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

Margaux L A Hujoel; Robert E Handsaker; Maxwell A Sherman; Nolan Kamitaki; Alison R Barton; Ronen E Mukamel; Chikashi Terao; Steven A McCarroll; Po-Ru Loh

doi:10.1038/s41588-024-01684-z

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

Nat Genet. 2024 Apr;56(4):569-578. doi: 10.1038/s41588-024-01684-z. Epub 2024 Mar 28.

Authors

Margaux L A Hujoel^{1

2

3}, Robert E Handsaker^{4

5

6}, Maxwell A Sherman^{7

8

4

9

10}, Nolan Kamitaki^{7

8

4

11}, Alison R Barton^{7

8

11

12}, Ronen E Mukamel^{7

8

4}, Chikashi Terao^{13

14

15}, Steven A McCarroll^{4

5

6}, Po-Ru Loh^{16

17

18}

Affiliations

¹ Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. mhujoel@broadinstitute.org.
² Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. mhujoel@broadinstitute.org.
³ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. mhujoel@broadinstitute.org.
⁴ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁵ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA, USA.
⁶ Department of Genetics, Harvard Medical School, Boston, MA, USA.
⁷ Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
⁸ Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
⁹ Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
¹⁰ Serinus Biosciences Inc., New York, NY, USA.
¹¹ Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
¹² Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA.
¹³ Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
¹⁴ Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan.
¹⁵ Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan.
¹⁶ Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. poruloh@broadinstitute.org.
¹⁷ Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. poruloh@broadinstitute.org.
¹⁸ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA. poruloh@broadinstitute.org.

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.

MeSH terms

DNA Copy Number Variations* / genetics
Diabetes Mellitus, Type 2* / genetics
Exons
Genetic Association Studies
Humans
Phenotype

Abstract

MeSH terms

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