Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems
Adapting the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems

Multiple trait single step Bayesian GWAS on pooled data

Authors: 
Sánchez J.P., Legarra A., Piles M.
Publication date: 
11 February 2018
Full title: 
Multiple trait single step Bayesian GWAS on pooled data
Publishing information: 
WCGALP, 11-16 February 2018, Auckland, New Zealand
Abstract: 

Socially affected traits must be recorded in group-housed animals. In these conditions sometimes it is not possible to obtain individual records of certain traits and in some species. Previous studies have addressed the issue on how to use group (i.e. pooled) data to estimate genetic parameters and to predict breeding values, but the value of this pooled data to conduct QTL mapping studies has not been assessed yet. Our objective was to present a method, based on a Bayesian single step genomic evaluation, in which the SNPs effects were assumed to follow the prior of Bayesian Cπ model, allowing thus a variable selection approach to pinpointing the genome regions most likely harbouring QTLs. The method was applied to a multi-trait simulated data set, in which for one of the traits pooled records were generated summing groups of 10 individual records. Our results show that an important loss of power was observed when pooled data were used, but even though one of the true QTLs can be detected with a probability of being associated to the trait of 0.83. This QTL was associated to a mutation explaining 16% of the genetic variance and with a frequency of 0.46. Other mutation with an even greater effect (22%) but with lower frequency (0.38) could not be detected. It can be concluded that although the proposed model can be used for QTL mapping when grouped data are available its power is limited and only strongly associated regions are likely to be declared as QTLs.

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