Key messageRice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement.AbstractSo far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F5–F7 lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r 2 = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.Electronic supplementary materialThe online version of this article (10.1007/s00122-017-3011-4) contains supplementary material, which is available to authorized users.

Fertilization sensitivity to heat in rice is a major issue within climate change scenarios in the tropics. A panel of 167 indica landraces and improved varieties was phenotyped for spikelet sterility (SPKST) under 38°C during anthesis and for several secondary traits potentially affecting panicle micro-climate and thus the fertilization process. The panel was genotyped with an average density of one marker per 29 kb using genotyping by sequencing. Genome-wide association analyses (GWAS) were conducted using three methods based on single marker regression, haplotype regression and simultaneous fitting of all markers, respectively. Fourteen loci significantly associated with SPKST under at least two GWAS methods were detected. A large number of associations was also detected for the secondary traits. Analysis of co-localization of SPKST associated loci with QTLs detected in progenies of bi-parental crosses reported in the literature allowed to narrow -down the position of eight of those QTLs, including the most documented one, qHTSF4.1. Gene families underlying loci associated with SPKST corresponded to functions ranging from sensing abiotic stresses and regulating plant response, such as wall-associated kinases and heat shock proteins, to cell division and gametophyte development. Analysis of diversity at the vicinity of loci associated with SPKST within the rice three thousand genomes, revealed widespread distribution of the favourable alleles across O. sativa genetic groups. However, few accessions assembled the favourable alleles at all loci. Effective donors included the heat tolerant variety N22 and some Indian and Taiwanese varieties. These results provide a basis for breeding for heat tolerance during anthesis and for functional validation of major loci governing this trait.

One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named , which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel “trick” concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding package and all scripts have been made publicly available.