Methods for association analysis and meta-analysis of rare variants in families

Shuang Feng, Giorgio Pistis, He Zhang, Matthew Zawistowski, Antonella Mulas, Magdalena Zoledziewska, Oddgeir L. Holmen, Fabio Busonero, Serena Sanna, Kristian Hveem, Cristen Willer, Francesco Cucca, Dajiang J. Liu, Gonçalo R. Abecasis

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Advances in exome sequencing and the development of exome genotyping arrays are enabling explorations of association between rare coding variants and complex traits. To ensure power for these rare variant analyses, a variety of association tests that group variants by gene or functional unit have been proposed. Here, we extend these tests to family-based studies. We develop family-based burden tests, variable frequency threshold tests and sequence kernel association tests. Through simulations, we compare the performance of different tests. We describe situations where family-based studies provide greater power than studies of unrelated individuals to detect rare variants associated with moderate to large changes in trait values. Broadly speaking, we find that when sample sizes are limited and only a modest fraction of all trait-associated variants can be identified, family samples are more powerful. Finally, we illustrate our approach by analyzing the relationship between coding variants and levels of high-density lipoprotein (HDL) cholesterol in 11,556 individuals from the HUNT and SardiNIA studies, demonstrating association for coding variants in the APOC3, CETP, LIPC, LIPG, and LPL genes and illustrating the value of family samples, meta-analysis, and gene-level tests. Our methods are implemented in freely available C++ code.

Original languageEnglish (US)
Pages (from-to)227-238
Number of pages12
JournalGenetic Epidemiology
Volume39
Issue number4
DOIs
StatePublished - May 1 2015

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Genetics(clinical)

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