Utilizing population controls in rare-variant case-parent association tests.


Journal Article

There is great interest in detecting associations between human traits and rare genetic variation. To address the low power implicit in single-locus tests of rare genetic variants, many rare-variant association approaches attempt to accumulate information across a gene, often by taking linear combinations of single-locus contributions to a statistic. Using the right linear combination is key-an optimal test will up-weight true causal variants, down-weight neutral variants, and correctly assign the direction of effect for causal variants. Here, we propose a procedure that exploits data from population controls to estimate the linear combination to be used in an case-parent trio rare-variant association test. Specifically, we estimate the linear combination by comparing population control allele frequencies with allele frequencies in the parents of affected offspring. These estimates are then used to construct a rare-variant transmission disequilibrium test (rvTDT) in the case-parent data. Because the rvTDT is conditional on the parents' data, using parental data in estimating the linear combination does not affect the validity or asymptotic distribution of the rvTDT. By using simulation, we show that our new population-control-based rvTDT can dramatically improve power over rvTDTs that do not use population control information across a wide variety of genetic architectures. It also remains valid under population stratification. We apply the approach to a cohort of epileptic encephalopathy (EE) trios and find that dominant (or additive) inherited rare variants are unlikely to play a substantial role within EE genes previously identified through de novo mutation studies.

Full Text

Cited Authors

  • Jiang, Y; Satten, GA; Han, Y; Epstein, MP; Heinzen, EL; Goldstein, DB; Allen, AS

Published Date

  • June 5, 2014

Published In

Volume / Issue

  • 94 / 6

Start / End Page

  • 845 - 853

PubMed ID

  • 24836453

Pubmed Central ID

  • 24836453

Electronic International Standard Serial Number (EISSN)

  • 1537-6605

Digital Object Identifier (DOI)

  • 10.1016/j.ajhg.2014.04.014


  • eng

Conference Location

  • United States