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Open Access Research

The distribution of the effects of genes affecting quantitative traits in livestock

Ben Hayes1* and Mike E Goddard12

Author Affiliations

1 Institute of Land and Food Resources, University of Melbourne, Parkville, Victoria, 3052, Australia

2 Department of Natural Resources and Environment, Victorian Institute of Animal Science, Attwood, Victoria, 3049, Australia

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Genetics Selection Evolution 2001, 33:209-229  doi:10.1186/1297-9686-33-3-209


The electronic version of this article is the complete one and can be found online at: http://www.gsejournal.org/content/33/3/209


Received:24 January 2000
Accepted:2 January 2001
Published:15 May 2001

© 2001 INRA, EDP Sciences

Abstract

Meta-analysis of information from quantitative trait loci (QTL) mapping experiments was used to derive distributions of the effects of genes affecting quantitative traits. The two limitations of such information, that QTL effects as reported include experimental error, and that mapping experiments can only detect QTL above a certain size, were accounted for. Data from pig and dairy mapping experiments were used. Gamma distributions of QTL effects were fitted with maximum likelihood. The derived distributions were moderately leptokurtic, consistent with many genes of small effect and few of large effect. Seventeen percent and 35% of the leading QTL explained 90% of the genetic variance for the dairy and pig distributions respectively. The number of segregating genes affecting a quantitative trait in dairy populations was predicted assuming genes affecting a quantitative trait were neutral with respect to fitness. Between 50 and 100 genes were predicted, depending on the effective population size assumed. As data for the analysis included no QTL of small effect, the ability to estimate the number of QTL of small effect must inevitably be weak. It may be that there are more QTL of small effect than predicted by our gamma distributions. Nevertheless, the distributions have important implications for QTL mapping experiments and Marker Assisted Selection (MAS). Powerful mapping experiments, able to detect QTL of 0.1σp, will be required to detect enough QTL to explain 90% the genetic variance for a quantitative trait.

Keywords:
distribution of gene effects; quantitative trait loci; genetic variance; marker assisted selection

Research

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