Whole-Genome Quantitative Trait Locus Mapping Reveals Major Role of Epistasis on Yield of Rice

Abstract

Although rice yield has been doubled in most parts of the world since 1960s, thanks to the advancements in breeding technologies, the biological mechanisms controlling yield are largely unknown. To understand the genetic basis of rice yield, a number of quantitative trait locus (QTL) mapping studies have been carried out, but whole-genome QTL mapping incorporating all interaction effects is still lacking. In this paper, we exploited whole-genome markers of an immortalized F2 population derived from an elite rice hybrid to perform QTL mapping for rice yield characterized by yield per plant and three yield component traits. Our QTL model includes additive and dominance main effects of 1,619 markers and all pair-wise interactions, with a total of more than 5 million possible effects. The QTL mapping identified 54, 5, 28 and 4 significant effects involving 103, 9, 52 and 7 QTLs for the four traits, namely the number of panicles per plant, the number of grains per panicle, grain weight, and yield per plant. Most identified QTLs are involved in digenic interactions. An extensive literature survey of experimentally characterized genes related to crop yield shows that 19 of 54 effects, 4 of 5 effects, 12 of 28 effects and 2 of 4 effects for the four traits, respectively, involve at least one QTL that locates within 2 cM distance to at least one yield-related gene. This study not only reveals the major role of epistasis influencing rice yield, but also provides a set of candidate genetic loci for further experimental investigation.

Climate sensitivity of Cryptomeria japonica in two contrasting environments: Perspectives from QTL mapping

“…The phenotypic variance explained (PVE) by i-th QTL was estimated as PVE i ¼b 2 i s x i =s y , whereb is the estimated value of parameter β, σ x is the variance of the marker genotype, and σ y is the variance of climate sensitivity traits. PVE by all QTLs was estimated as PVE all ¼ s^y =s y , where s^y is the variance of the estimated value of the climate sensitivity traits, which is obtained from a multiple linear regression using significant QTLs as explanatory variables [45]. The prediction accuracy of significant QTLs was tested by correlating the observed and predicted values using five-fold cross validation.…”

Modeling Epistasis in Genomic Selection

“…It is hence a biologically influential component contributing to the genetic architecture of quantitative traits (Mackay 2014). The influence of epistasis on genome-wide QTL mapping ranges from limited (e.g., Buckler et al 2009;Tian et al 2011) to high (e.g., Carlborg et al 2006;Würschum et al 2011;Huang et al 2014). These discrepancies can be explained by the complexities of the examined traits, which are controlled by many loci exhibiting small effects entailing a low QTL detection power.…”

Fast proximal gradient optimization of the empirical Bayesian Lasso for multiple quantitative trait locus mapping

“…Our recent studies demonstrated that EBlasso has a broad range of applications such as whole-genome QTL mapping and pathway-based genome-wide association study (GWAS) [15,16,21]. When the number of possible effects is very large in QTL models with both main and epistatic effects, the computation time becomes a critical concern.…”

“…Both EBlasso and EBEN outperform other shrinkage methods including Lasso and MCMC-based Bayesian shrinkage methods in terms of PD and FDR. EBlasso has been applied to whole-genome QTL mapping [15] and pathway-based genome-wide association study (GWAS) [16], where linear regression models with millions of variables were inferred. However, optimized by a greedy coordinate ascent algorithm, the accuracy and computational speed may not be optimal for both methods.…”