Article
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 782450549, USA.
Genetic Epidemiology (Impact Factor: 2.6). 01/1999; 17 Suppl 1(S1):S16973. DOI: 10.1002/gepi.1370170729 Source: PubMed
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 "Using permutation to obtain empirical Pvalues 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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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 Pvalue 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 genomewide 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. 
 "In highly heritable phenotypes, above 60%, a genomewide 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 identicalbydescent (IBD) status at each locus [32]. Significant results were confirmed by computing empirical pvalues for each LODscore using a permutation approach [33]. Chromosomal regions were prioritised if the LOD score was greater than 2. "
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ABSTRACT: Human height is a highly heritable and complex trait but finding important genes has proven more difficult than expected. One reason might be the composite measure of height which may add heterogeneity and noise. The aim of this study was to conduct a genomewide linkage scan to identify quantitative trait loci (QTL) for lengths of spine, femur, tibia, humerus and radius. These were investigated as alternative measures for height in a large, populationbased twin sample with the potential to find genes underlying bone size and bone diseases. 3,782 normal Caucasian females, 1880 years old, with whole body dual energy Xray absorptiometry (DXA) images were used. A novel and reproducible method, linear pixel count (LPC) was used to measure skeletal sizes on DXA images. Intraclass correlations and heritability estimates were calculated for lengths of spine, femur, tibia, humerus and radius on monozygotic (MZ; n = 1,157) and dizygotic (DZ; n = 2,594) twins. A genomewide linkage scan was performed on 2000 DZ twin subjects. All skeletal sites excluding spine were highly correlated. Intraclass correlations showed results for MZ twins to be significantly higher than DZ twins for all traits. Heritability results were as follows: spine, 66%; femur, 73%; tibia, 65%; humerus, 57%; radius, 68%. Results showed reliable evidence of highly suggestive linkage on chromosome 5 for spine (LOD score = 3.0) and suggestive linkage for femur (LOD score = 2.19) in the regions of 105cM and 155cM respectively. We have shown strong heritability of all skeletal sizes measured in this study and provide preliminary evidence that spine length is linked to the chromosomal region 5q155q23.1. Bone size phenotype appears to be more useful than traditional height measures to uncover novel genes. Replication and further fine mapping of this region is ongoing to determine potential genes influencing bone size and diseases affecting bone. 
 "Under a polygenic model of inheritance there is in fact theoretical largesample justification for assuming withinpedigree 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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ABSTRACT: We determine the power of variance component linkage analysis in the case of discrete, dichotomous traits analyzed under a classical liability threshold model. For simplicity we consider randomly ascertained samples and an additive model of variation incorporating a qtl, residual additive genetic factors, and individualspecific random environmental effects. We derive an expression for the power of variance component linkage analysis in arbitrary relative pairs, and compare the power of discrete and quantitative trait linkage analysis in the specific case of sibpairs. The predicted sample sizes required in linkage analysis of sibpairs are confirmed by analysis of simulated data. Unlike the affectedsibpair method, the power of discrete trait variance component analysis increases with trait prevalence. The relative efficiency of a discrete trait for linkage analysis increases with population trait prevalence, but does not exceed about 40% and is typically much less.