Haplotype sharing correlation analysis using family data: A comparison with family-based association test in the presence of allelic heterogeneity
Department of Biostatistics, City of Hope National Medical Center, Duarte, California 91010-3000, USA. Genetic Epidemiology
(Impact Factor: 2.6).
07/2004; 27(1):43-52. DOI: 10.1002/gepi.20005
The haplotype-sharing correlation (HSC) method for association analysis using family data is revisited by introducing a permutation procedure for estimating region-wise significance at each marker on a study segment. In simulation studies, the HSC method has a correct type 1 error rate in both unstructured and structured populations. The HSC signals on disease segments occur in the vicinity of a true disease locus on a restricted region without recombination hotspots. However, the peak signal may not pinpoint the true disease location in a small region with dense markers. The HSC method is shown to have higher power than single- and multilocus family-based association test (FBAT) methods when the true disease locus is unobserved among the study markers, and especially under conditions of weak linkage disequilibrium and multiple ancestral disease alleles. These simulation results suggest that the HSC method has the capacity to identify true disease-associated segments under allelic heterogeneity that go undetected by the FBAT method that compares allelic or haplotypic frequencies.
Available from: Ingrid Borecki
- "Also, for the detection of linkage of complex trait loci, TDT-type methods may have greater power than traditional linkage analyses under certain circumstances (Risch & Merikangas, 1996). Motivated by the increasing availability of high-density single nucleotide polymorphism (SNP) markers within genes and across the genome, recent developments of TDT-type tests have been focused on how to efficiently use haplotype information from multiple closely linked markers (Lazzeroni & Lange, 1998; Clayton, 1999; Clayton & Jones, 1999; Dudbridge et al. 2000; Bourgain et al. 2000, 2001, 2002; Rabinowitz & Laird, 2000; Zhao et al. 2000; Li et al. 2001; Seltman et al. 2001, 2003; Zhang et al. 2003; Qian, 2004). In general, simultaneously studying multiple markers in genetic dissection of complex traits tends to more powerful than a single marker (Akey et al. 2001; Morris & Kaplan, 2002). "
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ABSTRACT: Taking advantage of increasingly available high-density single nucleotide polymorphisms (SNP) markers across the genome, various types of transmission/disequilibrium tests (TDT) using haplotype information have been developed. A practical challenge arising in such studies is the possibility that transmitted haplotypes have inherited disease-causing mutations from different ancestral chromosomes, or do not bear any disease-causing mutations (founder heterogeneity). To reduce the loss of signal strength due to founder heterogeneity, we propose an SP-TDT test that combines a sequential peeling procedure with the haplotype similarity based TDT method. The proposed SP-TDT method is applicable to any size of nuclear family with or without ambiguous phase information. Simulation studies suggest that the SP-TDT method has the correct type I error rate in stratified populations, and enhanced power compared with some existing haplotype similarity based TDT methods. Finally, we apply the proposed method to study the association of the leptin gene with obesity from the National Heart, Lung, and Blood Institute Family Heart Study.
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ABSTRACT: Moderately dense maps of single-nucleotide polymorphism (SNP) markers across the human genome for both the simulated data set and data from the Collaborative Study of the Genetics of Alcoholism were available at Genetic Analysis Workshop 14 for the first time. This allowed examination of various novel and existing methods for haplotype analyses. Three contributors applied Mantel statistics in different ways for both linkage and association analysis by using the shared length between two haplotypes at a marker locus as a measure of genetic similarity. The results indicate that haplotype-sharing based on Mantel statistics can be a powerful approach and needs further methodological evaluation. Four contributors investigated haplotype-tagging SNP (htSNP) selection procedures, two contributors examined the use of multilocus haplotypes compared to single loci in association tests, and two contributors compared the accuracy of various methods for reconstructing haplotypes and estimating haplotype frequencies for both pedigree data and data from unrelated individuals. For all three different tasks, software packages and procedures gave similar results in regions of high linkage disequilibrium (LD). However, they were not as consistent in regions of moderate to low LD. One coalescence-based approach for estimating haplotype frequencies, coupled with a Markov chain Monte Carlo technique, outperformed the other haplotype frequency estimation methods in regions of low LD. In conclusion, regardless of the task, results were similar in chromosomal regions of high LD. However, based on the differing results observed here, methodological improvements are required for chromosomal regions of low to moderate LD.
Available from: Duncan C Thomas
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ABSTRACT: The potential value of haplotypes has attracted widespread interest in the mapping of complex traits. Haplotype sharing methods take the linkage disequilibrium information between multiple markers into account, and may have good power to detect predisposing genes. We present a new approach based on Mantel statistics for spacetime clustering, which is developed in order to improve the power of haplotype sharing analysis for gene mapping in complex disease.
The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes for case-only and case-control studies. The genetic similarity is measured as the shared length between haplotypes around a putative disease locus. The phenotypic similarity is measured as the mean-corrected cross-product based on the respective phenotypes. We analyzed two tests for statistical significance with respect to type I error: (1) assuming asymptotic normality, and (2) using a Monte Carlo permutation procedure. The results were compared to the chi(2) test for association based on 3-marker haplotypes.
The results of the type I error rates for the Mantel statistics using the permutational procedure yielded pointwise valid tests. The approach based on the assumption of asymptotic normality was seriously liberal.
Power comparisons showed that the Mantel statistics were better than or equal to the chi(2) test for all simulated disease models.
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