Boosting predictabilities of agronomic traits in rice using bivariate genomic selection

The multivariate genomic selection (GS) models have not been adequately studied and their potential remains unclear. In this study, we developed a highly efficient bivariate (2D) GS method and demonstrated its significant advantages over the univariate (1D) rival methods using a rice dataset, where four traditional traits (i.e. yield, 1000-grain weight, grain number and tiller number) as well as 1000 metabolomic traits were analyzed. The novelty of the method is the incorporation of the HAT methodology in the 2D BLUP GS model such that the computational efficiency has been dramatically increased by avoiding the conventional cross-validation. The results indicated that (1) the 2D BLUP-HAT GS analysis generally produces higher predictabilities for two traits than those achieved by the analysis of individual traits using 1D GS model, and (2) selected metabolites may be utilized as ancillary traits in the new 2D BLUP-HAT GS method to further boost the predictability of traditional traits, especially for agronomically important traits with low 1D predictabilities.

[1]  M. Calus,et al.  Accuracy of multi-trait genomic selection using different methods , 2011, Genetics Selection Evolution.

[2]  Rohan L. Fernando,et al.  Extension of the bayesian alphabet for genomic selection , 2011, BMC Bioinformatics.

[3]  J. Danesh,et al.  GUESS-ing Polygenic Associations with Multiple Phenotypes Using a GPU-Based Evolutionary Stochastic Search Algorithm , 2013, PLoS genetics.

[4]  M P L Calus,et al.  Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM. , 2007, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[5]  M. Asins,et al.  Present and future of quantitative trait locus analysis in plant breeding , 2002 .

[6]  Laxmi Parida,et al.  Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction , 2016, Bioinform..

[7]  M. Goddard,et al.  Prediction of total genetic value using genome-wide dense marker maps. , 2001, Genetics.

[8]  José Crossa,et al.  Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree , 2009, Genetics.

[9]  C. R. Henderson,et al.  Best linear unbiased estimation and prediction under a selection model. , 1975, Biometrics.

[10]  Xiang Zhou,et al.  Polygenic Modeling with Bayesian Sparse Linear Mixed Models , 2012, PLoS genetics.

[11]  R. Fernando,et al.  Persistence of accuracy of genomic estimated breeding values over generations in layer chickens , 2011, Genetics Selection Evolution.

[12]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[13]  B. Hayes,et al.  Accuracy of estimated genomic breeding values for wool and meat traits in a multi-breed sheep population , 2010 .

[14]  Kazuki Saito,et al.  Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis. , 2012, The Plant journal : for cell and molecular biology.

[15]  Li Ma,et al.  Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model , 2015, Heredity.

[16]  Maciej Haranczyk,et al.  Metal–organic framework with optimally selective xenon adsorption and separation , 2016, Nature Communications.

[17]  Jean-Luc Jannink,et al.  Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy , 2012, Genetics.

[18]  Shizhong Xu,et al.  Metabolomic prediction of yield in hybrid rice. , 2016, The Plant journal : for cell and molecular biology.

[19]  Andrés Legarra,et al.  Performance of Genomic Selection in Mice , 2008, Genetics.

[20]  Dorian Garrick,et al.  Genomic Prediction from Multiple-Trait Bayesian Regression Methods Using Mixture Priors , 2018, Genetics.

[21]  Takeshi Hayashi,et al.  A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits , 2012, BMC Bioinformatics.

[22]  M. Lund,et al.  Model comparison on genomic predictions using high-density markers for different groups of bulls in the Nordic Holstein population. , 2013, Journal of dairy science.

[23]  Jinghua Xiao,et al.  Gains in QTL Detection Using an Ultra-High Density SNP Map Based on Population Sequencing Relative to Traditional RFLP/SSR Markers , 2011, PloS one.

[24]  Robenzon E. Lorenzana,et al.  Accuracy of genotypic value predictions for marker-based selection in biparental plant populations , 2009, Theoretical and Applied Genetics.

[25]  P. Visscher,et al.  Multi-trait analysis of genome-wide association summary statistics using MTAG , 2017, Nature Genetics.

[26]  Bo Huang,et al.  Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology , 2016, Scientific Reports.

[27]  M. Zaman-Allah,et al.  Translating High-Throughput Phenotyping into Genetic Gain , 2018, Trends in plant science.

[28]  Shizhong Xu.  Predicted Residual Error Sum of Squares of Mixed Models: An Application for Genomic Prediction , 2017, G3: Genes, Genomes, Genetics.

[29]  F. Seefried,et al.  Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction , 2011, Genetics Selection Evolution.

[30]  Julong Wei,et al.  Identification of optimal prediction models using multi-omic data for selecting hybrid rice , 2019, Heredity.

[31]  Bjarni J. Vilhjálmsson,et al.  A mixed-model approach for genome-wide association studies of correlated traits in structured populations , 2012, Nature Genetics.

[32]  Jie Luo,et al.  Comparative and parallel genome-wide association studies for metabolic and agronomic traits in cereals , 2016, Nature Communications.

[33]  Cai-guo Xu,et al.  Genetic analysis of the metabolome exemplified using a rice population , 2013, Proceedings of the National Academy of Sciences.

[34]  S. Cloutier,et al.  Evaluation of Genomic Prediction for Pasmo Resistance in Flax , 2018, International journal of molecular sciences.

[35]  Controlling the Overfitting of Heritability in Genomic Selection through Cross Validation , 2017, Scientific Reports.

[36]  Guosheng Su,et al.  Comparison of single-trait and multiple-trait genomic prediction models , 2014, BMC Genetics.

[37]  M. Lund,et al.  Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances. , 2014, Journal of dairy science.