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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 (rMG) 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; rMGshowed 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.

Increasing the whole‐kernel methionine [MET; 2‐amino‐4‐(methylthio)‐butyric acid] level of maize (Zea maysL.) would improve the quality of the grain for poultry feed. Our objective was to increase the MET levels of A632, B73, Mo17, A632/Mo17, B73/A632, and B73/Mo17, with the high‐MET inbred BSSS53 as a donor parent. Backcross‐derived lines were evaluated per se in trials during 3 yr, and hybrids among subsets of these lines were evaluated in three replicates during 1998. Methionine levels of the best A632 recoveries were increased by 17% after three backcrosses and 11% after four backcrosses. Methionine levels of the best B73 recoveries were increased by 20% after three backcrosses and 17% after four backcrosses. In the Mo17 genetic background, MET levels of the best recoveries were increased by 55% after three backcrosses and 31% after four backcrosses. Crosses among the highest MET backcross‐derived lines produced hybrids with significant 23 to 43% increases in whole‐kernel MET compared with corresponding control hybrids. Significant increases in the MET levels of experimental hybrids indicate that BSSS53 is a useful donor source of whole‐kernel MET and that selection within normal dent maize germplasm can be effectively utilized to produce high‐MET hybrids.

Chromosomal loci containing genes affecting antinociceptive sensitivity to morphine have been identified, but virtually nothing is known about the genetic mediation of sensitivity to over-the-counter analgesics. Such knowledge would be of great clinical interest, as prodigious interindividual variability has been noted in the efficacy of these ubiquitously used drugs. In the present study, we assessed heritability and genetic correlations among three over-the-counter analgesics in mice of 12 inbred mouse strains on the 0.9% acetic acid (i.p.) writhing test. Analgesics included the centrally acting analgesic, acetaminophen (150 mg/kg, s.c.), and the nonsteroidal anti-inflammatory drugs (NSAIDs), indomethacin (40 mg/kg, s.c.) and lysine-acetylsalicylic acid (800 mg/kg, s.c.). Significant strain differences in sensitivity to each of the drugs were observed, with narrow-sense heritability estimates ranging from 23 to 45%. Similar strains were sensitive and resistant, respectively, to the two NSAIDs (rs= 0.64). In contrast, a completely different pattern of sensitivities was observed for acetaminophen, implying genetic dissociation (rs= 0.29 and 0.02) compared with the NSAIDs. Additional experiments were performed on two strains, C57BL/6 and DBA/2, with extreme sensitivities to acetaminophen. Plasma acetaminophen levels in these strains were not significantly different during the time of antinociception assessment, suggesting the existence of genetic factors affecting acetaminophen pharmacodynamics rather than pharmacokinetics.

Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K–40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms.

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.