For traits undergoing a complex mode of inheritance, involving genes of large, moderate and small effects, gene detection based solely on P-values is of limited utility. Such GWAS approach has been broadly widespread, not only in livestock, but most of all in humans, plants and laboratory species. However, it has been demonstrated, that this methodology has serious limitations in terms of power to detect variants of moderate effects as well as in terms of repeatability of results across data sets. Here alternative approaches towards statistical modelling of complex traits are proposed, which utilize functional information on traits to allow for the discovery of genes not only with high, but also moderate effects on phenotypes and, more importantly, to identify physiological key processes for phenotype determination. Dairy cattle data sets originating from high high-throughput technologies – SNP microarray and whole genome sequence are used as examples.
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Methods and Tools: Statistical methods - linear and nonlinear models, , 204, 2014
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