Statistically efficient association analysis of quantitative traits with haplotypes and untyped SNPs in family studies
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METHODOLOGY ARTICLE
Open Access
Statistically efficient association analysis of quantitative traits with haplotypes and untyped SNPs in family studies Guoqing Diao1*
and Dan-yu Lin2
Abstract Background: Associations between haplotypes and quantitative traits provide valuable information about the genetic basis of complex human diseases. Haplotypes also provide an effective way to deal with untyped SNPs. Two major challenges arise in haplotype-based association analysis of family data. First, haplotypes may not be inferred with certainty from genotype data. Second, the trait values within a family tend to be correlated because of common genetic and environmental factors. Results: To address these challenges, we present an efficient likelihood-based approach to analyzing associations of quantitative traits with haplotypes or untyped SNPs. This approach properly accounts for within-family trait correlations and can handle general pedigrees with arbitrary patterns of missing genotypes. We characterize the genetic effects on the quantitative trait by a linear regression model with random effects and develop efficient likelihood-based inference procedures. Extensive simulation studies are conducted to examine the performance of the proposed methods. An application to family data from the Childhood Asthma Management Program Ancillary Genetic Study is provided. A computer program is freely available. Conclusions: Results from extensive simulation studies show that the proposed methods for testing the haplotype effects on quantitative traits have correct type I error rates and are more powerful than some existing methods. Keywords: Complex diseases, EM algorithm, Gene-environment interactions, Haplotype analysis, Hardy-Weinberg equilibrium, Unphased genotype, Variance-component models
Background With the advances in high-throughput genotyping technologies and the availability of dense SNP maps across the human genome [1], haplotype-based association analysis plays an increasingly important role in mapping genes that influence complex human diseases. Haplotypes, which are specific combinations of alleles at several tightly linked SNPs on a chromosome, incorporate the linkage disequilibrium information and pertain to the functional properties of proteins through the amino acids sequences. *Correspondence: [email protected] Department of Biostatistics and Bioinformatics, The George Washington University, Washington, District of Columbia, USA Full list of author information is available at the end of the article 1
Association analysis based on haplotypes tends to be more powerful than the analysis of individual SNPs, especially when the causal SNPs are not directly typed or when multiple mutations occur in the cis position [2–6]. Standard genotyping procedures only measure unphased genotypes rather than haplotypes. Haplotypes are ambiguous if the genotypes of a subject are heterozygous at more than one marker locus. The ambiguity of the gametic phase information poses a major challenge in the haplotype analysis. For populatio
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