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 78245-0549, USA.

Genetic Epidemiology (Impact Factor: 2.95). 02/1999; 17 Suppl 1:S169-73. DOI: 10.1002/gepi.1370170729 Source: PubMed

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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. · 2.84 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**We describe a variance-components method for multipoint linkage analysis that allows joint consideration of a discrete trait and a correlated continuous biological marker (e.g., a disease precursor or associated risk factor) in pedigrees of arbitrary size and complexity. The continuous trait is assumed to be multivariate normally distributed within pedigrees, and the discrete trait is modeled by a threshold process acting on an underlying multivariate normal liability distribution. The liability is allowed to be correlated with the quantitative trait, and the liability and quantitative phenotype may each include covariate effects. Bivariate discrete-continuous observations will be common, but the method easily accommodates qualitative and quantitative phenotypes that are themselves multivariate. Formal likelihood-based tests are described for coincident linkage (i.e., linkage of the traits to distinct quantitative-trait loci [QTLs] that happen to be linked) and pleiotropy (i.e., the same QTL influences both discrete-trait status and the correlated continuous phenotype). The properties of the method are demonstrated by use of simulated data from Genetic Analysis Workshop 10. In a companion paper, the method is applied to data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate linkage analysis of alcoholism diagnoses and P300 amplitude of event-related brain potentials.The American Journal of Human Genetics 11/1999; 65(4):1134-47. · 10.99 Impact Factor - [Show abstract] [Hide abstract]

**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; · 2.95 Impact Factor

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