An empirical test of the significance of an observed quantitative trait locus effect that preserves additive genetic variation

Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas 78245-0549, USA.
Genetic Epidemiology (Impact Factor: 2.6). 01/1999; 17 Suppl 1(S1):S169-73. DOI: 10.1002/gepi.1370170729
Source: PubMed


We propose a constrained permutation test that assesses the significance of an observed quantitative trait locus effect against a background of genetic and environmental variation. Permutations of phenotypes are not selected at random, but rather are chosen in a manner that attempts to maintain the additive genetic variability in phenotypes. Such a constraint maintains the nonindependence among observations under the null hypothesis of no linkage. The empirical distribution of the lod scores calculated using permuted phenotypes is compared to that obtained using phenotypes simulated from the assumed underlying multivariate normal model. We make comparisons of univariate analyses for both a quantitative phenotype that appears consistent with a multivariate normal model and a quantitative phenotype containing pronounced outliers. An example of a bivariate analysis is also presented.

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    • "Using permutation to obtain empirical P-values in the context of linkage analysis has been described previously (Iturria et al. 1999; Wan et al. 1997). In brief, consider a variance components linkage analysis of in a sample of full sibling pairs. "
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    • "In highly heritable phenotypes, above 60%, a genome-wide scan was implemented using a modified version of the Haseman and Elston method [31] in a generalised linear model in which the square of the sibling differences was regressed on estimated identical-by-descent (IBD) status at each locus [32]. Significant results were confirmed by computing empirical p-values for each LOD-score using a permutation approach [33]. Chromosomal regions were prioritised if the LOD score was greater than 2. "
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    PLoS ONE 02/2008; 3(3):e1752. DOI:10.1371/journal.pone.0001752 · 3.23 Impact Factor
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    • "Under a polygenic model of inheritance there is in fact theoretical large-sample justification for assuming within-pedigree multivariate normality of the phenotype vector (Lange, 1978, 1997), but in the presence of a QTL the liability phenotype reflects a mixture of genotypic distributions and the assumption of normality is obligately violated (Morton & MacLean, 1974; Elston, 1980; Blangero, 1998). Numerous studies have shown, however, that the assumption of normality is robust to reasonable violations (Beaty et al. 1985; Searle et al. 1992; Amos et al. 1996; Allison et al. 1999; Iturria et al. 1999; Blangero et al. 2001). Indeed, if only two variance components are modelled, one of which is "
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