A range of simple summary genome-wide statistics for detecting genetic linkage using high density marker data

Ian W. Saunders, Garry N. Hannan, Jesper Brohede, Graham G. Giles, Mark A. Jenkins, John L. Hopper, Melissa C. Southey

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

A simple approach to design and analysis of genome-wide linkage scans is described, based on an approximation to the joint distribution of likelihood ratio statistics at a large number of single nucleotide polymorphism (SNP) loci. The approximation is readily calculated and makes it feasible to study the test properties of a range of summary statistics for the entire sequence of point-wise test values. Both the null distribution in the absence of genetic effects and the alternative distribution under various models of single or multi-gene inheritance can readily be simulated. This allows the power of a variety of statistics to be evaluated. The most powerful statistics proved to be the "quantile statistics" defined as quantiles of the set of pointwise test statistics. As a proof of principle study, the method was applied to a small dataset of 40 individuals from 8 families known to be segregating mutations in one or other of the DNA mismatch repair genes, MLH1 and MSH2. These included seven affected sib pairs and 11 discordant sib pairs, who were genotyped at 57,429 autosomal SNPs using the Affymetrix GeneChip Human Mapping 50 k Xba 240 Array. While the sample size was not sufficient to detect the linkage, there was a clear signal at the MLH1 location indicating that relatively small sample sizes would have been adequate to detect linkage to this gene.

Original languageEnglish
Pages (from-to)565-576
Number of pages12
JournalGenetic Epidemiology
Volume31
Issue number6
DOIs
Publication statusPublished - 1 Sep 2007
Externally publishedYes

Keywords

  • Colon cancer
  • Sample size
  • Sib pair study
  • Statistical power

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