Selection and subsequent analysis of sib pair data for QTL detection

D G Chatziplis, H Hamann, C S Haley

Research output: Contribution to journalArticlepeer-review

Abstract

Haseman and Elston (1972) developed a robust regression method for the detection of linkage between a marker and a quantitative trait locus (QTL) using sib pair data. The principle underlying this method is that the difference in phenotypes between pairs of sibs becomes larger as they share a decreasing number of alleles at a particular QTL identical by descent (IBD) from their parents. In this case, phenotypically very different sibs will also on average share a proportion of alleles IBD at any marker linked to the QTL that is lower than the expected value of 0.5. Thus, the deviation of the proportion of marker alleles IBD from the expected value in pairs of sibs selected to be phenotypically different (i.e. discordant) can provide a test for the presence of a QTL. A simple regression method for QTL detection in sib pairs selected for high phenotypic differences is presented here. The power of the analytical method was found to be greater than the power obtained using the standard analysis when samples of sib pairs with high phenotypic differences were used. However, the use of discordant sib pairs was found to be less powerful for QTL detection than alternative selective genotyping schemes based on the phenotypic values of the sibs except with intense selection, when its advantage was only marginal. The most effective selection scheme overall was the use of sib pairs from entire families selected on the basis of high within-family variance for the trait in question. There is little effect of selection on QTL position estimates, which are in good agreement with the simulated values. However, QTL variance estimates are biased to a greater or lesser degree, depending on the selection method.
Original languageEnglish
Pages (from-to)177-86
Number of pages10
JournalGenetics Research
Volume78
Issue number2
Publication statusPublished - Oct 2001

Keywords

  • Biological Evolution
  • Computer Simulation
  • Female
  • Genotype
  • Humans
  • Male
  • Mathematics
  • Models, Statistical
  • Nuclear Family
  • Quantitative Trait, Heritable
  • Selection, Genetic

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