A comparison of statistical methods for genomic selection in a mice population

BMC Genetics

Sandra A Queiroz²  Roberto Carvalheiro¹  Haroldo HR Neves² 

[1] GenSys Consultores Associados S/S Ltda., Porto Alegre, Rio Grande do Sul, Brazil;Departamento de Zootecnia, FCAV, UNESP, Jaboticabal, CEP: 14.884-900, SP, Brazil

关键词: Subset selection;    SNP;    Ridge regression;    Random forest;    LASSO;    Kernel regression;   

Others  :  1089673
DOI  :  10.1186/1471-2156-13-100

 received in 2012-08-14, accepted in 2012-10-31,  发布年份 2012

【 摘 要 】

Background

The availability of high-density panels of SNP markers has opened new perspectives for marker-assisted selection strategies, such that genotypes for these markers are used to predict the genetic merit of selection candidates. Because the number of markers is often much larger than the number of phenotypes, marker effect estimation is not a trivial task. The objective of this research was to compare the predictive performance of ten different statistical methods employed in genomic selection, by analyzing data from a heterogeneous stock mice population.

Results

For the five traits analyzed (W6W: weight at six weeks, WGS: growth slope, BL: body length, %CD8+: percentage of CD8+ cells, CD4+/ CD8+: ratio between CD4+ and CD8+ cells), within-family predictions were more accurate than across-family predictions, although this superiority in accuracy varied markedly across traits. For within-family prediction, two kernel methods, Reproducing Kernel Hilbert Spaces Regression (RKHS) and Support Vector Regression (SVR), were the most accurate for W6W, while a polygenic model also had comparable performance. A form of ridge regression assuming that all markers contribute to the additive variance (RR_GBLUP) figured among the most accurate for WGS and BL, while two variable selection methods ( LASSO and Random Forest, RF) had the greatest predictive abilities for %CD8+ and CD4+/ CD8+. RF, RKHS, SVR and RR_GBLUP outperformed the remainder methods in terms of bias and inflation of predictions.

Conclusions

Methods with large conceptual differences reached very similar predictive abilities and a clear re-ranking of methods was observed in function of the trait analyzed. Variable selection methods were more accurate than the remainder in the case of %CD8+ and CD4+/CD8+ and these traits are likely to be influenced by a smaller number of QTL than the remainder. Judged by their overall performance across traits and computational requirements, RR_GBLUP, RKHS and SVR are particularly appealing for application in genomic selection.

【 授权许可】

   
2012 Neves et al.; licensee BioMed Central Ltd.

【 图 表 】

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