Genomic selection (GS) reports on milk fatty acid (FA) profiles have been published quite recently and are still few despite this trait represents the most important aspect of milk nutritional and sensory quality. Reasons for this can be found in the high costs of phenotype recording but also in issues related to its nature of complex trait constituted by multiple genetically correlated variables with low heritabilities. One possible strategy to deal with such constraint is represented by the use of dimension reduction methods. We analysed 40 individual FAs from Italian Brown Swiss, Holstein and Simmental milk through multivariate factor analysis (MFA) to study the genetics of milk FA-related latent variables (factors) and assess their potential use in breeding. A total of nine factors were obtained, and their genetic parameters were inferred under a Bayesian framework using two statistical approaches: the classical pedigree best linear unbiased prediction (ABLUP) and the single-step genomic BLUP (ssGBLUP). The resulting factorial solutions were able to represent groups of FAs with common origin and function and can be considered concise pathway-level phenotypes. The heritability (h2 ) values showed relevant variations across different factors in each breed (0.03≤h2 ≤0.38). The accuracies of breeding values predicted were low to high, ranging from 0.13 to 0.72 and from 0.18 to 0.74 considering the pedigree and the genomic model, respectively. The gain in accuracy in genetic prediction due to the addition of genomic information was ~30% and ~5% in validation and training groups respectively, confirming the contribution of genomic information in yielding more accurate predictions compared to the traditional ABLUP methodology. Our results suggest that MFA in combination with GS can be a valuable tool in dairy cattle breeding and deserves to be further investigated for use in future breeding programs to improve cow's milk FA-related traits.

Qingyuan partridge chicken is an important indigenous chicken in China. In its breeding schemes, chickens are usually selected at the age of 105-day-old for five traits, including body weight (BW), shank length (SL), shank girth (SG), comb height (CH) and feather maturity (FM). At present, genetic parameters of the aforementioned traits have still not been studied in Qingyuan partridge chickens. The objectives of this study were the following: (1) to investigate whether the optimal statistical models of these traits need to consider maternal genetic and permanent environmental effects in late-feathering Qingyuan partridge hens, and (2) to estimate genetic parameters for these traits based on the optimal models. The numbers of records for BW, SL, SG, CH and FM were 13,721, 13,671, 13,670, 13,669 and 13,672, respectively. Variance components were estimated using average information-restricted maximum likelihood method, and the optimal model was determined based on Bayesian information criterion. More specifically, the optimal model for BW considered maternal genetic and permanent environmental effects in addition to direct additive genetic effect; SL, SG and FM considered direct and maternal genetic effects; and CH considered direct and maternal genetic effects, and the covariance between them. The direct heritabilities of these traits estimated using the optimal models were 0.21±0.04, 0.30±0.05, 0.40±0.05, 0.59±0.09 and 0.09±0.04, respectively; the maternal heritabilities were 0.01±0.04, 0.05±0.05, 0.04±0.05, 0.09±0.09 and 0.03±0.04, respectively. Maternal genetic effect evidently played an important part in FM and maternal heritability accounted for 30 per cent of total heritability. Furthermore, the direct and maternal genetic effects for CH were estimated to be negatively and moderately correlated (-0.51±0.11). For all traits, neglecting existent maternal effects biased the estimation of direct heritability. Therefore, to implement optimum breeding strategies for improvement of these traits in Qingyuan partridge hens, maternal effects should be taken into consideration.

Breeding or genetic improvement refers to the process of artificial selection following domestication; as such, it has had a major influence on modern agriculture and animal production. Improvement generally focuses on traits that greatly affect the economic performance. Therefore, understanding the genetic basis underlying improvement will contribute to the identification of genes controlling economic traits and will facilitate future crop and animal breeding. However, genome-wide study of the molecular basis underlying improvement remains rare. The silkworm is a unique, entirely domesticated economically important invertebrate; genetic improvement has had a huge effect on the silkworm regarding silk-related traits. Herein, we performed whole-genomic sequencing on local and genetically improved silkworm lines to identify the genomic regions under strong selection in silkworm breeding/improvement. By genomic-wide selective sweeping analysis, we identified 24 genomic regions with strong selection signals, eight of which contained 13 candidate genes underlying silkworm breeding. Interestingly, six of these genes were annotated with functions related to neural signal response. Among the six genes, BGIBMGA004050 encodes silkworm CREB-regulated_transcription_coactivator_1 (BmCRTC1), which was reported to be involved in energy-sensing pathways. These results suggested that improvement may have affected the nervous system of the silkworm. This research will provide new insights into the genetic basis underlying the genetic improvement of silkworms and possibly of other species.

The aim of this study was to estimate the genetic parameters for the morphometric measurements of withers height (WH), thoracic circumference (TC) and cannon bone circumference (CBC) of Criollo horses, stratified for maternal and paternal effects. Statistical genetic design of factor crossings was used to evaluate the offspring of full siblings and half-siblings. Fifty stallions were selected (n=50) who had been crossed with six mares each (n=6), to provide 300 parental pairings in which two offspring were born per mare (n=600). WH in females and TC in males were highly influenced by additive genetic effects, while the other morphometric measures were found to be closely related to the general effects of the environment. WH had the greatest additive genetic influence on female offspring (47.06%), while TC presented the greatest additive genetic influence on male offspring (58.73%). When comparing the parental influence on the morphometric characteristics evaluated, TC and CBC had greater additive genetic influence from maternal effects, regardless of offspring sex. However, the WH in female offspring showed greater maternal additive genetic contribution, while in males this characteristic presented greater paternal additive genetic influence. For WH in male offspring, narrow sense heritability ( : 0.26) was more influenced by paternal effects, while for WH in females ( : 0.47), TC in females ( : 0.08) and males ( : 0.59), and CBC in females ( : 0.07) and males ( : 0.05) the maternal effects were the most important. Therefore, the phenotypic expression of WH in males and females and TC in males is influenced by the additive genetic effect of the genes. On the other hand, the TC in female progenies and CBC in progenies of both sexes are closely related to the effects of the environment. The results obtained in this study could be useful in the selection process of Criollo horses.

This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single-step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single-step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance-covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.

Yunnan as a frontier zone that connects China with South and Southeast Asia, has 11 well-recognized goat breeds. However, the knowledge about maternal origins, population structure and demographic history of Chinese indigenous goats from Yunnan is limited. In this study, we analysed a 481-bp fragment of first hypervariable segment (HVSI) of the mitochondrial DNA (mtDNA) control region sequences of 749 individuals from 10 Yunnan indigenous goat breeds, of which 556 sequences were newly determined. There were 110 polymorphic sites that defined 158 haplotypes among all sequences. The haplotype and nucleotide diversity of these breeds ranged from 0.782±0.079 to 0.982±0.015 and from 0.028±0.003 to 0.043±0.005, respectively. Phylogenetic analysis identified two lineages A and B, of which the lineage A had higher frequency (68.1%) and distributed in all Yunnan breeds. We combined previously reported sequences with our sequences belonging to the lineage B and detected two subclades B1 and B2, in which the B1 subclade shared individuals from Eastern Asia, Southeast Asia and Southern Asia. Given higher level of diversity and more unique haplotypes, the B2 subclade probably originated from Southwestern China. The haplotype network, analysis of molecular variance (AMOVA) and a Mantel test revealed no significant phylogeographic structuring among Yunnan goat breeds. This can be explained by high gene flow and genetic admixture among these breeds from different geographic regions in Yunnan. Additionally, both the lineages A and B reflected different demographic histories. This study will provide a scientific basis for the conservation and utilization of Yunnan indigenous goats.