Titlebook: Design, Analysis, and Interpretation of Genome-Wide Association Scans; Daniel O. Stram Book 2014 Springer Science+Business Media New York

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https://doi.org/10.1007/978-1-4614-9443-0Biostatistics; GWAS studies; Genetic disease variation; Genome studies; Risk prediction; Statistics in pu

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978-1-4939-4952-6Springer Science+Business Media New York 2014

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Statistics for Biology and Healthhttp://image.papertrans.cn/d/image/268802.jpg

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Vampire with a Brain Is a Good ITP Hammerchapter focuses specifically on gaining a basic grounding in three general topics:.In addition to these concepts, several important tools for the investigation of linkage disequilibrium and of population heterogeneity are introduced: specifically data from the HapMap project and data manipulation an

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Pascal Fontaine,Hans-J?rg Schurrthe phenotype is discrete (disease/no disease) then case–control methods, . and ., are typically utilized. . for generalized linear models is reviewed, and the ., ., and . tests are defined and discussed. The analysis of data from nuclear family-based designs is also briefly introduced. Issues regar

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Frontiers of Computational Science a particular study. This chapter first reviews haplotype frequency estimation and .. It gives the details of EM estimation and haplotype imputation for a small number of SNPs using data from unrelated subjects and then considers the extension of this method to larger number of SNPs. The partition-l

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https://doi.org/10.1007/978-981-15-4818-5s that have been genotyped in a particular study. Imputed SNP analysis is in certain respects identical to haplotype analysis since the imputed SNPs are on specific haplotypes or haplotype combinations. Testing imputed SNPs as well as genotyped SNPs is thus simply a more focused kind of haplotype an