Multiple Genetic Variant Association Testing by Collapsing and Kernel Methods With Pedigree or Population Structured Data

Daniel J Schaid, Shannon K. Mcdonnell, Jason P. Sinnwell, Stephen N Thibodeau

Research output: Contribution to journalArticle

56 Scopus citations


Searching for rare genetic variants associated with complex diseases can be facilitated by enriching for diseased carriers of rare variants by sampling cases from pedigrees enriched for disease, possibly with related or unrelated controls. This strategy, however, complicates analyses because of shared genetic ancestry, as well as linkage disequilibrium among genetic markers. To overcome these problems, we developed broad classes of "burden" statistics and kernel statistics, extending commonly used methods for unrelated case-control data to allow for known pedigree relationships, for autosomes and the X chromosome. Furthermore, by replacing pedigree-based genetic correlation matrices with estimates of genetic relationships based on large-scale genomic data, our methods can be used to account for population-structured data. By simulations, we show that the type I error rates of our developed methods are near the asymptotic nominal levels, allowing rapid computation of P-values. Our simulations also show that a linear weighted kernel statistic is generally more powerful than a weighted "burden" statistic. Because the proposed statistics are rapid to compute, they can be readily used for large-scale screening of the association of genomic sequence data with disease status.

Original languageEnglish (US)
Pages (from-to)409-418
Number of pages10
JournalGenetic Epidemiology
Issue number5
StatePublished - Jul 2013



  • Burden test
  • Genome sequence data
  • Kernel statistic
  • Pedigree data
  • Rare variants

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

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