Fertility transmission (FT) is a phenomenon with a cultural and/or genetic basis, whereby a positive correlation exists between the number of offspring of an individual and that of his/her parents. Theoretical studies using a haploid individual-based model have shown that FT increases the variance and intergenerational correlation in reproductive success and results in an imbalance in the coalescent tree of sampled genes. This phenomenon has been documented in several demographic studies conducted on the correlation in fertility between generations, or through the reconstruction of the genealogical trees of mitochondrial DNA sequences. However, as mtDNA is a single locus, potentially subject to other forces (e.g. natural selection), it is of interest to extend the theory of FT to nuclear loci. We show that because random mating between individuals leads to a mixing of their fertility profiles, FT in these cases will have less influence on the variance and intergenerational correlation of reproductive success. This, in turn, results in less impact on the shape of the coalescent trees. Nevertheless, in the presence of FT, high heterogeneity in reproductive success and homogamy for family size will increase the imbalance in the coalescent tree. Thus, FT should be easier to detect when occurring in conjunction with these other factors. We also show the utility of analysing different kinds of loci (X-linked, Y-linked, mitochondrial and autosomal) to assess whether FT is matrilineal, patrilineal or biparental. Finally, we demonstrate that the shape of the coalescent tree depends upon population size, in contrast to the classical Kingman's model.

The aim of this study was to investigate whether the use of sainfoin-based condensed tannins (CT) enhances feed value when given with tannin-free legumes (lucerne) to sheep. The experiments were conducted with fresh sainfoin and lucerne harvested at two stages (vegetative stage as compared with early flowering) in the first growth cycle. Fresh sainfoin and lucerne forages were combined in ratios of 100 : 0, 75 : 25, 25 : 75 and 0 : 100 (denoted S100, S75, S25 and S0, respectively). Voluntary intake, organic matter digestibility (OMD) and nitrogen (N) retention were measured in sheep fed the different sainfoin and lucerne mixtures. Loss of dry matter (DM) and N from polyester bags suspended in the rumen, abomasum and small intestine (SI) was also measured using rumen-fistulated sheep and intestinally fistulated sheep. The CT content in sainfoin (S100) decreased with increasing percentage of lucerne in the mixture (mean value from 58 g/kg DM for S100 to 18 g/kg DM for S25) and with growth stage (S100: 64 to 52 g/kg DM). OMD did not differ between different sainfoin/lucerne mixture ratios. Sainfoin and lucerne had an associative effect (significant quadratic contrast) on voluntary intake, N intake, total-tract N digestibility, N in faeces and urine (g/g N intake) and N retained (g/g N intake). Compared with lucerne mixtures (S0 and S25), high-sainfoin-content mixtures (S100 and S75) increased the in situ estimates of forage N escaping from the rumen (from 0.162, 0.188 for S0 and S25 to 0.257, 0.287 for S75 and S100) but decreased forage N intestinal digestibility (from 0.496, 0.446 for S0 and S25 to 0.469, 0.335 for S75 and S100). The amount of forage N disappearing from the bags in the SI (per g forage N) was the highest for high-sainfoin mixtures (from 0.082, 0.108 for S100 and S75 to 0.056, 0.058 for S25 and S0, P < 0.001). Rumen juice total N (tN) and ammonia N (NH3-N) values were the lowest in the high-sainfoin diet (mean tN 0.166 mg/g in S100 as compared with 0.514 mg/g in S0; mean NH3-N 0.104 mg/g in S100 as compared with 0.333 mg/g in S0, P < 0.001).

Dry matter grain yield per plot from three genetically homogeneous single-cross maize hybrids were analysed to investigate whether environmental variance depends on genotype. Three genotypes were tested at 20 locations in 3 years. The data were analysed using a non-parametric approach and fully parametric Bayesian models. Both analyses reveal effects of genotype on environmental variation. The Bayesian analyses indicate that genotype by location–year interactions are the most important effects acting at the level of the mean. The best-fitting Bayesian model is one postulating genotype by location–year interactions acting on the mean and main effects of genotype and of location–year on the variance. Despite the detection of genotypic effects acting on the variance, location–year effects constitute the biggest relative source of variance heterogeneity.

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.

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.

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.

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.

Aim of this work was to evaluate if long-term dietary supplementation of potassium iodide (KI) to dairy goats can influence metabolic and hormonal parameters. Thirty Sarda crossbred dairy goats were divided into three groups, which were orally administered 0 (control group; CON), 0.45 (low iodine group; LI) or 0.90 (high iodine group; HI) mg of KI/day, respectively. The daily dose of KI (76.5% of iodine) was administered as salt dissolved in water for 8 weeks. Plasma contents of nonesterified fatty acids (NEFA), urea, glucose, insulin, free triiodothyronine (FT3) and thyroxine (FT4) were determined weekly. Iodine supplementation increased significantly the FT3 hormone (P = 0.007) and FT3/FT4 ratio (P = 0.001) and tended to influence the FT4 hormone (P = 0.059). An iodine level × week of sampling interaction for NEFA (P = 0.013) evidenced a temporary concentration increase in supplemented groups. The ‘Revised Quantitative Insulin Sensitivity Check Index’ increased with KI supplementation (P ⩽ 0.01). Blood urea nitrogen (BUN) and insulin were lowered (P ⩽ 0.01) by iodine supplementation (groups LI and HI; P ⩽ 0.01). The glucose concentration evidenced an iodine level × week of sampling interaction (P = 0.025) due to an unexpected and temporary increase of its concentration in the CON group. Glucose concentration was decreased by KI supplementation only in LI group (P < 0.05). In conclusion, the daily supplementation of low doses of KI can improve insulin sensitivity and decrease BUN in dairy goats.