Most parsimonious haplotype allele sharing determination

<p>Abstract</p> <p>Background</p> <p>The "common disease – common variant" hypothesis and genome-wide association studies have achieved numerous successes in the last three years, particularly in genetic mapping in human diseases. Nevertheless, the power of the association study methods are still low, in particular on quantitative traits, and the description of the full allelic spectrum is deemed still far from reach. Given increasing density of single nucleotide polymorphisms available and suggested by the block-like structure of the human genome, a popular and prosperous strategy is to use haplotypes to try to capture the correlation structure of SNPs in regions of little recombination. The key to the success of this strategy is thus the ability to unambiguously determine the haplotype allele sharing status among the members. The association studies based on haplotype sharing status would have significantly reduced degrees of freedom and be able to capture the combined effects of tightly linked causal variants.</p> <p>Results</p> <p>For pedigree genotype datasets of medium density of SNPs, we present two methods for haplotype allele sharing status determination among the pedigree members. Extensive simulation study showed that both methods performed nearly perfectly on breakpoint discovery, mutation haplotype allele discovery, and shared chromosomal region discovery.</p> <p>Conclusion</p> <p>For pedigree genotype datasets, the haplotype allele sharing status among the members can be deterministically, efficiently, and accurately determined, even for very small pedigrees. Given their excellent performance, the presented haplotype allele sharing status determination programs can be useful in many downstream applications including haplotype based association studies.</p

Linkage Analysis For Categorical Traits And Ancestry Assignment In Admixed Individuals

A major goal in genetics is the identification of loci that contribute to diseases and other traits. With my Ph.D. research, I have developed methods that address two important challenges in this search: First, I addressed the challenge of choosing an appropriate disease model by developing a Gibbs sampler and an elimination algorithm to perform linkage analysis for categorical traits. Second, I addressed the challenge of population stratification due to admixture by developing a Principal Components-based approach to the assignment of ancestry at local regions along the genome of phased haplotypes in admixed individuals. Choosing an appropriate disease model is critical for maximizing power to detect disease loci. Many complex heritable diseases feature nominal or ordinal phenotypic measurements for which traditional methods of linkage analysis, which model traits as binary or continuous, are not well-suited. To address this challenge, I developed a Gibbs sampling approach (LOCate) and an elimination algorithm approach (LOCate2) to assess linkage for categorical traits. I validated the methods on simulated data and found that my approaches have increased power versus existing methods for ordinal linkage analysis. I also used these methods to analyze several data sets of categorical traits in humans and dogs, and found increased LOD scores at candidate loci when the traits were treated as categorical rather than binary. This will be useful for mapping genes for many complex traits. Identifying ancestry along each chromosome in admixed individuals is of interest for admixture mapping, understanding the population genetic history of admixture events, and identifying recent targets of selection. I developed a Principal Components-based forward-backward algorithm for determining local ancestry from a high-density, genomewide set of SNP genotypes of admixed individuals. Simulations show that the method is robust to misspecification of ancestral populations and the number of generations since admixture. I also applied my method to assess 3-way European, Native American, and African admixture among four Latino populations, and identified regions of extreme levels of African and Native American ancestry which may have experienced selection during admixture. This method is fast, accurate, and applicable to phased haplotypes with admixture from two or more populations