Assessing the value of phenotypic information from non-genotyped animals for QTL mapping of complex traits in real and simulated populations

BMC Genetics

Research Article

Flavio S. Schenkel¹  Thaise P. Melo²  Luciana Takada²  Roberto Carvalheiro²  Marina M. Dias²  Fernando Baldi²  Henrique N. Oliveira²  Lucia G. Albuquerque²  Haroldo H. R. Neves³ 

[1] Centre for Genetic Improvement of Livestock, University of Guelph, N1G2W1, Guelph, ON, Canada;UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil;UNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, 14884-900, São Paulo, Brazil;GenSys Consultores Associados S/C Ltda, 90680-000, Porto Alegre, Brazil;

关键词: GBLUP;    GWAS;    Single-step;   

DOI  :  10.1186/s12863-016-0394-1

 received in 2015-09-22, accepted in 2016-06-09,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundQTL mapping through genome-wide association studies (GWAS) is challenging, especially in the case of low heritability complex traits and when few animals possess genotypic and phenotypic information. When most of the phenotypic information is from non-genotyped animals, GWAS can be performed using the weighted single-step GBLUP (WssGBLUP) method, which permits to combine all available information, even that of non-genotyped animals. However, it is not clear to what extent phenotypic information from non-genotyped animals increases the power of QTL detection, and whether factors such as the extent of linkage disequilibrium (LD) in the population and weighting SNPs in WssGBLUP affect the importance of using information from non-genotyped animals in GWAS. These questions were investigated in this study using real and simulated data.ResultsAnalysis of real data showed that the use of phenotypes of non-genotyped animals affected SNP effect estimates and, consequently, QTL mapping. Despite some coincidence, the most important genomic regions identified by the analyses, either using or ignoring phenotypes of non-genotyped animals, were not the same. The simulation results indicated that the inclusion of all available phenotypic information, even that of non-genotyped animals, tends to improve QTL detection for low heritability complex traits. For populations with low levels of LD, this trend of improvement was less pronounced. Stronger shrinkage on SNPs explaining lower variance was not necessarily associated with better QTL mapping.ConclusionsThe use of phenotypic information from non-genotyped animals in GWAS may improve the ability to detect QTL for low heritability complex traits, especially in populations in which the level of LD is high.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

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