Copy number variants (CNVs) are pieces of genomic DNA of 1000 base pairs or longer which occur in a given genome at a different frequency than in a reference genome. Their importance as a source for phenotypic variability has been recognized only in the last couple of years. Chromosomal deletions can be seen as a special case of CNVs where stretches of DNA are missing in certain lines when compared to the reference genome of the mouse line C57BL/6, for example. Based upon more than 8 million single nucleotide polymorphisms (SNPs) in the fifteen inbred mouse lines which were determined in a whole genome chip based resequencing project by Perlegen Sciences, we detected 20 166 such long chromosomal deletions. They cover altogether between 4.4 million and 8.8 million base pairs, depending on the mouse line. Thus, their extent is comparable to that of SNPs. The chromosomal deletions were found by searching for clusters of missing values in the genotyping data by applying bioinformatics and biostatistical methods. In contrast to isolated missing values, clusters are likely the consequence of missing DNA probe rather than of a failed hybridization or deficient oligos. We analyzed these deletion sites in various ways. Twenty-two percent of these deletion sites overlap with exons; they could therefore affect a gene’s functioning. The corresponding genes seem to exist in alternative forms, a phenomenon that reminds of the alternative forms of mRNA generated during gene splicing. We furthermore detected statistically significant association between hundreds of deletion sites and fat weight at the age of eight weeks.

Before a breeder invests selection pressure on a trait of interest, it needs to be established whether that trait is actually heritable. Some traits may not have been measured widely in pedigreed populations, for example, a disease or deformity may become more prevalent than previously, but is still relatively rare. One approach to detect inheritance would be to screen a commercial population to obtain a sample of “affecteds” (the test group) and to also obtain a random control group. These individuals are then genotyped with a set of genetic markers and the relationships between individuals within each group estimated. If the relatedness is higher in the test group than in the control group, this provides initial evidence for the trait being heritable. A power simulation shows that this approach is feasible with moderate resources.

Quantitative trait loci (QTL) and their additive, dominance and epistatic effects play a critical role in complex trait variation. It is often infeasible to detect multiple interacting QTL due to main effects often being confounded by interaction effects. Positioning interacting QTL within a small region is even more difficult. We present a variance component approach nested in an empirical Bayesian method, which simultaneously takes into account additive, dominance and epistatic effects due to multiple interacting QTL. The covariance structure used in the variance component approach is based on combined linkage disequilibrium and linkage (LDL) information. In a simulation study where there are complex epistatic interactions between QTL, it is possible to simultaneously fine map interacting QTL using the proposed approach. The present method combined with LDL information can efficiently detect QTL and their dominance and epistatic effects, making it possible to simultaneously fine map main and epistatic QTL.

In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.

This paper presents a quick, easy to implement and versatile way of using stochastic simulations to investigate the power and design of using single nucleotide polymorphism (SNP) arrays for genome-wide association studies in farm animals. It illustrates the methodology by discussing a small example where 6 experimental designs are considered to analyse the same resource consisting of 6 006 animals with pedigree and phenotypic records: (1) genotyping the 30 most widely used sires in the population and all of their progeny (515 animals in total), (2) genotyping the 100 most widely used sires in the population and all of their progeny (1 102 animals in total), genotyping respectively (3) 515 and (4) 1 102 animals selected randomly or genotyping respectively (5) 515 and (6) 1 102 animals from the tails of the phenotypic distribution. Given the resource at hand, designs where the extreme animals are genotyped perform the best, followed by designs selecting animals at random. Designs where sires and their progeny are genotyped perform the worst, as even genotyping the 100 most widely used sires and their progeny is not as powerful of genotyping 515 extreme animals.

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) suggested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http://agbu.une.edu.au/~kmeyer/wombat.html

Objective: To evaluate the effect and profitability of using the quantitative trait loci (QTL)-linked direct marker (DR marker) in gene-assisted selection (GAS). Methods: Three populations (100, 200, or 300 sows plus 10 boars within each group) with segregating QTL were simulated stochastically. Five economic traits were investigated, including number of born alive (NBA), average daily gain to 100 kg body weight (ADG), feed conversion ratio (FCR), back fat at 100 kg body weight (BF) and intramuscular fat (IMF). Selection was based on the estimated breeding value (EBV) of each trait. The starting frequencies of the QTL’s favorable allele were 0.1, 0.3 and 0.5, respectively. The economic return was calculated by gene flow method. Results: The selection efficiency was higher than 100% when DR markers were used in GAS for 5 traits. The selection efficiency for NBA was the highest, and the lowest was for ADG whose QTL had the lowest variance. The mixed model applied DR markers and obtained higher extra genetic gain and extra economic returns. We also found that the lower the frequency of the favorable allele of the QTL, the higher the extra return obtained. Conclusion: GAS is an effective selection scheme to increase the genetic gain and the economic returns in pig breeding.

A novel chitinolytic and chitosanolytic bacterium, Sphingomonas sp. CJ-5, has been isolated and characterized. It secretes both chitinase and chitosanase into surrounding medium in response to chitin or chitosan induction. To characterize the enzymes, both chitinase and chitosanase were purified by ammonium sulfate precipitation, Sephadex G-200 gel filtration and DEAE-Sepharose Fast Flow. SDS-PAGE analysis demonstrated molecular masses of chitinase and chitosanase were 230 kDa and 45 kDa respectively. The optimum hydrolysis conditions for chitinase were about pH 7.0 and 36 °C, and these for chitosanase were pH 6.5 and 56 °C, respectively. Both enzymes were quite stable up to 45 °C for one hour at pH 5~8. These results show that CJ-5 may have potential for industrial application particularly in recycling of chitin wastes.

Neuropeptide Y (NPY) is one of the most important orexigenic agents in central regulation of feeding behavior, body weight and energy homeostasis in domestic chickens. To examine differences in the hypothalamic NPY between layer-type and meat-type of chickens, which are two divergent kinds of the domestic chickens in feeding behavior and body weight, we detected mRNA levels of NPY in hypothalamic infundibular nucleus (IN), paraventricular nucleus (PVN) and lateral hypothalamic area (LHA) of these two types of chickens using one-step real time RT-PCR. The meat-type chicken had more food daily (about 1.7 folds) and greater body weights (about 1.5 folds) and brain weights than the layer-type chicken at the age of 14 d. In the meat-type of chicken, NPY mRNA levels of the IN and PVN were significantly greater than those of the LHA, and were not significantly different between the IN and PVN. However, in the layer-type of chicken, NPY mRNA levels were significantly greater in the IN than those in the LHA and PVN, and were not significantly different between the PVN and LHA. In all these hypothalamic regions, the layer-type of chicken had significantly higher NPY mRNA levels than the meat-type chicken did. These results suggest the expression of NPY in the hypothalamus has a type-dependent pattern in domestic chickens.

Sequential methodology based on the application of three types of experimental designs was used to optimize the fermentation conditions for elastase production from mutant strain ZJUEL31410 of Bacillus licheniformis in shaking flask cultures. The optimal cultivation conditions stimulating the maximal elastase production consist of 220 r/min shaking speed, 25 h fermentation time, 5% (v/v) inoculums volume, 25 ml medium volume in 250 ml Erlenmeyer flask and 18 h seed age. Under the optimized conditions, the predicted maximal elastase activity was 495 U/ml. The application of response surface methodology resulted in a significant enhancement in elastase production. The effects of other factors such as elastin and the growth factor (corn steep flour) on elastase production and cell growth were also investigated in the current study. The elastin had no significant effect on enzyme-improved production. It is still not clear whether the elastin plays a role as a nitrogen source or not. Corn steep flour was verified to be the best and required factor for elastase production and cell growth by Bacillus licheniformis ZJUEL31410.