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Hum Mutat. 2019 Apr 24. doi: 10.1002/humu.23765. [Epub ahead of print]

Heterozygosity mapping for human dominant trait variants.

Author information

1
Diagnostics and Therapeutics of Intractable Diseases, Intractable Disease Research Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan.
2
Division of Genomic Medicine Research, Medical Genomics Center, National Center for Global Health and Medicine, Tokyo, Japan.
3
Laboratory of Statistical Genetics, Rockefeller University, New York, New York.
4
School of Statistics, Shanxi University of Finance and Economics, Taiyuan, China.
5
Department of Anatomy, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand.
6
McGill University and Genome Québec Innovation Centre, Montréal, Québec, Canada.
7
Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
8
Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Pennsylvania, Philadelphia.
9
Center for Statistical Genetics, Baylor College of Medicine, Houston, Texas.
10
Institute of Biotechnology, Amity University, Gwalior, Madhya Pradesh, India.
11
Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan.

Abstract

Homozygosity mapping is a well-known technique to identify runs of homozygous variants that are likely to harbor genes responsible for autosomal recessive disease, but a comparable method for autosomal dominant traits has been lacking. We developed an approach to map dominant disease genes based on heterozygosity frequencies of sequence variants in the immediate vicinity of a dominant trait. We demonstrate through theoretical analysis that DNA variants surrounding an inherited dominant disease variant tend to have increased heterozygosity compared with variants elsewhere in the genome. We confirm existence of this phenomenon in sequence data with known dominant pathogenic variants obtained on family members and in unrelated population controls. A computer-based approach to estimating empirical significance levels associated with our test statistics shows genome-wide p-values smaller than 0.05 for many but not all of the individuals carrying a pathogenic variant.

KEYWORDS:

ALSPAC; computer simulation; gene mapping; genetic association analysis; sequence variants

PMID:
31018026
DOI:
10.1002/humu.23765

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