64 research outputs found

    Most parsimonious haplotype allele sharing determination

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    <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

    Haplotype inference in general pedigrees with two sites

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    <p>Abstract</p> <p>Background</p> <p>Genetic disease studies investigate relationships between changes in chromosomes and genetic diseases. Single haplotypes provide useful information for these studies but extracting single haplotypes directly by biochemical methods is expensive. A computational method to infer haplotypes from genotype data is therefore important. We investigate the problem of computing the minimum number of recombination events for general pedigrees with two sites for all members.</p> <p>Results</p> <p>We show that this NP-hard problem can be parametrically reduced to the Bipartization by Edge Removal problem and therefore can be solved by an <it>O</it>(2<it><sup>k</sup></it> · <it>n</it><sup>2</sup>) exact algorithm, where <it>n</it> is the number of members and <it>k</it> is the number of recombination events.</p> <p>Conclusions</p> <p>Our work can therefore be useful for genetic disease studies to track down how changes in haplotypes such as recombinations relate to genetic disease.</p

    Estimation of a system of national accounts: implementation with mathematica

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    This study implements Mathematica to estimate a system of national accounts. The estimation methods applied are portrayed in Danilov and Magnus (2008), including the Bayesian estimation, restricted and unrestricted least-squares estimation and best linear unbiased estimation. Operationalizing these methods in the Mathematica environment is the main contribution of the current study. In light of the United Nations�e¤orts aimed to standardize across countries the compilation of national accounts, the Mathematica codes developed here should provide an important tool both for the estimation of unrealized or unavailable national accounts data and for conducting cross-country and within-country macroeconomic policy analysis.System of national accounts; Social Accounting Matrix; Bayesian estimation; Least-squares estimation; Best linear unbiased estimation; Linear programming

    Efficient haplotyping for families

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 81-83).Hapi is a novel dynamic programming algorithm for haplotyping nuclear families that outperforms contemporary family-based haplotyping algorithms. Haplotypes are useful for mapping and identifying genes which cause and contribute to the etiology of human disease, and for analyzing the products of meiosis to locate recombinations, enabling the identification of recombination hotspots and gene conversions. They can also be used to study population history, including expansion, contraction, and migration patterns in humans and other species. Hapi's efficiency is a result of eliminating or ignoring states and state transitions that are unnecessary for computing haplotypes. When applied to a dataset containing 103 families, Hapi performs over 3.8-320 times faster than state-of-the-art algorithms. These efficiency gains are practically important as they enable Hapi to haplotype family datasets which current algorithms are either unable to handle or are impractical for because of time constraints. Hapi infers both minimum-recombinant and maximum likelihood haplotypes, and because it applies to related individuals, the haplotypes it infers are highly accurate over large genomic distances.by Amy Lynne Williams.Ph.D

    Estimation of a system of national accounts: implementation with mathematica

    Get PDF
    This study implements Mathematica to estimate a system of national accounts. The estimation methods applied are portrayed in Danilov and Magnus (2008), including the Bayesian estimation, restricted and unrestricted least-squares estimation and best linear unbiased estimation. Operationalizing these methods in the Mathematica environment is the main contribution of the current study. In light of the United Nations�e¤orts aimed to standardize across countries the compilation of national accounts, the Mathematica codes developed here should provide an important tool both for the estimation of unrealized or unavailable national accounts data and for conducting cross-country and within-country macroeconomic policy analysis

    Estimation of a system of national accounts: implementation with mathematica

    Get PDF
    This study implements Mathematica to estimate a system of national accounts. The estimation methods applied are portrayed in Danilov and Magnus (2008), including the Bayesian estimation, restricted and unrestricted least-squares estimation and best linear unbiased estimation. Operationalizing these methods in the Mathematica environment is the main contribution of the current study. In light of the United Nations�e¤orts aimed to standardize across countries the compilation of national accounts, the Mathematica codes developed here should provide an important tool both for the estimation of unrealized or unavailable national accounts data and for conducting cross-country and within-country macroeconomic policy analysis
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