Accuracy of genomic selection for alfalfa biomass yield in different reference populations

BMC Genomics

Research Article

Paolo Annicchiarico¹  Nelson Nazzicari¹  Luciano Pecetti¹  Xuehui Li²  Yanling Wei³  E. Charles Brummer³ 

[1] Council for Agricultural Research and Economics (CREA), Research Centre for Fodder Crops and Dairy Productions, 29 viale Piacenza, 26900, Lodi, Italy;Department of Plant Sciences, North Dakota State University, 1340 Administration Avenue, 58108, Fargo, ND, USA;Plant Sciences Department, University of California, Davis, Plant Breeding Center, One Shields Avenue, 95616, Davis, CA, USA;

关键词: Alfalfa;    Breeding strategy;    Genomic selection;    Genotyping-by-sequencing;    Lucerne;    Medicago sativa;    Missing data imputation;    Yield;   

DOI  :  10.1186/s12864-015-2212-y

 received in 2015-07-07, accepted in 2015-11-13,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundGenomic selection based on genotyping-by-sequencing (GBS) data could accelerate alfalfa yield gains, if it displayed moderate ability to predict parent breeding values. Its interest would be enhanced by predicting ability also for germplasm/reference populations other than those for which it was defined. Predicting accuracy may be influenced by statistical models, SNP calling procedures and missing data imputation strategies.ResultsLandrace and variety material from twogenetically-contrasting reference populations, i.e., 124 elite genotypes adapted to the Po Valley (sub-continental climate; PV population) and 154 genotypes adapted to Mediterranean-climate environments (Me population), were genotyped by GBS and phenotyped in separate environments for dry matter yield of their dense-planted half-sib progenies. Both populations showed no sub-population genetic structure. Predictive accuracy was higher by joint rather than separate SNP calling for the two data sets, and using random forest imputation of missing data. Highest accuracy was obtained using Support Vector Regression (SVR) for PV, and Ridge Regression BLUP and SVR for Me germplasm. Bayesian methods (Bayes A, Bayes B and Bayesian Lasso) tended to be less accurate. Random Forest Regression was the least accurate model. Accuracy attained about 0.35 for Me in the range of 0.30-0.50 missing data, and 0.32 for PV at 0.50 missing data, using at least 10,000 SNP markers. Cross-population predictions based on a smaller subset of common SNPs implied a relative loss of accuracy of about 25 % for Me and 30 % for PV. Genome-wide association analyses based on large subsets of M. truncatula-aligned markers revealed many SNPs with modest association with yield, and some genome areas hosting putative QTLs. A comparison of genomic vs. conventional selection for parent breeding value assuming 1-year vs. 5-year selection cycles, respectively, indicated over three-fold greater predicted yield gain per unit time for genomic selection.ConclusionsGenomic selection for alfalfa yield is promising, based on its moderate prediction accuracy, moderate value of cross-population predictions, and lack of sub-population structure. There is limited scope for searching individual QTLs with overwhelming effect on yield. Some of our results can contribute to better design of genomic selection experiments for alfalfa and other crops with similar mating systems.

【 授权许可】

CC BY   
© Annicchiarico et al. 2015