Heterosis is widely used in genetic crop improvement; however, the genetic basis of heterosis is incompletely understood. The use of whole-genome segregating populations poses a problem for establishing the genetic basis of heterosis, in that interactions often mask the effects of individual loci. However, introgression line (IL) populations permit the partitioning of heterosis into defined genomic regions, eliminating a major part of the genome-wide epistasis. In our previous study, based on mid-parental heterosis (HMP) value with single-point analysis, 42 heterotic loci (HLs) associated with six yield-related traits were detected in wild and cultivated rice using a set of 265 ILs of Dongxiang common wild rice (Oryza rufipogon Griff.). In this study, the genetic effects of HLs were determined as the combined effects of both additive and dominant gene actions, estimated from the performance values of testcross F1s and the dominance effects estimated from the HMP values of testcross F1s. We characterized the gene action type at each HL. Thirty-eight of the 42 HLs were over-dominant, and in the absence of epistasis, four HLs were dominant. Therefore, we favour that over-dominance is a major genetic basis of ‘wild-cultivar’ crosses at the single functional Mendelian locus level.

Seed filling rate of soybean has been shown to be a dynamic process in different developmental stages affected by both genotype and environment. The objective of the present study was to determine additive, epistatic and quantitative trait loci (QTLs)×environment interaction (QE) effects of the QTL underlying a seed filling rate of soybean. One hundred and forty-three recombinant inbred lines (RILs) derived from the cross of Charleston and Dongnong 594 were used with 2 years of field data (2004 and 2005). Eleven QTLs with significantly unconditional and conditional additive (a) effect and/or additive×environment interaction (ae) effect at different filling stages were identified. Of them six QTLs showed positive a effects and four QTLs had negative a effects on the seed filling rate during seed development. aa and aae effects of 12 pairs of QTLs were identified by unconditional mapping from the initial stage to the final stage. Thirteen pairs of QTLs underlying the seed filling rate with aa and aae effects were identified by conditional mapping. QTLs with aa and aae (additive×additive×environment) effects appeared to vary at different filling stages. Our results demonstrated that the mass filling rate in soybean seed were under genetic and environmental control.

A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 0·5. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 0·89 was obtained by ssGBLUP after one iteration, which was 0·01 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 0·82 and 0·74 for ssGBLUP and CGWAS with m=8, and 0·83 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.

Owing to their ability and flexibility to describe individual gene expression at different time points, random regression (RR) analyses have become a popular procedure for the genetic analysis of dynamic traits whose phenotypes are collected over time. Specifically, when modelling the dynamic patterns of gene expressions in the RR framework, B-splines have been proved successful as an alternative to orthogonal polynomials. In the so-called Bayesian B-spline quantitative trait locus (QTL) mapping, B-splines are used to characterize the patterns of QTL effects and individual-specific time-dependent environmental errors over time, and the Bayesian shrinkage estimation method is employed to estimate model parameters. Extensive simulations demonstrate that (1) in terms of statistical power, Bayesian B-spline mapping outperforms the interval mapping based on the maximum likelihood; (2) for the simulated dataset with complicated growth curve simulated by B-splines, Legendre polynomial-based Bayesian mapping is not capable of identifying the designed QTLs accurately, even when higher-order Legendre polynomials are considered and (3) for the simulated dataset using Legendre polynomials, the Bayesian B-spline mapping can find the same QTLs as those identified by Legendre polynomial analysis. All simulation results support the necessity and flexibility of B-spline in Bayesian mapping of dynamic traits. The proposed method is also applied to a real dataset, where QTLs controlling the growth trajectory of stem diameters in Populus are located.

Using a dataset of karyotypic changes reported for bovids and the house mouse (Mus musculus domesticus) together with information from the cattle (Bos taurus) and mouse genomes, we examined two principal variables that have been proposed to predict chromosomal positioning in the nucleus, chromosome size and GC content. These were expected to influence the distribution of Robertsonian (Rb) fusions, the predominant mode of chromosomal change in both taxa. We found the largest chromosomes to be most frequently involved in fusions in bovids, and confirm earlier reports that chromosomes of intermediate size were the most frequent fusers in mice. We then tested whether chromosomal positioning can explain Rb fusion frequencies. We classified chromosomes into groups by size and considered the frequency of interactions between specific groups. Among the interactions, mouse chromosomes showed a slight tendency to fuse with neighbouring chromosomes, in line with expectations of chromosomal positioning, but also resembling predictions from meiotic spindle-induced bias. Bovids, on the other hand, showed no trend in interactions, with small chromosomes being the least frequent partner for all size classes. We discuss the results in terms of nuclear organization at various cell cycle stages and the proposed mechanisms of Rb fusion formation, and note that the difference can be explained by (i) considering bovid species generally to be characterized by a greater intermingling of chromosomal size classes than the house mouse, or (ii) by the vastly different timescales underpinning their evolutionary histories.