Protein-altering variants at copy number-variable regions influence diverse human phenotypes
Margaux L A Hujoel1,2,3, Robert E Handsaker4,5,6, Maxwell A Sherman7,8,4,9,10
1Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. mhujoel@broadinstitute.org.
Nature Genetics
|March 29, 2024
Summary
Copy number variants (CNVs) analysis revealed significant associations between genetic variations and 41 quantitative traits. This study highlights the impact of previously overlooked genomic variations on human health and disease risk.
Area of Science:
- Genomics
- Human Genetics
- Bioinformatics
Background:
- Copy number variants (CNVs) represent large genetic variations but are often under-ascertained in genetic association studies.
- Previous analyses have limitations in detecting subexonic CNVs and variations within segmental duplications.
Purpose of the Study:
- To ascertain protein-altering CNVs from large-scale whole-exome sequencing data.
- To investigate the association of these CNVs with quantitative traits.
- To explore the contribution of previously undetected genomic variations to human traits and diseases.
Main Methods:
- Utilized UK Biobank whole-exome sequencing data (n=468,570).
- Employed haplotype-informed methods for ascertaining protein-altering CNVs, including subexonic CNVs and variations in segmental duplications.
- Integrated CNVs into analyses of rare variants predicted to cause gene loss of function (LOF).
Main Results:
- Identified 100 associations between predicted LOF variants (including CNVs) and 41 quantitative traits.
- A partial deletion in RGL3 exon 6 showed a strong protective effect against hypertension risk (OR=0.86).
- Variations in segmental duplications contributed significantly to type 2 diabetes risk, chronotype, and blood cell traits.
Conclusions:
- Protein-altering CNVs and variations in segmental duplications are crucial genetic factors influencing quantitative traits.
- New insights into human genetics can be gained by analyzing genomic variations previously missed by standard methods.
- This study underscores the importance of comprehensive CNV analysis in understanding genetic contributions to health and disease.
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