Format

Send to

Choose Destination
Genet Epidemiol. 2017 May;41(4):309-319. doi: 10.1002/gepi.22037. Epub 2017 Feb 13.

Gene-based segregation method for identifying rare variants in family-based sequencing studies.

Author information

1
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
2
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States of America.
3
Department of Public Health Science, Seoul National University, Seoul, Republic of Korea.
4
Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
5
Department of Biostatistics, Anschutz Medical Campus, University of Colorado, Aurora, Colorado, United States of America.
6
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
7
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
8
University of Washington Center for Mendelian Genomics, Seattle, Washington, United States of America.

Abstract

Whole-exome sequencing using family data has identified rare coding variants in Mendelian diseases or complex diseases with Mendelian subtypes, using filters based on variant novelty, functionality, and segregation with the phenotype within families. However, formal statistical approaches are limited. We propose a gene-based segregation test (GESE) that quantifies the uncertainty of the filtering approach. It is constructed using the probability of segregation events under the null hypothesis of Mendelian transmission. This test takes into account different degrees of relatedness in families, the number of functional rare variants in the gene, and their minor allele frequencies in the corresponding population. In addition, a weighted version of this test allows incorporating additional subject phenotypes to improve statistical power. We show via simulations that the GESE and weighted GESE tests maintain appropriate type I error rate, and have greater power than several commonly used region-based methods. We apply our method to whole-exome sequencing data from 49 extended pedigrees with severe, early-onset chronic obstructive pulmonary disease (COPD) in the Boston Early-Onset COPD study (BEOCOPD) and identify several promising candidate genes. Our proposed methods show great potential for identifying rare coding variants of large effect and high penetrance for family-based sequencing data. The proposed tests are implemented in an R package that is available on CRAN (https://cran.r-project.org/web/packages/GESE/).

KEYWORDS:

Mendelian disease; extended families; filtering approach; whole exome sequencing

PMID:
28191685
PMCID:
PMC5397337
DOI:
10.1002/gepi.22037
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center