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 400083
Title Estimating Indirect Genetic Effects: Precision of Estimates and Optimum Designs
Author(s) Bijma, P.
Source Genetics 186 (2010). - ISSN 0016-6731 - p. 1013 - 1028.
Department(s) Animal Breeding and Genetics
Publication type Refereed Article in a scientific journal
Publication year 2010
Keyword(s) selection incorporating interaction - multilevel selection - interacting phenotypes - variance-components - biological groups - evolution - parameters - individuals - model - inheritance
Abstract Social interactions among individuals are abundant both in natural and domestic populations. Such social interactions cause phenotypes of individuals to depend on genes carried by other individuals, a phenomenon known as indirect genetic effects (IGE). Because IGEs have drastic effects on the rate and direction of response to selection, knowledge of their magnitude and relationship to direct genetic effects (DGE) is indispensable for understanding response to selection. Very little is known, however, of statistical power and optimum experimental designs for estimating IGEs. This work, therefore, presents expressions for the standard errors of the estimated (co)variances of DGEs and IGEs and identifies optimum experimental designs for their estimation. It also provides an expression for optimum family size and a numerical investigation of optimum group size. Designs with groups composed of two families were optimal and substantially better than designs with groups composed at random with respect to family. Results suggest that IGEs can be detected with 1000–2000 individuals and/or 250–500 groups when using optimum designs. Those values appear feasible for agriculture and aquaculture and for the smaller laboratory species. In summary, this work provides the tools to optimize and quantify the required size of experiments aiming to identify IGEs. An R-package SE.IGE is available, which predicts SEs and identifies optimum family and group sizes
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