Incorporating single-locus tests into haplotype cladistic analysis in case-control studies.
In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.
Liu
Jianfeng
J
Department of Orthopedic Surgery, School of Medicine, University of Missouri-Kansas City, Kansas City, Missouri, United States of America.
Papasian
Chris
C
Deng
Hong-Wen
HW
D000465:Algorithms; D000483:Alleles; D001172:Arthritis, Rheumatoid / Q000235:genetics; D001499:Bayes Theorem; D016022:Case-Control Studies; D016000:Cluster Analysis; D003198:Computer Simulation; D005189:False Positive Reactions; D005787:Gene Frequency; D005819:Genetic Markers; D014644:Genetic Variation; D015894:Genome, Human; D005838:Genotype; D006239:Haplotypes; D006579:Heterozygote; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D016015:Logistic Models; D037601:Mannose-Binding Lectin / Q000235:genetics; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D013269:Stochastic Processes
D005819:Genetic Markers; C445619:MBL2 protein, human; D037601:Mannose-Binding Lectin