Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a Barley case study

Abstract

We compared the accuracies of four genomic-selection prediction methods as affected by marker density, level of linkage disequilibrium (LD), quantitative trait locus (QTL) number, sample size, and level of replication in populations generated from multiple inbred lines. Marker data on 42 two-row spring barley inbred lines were used to simulate high and low LD populations from multiple inbred line crosses: the first included many small full-sib families and the second was derived from five generations of random mating. True breeding values (TBV) were simulated on the basis of 20 or 80 additive QTL. Methods used to derive genomic estimated breeding values (GEBV) were random regression best linear unbiased prediction (RR-BLUP), Bayes-B, a Bayesian shrinkage regression method, and BLUP from a mixed model analysis using a relationship matrix calculated from marker data. Using the best methods, accuracies of GEBV were comparable to accuracies from phenotype for predicting TBV without requiring the time and expense of field evaluation. We identified a trade-off between a method's ability to capture marker-QTL LD vs. marker-based relatedness of individuals. The Bayesian shrinkage regression method primarily captured LD, the BLUP methods captured relationships, while Bayes-B captured both. Under most of the study scenarios, mixed-model analysis using a marker-derived relationship matrix (BLUP) was more accurate than methods that directly estimated marker effects, suggesting that relationship information was more valuable than LD information. When markers were in strong LD with large-effect QTL, or when predictions were made on individuals several generations removed from the training data set, however, the ranking of method performance was reversed and BLUP had the lowest accuracy.

Figure 1.—

Decline of LD as measured by against distance in centimorgans for all markers with minor allele frequency >0.2 (858 markers in total). (A–C) in the original 42 lines, design 1 and design 2, respectively. Gray lines are smoothed running averages.

Figure 2.—

Correlation between simulated and predicted breeding values in individuals derived from one generation of randomly mating the training population (accuracy). (A and B) Analyses with dense markers. (C and D) Analyses with sparse markers. (B) Results with observed QTL. (A, C, and D) Results with unobserved QTL. The standard error for each point is small (<0.002) and is not shown. Note that the y-axis scale for B is different from that for A, C, and D.

Figure 3.—

Same as for Figure 2, but predictions are for individuals derived from four generations of randomly mating the training population.

Figure 4.—

Prediction accuracy in individuals derived from one generation of random mating with different population sizes under sparse markers with 80 QTL. All scenarios are under the 80 QTL setting. (A) Results with unobserved QTL. (B) Results with observed QTL. Note that the y-axis scale for A is different from that for B.