Abstract
Haplotypes are composed of specific combinations of alleles at the several loci on the same chromosome. Because haplotypes incorporate linkage disequilibrium (LD) information from multiple loci, haplotype-based association analyses can provide greater powers than the single-marker analysis in the association studies. However, when we construct haplotypes using many markers simultaneously, we may be confronted with a sparseness problem due to a large number of haplotypes. In this paper, we propose the principal-component (PC) association test as an alternative to the haplotype-based association test. We define the PC scores from the LD blocks and perform the association test using logistic regression. The proposed PC test was applied to the analysis of the Genetic Analysis Workshop 15 simulated data set. By knowing the answers of Problem 3, we evaluated the performance of the PC test and the haplotype-based association test using Akaike Information Criterion (AIC), power, and type I error. The PC test performed better than the haplotype-based association test in the sense that the former tends to have smaller AIC values and slightly greater power than the latter.
Highlights
Several studies have shown that the use of haplotypes may offer more powerful information on genetic association with traits than the use of single-nucleotide polymorphisms (SNPs) [1,2]
By knowing the answers of Problem 3, we evaluated the performance of the PC test and the haplotype-based association test using Akaike Information Criterion (AIC) [7], power, and type I error
Using Akaike Information Criterion (AIC), power, and type I error, we evaluated the performances of the PC test and the haplotype-based association test
Summary
Several studies have shown that the use of haplotypes may offer more powerful information on genetic association with traits than the use of single-nucleotide polymorphisms (SNPs) [1,2]. If markers have low LD relationship in the LD block, a large number of haplotypes are constructed. Several methods have been proposed to test whether the haplotypes are associated with the disease trait. In these association studies, haplotypes are treated as covariates in logistic regression models [3,4,5,6]. The haplotypebased association test has a problem when the number of haplotypes is large. When there are many haplotypes, parameter estimation is difficult due to the large number of parameters as well as the sparseness of data
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