Genome-wide prediction using Bayesian additive regression trees

Genetics Selection Evolution

Research Article

Patrik Waldmann¹ 

[1] Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences (SLU), Box 7023, 750 07, Uppsala, Sweden;

关键词: Quantitative Trait Locus;    Markov Chain Monte Carlo;    Random Forest;    Regression Tree;    Lasso;   

DOI  :  10.1186/s12711-016-0219-8

 received in 2016-03-04, accepted in 2016-05-26,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundThe goal of genome-wide prediction (GWP) is to predict phenotypes based on marker genotypes, often obtained through single nucleotide polymorphism (SNP) chips. The major problem with GWP is high-dimensional data from many thousands of SNPs scored on several thousands of individuals. A large number of methods have been developed for GWP, which are mostly parametric methods that assume statistical linearity and only additive genetic effects. The Bayesian additive regression trees (BART) method was recently proposed and is based on the sum of nonparametric regression trees with the priors being used to regularize the parameters. Each regression tree is based on a recursive binary partitioning of the predictor space that approximates an unknown function, which will automatically model nonlinearities within SNPs (dominance) and interactions between SNPs (epistasis). In this study, we introduced BART and compared its predictive performance with that of the LASSO, Bayesian LASSO (BLASSO), genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space (RKHS) regression and random forest (RF) methods.ResultsTests on the QTLMAS2010 simulated data, which are mainly based on additive genetic effects, show that cross-validated optimization of BART provides a smaller prediction error than the RF, BLASSO, GBLUP and RKHS methods, and is almost as accurate as the LASSO method. If dominance and epistasis effects are added to the QTLMAS2010 data, the accuracy of BART relative to the other methods was increased. We also showed that BART can produce importance measures on the SNPs through variable inclusion proportions. In evaluations using real data on pigs, the prediction error was smaller with BART than with the other methods.ConclusionsBART was shown to be an accurate method for GWP, in which the regression trees guarantee a very sparse representation of additive and complex non-additive genetic effects. Moreover, the Markov chain Monte Carlo algorithm with Bayesian back-fitting provides a computationally efficient procedure that is suitable for high-dimensional genomic data.

【 授权许可】

CC BY   
© The Author(s) 2016

【 图 表 】

Fig. 2

Fig. 2

12951_2015_155_Article_IEq56.gif

Fig. 3

12951_2017_255_Article_IEq36.gif

Fig. 2

Fig. 2

Fig. 2

Fig. 1

Fig. 1

12936_2017_1963_Article_IEq63.gif

Fig. 3

Fig. 1

12936_2017_1932_Article_IEq37.gif

Fig. 2

Fig. 1

Fig. 1

Fig. 2

12951_2015_155_Article_IEq54.gif

12951_2015_155_Article_IEq53.gif

12951_2017_255_Article_IEq34.gif

12951_2017_255_Article_IEq33.gif

12936_2016_1316_Article_IEq8.gif

Fig. 3

Fig. 8

12936_2017_1963_Article_IEq60.gif

Fig. 1

Fig. 2

Fig. 7

Fig. 9

Fig. 5

Fig. 3

Fig. 2

12951_2017_255_Article_IEq32.gif

Fig. 1

40170_2023_319_Article_IEq3.gif

13046_2023_2867_Article_IEq1.gif

Fig. 1

Fig. 2

Fig. 6

12951_2015_155_Article_IEq49.gif

Fig. 4

Fig. 3

Fig. 3

Fig. 2

Fig. 2

Fig. 1

Fig. 1

Fig. 1

Fig. 6

12936_2015_894_Article_IEq28.gif

Fig. 5

Fig. 1

Fig. 2

12951_2015_155_Article_IEq47.gif

Fig. 4

Fig. 5

Fig. 7

Fig. 3

Fig. 3

Fig. 6

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 3

12951_2015_155_Article_IEq44.gif

Fig. 3

Fig. 1

Fig. 1

Fig. 1

Fig. 4

12951_2017_297_Article_IEq1.gif

Fig. 3

Fig. 2

Fig. 1

Fig. 2

Fig. 2

Fig. 4

Fig. 2

Fig. 3

Fig. 1

Fig. 3

Fig. 2

Fig. 1

Fig. 6

Fig. 1

12936_2017_2075_Article_IEq45.gif

Fig. 3

Fig. 2

Fig. 2

Fig. 3

Fig. 1

Fig. 8

12951_2015_155_Article_IEq42.gif

Fig. 2

12951_2015_155_Article_IEq40.gif

Fig. 2

Fig. 4

Fig. 1

Fig. 3

Fig. 1

12936_2017_1963_Article_IEq15.gif

Fig. 4

12951_2017_277_Article_IEq1.gif

12936_2017_2075_Article_IEq41.gif

12936_2017_1938_Article_IEq2.gif

Fig. 2

Fig. 3

12864_2015_2198_Article_IEq1.gif

12951_2015_155_Article_IEq39.gif

Fig. 3

12936_2017_1866_Article_IEq9.gif

Fig. 2

Fig. 1

Fig. 1

12936_2015_894_Article_IEq17.gif

Fig. 2

Fig. 1

12951_2015_155_Article_IEq37.gif

13690_2023_1196_Figb_HTML.png

13690_2023_1196_Figa_HTML.png

Fig. 1

12944_2023_1927_Article_IEq25.gif

12944_2023_1927_Article_IEq24.gif

Fig. 2

Fig. 5

12936_2017_2075_Article_IEq38.gif

Fig. 3

Fig. 1

12888_2023_5256_Article_IEq1.gif

Fig. 3

12944_2023_1927_Article_IEq7.gif

12944_2023_1927_Article_IEq6.gif

Fig. 9

Fig. 1

12951_2015_155_Article_IEq34.gif

12951_2015_155_Article_IEq33.gif

12951_2015_155_Article_IEq32.gif

Fig. 1

12864_2016_2889_Article_IEq3.gif

Fig. 1

Fig. 2

Fig. 2

Fig. 2

Fig. 1

12951_2017_270_Article_IEq10.gif

Fig. 8

Fig. 3

Fig. 3

Fig. 7

12951_2015_111_Article_IEq5.gif

Fig. 1

12951_2015_155_Article_IEq30.gif