Haplotype sharing correlation analysis using family data: A comparison with family-based association test in the presence of allelic heterogeneity
ABSTRACT 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.
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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.Annals of Human Genetics 08/2005; 69(Pt 4):455-67. DOI:10.1046/j.1529-8817.2005.00168.x · 1.93 Impact Factor
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ABSTRACT: There are two aspects regarding the age of alleles that are relevant as indicators of the timing of mutational events. The first is to know which alleles are species-specific; the second is about the time of origin of species-specific alleles. Both aspects can be analyzed using haplotype-sharing methods, by using the length of shared haplotypes as a measure of the speed of coalescence to common ancestors. The availability of sequence data for closely related species makes it possible to infer the original SNP allele. The allele present in more than one species is the original allele. In general, original alleles are expected to be more frequent, because the cumulative effects of genetic drift determine the maximum frequency a new mutant can reach. The human species is relatively young, and founder effects are still observable as extended linkage disequilibrium. Coalescence to a single founder takes place in human populations over a time frame that is so small that original haplotypes spanning several markers are still observable in current high-density SNP genotyping arrays. We show here that the length of shared haplotypes surrounding alleles is an indicator of the relative ages of alleles, and it is applicable to original and species-specific alleles.Human Heredity 10/2009; 69(1):52-9. DOI:10.1159/000243154 · 1.64 Impact Factor
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ABSTRACT: Haplotype sharing analysis is a well-established option for the investigation of the etiology of complex diseases. The statistical power of haplotype association methods depends strongly on how the information of unobserved haplotypes can be captured by multilocus genotypes. In this study we combine an entropy-based marker selection algorithm (EMS), with a haplotype sharing-based Mantel statistics into a new algorithm. Genetic markers are iteratively selected by their multilocus linkage disequilibrium (LD), which is assessed by a normalized entropy difference. The initial marker set is gradually enlarged to increase the available information on the amount of sharing around a potential susceptibility marker. Markers are rejected from joint phasing if they do not increase the multilocus LD. In simulated candidate gene studies, the Mantel statistics combined with the new EMS performs as well or better at detecting the disease single nucleotide polymorphism-or in indirect association analysis its flanking markers-than the Mantel statistics without selection of markers prior to haplotype estimation and the Mantel statistics using sliding windows of size five. It is therefore appealing to apply our selection approach for haplotype-based association analysis, since marker selection driven by the observed data avoids both the arbitrary choice of markers when using a fixed window size, as well as the estimation of haplotype block structure.Genetic Epidemiology 05/2010; 34(4):354-63. DOI:10.1002/gepi.20491 · 2.95 Impact Factor