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Association screening for genes with multiple potentially rare variants: an inverse-probability weighted clustering approach.

Authors
  • Liu, Ying
  • Huang, Chien Hsun
  • Hu, Inchi
  • Lo, Shaw-Hwa
  • Zheng, Tian
Type
Published Article
Journal
BMC Proceedings
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Jan 01, 2011
Volume
5 Suppl 9
Identifiers
DOI: 10.1186/1753-6561-5-S9-S106
PMID: 22373536
Source
Medline
License
Unknown

Abstract

Both common variants and rare variants are involved in the etiology of most complex diseases in humans. Developments in sequencing technology have led to the identification of a high density of rare variant single-nucleotide polymorphisms (SNPs) on the genome, each of which affects only at most 1% of the population. Genotypes derived from these SNPs allow one to study the involvement of rare variants in common human disorders. Here, we propose an association screening approach that treats genes as units of analysis. SNPs within a gene are used to create partitions of individuals, and inverse-probability weighting is used to overweight genotypic differences observed on rare variants. Association between a phenotype trait and the constructed partition is then evaluated. We consider three association tests (one-way ANOVA, chi-square test, and the partition retention method) and compare these strategies using the simulated data from the Genetic Analysis Workshop 17. Several genes that contain causal SNPs were identified by the proposed method as top genes.

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