An empirical test of the significance of an observed quantitative trait locus effect that preserves additive genetic variation.
ABSTRACT 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: 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 genome-wide 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, population-based twin sample with the potential to find genes underlying bone size and bone diseases. 3,782 normal Caucasian females, 18-80 years old, with whole body dual energy X-ray 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 genome-wide 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 5q15-5q23.1. 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.PLoS ONE 02/2008; 3(3):e1752. · 3.73 Impact Factor
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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.Behavior Genetics 10/2008; 39(1):91-100. · 2.61 Impact Factor
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ABSTRACT: In humans, mitochondria contain their own DNA (mtDNA) that is inherited exclusively from the mother. The mitochondrial genome encodes 13 polypeptides that are components of oxidative phosphorylation to produce energy. Any disruption in these genes might interfere with energy production and thus contribute to metabolic derangement. Mitochondria also regulate several important cellular activities including cell death and calcium homeostasis. Aided by sharply declining costs of high-density genotyping, hundreds of mitochondrial variants will soon be available in several cohorts with pedigree structures. Association testing of mitochondrial variants with disease traits using pedigree data raises unique challenges because of the difficulty in separating the effects of nuclear and mitochondrial genomes, which display different modes of inheritance. Failing to correctly account for these effects might decrease power or inflate type I error in association tests. In this report, we sought to identify the best strategy for association testing of mitochondrial variants when genotype and phenotype data are available in pedigrees. We proposed several strategies to account for polygenic effects of the nuclear and mitochondrial genomes and we performed extensive simulation studies to evaluate type I error and power of these strategies. In addition, we proposed two permutation tests to obtain empirical P values for these strategies. Furthermore, we applied two of the analytical strategies to association analysis of 196 mitochondrial variants with blood pressure and fasting blood glucose in the pedigree rich, Framingham Heart Study. Finally, we discussed strategies for study design, genotyping, and data cleaning in association testing of mtDNA in pedigrees.Genetic Epidemiology 01/2013; · 4.02 Impact Factor