On the basis of growing environment, maize can largely be classified into temperate and tropical groups, leaving extensive genetic variation and evolutionary signatures in the maize genome. To identify candidate genes governing flowering time and photoperiod sensitivity, selective signature analysis and SNP- and haplotype-based GWAS were performed using 39,350 high-quality SNP markers in temperate and tropical maize groups consisting of 410 inbred lines phenotyped in three representative experiments in different latitudes. Selective signature analysis revealed 106 selective-sweep regions containing 423 candidate genes involved mainly in biological regulation and biosynthesis pathways. Among these genes, 25 overlapped with known genes governing flowering time and photoperiod sensitivity and 37 were also detected by GWAS for days to tassel, anthesis-silk interval, and photoperiod sensitivity measured by days to silking. Only two of the candidate genes governing flowering time overlapped selective signals. Most haplotype alleles within significant haplotype loci showed the same direction of effect on flowering time and photoperiod sensitivity. The inbred lines carrying GATT at HapL499 (haplotype locus 499) on chromosome 1 had relatively short flowering times. Lines carrying CA at HapL4054 on chromosome 10, TA at HapL4055 on chromosome 10, and GTTGT at HapL978 on chromosome 2 were less sensitive to photoperiod than lines carrying other haplotype alleles. Haplotype loci associated with flowering time and photoperiod sensitivity explained respectively 17.5%–18.6% and 11.2%–15.5% of phenotypic variation. Candidate genes and favorable haplotypes identified in this study may support the more efficient utilization of maize germplasm groups.

Genomic selection (GS) as a promising molecular breeding strategy has been widely implemented and evaluated for plant breeding, because it has remarkable superiority in enhancing genetic gain, reducing breeding time and expenditure, and accelerating the breeding process. In this study the factors affecting prediction accuracy (r_MG) in GS were evaluated systematically, using six agronomic traits (plant height, ear height, ear length, ear diameter, grain yield per plant and hundred-kernel weight) evaluated in one natural and two biparental populations. The factors examined included marker density, population size, heritability, statistical model, population relationships and the ratio of population size between the training and testing sets, the last being revealed by resampling individuals in different proportions from a population. Prediction accuracy continuously increased as marker density and population size increased and was positively correlated with heritability; r_MG showed a slight gain when the training set increased to three times as large as the testing set. Low predictive performance between unrelated populations could be attributed to different allele frequencies, and predictive ability and prediction accuracy could be improved by including more related lines in the training population. Among the seven statistical models examined, including ridge regression best linear unbiased prediction (RR-BLUP), genomic BLUP (GBLUP), BayesA, BayesB, BayesC, Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), and reproducing kernel Hilbert space (RKHS), the RKHS and additive-dominance model (Add + Dom model) showed credible ability for capturing non-additive effects, particularly for complex traits with low heritability. Empirical evidence generated in this study for GS-relevant factors will help plant breeders to develop GS-assisted breeding strategies for more efficient development of varieties.

The International Crop Science Congress (ICSC) is a regularly held event allowing crop scientists to integrate current knowledge into a global context and international applications. The 7th ICSC was held on August 14–19, 2016 in Beijing, China, with the theme “Crop Science: Innovation and Sustainability”. As a companion production for this great congress, the nine papers collected in this special issue feature important fields of crop science in China. This editorial first briefly introduces the 7th ICSC, followed by a brief discussion of the current status of, constraints to, and innovations in Chinese agriculture and crop science. Finally, the main scientific points of the papers published in this special issue are surveyed, covering important advances in hybrid rice breeding, minor cereals, food legumes, rapeseed, crop systems, crop management, cotton, genomics-based germplasm research, and QTL mapping. In a section describing future prospects, it is indicated that China faces a full transition from traditional to modern agriculture and crop science.