Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 538367
Title Detecting epistatic selection with partially observed genotype data by using copula graphical models
Author(s) Behrouzi, Pariya; Wit, Ernst C.
Source Journal of the Royal Statistical Society. Series C: Applied Statistics (2018). - ISSN 0035-9254
DOI https://doi.org/10.1111/rssc.12287
Department(s) Biometris (WU MAT)
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Epistasis - Epistatic selection - Gaussian copula - Graphical models - Linkage disequilibrium - Penalized inference
Abstract

In cross-breeding experiments it can be of interest to see whether there are any synergistic effects of certain genes. This could be by being particularly useful or detrimental to the individual. This type of effect involving multiple genes is called epistasis. Epistatic interactions can affect growth, fertility traits or even cause complete lethality. However, detecting epistasis in genomewide studies is challenging as multiple-testing approaches are underpowered. We develop a method for reconstructing an underlying network of genomic signatures of high dimensional epistatic selection from multilocus genotype data. The network captures the conditionally dependent short- and long-range linkage disequilibrium structure and thus reveals 'aberrant' marker-marker associations that are due to epistatic selection rather than gametic linkage. The network estimation relies on penalized Gaussian copula graphical models, which can account for a large number of markers p and a small number of individuals n. We demonstrate the efficiency of the proposed method on simulated data sets as well as on genotyping data in Arabidopsis thaliana and maize.

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