Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize

Abstract

Deleterious alleles have long been proposed to play an important role in patterning phenotypic variation and are central to commonly held ideas explaining the hybrid vigor observed in the offspring of a cross between two inbred parents. We test these ideas using evolutionary measures of sequence conservation to ask whether incorporating information about putatively deleterious alleles can inform genomic selection (GS) models and improve phenotypic prediction. We measured a number of agronomic traits in both the inbred parents and hybrids of an elite maize partial diallel population and re-sequenced the parents of the population. Inbred elite maize lines vary for more than 350,000 putatively deleterious sites, but show a lower burden of such sites than a comparable set of traditional landraces. Our modeling reveals widespread evidence for incomplete dominance at these loci, and supports theoretical models that more damaging variants are usually more recessive. We identify haplotype blocks using an identity-by-decent (IBD) analysis and perform genomic prediction analyses in which we weigh blocks on the basis of complementation for segregating putatively deleterious variants. Cross-validation results show that incorporating sequence conservation in genomic selection improves prediction accuracy for grain yield and other fitness-related traits as well as heterosis for those traits. Our results provide empirical support for an important role for incomplete dominance of deleterious alleles in explaining heterosis and demonstrate the utility of incorporating functional annotation in phenotypic prediction and plant breeding.

Fig 1. Heterosis and deleterious variants.

(a) Boxplots (median and interquartile range) of percent mid-parent heterosis (MPH). (b) Proportion of deleterious alleles in landraces (LR, green) and elite maize (MZ, blue) lines. (c) The allele frequency of the minor alleles in the multi-species alignment in bins of 0.01 GERP score (including GERP < = 0 sites). (d) The mean GERP score for putatively deleterious sites (GERP >0). Each point represents a 1 Mb window. In (c) and (d) the solid blue and dashed black lines define the best-fit regression line and its 95% confidence interval.

Fig 2. Variance explained and degree of dominance (k) of GERP-SNPs for traits per se.

(a) Total per-SNP variance explained for grain yield trait per se by GERP-SNPs (red lines) and randomly sampled SNPs (grey beanplots). (b) Density plots of the degree of dominance (k). Extreme values of k were truncated at 2 and -2. (c-e) Linear regressions of additive effects (c), dominance effects (d), and degree of dominance (e) of seven traits per se against SNP GERP scores. Solid and dashed lines represent significant and nonsignificant linear regressions, with grey bands representing 95% confidence intervals. Data are only shown for SNPs that explain more than the mean genome-wide per-SNP variance (see Methods for details).

Fig 3. Genomic prediction models incorporating GERP.

(a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b) under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent prediction accuracy estimated from cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Prediction accuracy using estimated values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on the right (grey). Horizontal bars indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the beans indicate prediction accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are shown in S13 Fig.