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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    ABSTRACT: Linkage analysis in multivariate or longitudinal context presents both statistical and computational challenges. The permutation test can be used to avoid some of the statistical challenges, but it substantially adds to the computational burden. Utilizing the distributional dependencies between p (defined as the proportion of alleles at a locus that are identical by descent (IBD) for a pairs of relatives, at a given locus) and the permutation test we report a new method of efficient permutation. In summary, the distribution of p for a sample of relatives at locus x is estimated as a weighted mixture of p drawn from a pool of 'representative' p distributions observed at other loci. This weighting scheme is then used to sample from the distribution of the permutation tests at the representative loci to obtain an empirical P-value at locus x (which is asymptotically distributed as the permutation test at loci x). This weighted mixture approach greatly reduces the number of permutation tests required for genome-wide scanning, making it suitable for use in multivariate and other computationally intensive linkage analyses. In addition, because the distribution of p is a property of the genotypic data for a given sample and is independent of the phenotypic data, the weighting scheme can be applied to any phenotype (or combination of phenotypes) collected from that sample. We demonstrate the validity of this approach through simulation.
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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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    • "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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