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

Dandi Qiao, Christoph Lange, Nan M. Laird, Sungho Won, Craig P. Hersh, Jarrett Morrow, Brian D. Hobbs, Sharon M. Lutz, Ingo Ruczinski, Terri H. Beaty, Edwin K. Silverman, Michael H. Cho

Research output: Contribution to journalArticlepeer-review

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/).

Original languageEnglish (US)
Pages (from-to)309-319
Number of pages11
JournalGenetic epidemiology
Volume41
Issue number4
DOIs
StatePublished - May 2017

Keywords

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

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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