicted to cause gene loss of function (LOF) identified 100 associations of predicted LOF variants with 41 quantitative traits. A low-frequency partial deletion of RGL3 exon 6 conferred one of the strongest protective effects of gene LOF on hypertension risk (odds ratio = 0.86 (0.82-0.90)). Protein-coding variation in rapidly evolving gene families within segmental duplications-previously invisible to most analysis methods-generated some of the human genome's largest contributions to variation in type 2 diabetes risk, chronotype and blood cell traits. These results illustrate the potential for new genetic insights from genomic variation that has escaped large-scale analysis to date.

SUBMITTER: Hujoel MLA

PROVIDER: S-EPMC11018521 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Protein-altering variants at copy number-variable regions influence diverse human phenotypes.

Hujoel Margaux L A MLA Handsaker Robert E RE Sherman Maxwell A MA Kamitaki Nolan N Barton Alison R AR Mukamel Ronen E RE Terao Chikashi C McCarroll Steven A SA Loh Po-Ru PR

Nature genetics 20240328 4

Copy number variants (CNVs) are among the largest genetic variants, yet CNVs have not been effectively ascertained in most genetic association studies. Here we ascertained protein-altering CNVs from UK Biobank whole-exome sequencing data (n = 468,570) using haplotype-informed methods capable of detecting subexonic CNVs and variation within segmental duplications. Incorporating CNVs into analyses of rare variants predicted to cause gene loss of function (LOF) identified 100 associations of predic ...[more]

PMID: 38548989

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Project description:BACKGROUND: Algorithms and software for CNV detection have been developed, but they detect the CNV regions sample-by-sample with individual-specific breakpoints, while common CNV regions are likely to occur at the same genomic locations across different individuals in a homogenous population. Current algorithms to detect common CNV regions do not account for the varying reliability of the individual CNVs, typically reported as confidence scores by SNP-based CNV detection algorithms. General methodologies for identifying these recurrent regions, especially those directed at SNP arrays, are still needed. RESULTS: In this paper, we describe two new approaches for identifying common CNV regions based on (i) the frequency of occurrence of reliable CNVs, where reliability is determined by high confidence scores, and (ii) a weighted frequency of occurrence of CNVs, where the weights are determined by the confidence scores. In addition, motivated by the fact that we often observe partially overlapping CNV regions as a mixture of two or more distinct subregions, regions identified using the two approaches can be fine-tuned to smaller sub-regions using a clustering algorithm. We compared the performance of the methods with sequencing-based results in terms of discordance rates, rates of departure from Hardy-Weinberg equilibrium (HWE) and average frequency and size of the identified regions. The discordance rates as well as the rates of departure from HWE decrease when we select CNVs with higher confidence scores. We also performed comparisons with two previously published methods, STAC and GISTIC, and showed that the methods we consider are better at identifying low-frequency but high-confidence CNV regions. CONCLUSIONS: The proposed methods for identifying common CNV regions in multiple individuals perform well compared to existing methods. The identified common regions can be used for downstream analyses such as group comparisons in association studies.

Dataset Information

Protein-altering variants at copy number-variable regions influence diverse human phenotypes.

Publications

Protein-altering variants at copy number-variable regions influence diverse human phenotypes.

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