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PubMedDate not supplied

A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information.

A general approach to family-based examinations of association between marker alleles and traits is proposed. The approach is based on computing p values by comparing test statistics for association to their conditional distributions given the minimal sufficient statistic under the null hypothesis for the genetic model, sampling plan and population admixture. The approach can be applied with any test statistic, so any kind of phenotype and multi-allelic markers may be examined, and covariates may be included in analyses. By virtue of the conditioning, the approach results in correct type I error probabilities regardless of population admixture, the true genetic model and the sampling strategy. An algorithm for computing the conditional distributions is described, and the results of the algorithm for configurations of nuclear families are presented. The algorithm is applicable with all pedigree structures and all patterns of missing marker allele information.

Algorithms↗
PubMed2006

Comprehensive association testing of common mitochondrial DNA variation in metabolic disease.

Many lines of evidence implicate mitochondria in phenotypic variation: (a) rare mutations in mitochondrial proteins cause metabolic, neurological, and muscular disorders; (b) alterations in oxidative phosphorylation are characteristic of type 2 diabetes, Parkinson disease, Huntington disease, and other diseases; and (c) common missense variants in the mitochondrial genome (mtDNA) have been implicated as having been subject to natural selection for adaptation to cold climates and contributing to "energy deficiency" diseases today. To test the hypothesis that common mtDNA variation influences human physiology and disease, we identified all 144 variants with frequency >1% in Europeans from >900 publicly available European mtDNA sequences and selected 64 tagging single-nucleotide polymorphisms that efficiently capture all common variation (except the hypervariable D-loop). Next, we evaluated the complete set of common mtDNA variants for association with type 2 diabetes in a sample of 3,304 diabetics and 3,304 matched nondiabetic individuals. Association of mtDNA variants with other metabolic traits (body mass index, measures of insulin secretion and action, blood pressure, and cholesterol) was also tested in subsets of this sample. We did not find a significant association of common mtDNA variants with these metabolic phenotypes. Moreover, we failed to identify any physiological effect of alleles that were previously proposed to have been adaptive for energy metabolism in human evolution. More generally, this comprehensive association-testing framework can readily be applied to other diseases for which mitochondrial dysfunction has been implicated.

Body Mass Index↗
PubMed2003

Genetic association tests with age at onset.

Many diseases or traits exhibit a varying age at onset. Recent data examples of prostate cancer and childhood diabetes show that compared to simply treating the disease outcome as affected vs. unaffected, incorporation of age-at-onset information into the transmission/disequilibrium type of test (TDT) does not appear to change the results much. In this paper, we evaluate the power of TDT as a function of age at onset, and show that age-at-onset information is most useful when the disease is common, or the relative risk associated with the high-risk genotype varies with age. Moreover, an extremely old unaffected subject can contribute substantially to the power of the TDT, sometimes as much as old-onset subjects. We propose a modified test statistic for testing no association between the marker at the candidate locus and age at onset. The simulation study was conducted to evaluate the finite sample properties of proposed and the TDT test statistics under various sampling schemes for trios of parents and offspring, as well as for sibling clusters where unaffected siblings were used as controls.

Age of Onset↗
PubMed1994

Instructions for additional qualitative scoring of the initial-letter Word-association Test.

An additional scoring method is based on grouping test-words according to whether the same sign is given by subjects to the test-words. In this way five test-word categories are formed, Eros (test-words with double plus signs), demi-Eros (single plus sign), demi-Thanatos (single minus), Thanatos (double minus), and Deviant (+/- and theta signs). The next step in scoring is to count the number of test-words in a given scoring category whose meanings do not conform. The greater the discrepancy between the test-word category and its meaning, the less well adapted is the subject. Several illustrative protocols are discussed.

Adult↗
PubMed2005

Sibship T2 association tests of complex diseases for tightly linked markers.

For population case-control association studies, the false-positive rates can be high due to inappropriate controls, which can occur if there is population admixture or stratification. Moreover, it is not always clear how to choose appropriate controls. Alternatively, the parents or normal sibs can be used as controls of affected sibs. For late-onset complex diseases, parental data are not usually available. One way to study late-onset disorders is to perform sib-pair or sibship analyses. This paper proposes sibship-based Hotelling's T2 test statistics for high-resolution linkage disequilibrium mapping of complex diseases. For a sample of sibships, suppose that each sibship consists of at least one affected sib and at least one normal sib. Assume that genotype data of multiple tightly linked markers/haplotypes are available for each individual in the sample. Paired Hotelling's T2 test statistics are proposed for high-resolution association studies using normal sibs as controls for affected sibs, based on two coding methods: 'haplotype/allele coding' and 'genotype coding'. The paired Hotelling's T2 tests take into account not only the correlation among the markers, but also take the correlation within each sib-pair. The validity of the proposed method is justified by rigorous mathematical and statistical proofs under the large sample theory. The non-centrality parameter approximations of the test statistics are calculated for power and sample size calculations. By carrying out power and simulation studies, it was found that the non-centrality parameter approximations of the test statistics were accurate. By power and type I error analysis, the test statistics based on the 'haplotype/allele coding' method were found to be advantageous in comparison to the test statistics based on the 'genotype coding' method. The test statistics based on multiple markers can have higher power than those based on a single marker. The test statistics can be applied not only for bi-allelic markers, but also for multi-allelic markers. In addition, the test statistics can be applied to analyse the genetic data of multiple markers which contain double heterozygotes--that is, unknown linkage phase data. An SAS macro, Hotel_sibs.sas, is written to implement the method for data analysis.

Diabetes Mellitus↗
PubMed2006

Power and sample size for testing associations of haplotypes with complex traits.

Evaluation of the association of haplotypes with either quantitative traits or disease status is common practice, and under some situations provides greater power than the evaluation of individual marker loci. The focus on haplotype analyses will increase as more single nucleotide polymorphisms (SNPs) are discovered, either because of interest in candidate gene regions, or because of interest in genome-wide association studies. However, there is little guidance on the determination of the sample size needed to achieve the desired power for a study, particularly when linkage phase of the haplotypes is unknown, and when a subset of tag-SNP markers is measured. There is a growing wealth of information on the distribution of haplotypes in different populations, and it is not unusual for investigators to measure genetic markers in pilot studies in order to gain knowledge of the distribution of haplotypes in the target population. Starting with this basic information on the distribution of haplotypes, we derive analytic methods to determine sample size or power to test the association of haplotypes with either a quantitative trait or disease status (e.g., a case-control study design), assuming that all subjects are unrelated. Our derivations cover both phase-known and phase-unknown haplotypes, allowing evaluation of the loss of efficiency due to unknown phase. We also extend our methods to when a subset of tag-SNPs is chosen, allowing investigators to explore the impact of tag-SNPs on power. Simulations illustrate that the theoretical power predictions are quite accurate over a broad range of conditions. Our theoretical formulae should provide useful guidance when planning haplotype association studies.

Computer Simulation↗