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Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

Gilbert, Houston N. Working Paper Advanced Search. Privacy Copyright. Skip to main content. Collection of Biostatistics Research Archive. Turner, S. R package version 0. Dowle, M.


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Resampling-based multiple hypothesis testing

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Genetics and Genomics

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Electronic Journal of Statistics

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Genetics , — Download references. This paper is part of a larger project on the genetics of social and economic behavior. The idea for this paper arose in the workshop that regularly takes place in the context of this project at the University of Zurich, and which is based on the collaboration of teams at universities in Berlin, Barcelona, Mainz, and Zurich. This work is licensed under a Creative Commons Attribution 4.

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Subjects Computational science Genome-wide association studies Statistical methods. Abstract The standard approach to the analysis of genome-wide association studies GWAS is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. The core idea is a two-step algorithm consisting of a machine learning and SNP selection step that drastically reduces the number of candidate SNPs by selecting only a small subset of the most predictive SNPs; and a statistical testing step where only the SNPs selected in step 1 are tested for association.

Full size image. Machine learning and SNP-selection step colored in red. Statistical testing step colored in blue. Problem Setting and Methodology In this section, we formally describe the statistical problem under investigation and propose a novel methodology for tackling it — based on a combination of machine learning and statistical testing techniques. Problem Setting and Notation Let n denote the number of subjects in the study and d the number of SNPs under investigation.

Table 1: Tabular representation of single SNP data. Full size table. Results Validation Validation using simulated phenotypes To assess the performance of the proposed COMBI method in comparison to other methods in a controlled environment, we conducted a number of simulation experiments with semi-real data. Figure 2: Illustration of validation methodology. Figure 3: Genome-wide scan for seven diseases. Discussion Several related machine learning methods have been successfully used in the context of statistical genomics.

These approaches can be classified into two groups: Methods that construct a model from genetic data in order to carry out accurate predictions on a phenotype 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , Additional Information How to cite this article : Mieth, B.

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Multiple Testing Procedures with Applications to Genomics | Sandrine Dudoit | Springer

References 1. PubMed Article Google Scholar 8. PubMed Article Google Scholar PubMed Google Scholar Article Google Scholar Google Scholar Acknowledgements This paper is part of a larger project on the genetics of social and economic behavior. Supplementary information PDF files 1. Supplementary Information. Ebooks cut down on the employ of paper, as advocated by environmental enthusiasts. Right now there are no fixed timings for study. There will be no question of waiting-time for new editions. Right now there is no transportation to be able to the eBook shop. The particular books in an eBook store can be downloaded instantly, sometimes for free, at times for any fee.

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