The study aimed to analyze the genetic structure of parental lines of Chinese Simmental beef cattle and Chinese local cattle by the 770K SNP markers covering the whole genome of cattle and trait-related SNP markers, and predict heterosis of different hybrid combinations in growth, carcass and meat quality traits by genetic distance (GD) of parental lines. One thousand two hundred and twenty-two Chinese Simmental beef cattle in Ulgai of Xilin Gol League, Inner Mongolia and 8 Chinese local cattle breeds including 190 were selected to form 8 hybrid combinations, and the genetic structure of parental lines were analyzed. Parent populations were genotyped using Illumina BovineHD SNPs array(770K). The QTLs corresponding to the target traits were screened by cattle QTL database and the SNP loci of the whole genome were mapped to obtain trait-related SNP markers. The state homology matrix was constructed by using SNP markers, and the genetic distance between the parent lines of each hybrid combination was calculated. All parent lines were clustered into 3 groups, Chinese Simmental beef cattle was clustered into one category, northern cattle (Menggu cattle, Xizang cattle and Chaidamu cattle) and southern cattle (Zhaotong cattle, Pingwu cattle, Nandan cattle, Wenshan cattle and Liangshan cattle) were clustered into one category, respectively. Chinese Simmental beef cattle and northern breeds of Chinese local cattle were relatively closer on the PCA map, indicating that the genetic relationship between them was relatively closer, and the difference in the genetic background was smaller. Among all the traits, the parent combinations with the largest genetic distance were Chinese Simmental beef cattle and Nandan cattle. The genetic distance between Chinese Simmental beef cattle and Zhaotong cattle was the smallest for marbling score, and the smallest genetic distance of other traits was all between Chinese Simmental beef cattle and Pingwu cattle. In summary, the possible combination with better heterosis in all traits was between Chinese Simmental beef cattle and Nandan cattle.

In maize hybrid breeding, complementary pools of parental lines with reshuffled genetic variants are established for superior hybrid performance. To comprehensively decipher the genetics of heterosis, we present a new design of multiple linked F1 populations with 42,840 F1 maize hybrids, generated by crossing a synthetic population of 1428 maternal lines with 30 elite testers from diverse genetic backgrounds and phenotyped for agronomic traits.We show that, although yield heterosis is correlated with the widespread, minor-effect epistatic QTLs, it may be resulted from a few major-effect additive and dominant QTLs in early developmental stages. Floral transition is probably one critical stage for heterosis formation, in which epistatic QTLs are activated by paternal contributions of alleles that counteract the recessive, deleterious maternal alleles. These deleterious alleles, while rare, epistatically repress other favorable QTLs. We demonstrate this with one example, showing that Brachytic2 represses the Ubiquitin3 locus in the maternal lines; in hybrids, the paternal allele alleviates this repression, which in turn recovers the height of the plant and enhances the weight of the ear. Finally, we propose a molecular design breeding by manipulating key genes underlying the transition from vegetative-to-reproductive growth.The new population design is used to dissect the genetic basis of heterosis which accelerates maize molecular design breeding by diminishing deleterious epistatic interactions.

Exploitation of crop heterosis is crucial for increasing global agriculture production. However, the quantitative genomic analysis of heterosis was lacking, and there is currently no effective prediction tool to optimize cross-combinations. Here 2,839 rice hybrid cultivars and 9,839 segregation individuals were resequenced and phenotyped. Our findings demonstrated that indica-indica hybrid-improving breeding was a process that broadened genetic resources, pyramided breeding-favorable alleles through combinatorial selection and collaboratively improved both parents by eliminating the inferior alleles at negative dominant loci. Furthermore, we revealed that widespread genetic complementarity contributed to indica-japonica intersubspecific heterosis in yield traits, with dominance effect loci making a greater contribution to phenotypic variance than overdominance effect loci. On the basis of the comprehensive dataset, a genomic model applicable to diverse rice varieties was developed and optimized to predict the performance of hybrid combinations. Our data offer a valuable resource for advancing the understanding and facilitating the utilization of heterosis in rice.© 2023. The Author(s).

Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or breeding values of individuals are generated by substitution effects, which involve both biological additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the genotypic value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts.

Genomic prediction methods based on multiple markers have potential to include nonadditive effects in prediction and analysis of complex traits. However, most developments assume a Hardy-Weinberg equilibrium (HWE). Statistical approaches for genomic selection that account for dominance and epistasis in a general context, without assuming HWE (, crosses or homozygous lines), are therefore needed. Our method expands the natural and orthogonal interactions (NOIA) approach, which builds incidence matrices based on genotypic (not allelic) frequencies, to include genome-wide epistasis for an arbitrary number of interacting loci in a genomic evaluation context. This results in an orthogonal partition of the variances, which is not warranted otherwise. We also present the partition of variance as a function of genotypic values and frequencies following Cockerham's orthogonal contrast approach. Then we prove for the first time that, even not in HWE, the multiple-loci NOIA method is equivalent to construct epistatic genomic relationship matrices for higher-order interactions using Hadamard products of additive and dominant genomic orthogonal relationships. A standardization based on the trace of the relationship matrices is, however, needed. We illustrate these results with two simulated F (not in HWE) populations, either in linkage equilibrium (LE), or in linkage disequilibrium (LD) and divergent selection, and pure biological dominant pairwise epistasis. In the LE case, correct and orthogonal estimates of variances were obtained using NOIA genomic relationships but not if relationships were constructed assuming HWE. For the LD simulation, differences were smaller, due to the smaller deviation of the F from HWE. Wrongly assuming HWE to build genomic relationships and estimate variance components yields biased estimates, inflates the total genetic variance, and the estimates are not empirically orthogonal. The NOIA method to build genomic relationships, coupled with the use of Hadamard products for epistatic terms, allows the obtaining of correct estimates in populations either in HWE or not in HWE, and extends to any order of epistatic interactions.Copyright © 2017 by the Genetics Society of America.

A major part of the growth performance in birds is the result of the combined effects of genes, including the general and specific combining ability that requires the design of an optimal mating system. The aim of this study was to fit the best model for each body weight trait from hatch to 45 days and the estimation of the variance components and the genetic parameters for the body weight traits of a crossbred population. This crossbred population was created by 4 strains of Japanese quail, including the Italian Speckled (A), Tuxedo (B), Pharaoh (C), Texas A&M (D), and the body weights of the different combinations were analyzed by 24 models including the direct genetic effect, the non-additive genetic effects including dominance and epistatic effects, the maternal permanent environmental and maternal genetic effects. The selection of the best fit model of each trait was performed based on the deviance information criteria. The variance components were estimated using a single-trait animal model analyzed with Gibbs sampling. At the early stage of bird growth, maternal genetic and maternal permanent environmental effects had a considerable contribution to the best model, but the contribution of these effects reduced with an increase in the bird's age and the additive variance contribution increased. Adding non-additive genetic effects (dominance and epistasis) to the models significantly reduced the variance of the error and the additive genetic variance estimated with high accuracy. The estimated heritability of body weight traits for the body weights of hatch, 5, 10, 15, 20, 25, 30, 35, 40, and 45 d were 0.316, 0.170, 0.251, 0.153, 0.132, 0.164, 0.290, 0.425, 0.476, and 0.362, respectively. The ratio of maternal genetic and maternal permanent environmental was considerable especially on early age body weight but the ratio of dominance and epistatic variances on each of the body weight traits was less than 4.5% of the total variance, but led to a more accurate estimates of the direct additive genetic.

Non-additive genetic effects are important to increase the accuracy of estimating genetic parameters for growth traits. The aim of this study was to estimate genetic parameters and variance components, specially dominance and epistasis genetic effects, for growth traits (birth weight (BW), weaning weight (WW), 3 (W3), 6 (W6), 9 (W9), and 12 (W12) month weight) in Adani goats. Analyses were carried out using Bayesian method via Gibbs sampler animal model by fitting of 18 different models. All fixed effects (sex, type of birth, age of dam, and year) showed significant effects on BW, WW, W3, and W6, whereas the type of birth and age of dam were not significant on W9 and W12. With the best model, direct heritability estimates were 0.347, 0.178, 0.158, 0.359, 0.278, and 0.281 for BW, WW, W3, W6, W9, and W12 traits, respectively. Maternal permanent environmental effect was significant for BW and WW, but maternal genetic effect was significant only for W3. Dominance and epitasis effects were significant almost for all traits and as a proportion of phenotypic variance were ranged from 0.115 to 0.258 and 0.107 to 0.218, respectively. The range of accuracy of breeding values estimated for growth traits with appropriate evaluation models was from 0.521 to 0.652, 0.616 to 0.694, and 0.548 to 0.684 for the all animals, 10% of the best males and 50% of the best females, respectively. When dominance and epistasis effects added to models, the error variance was reduced and the accuracy of estimated breeding values increased. The accuracy of the best model showed a significant difference with the accuracy of other models (p < 0.01). The result of the present study suggests that non-additive genetic effects should be in genetic evaluation models for goat growth traits because of its effect on accuracy of estimated breeding values.

Epistatic genomic relationship matrices for interactions of any-order can be constructed using the Hadamard products of orthogonal additive and dominance genomic relationship matrices and standardization based on the trace of the resulting matrices. Variance components for litter size in pigs were estimated by Bayesian methods for five nested models with additive, dominance, and pairwise epistatic effects in a pig dataset, and including genomic inbreeding as a covariate.Estimates of additive and non-additive (dominance and epistatic) variance components were obtained for litter size. The variance component estimates were empirically orthogonal, i.e. they did not change when fitting increasingly complex models. Most of the genetic variance was captured by non-epistatic effects, as expected. In the full model, estimates of dominance and total epistatic variances (additive-by-additive plus additive-by-dominance plus dominance-by-dominance), expressed as a proportion of the total phenotypic variance, were equal to 0.02 and 0.04, respectively. The estimate of broad-sense heritability for litter size (0.15) was almost twice that of the narrow-sense heritability (0.09). Ignoring inbreeding depression yielded upward biased estimates of dominance variance, while estimates of epistatic variances were only slightly affected.Epistatic variance components can be easily computed using genomic relationship matrices. Correct orthogonal definition of the relationship matrices resulted in orthogonal partition of genetic variance into additive, dominance, and epistatic components, but obtaining accurate variance component estimates remains an issue. Genomic models that include non-additive effects must also consider inbreeding depression in order to avoid upward bias of estimates of dominance variance. Inclusion of epistasis did not improve the accuracy of prediction of breeding values.

Non-additive genetic effects are usually ignored in animal breeding programs due to data structure (e.g., incomplete pedigree), computational limitations and over-parameterization of the models. However, non-additive genetic effects may play an important role in the expression of complex traits in livestock species, such as fertility and reproduction traits. In this study, components of genetic variance for additive and non-additive genetic effects were estimated for a variety of fertility and reproduction traits in Holstein cattle using pedigree and genomic relationship matrices. Four linear models were used: (a) an additive genetic model; (b) a model including both additive and epistatic (additive by additive) genetic effects; (c) a model including both additive and dominance effects; and (d) a full model including additive, epistatic and dominance genetic effects. Nine fertility and reproduction traits were analysed, and models were run separately for heifers (N = 5,825) and cows (N = 6,090). For some traits, a larger proportion of phenotypic variance was explained by non-additive genetic effects compared with additive effects, indicating that epistasis, dominance or a combination thereof is of great importance. Epistatic genetic effects contributed more to the total phenotypic variance than dominance genetic effects. Although these models varied considerably in the partitioning of the components of genetic variance, the models including a non-additive genetic effect did not show a clear advantage over the additive model based on the Akaike information criterion. The partitioning of variance components resulted in a re-ranking of cows based solely on the cows' additive genetic effects between models, indicating that adjusting for non-additive genetic effects could affect selection decisions made in dairy cattle breeding programs. These results suggest that non-additive genetic effects play an important role in some fertility and reproduction traits in Holstein cattle.© 2020 Blackwell Verlag GmbH.

Genomic analyses commonly explore the additive genetic variance of traits. The non-additive variance, however, is usually small but often significant in dairy cattle. This study aimed at dissecting the genetic variance of eight health traits that recently entered the total merit index in Germany and the somatic cell score (SCS), as well as four milk production traits by analysing additive and dominance variance components. The heritabilities were low for all health traits (between 0.033 for mastitis and 0.099 for SCS), and moderate for the milk production traits (between 0.261 for milk energy yield and 0.351 for milk yield). For all traits, the contribution of dominance variance to the phenotypic variance was low, varying between 0.018 for ovarian cysts and 0.078 for milk yield. Inbreeding depression, inferred from the SNP-based observed homozygosity, was significant only for the milk production traits. The contribution of dominance variance to the genetic variance was larger for the health traits, ranging from 0.233 for ovarian cysts to 0.551 for mastitis, encouraging further studies that aim at discovering QTLs based on their additive and dominance effects.© 2023 The Authors. Journal of Animal Breeding and Genetics published by John Wiley & Sons Ltd.

Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively.We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance.Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.© 2023. BioMed Central Ltd., part of Springer Nature.

【目的】通过1 500个陆地棉杂交组合分析杂种优势与其亲本间数量性状遗传距离的相关性,探讨能否利用大规模杂交组合亲本间遗传距离提高陆地棉杂种优势预测效果,以期为棉花杂交育种和杂种优势利用提供理论指导。【方法】选择来自15个国家和中国23个省（市）的305份陆地棉核心种质为亲本,采用L×T（Line×Tester）杂交设计配制1 500个杂交组合。2012—2013年,在中国南北方13个生态环境下考察其株高、单铃重、衣分、纤维长度等10个产量和纤维品质相关性状,分析F₁杂种优势、亲本间遗传距离和群体结构,并采用4种方式（Cor1—Cor4）计算遗传距离与杂种优势的相关性。【结果】10个性状中亲优势（MPH）均值的变幅为1.70%—7.40%,平均为4.36%,按父本不同将F₁分成5组（A,E）,其MPH均值A>E>B>C>D;超亲优势（HB）均值的变幅为-4.17%—1.87%,平均为-0.17%,A、B和E组的HB均值皆为正。10个性状在5组中除D、E组的马克隆值之外,其他性状普遍具有明显的中亲优势,其中,单铃重和纤维长度的中亲优势在5组中均以正优势为主（达80%以上）,最大值分别为34.01%和9.83%,对应的超亲优势分别为24.25%和5.80%。F₁和亲本差异显著性分析表明单铃重、株高、纤维长度、伸长率和整齐度指数整体表现出一定的超亲优势。父本（测试种）与300个母本之间的遗传距离介于2.280—61.430,平均为21.550,5个测试种与母本间的平均遗传距离D>C>E>A>B,其中,最近值为11.721,最远值为33.271。按最小方差聚类,将305个陆地棉亲本划分为2个主群,包括5个亚群。4种遗传距离与杂种优势的相关性分析结果显示,因样本量、遗传距离变幅和父本不同其结果有所差异,相关性随样本量的增大而有所增强。其中,Cor1是Cor2结果的整体体现;Cor3与Cor1和Cor2相比,部分性状的中亲优势与遗传距离的相关性有所不同;Cor4的相关性最弱。综合来看,遗传距离与衣分、断裂比强度、整齐度指数和纺纱均匀性指数的中亲优势呈显著正相关,遗传距离与其他性状的中亲优势的相关性因采用的分析方案不同,结果有所不同;在4种方案中,除整齐度指数外,遗传距离与超亲优势的相关性整体表现负相关。其中,遗传距离与马克隆值、纤维长度和衣分的超亲优势相关性较强。【结论】陆地棉亲本间数量性状遗传距离与杂种优势有一定的线性关系,不同性状的杂种优势与遗传距离的相关性存在正负和强弱差异,且样本量越大相关性越强。说明基于大规模杂交组合研究陆地棉亲本间遗传距离与杂种优势的关系效果显著。

【Objective】The correlation between heterosis and genetic distance (GD) of quantitative traits between parents was analyzed by 1500 hybrid combinations in upland cotton, and the possibility of using GD between parents of large-scale combinations to improve the efficiency of hybrid vigour prediction of upland cotton was discussed in order to provide theoretical guidance for cotton hybrid breeding and utilization of heterosis.【Method】305 upland cotton core collections from 15 countries and 23 provinces (municipalities) of China were selected as parents, and 1500 cross combinations were produced by L×T (Line×Tester) cross design. From 2012 to 2013, ten yield and fiber quality related traits, including plant height (PH), boll weight (BW), boll number per plant (BN), lint percentage (LP), fiber length (FL), fiber strength (FS), fiber elongation (FE), fiber length uniformity (FU), micronaire (MIC) and spinning consistent index (SCI), were investigated in 13 ecological conditions in north and south China. F₁ hybrids mid-parent heterosis (MPH), heterobeltiosis (HB), GD between parents and population structure were analyzed. The correlation between GD and hybrid vigour was calculated by four schemes (Cor1-Cor4). 【Result】The mean values of MPH of the ten traits ranged from 1.70% to 7.40%, with an average of 4.36%, and F₁ hybrids were divided into 5 groups (A-E) according to different male parents, the mean values of MPH: A>E>B>C>D. The mean values of HB ranged from -4.17% to 1.87%, with an average of -0.17%, and the average values of group A, B, and E were positive. In 5 groups, except for MIC of group D and E, other 9 traits had obvious MPH, among them, MPH of BW and FL were mainly positive (more than 80%) in the 5 groups, the maximum MPH values were 34.01% and 9.83% respectively, and the corresponding HB values were 24.25% and 5.80% respectively. The significant difference analysis between F₁ hybrids and their parents indicated that BW, PH, FL, FE, and FU showed some HB. The GDs between male parents (testers) and 300 female parents ranged from 2.280 to 61.430, with an average of 21.550. The mean GDs between 5 testers and female parents: D>C>E>A>B, in which the nearest value was 11.721, and the farthest value was 33.271. According to “Ward” clustering method, 305 upland cotton parents were divided into two groups, including five subgroups. The results of four correlation analysis methods between GD and heterosis showed that the consequences varied with the sample size, the range of GD, and the male parent, the correlation increased with the sample size. Cor1 was the overall embodiment of Cor2 results; compared with Cor1 and Cor2, Cor3 had different correlations between MPH and GD in some traits; Cor4 had the weakest correlations. To sum up, the genetic distance was positively correlated with the MPH of LP, FS, FU, and SCI, the correlation between GD and MPH of other traits was different due to the different analysis schemes. In the four schemes, except for FU, the relationship between GD and HB was negatively correlated on the whole, and there was a strong correlation between genetic distance and HB of MIC, FL and LP. 【Conclusion】There is a linear relationship between GD of quantitative traits and hybrid vigour in upland cotton. The correlations are positive or negative, strong or weak due to different traits, and the larger the sample size, the stronger the correlation. Thus, the large-scale hybrid combinations are used to well study the relationship between GD and heterosis in upland cotton.

A distance measure for populations diverging by drift only is based on the coancestry coefficient theta, and three estimators of the distance D = -ln(1 - theta) are constructed for multiallelic, multilocus data. Simulations of a monoecious population mating at random showed that a weighted ratio of single-locus estimators performed better than an unweighted average or a least squares estimator. Jackknifing over loci provided satisfactory variance estimates of distance values. In the drift situation, in which mutation is excluded, the weighted estimator of D appears to be a better measure of distance than others that have appeared in the literature.

Genotypes for 24 microsatellite markers, dispersed across the chicken genome, were used to predict progeny performance and heterosis for egg production (number and mass) in 'layers' (egg-type chickens). These markers were used to evaluate genetic distance between each of 39 sires sampled from two-layer male-lines; Rhode Island Red (RIR) and White egg Leghorn (Leghorn), and a DNA pool of 30 randomly sampled females from a Brown-egg female line (Silver). Each sire was analysed for egg production across months in the laying period and cumulatively in each of three subperiods; onset (2 month), mid (9 month) and late (1 month). The average Reynolds' genetic distance between Leghorn sires and the Silver female line (theta;=0.6) was significantly higher than that between RIR sires and the Silver female line (theta;=0.5). Neither performance nor heterosis values in the RIR sire's daughters were associated with genetic distance values between sires and the Silver female line. On the other hand, performance as well as heterosis values of Leghorn's daughters were positively associated with genetic distance. This association was particularly evident in the mid-subperiod. If 25% of the most genetically distant Leghorn sires from the Silver female line had been selected in a single generation on the basis of DNA markers information only, average egg production of the crossbred daughters would have been improved by about nine eggs (3%). In principle, further improvement is possible if selection to increase genetic distance between the parental lines is carried on.

To assess the value of DNA fingerprints for the prediction of heterosis in chickens, retrospective analyses of data from three crossbreeding experiments and DNA fingerprints (DEP) of parental strains were conducted using two minisatellite and one middle-repetitive DNA probes. DEP bands were assessed on pooled DNA samples of 10-15 individuals per parental genetic group. The number of DEP bands evaluated in the experiments ranged from 81 to 139. The probes varied in their predictive value, but predictability of heterosis generally increased with multiple probes. Highly significant correlations (0.68-0.87) between band sharing ratios (SH) and heterosis were found in 25 crosses of White Leghorns in the first egg production cycle for age at sexual maturity, egg production, and mature body weight: traits with heterosis of 10% or more of the means. Regressions on SH explained 78.4% of the variation in heterosis in age at sexual maturity, 60.2% in egg production and 46.4% in mature body weight. For "broiler" traits with heterosis of < 1%, none of the correlations, based on 13 crosses, were significant. It was concluded that multilocus probe DFP of pooled DNA samples show promise as predictors of heterosis.

旨在利用覆盖全基因组和性状的特异性SNPs标记预测和牛、西门塔尔牛与荷斯坦牛杂种优势，为牛杂种优势利用和选种选配提供参考依据。本研究分别利用牛Illumina Bovine HD 770 K和GGP Bovine 100 K芯片对464头和牛、1 222头西门塔尔牛和43头荷斯坦牛3个亲本群体进行基因型分型，并通过牛QTLs数据库筛选与目的性状对应的QTLs，对比牛参考基因组映射得到与初生重、周岁重、胴体重性状相关的特异性SNPs；然后构建覆盖全基因组和性状特异性SNPs两种标记状态同源矩阵，通过计算杂交组合亲本间的遗传距离来预测品种间杂种优势，并利用配合力分析验证较优组合的实际杂交效果。结果表明，基于全基因组和性状特异性SNPs计算的各杂交组合遗传距离差异不显著。在全基因组水平上，西门塔尔牛♂&#215;荷斯坦牛♀（S&#215;H）与和牛♂&#215;荷斯坦牛♀（W&#215;H）亲本间杂交组合遗传距离分别为0.346 1和0.338 9；在初生重、周岁重和胴体重性状上，S&#215;H亲本间遗传距离分别为0.343 1、0.348 7和0.336 7，而W&#215;H遗传距离分别为0.337 6、0.340 7和0.329 2；两种SNPs标记计算的遗传距离均为S&#215;H较大，W&#215;H次之。因此，在初生重、周岁重、胴体重性状上，S&#215;H为较优杂交组合。通过分析德系西门塔尔牛♂&#215;荷斯坦牛♀实际杂交群体的配合力，发现10个父系在初生重性状上一般配合力和特殊配合力均为正效应，最高效应值分别达到3.760 9和8.931 2。西门塔尔牛与荷斯坦牛杂交可在初生重、周岁重和胴体重获得较高的杂种优势。

The aim of the study was to predict heterosis of different crosses for growth and carcass traits by single nucleotide polymorphism (SNP) markers at the whole genome level in Simmental, Wagyu, and Holstein. The Illumina Bovine HD 770 K and GGP Bovine 100 K chips were used to detect genotypes of 1 222 Simmental, 464 Wagyu and 43 Holstein, respectively. The trait-associated SNP markers were obtained by blasting Illumina Bovine HD 770 K and GGP Bovine 100K chips with QTLs database of birth weight, yearling weight, and carcass weight in NCBI. The genetic distance (GD) was used to predict heterosis of different crosses based on genomic SNPs and trait-associated SNP markers. The combining ability was estimated to verify heterosis effect of Simmental and Holstein in practice. The results showed that the genetic distances were no significant difference between genome-wide and trait-associated SNPs in two hybrid groups. At the genome-wide level, the genetic distances of Simmental(♂)&#215;Holstein(♀) and Wagyu(♂)&#215;Holstein (♀) were 0.346 1 and 0.338 9, respectively. For trait-associated SNPs with birth weight, yearling weight and carcass weight, the genetic distances of Simmental(♂)&#215;Holstein(♀) were 0.343 1, 0.348 7 and 0.336 7, it was 0.337 6, 0.340 7 and 0.329 2 in Wagyu(♂)&#215;Holstein (♀), respectively. In short, Simmental(♂)&#215;Holstein(♀) had the larger GD than Wagyu(♂)&#215;Holstein (♀). Therefore, Simmental(♂)&#215;Holstein(♀) would obtain greater heterosis in birth weight, yearling weight and carcass weight. According to combining ability test of German Simmental&#215;Holstein, it was found that general and specific combining ability of 10 German Simmental sires had positive effect on birth weight, and the highest value were 3.760 9, 8.931 2, respectively. Hybridization of Simmental and Holstein would obtain greater heterosis for birth weight, yearling weight, and carcass weight.

基因组选择是指利用覆盖在全基因组范围内的分子标记信息来估计个体育种值。利用基因组信息能够避免因系谱错误带来的诸多问题，提高选择准确性并缩短育种世代间隔。根据统计模型的不同，基因组选择方法可大致分为基于BLUP（best linear unbiased prediction, BLUP）理论的方法、基于贝叶斯理论的方法和其他方法。目前应用较多的是GBLUP及其改进方法ssGBLUP。准确性是基因组选择模型最常用的评价指标，用来衡量真实值和估计值之间的相似程度。影响准确性的因素可以从模型中体现，大致分为可控因素和不可控因素。传统基因组选择方法促进了动物育种的快速发展，但这些方法目前都面临着多群体、多组学和计算等诸多挑战，不能捕获基因组高维数据间的非线性关系。作为人工智能的一个分支，机器学习是最贴近生物掌握自然语言处理能力的一种方式。机器学习从数据中提取特征并自动总结规律，利用该规律与新数据进行预测。对于基因组信息，机器学习无需进行分布假设，且所有的标记信息都能够被考虑进模型当中。相比于传统的基因组选择方法，机器学习更容易捕获基因型之间、表型与环境之间的复杂关系。因此，机器学习在动物基因组选择中具有一定的优势。根据训练期间接受的监督数量和监督类型，机器学习可分为监督学习、无监督学习、半监督学习和强化学习等。它们的主要区别为输入的数据是否带有标签。目前在动物基因组选择中应用的机器学习方法均为监督学习。监督学习可以处理分类和回归问题，需要向算法提供有标签的数据和所需的输出。近年来机器学习在动物基因组选择中的应用不断增多，特别是在奶牛和肉牛中发展较快。本文将机器学习算法划分为单个算法、集成算法和深度学习3类，综述其在动物基因组选择中的研究进展。单个算法中最常用的是KRR和SVR，两者都是通过核技巧来学习非线性函数，在原始空间中将数据映射到更高维的核空间。目前常用的核函数有线性核、余弦核、高斯核和多项式核等。深度学习又称为深度神经网络，由连接神经元的多个层组成。集成学习算法则是指将不同的学习器融合在一起进而得到一个较强的监督模型。近十年来，有关机器学习和深度学习的相关文献呈现了指数型的增长，在基因组选择方面的应用也在逐渐增多。尽管机器学习在某些方面存在明显的优势，但其在估计动物复杂性状基因组育种值时仍面临诸多挑战。部分模型的可解释性低，不利于数据、参数和特征的调整。数据的异质性、稀疏性和异常值也会造成机器学习的数据噪声。还有过拟合、大标记小样本和调参等问题。因此，在训练模型时需要谨慎处理每一个步骤。文章介绍了基因组选择传统方法及其面临的问题、机器学习的概念和分类，探讨了机器学习在动物基因组选择中的研究进展及目前存在的挑战，并给出了一个案例和一些应用的建议，以期为机器学习在动物基因组选择当中的应用提供一定参考。

Genomic selection is defined as using the molecular marker information that covered the whole genome to estimate individual’s breeding values. Using genome information can avoid many problems caused by pedigree errors so as to improve selection accuracy and shorten breeding generation intervals. According to different statistical models, methods of estimated genomic breeding value (GEBV) can be divided into based on BLUP (best linear unbiased prediction) theory, based on Bayesian theory and others. At present, GBLUP and its improved method ssGBLUP have been widely employed. Accuracy is the most used evaluation metric for genomic selection models, which is to evaluate the similarity between the true value and the estimated value. The factors that affect the accuracy can be reflected from the model, which can be divided into controllable factors and uncontrollable factors. Traditional genomic selection methods have promoted the rapid development of animal breeding, but these methods are currently facing many challenges such as multi-population, multi-omics, and computing. What’s more, they cannot capture the nonlinear relationship between high-dimensional genomic data. As a branch of artificial intelligence, machine learning is very close to biological mastery of natural language processing. Machine learning extracts features from data and automatically summarizes the rules and use to make predictions for new data. For genomic information, machine learning does not require distribution assumptions, and all marker information can be considered in the model. Compared with traditional genomic selection methods, machine learning can more easily capture complex relationships between genotypes, phenotypes, and the environment. Therefore, machine learning has certain advantages in animal genomic selection. According to the amount and type of supervision received during training, machine learning can be classified into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. The main difference is whether the input data is labeled. The machine learning methods currently applied in animal genomic selection are all supervised learning. Supervised learning can handle both classification and regression problems, requiring the algorithm to be provided with labeled data and the desired output. In recent years, the application of machine learning in animal genomic selection has been increasing, especially in dairy and beef cattle. In this review, machine learning algorithms are divided into three categories: single algorithm, ensemble algorithm and deep learning, and their research progress in animal genomic selection were summarized. The most used single algorithms are KRR and SVR, both of which use kernel tricks to learn nonlinear functions and map data to higher-dimensional kernel spaces in the original space. Currently commonly used kernel functions are linear kernel, cosine kernel, Gaussian kernel, and polynomial kernel. Deep learning, also known as a deep neural network, consists of multiple layers of connected neurons. An ensemble learning algorithm refers to fusing different learners together to obtain a stronger supervised model. In the past decade, the related literature on machine learning and deep learning has shown exponential growth. And its application in genomic selection is also gradually increasing. Although machine learning has obvious advantages in some aspects, it still faces many challenges in estimating the genetic breeding value of complex traits in animals. The interpretability of some models is low, which is not conducive to the adjustment of data, parameters, and features. Data heterogeneity, sparsity, and outliers can also cause data noise for machine learning. There are also problems such as overfitting, large marks and small samples, and parameter adjustment. Therefore, each step needs to be handled carefully while training the model. This paper introduced the traditional methods of genomic selection and the problems they face, the concept and classification of machine learning. We discussed the research progress and current challenges of machine learning in animal genomic selection. A Case and some application suggestions were given to provide a certain reference for the application of machine learning in animal genomic selection.

Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line  ×  tester mating design producing 36 F cross combinations. These Fs along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments.© 2024. The Author(s).

Leaf orientation traits of maize (Zea mays) are complex traits controlling by multiple loci with additive, dominance, epistasis, and environmental interaction effects. In this study, an attempt was made for identifying the causal loci, and estimating the additive, nonadditive, environmental specific genetic effects underpinning leaf traits (leaf length, leaf width, and upper leaf angle) of maize NAM population. Leaf traits were analyzed by using full genetic model and additive model of multiple loci. Analysis with full genetic model identified 38 similar to 47 highly significant loci (-log(10)P(EW) > 5), while estimated total heritability were 64.32 similar to 79.06% with large contributions due to dominance and dominance related epistasis effects (16.00 similar to 56.91%). Analysis with additive model obtained smaller total heritability (h(T)(2) (sic) 18.68 similar to 29.56%) and detected fewer loci (30 similar to 36) as compared to the full genetic model. There were 12 pleiotropic loci identified for the three leaf traits: eight loci for leaf length and leaf width, and four loci for leaf length and leaf angle. Optimal genotype combinations of superior line (SL) and superior hybrid (SH) were predicted for each of the traits under four different environments based on estimated genotypic effects to facilitate maker-assisted selection for the leaf traits.

Aim of the present study was first to identify genetic variants associated with egg number (EN) in female broilers, second to describe the mode of their gene action (additive and/or dominant) and third to provide a list with implicated candidate genes for the trait. A number of 2586 female broilers genotyped with the high density (~ 600 k) SNP array and with records on EN (mean = 132.4 eggs, SD = 29.8 eggs) were used. Data were analyzed with application of additive and dominant multi-locus mixed models.A number of 7 additive, 4 dominant and 6 additive plus dominant marker-trait significant associations were detected. A total number of 57 positional candidate genes were detected within 50 kb downstream and upstream flanking regions of the 17 significant markers. Functional enrichment analysis pinpointed two genes (BHLHE40 and CRTC1) to be involved in the 'entrainment of circadian clock by photoperiod' biological process. Gene prioritization analysis of the positional candidate genes identified 10 top ranked genes (GDF15, BHLHE40, JUND, GDF3, COMP, ITPR1, ELF3, ELL, CRLF1 and IFI30). Seven prioritized genes (GDF15, BHLHE40, JUND, GDF3, COMP, ELF3, CRTC1) have documented functional relevance to reproduction, while two more prioritized genes (ITPR1 and ELL) are reported to be related to egg quality in chickens.Present results have shown that detailed exploration of phenotype-marker associations can disclose the mode of action of genetic variants and help in identifying causative genes associated with reproductive traits in the species.

During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked.We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO's performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects.ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO.Supplementary data are available at Bioinformatics online.© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Differential display of mRNA was used to analyse the differences of gene expression in liver between chicken hybrids and their parents in a 4 x 4 diallel crosses in order to study the molecular basis of heterosis in chickens. The results indicated that patterns of gene expression in hybrids differ significantly from their parents. Four patterns of differential gene expression were revealed, which included: (i) bands only detected in the hybrid F1s (UNF1); (ii) bands only absent in the hybrid F1s (ABF1); (iii) bands only detected in the parental P1 or P2 lines (UNP1 and UNP2) and (iv) bands absent in the parental P1 or P2 lines (ABP1 and ABP2). In addition, correlations between patterns of gene expression and heterosis percentages of nine carcass traits of 8-week-old chickens were evaluated. Statistical results showed that negative correlations between heterosis percentages and the percentage of F1-specific bands (UNF1) were significant at P < 0.01 for breast muscle yield, leg muscle yield, wing weight, eviscerated weight and eviscerated weight with giblet of 8-week-old chickens, and at P < 0.05 for intermuscular fat width. Heterosis percentage was negatively correlated with ABP (bands present in the hybrid F1s and one parental line but absent in the other parental line, ABP1 and ABP2) for breast muscle yield, leg muscle yield, wing weight, eviscerated weight and eviscerated weight with giblet of 8-week-old chickens (P < 0.01). Bands detected only in the hybrid F1s but not in either of the parental lines (UNF1) and bands absent in parental P1 or P2 lines (which includes ABP1 and ABP2) may play important roles in chicken heterosis.

Heterosis was defined as the advantage of hybrid performance over its parents in terms of growth and productivity. Previous studies showed that differential gene expression between hybrids and their parents is responsible for the heterosis; however, information on systematic identification and characterization of the differentially expressed genes are limited. In this study, an interspecific hybrid between common wheat (Triticum aestivum. L., 2n = 6x = 42, AABBDD) line 3338 and spelt (Triticum spelta L. 2n = 6x = 42, AABBDD) line 2463 was found to be highly heterotic in both aerial growth and root related traits, and was then used for expression assay. A modified suppression subtractive hybridization (SSH) was used to generate four subtracted cDNA libraries, and 748 nonreduandant cDNAs were obtained, among which 465 had high sequence similarity to the GenBank entries and represent diverse of functional categories, such as metabolism, cell growth and maintenance, signal transduction, photosynthesis, response to stress, transcription regulation and others. The expression patterns of 68.2% SSH-derived cDNAs were confirmed by reverse Northern blot, and semi-quantitative RT-PCR exhibited the similar results (72.2%). And it was concluded that the genes differentially expressed between hybrids and their parents involved in diverse physiological process pathway, which might be responsible for the observed heterosis.

Although many phenotypic traits of chickens have been well documented, the genetic patterns of gene expression levels in chickens remain to be determined. In the present study, we crossed two chicken breeds, White Leghorn (WL) and Cornish (Cor), which have been selected for egg and meat production, respectively, for a few hundred years. We evaluated transcriptome abundance in the brain, muscle, and liver from the day-old progenies of pure-bred WL and Cor, and the hybrids of these two breeds, by RNA-Seq in order to determine the inheritance patterns of gene expression. Comparison among expression levels in the different groups revealed that most of the genes showed conserved expression patterns in all three examined tissues and that brain had the highest number of conserved genes, which indicates that conserved genes are predominantly important compared to others. On the basis of allelic expression analysis, in addition to the conserved genes, we identified the extensive presence of additive, dominant (Cor dominant and WL dominant), over-dominant, and under-dominant genes in all three tissues in hybrids. Our study is the first to provide an overview of inheritance patterns of the transcriptome in layers and broilers, and we also provide insights into the genetics of chickens at the gene expression level.

Heterosis is an important biological phenomenon that has been extensively utilized in agricultural breeding. However, negative heterosis is also pervasively observed in nature, which can cause unfavorable impacts on production performance. Compared with systematic studies of positive heterosis, the phenomenon of negative heterosis has been largely ignored in genetic studies and breeding programs, and the genetic mechanism of this phenomenon has not been thoroughly elucidated to date. Here, we used chickens, the most common agricultural animals worldwide, to determine the genetic and molecular mechanisms of negative heterosis.We performed reciprocal crossing experiments with two distinct chicken lines and found that the body weight presented widely negative heterosis in the early growth of chickens. Negative heterosis of carcass traits was more common than positive heterosis, especially breast muscle mass, which was over - 40% in reciprocal progenies. Genome-wide gene expression pattern analyses of breast muscle tissues revealed that nonadditivity, including dominance and overdominace, was the major gene inheritance pattern. Nonadditive genes, including a substantial number of genes encoding ATPase and NADH dehydrogenase, accounted for more than 68% of differentially expressed genes in reciprocal crosses (4257 of 5587 and 3617 of 5243, respectively). Moreover, nonadditive genes were significantly associated with the biological process of oxidative phosphorylation, which is the major metabolic pathway for energy release and animal growth and development. The detection of ATP content and ATPase activity for purebred and crossbred progenies further confirmed that chickens with lower muscle yield had lower ATP concentrations but higher hydrolysis activity, which supported the important role of oxidative phosphorylation in negative heterosis for growth traits in chickens.These findings revealed that nonadditive genes and their related oxidative phosphorylation were the major genetic and molecular factors in the negative heterosis of growth in chickens, which would be beneficial to future breeding strategies.

Egg-laying performance is economically important in poultry breeding programs. Crossbreeding between indigenous and elite commercial lines to exploit heterosis has been an upward trend in traditional layer breeding for niche markets. The objective of this study was to analyse the genetic background and to estimate the heterosis of longitudinal egg-laying traits in reciprocal crosses between an indigenous Beijing-You and an elite commercial White Leghorn layer line. Egg weights were measured for the first three eggs, monthly from 28 to 76 weeks of age, and at 86 and 100 weeks of age. Egg quality traits were measured at 32, 54, 72, 86, and 100 weeks of age. Egg production traits were measured from the start of lay until 43, 72, and 100 weeks of age. Heritabilities and phenotypic and genetic correlations were estimated. Heterosis was estimated as the percentage difference of performance of a crossbred from that of the parental average. Reciprocal cross differences were estimated as the difference between the reciprocal crossbreds as a percentage of the parental average.Estimates of heritability of egg weights ranged from 0.29 to 0.75. Estimates of genetic correlations between egg weights at different ages ranged from 0.72 to 1.00. Estimates of heritability for cumulative egg numbers until 43, 72, and 100 weeks of age were around 0.15. Estimates of heterosis for egg weight and cumulative egg number increased with age, ranging from 1.0 to 9.0% and from 1.4 to 11.6%, respectively. From 72 to 100 weeks of age, crossbreds produced more eggs per week than the superior parent White Leghorn (3.5 eggs for White Leghorn, 3.8 and 3.9 eggs for crossbreds). Heterosis for eggshell thickness ranged from 2.7 to 6.6% when using Beijing-You as the sire breed. No significant difference between reciprocal crosses was observed for the investigated traits, except for eggshell strength at 54 weeks of age.The heterosis was substantial for egg weight and cumulative egg number, and increased with age, suggesting that non-additive genetic effects are important in crossbreds between the indigenous and elite breeds. Generally, the crossbreds performed similar to or even outperformed the commercial White Leghorns for egg production persistency.© 2023. The Author(s).

Heterosis is routinely exploited to improve animal performance. However, heterosis and its underlying molecular mechanism for feed intake and efficiency have been rarely explored in chickens. Feed efficiency continues to be an important breeding goal trait since feed accounts for 60 to 70% of the total production costs in poultry. Here, we profiled the mRNA-lncRNA landscape of 96 samples of the hypothalamus, liver and duodenum mucosa from White Leghorn (WL), Beijing-You chicken (YY), and their reciprocal crosses (WY and YW) to elucidate the regulatory mechanisms of heterosis.We observed negative heterosis for both feed intake and residual feed intake (RFI) in YW during the laying period from 43 to 46 weeks of age. Analysis of the global expression pattern showed that non-additivity was a major component of the inheritance of gene expression in the three tissues for YW but not for WY. The YW-specific non-additively expressed genes (YWG) and lncRNA (YWL) dominated the total number of non-additively expressed genes and lncRNA in the hypothalamus and duodenum mucosa. Enrichment analysis of YWG showed that mitochondria components and oxidation phosphorylation (OXPHOS) pathways were shared among the three tissues. The OXPHOS pathway was enriched by target genes for YWL with non-additive inheritance of expression in the liver and duodenum mucosa. Weighted gene co-expression network analysis revealed divergent co-expression modules associated with feed intake and RFI in the three tissues from WL, YW, and YY. Among the negatively related modules, the OXPHOS pathway was enriched by hub genes in the three tissues, which supports the critical role of oxidative phosphorylation. Furthermore, protein quantification of ATP5I was highly consistent with ATP5I expression in the liver, which suggests that, in crossbred YW, non-additive gene expression is down-regulated and decreases ATP production through oxidative phosphorylation, resulting in negative heterosis for feed intake and efficiency.Our results demonstrate that non-additively expressed genes and lncRNA involved in oxidative phosphorylation in the hypothalamus, liver, and duodenum mucosa are key regulators of the negative heterosis for feed intake and RFI in layer chickens. These findings should facilitate the rational choice of suitable parents for producing crossbred chickens.© 2023. ’Institut National de Recherche en Agriculture, Alimentation et Environnement (INRAE).

Crossbreeding advantage in hybrids compared with their parents, termed heterosis, has been exhaustively exploited in chicken breeding over the last century. Reports for crossbreeding of elite laying chickens covering rearing and laying period remain infrequent. In this study, resource populations of Rhode Island Red (RIR) and White Leghorn (WL) pure-bred chickens were reciprocally crossed to generate 4 distinct groups that were evaluated for prelaying growth, egg production, and egg quality. Birds monitored for prelaying growth consists of 105 (RIR), 131 (WL), 207 (RIR × WL) and 229 (WL × RIR), and 30 pullets from each group were evaluated. Egg laying records were collected from 102, 89, 147, and 191 hens in the 4 populations, respectively. In addition, expression of 5 candidate genes for egg production in the ovarian follicles was measured by RT-qPCR. Results showed that BW of hatched chicks in the WL line was higher than the other populations. However, the 2 crossbreds grew faster than WL purebred throughout the prelaying period. Low to medium heterosis was observed for BW and body length before the onset of lay. White Leghorn and the hybrids commenced laying earlier than RIR pullets and egg production traits were favorable in the crossbreds compared with purebreds. Heterosis for egg number and clutch size was moderate in WL × RIR but low in RIR × WL hens. Expression of antimullerian hormone gene was high in WL and RIR × WL hybrids, suggesting WL parent-specific enhancing dominant expression. Shell weight was higher in the crossbreds than purebreds at 52 wk of age, but RIR hens laid eggs with higher shell ratio than the other populations (P < 0.05). Conversely, WL and the hybrids had higher eggshell strength than RIR birds (P < 0.05). Eggshell strength was the only egg quality trait that showed heterosis above 10% in WL × RIR hybrids at 32 and 52 wk of age. White Leghorn × RIR hens demonstrated higher percent heterosis for economic traits than birds of the reciprocal hybrid. This means that RIR breed is a better dam than a sire line for growth, egg laying, and egg quality traits.Copyright © 2020. Published by Elsevier Inc.

Although the majority of genes are expressed equally from both alleles, some genes are differentially expressed. Organisms possess characteristics to preferentially express a particular allele under regulatory factors, which is termed allele-specific expression (ASE). It is one of the important genetic factors that lead to phenotypic variation and can be used to identify the variance of gene regulation factors. ASE indicates mechanisms such as DNA methylation, histone modifications, and non-coding RNAs function. Here, we review a broad survey of progress in ASE studies, and what this simple yet very effective approach can offer in functional genomics, and possible implications toward our better understanding of the underlying mechanisms of complex traits.

The genetic basis of hybrid vigor in plants remains largely unsolved but strong evidence suggests that variation in transcriptional regulation can explain many aspects of this phenomenon. Natural variation in transcriptional regulation is highly abundant in virtually all species and thus a potential source of heterotic variability. Allele Specific Expression (ASE), which is tightly linked to parent of origin effects and modulated by complex interactions and, is generally considered to play a key role in explaining the differences between hybrids and parental lines. Here we discuss the recent developments in elucidating the role of transcriptional variation in a number of aspects of hybrid vigor, thereby bridging old paradigms and hypotheses with contemporary research in various species.Copyright © 2020 Botet and Keurentjes.

Hybrid mapping is a powerful approach to efficiently identify and characterize genes regulated through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we perform a comprehensive multi-omic characterization of regulatory variation across the brain, liver, muscle, and placenta through four developmental stages. We produce one of the largest multi-omic datasets in pigs to date, including 16 whole genome sequenced individuals, as well as 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We develop a read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We show that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identify 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. We integrate methylation, chromatin accessibility, and gene expression data to show that allele specific expression can be explained in great part by allele specific methylation and/or chromatin accessibility. This study provides a comprehensive characterization of regulatory variation across multiple tissues and developmental stages in pigs.© 2024. The Author(s).

Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of approximately 50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size N(e) = 100, the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.

Three recent breakthroughs have resulted in the current widespread use of DNA information: the genomic selection (GS) methodology, which is a form of marker-assisted selection on a genome-wide scale, and the discovery of large numbers of single-nucleotide markers and cost effective methods to genotype them. GS estimates the effect of thousands of DNA markers simultaneously. Nonlinear estimation methods yield higher accuracy, especially for traits with major genes. The marker effects are estimated in a genotyped and phenotyped training population and are used for the estimation of breeding values of selection candidates by combining their genotypes with the estimated marker effects. The benefits of GS are greatest when selection is for traits that are not themselves recorded on the selection candidates before they can be selected. In the future, genome sequence data may replace SNP genotypes as markers. This could increase GS accuracy because the causative mutations should be included in the data.

Most genomic prediction studies fit only additive effects in models to estimate genomic breeding values (GEBV). However, if dominance genetic effects are an important source of variation for complex traits, accounting for them may improve the accuracy of GEBV. We investigated the effect of fitting dominance and additive effects on the accuracy of GEBV for eight egg production and quality traits in a purebred line of brown layers using pedigree or genomic information (42K single-nucleotide polymorphism (SNP) panel). Phenotypes were corrected for the effect of hatch date. Additive and dominance genetic variances were estimated using genomic-based [genomic best linear unbiased prediction (GBLUP)-REML and BayesC] and pedigree-based (PBLUP-REML) methods. Breeding values were predicted using a model that included both additive and dominance effects and a model that included only additive effects. The reference population consisted of approximately 1800 animals hatched between 2004 and 2009, while approximately 300 young animals hatched in 2010 were used for validation. Accuracy of prediction was computed as the correlation between phenotypes and estimated breeding values of the validation animals divided by the square root of the estimate of heritability in the whole population. The proportion of dominance variance to total phenotypic variance ranged from 0.03 to 0.22 with PBLUP-REML across traits, from 0 to 0.03 with GBLUP-REML and from 0.01 to 0.05 with BayesC. Accuracies of GEBV ranged from 0.28 to 0.60 across traits. Inclusion of dominance effects did not improve the accuracy of GEBV, and differences in their accuracies between genomic-based methods were small (0.01-0.05), with GBLUP-REML yielding higher prediction accuracies than BayesC for egg production, egg colour and yolk weight, while BayesC yielded higher accuracies than GBLUP-REML for the other traits. In conclusion, fitting dominance effects did not impact accuracy of genomic prediction of breeding values in this population. © 2016 Blackwell Verlag GmbH.

In pig breeding, selection is usually carried out in purebred populations, although the final goal is to improve crossbred performance. Genomic selection can be used to select purebred parental lines for crossbred performance. Dominance is the likely genetic basis of heterosis and explicitly including dominance in the genomic selection model may be an advantage when selecting purebreds for crossbred performance. Our objectives were two-fold: (1) to compare the predictive ability of genomic prediction models with additive or additive plus dominance effects, when the validation criterion is crossbred performance; and (2) to compare the use of two pure line reference populations to a single combined reference population.We used data on litter size in the first parity from two pure pig lines (Landrace and Yorkshire) and their reciprocal crosses. Training was performed (1) separately on pure Landrace (2085) and Yorkshire (2145) sows and (2) the two combined pure lines (4230), which were genotyped for 38 k single nucleotide polymorphisms (SNPs). Prediction accuracy was measured as the correlation between genomic estimated breeding values (GEBV) of pure line boars and mean corrected crossbred-progeny performance, divided by the average accuracy of mean-progeny performance. We evaluated a model with additive effects only (MA) and a model with both additive and dominance effects (MAD). Two types of GEBV were computed: GEBV for purebred performance (GEBV) based on either the MA or MAD models, and GEBV for crossbred performance (GEBV-C) based on the MAD. GEBV-C were calculated based on SNP allele frequencies of genotyped animals in the opposite line.Compared to MA, MAD improved prediction accuracy for both lines. For MAD, GEBV-C improved prediction accuracy compared to GEBV. For Landrace (Yorkshire) boars, prediction accuracies were equal to 0.11 (0.32) for GEBV based on MA, and 0.13 (0.34) and 0.14 (0.36) for GEBV and GEBV-C based on MAD, respectively. Combining animals from both lines into a single reference population yielded higher accuracies than training on each pure line separately. In conclusion, the use of a dominance model increased the accuracy of genomic predictions of crossbred performance based on purebred data.

The number of teats in pigs is related to a sow's ability to rear piglets to weaning age. Several studies have identified genes and genomic regions that affect teat number in swine but few common results were reported. The objective of this study was to identify genetic factors that affect teat number in pigs, evaluate the accuracy of genomic prediction, and evaluate the contribution of significant genes and genomic regions to genomic broad-sense heritability and prediction accuracy using 41,108 autosomal single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing on 2936 Duroc boars.Narrow-sense heritability and dominance heritability of teat number estimated by genomic restricted maximum likelihood were 0.365 ± 0.030 and 0.035 ± 0.019, respectively. The accuracy of genomic predictions, calculated as the average correlation between the genomic best linear unbiased prediction and phenotype in a tenfold validation study, was 0.437 ± 0.064 for the model with additive and dominance effects and 0.435 ± 0.064 for the model with additive effects only. Genome-wide association studies (GWAS) using three methods of analysis identified 85 significant SNP effects for teat number on chromosomes 1, 6, 7, 10, 11, 12 and 14. The region between 102.9 and 106.0 Mb on chromosome 7, which was reported in several studies, had the most significant SNP effects in or near the PTGR2, FAM161B, LIN52, VRTN, FCF1, AREL1 and LRRC74A genes. This region accounted for 10.0% of the genomic additive heritability and 8.0% of the accuracy of prediction. The second most significant chromosome region not reported by previous GWAS was the region between 77.7 and 79.7 Mb on chromosome 11, where SNPs in the FGF14 gene had the most significant effect and accounted for 5.1% of the genomic additive heritability and 5.2% of the accuracy of prediction. The 85 significant SNPs accounted for 28.5 to 28.8% of the genomic additive heritability and 35.8 to 36.8% of the accuracy of prediction.The three methods used for the GWAS identified 85 significant SNPs with additive effects on teat number, including SNPs in a previously reported chromosomal region and SNPs in novel chromosomal regions. Most significant SNPs with larger estimated effects also had larger contributions to the total genomic heritability and accuracy of prediction than other SNPs.

An objective of commercial beef cattle crossbreeding programs is to simultaneously optimize use of additive (breed differences) and non-additive (heterosis) effects. A total of 6,794 multibreed and crossbred beef cattle with phenotype and Illumina BovineSNP50 genotype data were used to predict genomic heterosis for growth and carcass traits by applying two methods assumed to be linearly proportional to heterosis. The methods were as follows: 1) retained heterozygosity predicted from genomic breed fractions (HET1) and 2) deviation of adjusted crossbred phenotype from midparent value (HET2). Comparison of methods was based on prediction accuracy from cross-validation. Here, a mutually exclusive random sampling of all crossbred animals (n = 5,327) was performed to form five groups replicated five times with approximately 1,065 animals per group. In each run within a replicate, one group was assigned as a validation set, while the remaining four groups were combined to form the reference set. The phenotype of the animals in the validation set was assumed to be unknown; thus, it resulted in every animal having heterosis values that were predicted without using its own phenotype, allowing their adjusted phenotype to be used for validation. The same approach was used to test the impact of predicted heterosis on accuracy of genomic breeding values (GBV). The results showed positive heterotic effects for growth traits but not for carcass traits that reflect the importance of heterosis for growth traits in beef cattle. Heterosis predicted by HET1 method resulted in less variable estimates that were mostly within the range of estimates generated by HET2. Prediction accuracy was greater for HET2 (0.37-0.98) than HET1 (0.34-0.43). Proper consideration of heterosis in genomic evaluation models has debatable effects on accuracy of EBV predictions. However, opportunity exists for predicting heterosis, improving accuracy of genomic selection, and consequently optimizing crossbreeding programs in beef cattle.

In the last decade, genomic selection has become a standard in the genetic evaluation of livestock populations. However, most procedures for the implementation of genomic selection only consider the additive effects associated with SNP (Single Nucleotide Polymorphism) markers used to calculate the prediction of the breeding values of candidates for selection. Nevertheless, the availability of estimates of non-additive effects is of interest because: (i) they contribute to an increase in the accuracy of the prediction of breeding values and the genetic response; (ii) they allow the definition of mate allocation procedures between candidates for selection; and (iii) they can be used to enhance non-additive genetic variation through the definition of appropriate crossbreeding or purebred breeding schemes. This study presents a review of methods for the incorporation of non-additive genetic effects into genomic selection procedures and their potential applications in the prediction of future performance, mate allocation, crossbreeding, and purebred selection. The work concludes with a brief outline of some ideas for future lines of that may help the standard inclusion of non-additive effects in genomic selection.

In commercial fish, dominance effects could be exploited by predicting production abilities of the offspring that would be generated by different mating pairs and choosing those pairs that maximise the average offspring phenotype. Consequently, matings would be performed to reduce inbreeding depression. This can be achieved by applying mate selection (MS) that combines selection and mating decisions in a single step. An alternative strategy to MS would be to apply minimum coancestry mating (MCM) after selection based on estimated breeding values. The objective of this study was to evaluate, by computer simulations, the potential benefits that can be obtained by implementing MS or MCM based on genomic data for exploiting dominance effects when creating commercial fish populations that are derived from a breeding nucleus.The selected trait was determined by a variable number of loci with additive and dominance effects. The population consisted of 50 full-sib families with 30 offspring each. Males and females with the highest estimated genomic breeding values were selected in the nucleus and paired using the MCM strategy. Both MCM and MS were used to create the commercial population.For a moderate number of SNPs, equal or even higher mean phenotypic values are obtained by selecting on genomic breeding values and then applying MCM than by using MS when the trait exhibited substantial inbreeding depression. This could be because MCM leads to high levels of heterozygosity across the whole genome, even for loci affecting the trait that are in linkage equilibrium with the SNPs. In contrast, MS specifically promotes heterozygosity for SNPs for which a dominance effect has been detected.In most scenarios, for the management of aquaculture breeding programs it seems advisable to follow the MCM strategy when creating the commercial population, especially for traits with large inbreeding depression. Moreover, MCM has the appealing property of reducing inbreeding levels, with a corresponding reduction in inbreeding depression for traits beyond those included in the selection objective.

Pig and poultry production relies on crossbreeding of purebred populations to produce production animals. In those breeding schemes, selection takes place within the purebred population to improve crossbred performance (CB performance). The genetic correlation between purebred performance (PB performance) and CB performance () is, however, lower than unity for many traits. When is low, the use of CB performance in selection is required to achieve sizable genetic progress. The objectives of this paper were to describe the different components and importance of, and to review existing literature that report estimates in pigs. The has 3 components: 1) genotype by genotype interactions, 2) genotype by environment interactions, and 3) differences in trait measurements. We theoretically showed that direct selection for CB performance reduces the response to selection in purebreds for.

The genetic correlation between purebred and crossbred performance ([Formula: see text]) is an important parameter in pig and poultry breeding, because response to selection in crossbred performance depends on the value of [Formula: see text] when selection is based on purebred (PB) performance. The value of [Formula: see text] can be substantially lower than 1, which is partly due to differences in allele frequencies between parental lines when non-additive genetic effects are present. This relationship between [Formula: see text] and parental allele frequencies suggests that [Formula: see text] can be expressed as a function of genetic parameters for the trait in the parental lines. In this study, we derived expressions for [Formula: see text] based on genetic variances within, and the genetic covariance between parental lines. It is important to note that the variance components used in our expressions are not the components that are typically estimated in empirical data. The expressions were derived for a genetic model with additive and dominance effects (D), and additive and epistatic additive-by-additive effects (E). We validated our expressions using simulations of purebred parental lines and their crosses, where the parental lines were either selected or not. Finally, using these simulations, we investigated the value of [Formula: see text] for genetic models with both dominance and epistasis or with other types of epistasis, for which expressions could not be derived.Our simulations show that when non-additive effects are present, [Formula: see text] decreases with increasing differences in allele frequencies between the parental lines. Genetic models that involve dominance result in lower values of [Formula: see text] than genetic models that involve epistasis only. Using information of parental lines only, our expressions provide exact estimates of [Formula: see text] for models D and E, and accurate upper and lower bounds of [Formula: see text] for two other genetic models.This work lays the foundation to enable estimation of [Formula: see text] from information collected in PB parental lines only.

随着基因组学、基因组编辑技术的迅速发展以及显微注射技术、体细胞克隆技术的广泛应用，一套新型的育种策略和方法已经逐渐形成。这一套新型育种策略和方法可以称为分子编写育种（breeding by molecular writing, BMW）。该方法可以高效创制新的遗传标记并对其进行快速验证，也可以对基因组进行精确到分子水平的编写并定向培养新品种，不仅能打破生殖隔离，跨物种的引入新的性状，更可以对物种内个体间基因组进行精确到单个碱基的插入、删除和替换。如外源基因的精确整合，内源基因的精确删除、替换，SNP位点的复制、删除或替换等。该技术的优点是：可以在极大的降低非预期效应的同时，快速高效的将多种有益性状聚合到同一品种内。分子编写可进行以下四方面工作：（1）新型育种标记的创制及验证；（2）跨物种分子编写；（3）基因组中碱基序列的删除；（4）物种内分子编写。该育种技术可以不通过有性杂交，只引入一个或几个目标基因或SNP，快速获得目标性状突出的遗传稳定新种质，然后结合常规育种方法育成新品种。该方法将实现真正的个体和群体水平的基因（或分子）杂交育种，获得分子杂种优势，能够高效的解决长久以来困扰育种工作的诸多难题，大大提高育种效率，尤其在畜禽育种中具有重要应用前景，将会是未来育种的发展方向。文章详细论述了分子编写育种技术的基本概念、研究手段、研究内容、研究现状并展望了该技术的应用前景，为动物育种、畜禽繁殖等领域的研究及从业人员提供了参考。

With the rapid development of genomics and genome editing techniques, and the extensive application of techniques such as microinjection and somatic cell nuclear transfer, a new set of breeding strategies and methods has gradually formed, which we named breeding by molecular writing (BMW). Using BMW, directional breeding of new varieties can be achieved by molecular-level genome editing, which can not only break down reproductive barriers separating different taxa, allowing the cross-species introduction of new traits, but also enable single-nucleotide insertion, deletion, and substitution in individual genomes. This can include the precise integration of exogenous genes; precise deletion and substitution of endogenous genes; and replication, deletion, and substitution of SNP loci. The main advantage of BMW is the rapid and efficient gathering of several beneficial traits into one species, while significantly reducing unintended effects. Molecular writing can be used for the following tasks: (1) identification and verification of new breeding markers; (2) cross-species molecular writing; (3) base sequence deletion in genomes; and (4) molecular writing within species. The BMW technique allows the introduction of only one or several target genes or SNPs and the rapid acquisition of new stable genetic varieties with pronounced target characters without sexual hybridization, and can then create new varieties in combination with conventional breeding methods. BMW can achieve genetic (or molecular) hybridization breeding at the individual and group levels, acquiring molecular heterosis, efficiently resolving several long-standing difficulties in breeding, and significantly improving breeding efficiency. It has strong potential for application in livestock and poultry breeding, and is a key part of the future of breeding. This paper discusses the basic concepts, research methods, research contents, and current research status of breeding by molecular writing in detail, and presents the prospects of applying BMW, providing references for researchers and practitioners in fields such as animal breeding and livestock and poultry reproduction.

种业是农业发展的&#x0201c;芯片&#x0201d;,原始创新是农业可持续发展的根本动力。2021年中央一号文件明确提出推进中国农作物种业快速发展的要求,对中国农作物种业原始创新研究提出了明确期望。谷子是中国起源的传统粮饲兼用作物及特色杂粮作物,生产及消费规模均位居世界首位。作为粟类作物,谷子在中国农业生产及农耕文明的起始与发展过程中发挥了重要的推动和支撑作用,已有研究证实谷子在中国拥有悠久的栽培历史,并且形成了分布广泛且多样性丰富的各类种质资源。近年来,谷子在杂交品种选育及杂种优势利用、抗除草剂品种和适于机械化栽培品种推广、基因组学及功能基因研究等领域取得进展,在原始创新的推动下初步形成了以杂交品种和抗除草剂品种为经营主体的谷子种业体系,推动了谷子种业从无到有的突破。中国在谷子优异种质鉴定与创制方法研究、谷子高效育种技术途径研发、谷子关键性状的协调表达与调控规律解析、谷子良种繁育过程基本生物学属性研究以及谷子新品种真实性鉴定方法探索等方面原始创新的进步为谷子种业发展提供了支撑,形成了一套初具规模的种业原始创新技术。目前,谷子种业持续良性发展仍然面临包括优异突破性种质匮乏、育种技术水平相对滞后、品质与产量协调性不够、良种扩繁标准缺乏以及种业市场监管手段有待加强等诸多挑战。为了推进谷子种业持续快速、高效、深入的发展,未来中国谷子种业原始创新研究的主要方向包括：基于表型组学与基因组修饰技术、单倍体育种与全基因组选择技术和关键性状优异单倍型鉴定与整合技术的谷子规模化高效育种技术体系构建;基于种子发育生理调控、分子指纹及杂种优势高效利用技术的谷子种子高效生产储藏与监管技术体系构建;以及基于谷子种业产学研推一体化设计与高效整合的人才培养与创新体系构建等方面。

Seed industry was the ‘chip’ of agricultural development, and original innovation have played essential roles in maintaining stable development of modern agriculture. The No. 1 central document of China released in 2021 has put forward requirements of original innovation researches essential for supporting further developments of Chinese crop seed industry. Foxtail millet is a traditional crop species cultivated for both forage and grain food consumption in China, and to date, foxtail millet was still widely planted as minor cereals in China with the largest scale of field production and commercial consumption across the globe. Foxtail millet was originated from China and cultivated for thousands of years to ensure development of Chinese agricultural culture and field crop production. Original innovation in foxtail millet has promoted initial development of foxtail millet seed industry based on operation of herbicide-resistant varieties in recent decades, including breakthroughs in areas of heterosis utilization, herbicide resistant breeding, dwarfing variety creation and genomics study of this important crop species. Achievements of fundamental researches including germplasm characterization, development of breeding tools, coordination and regulation of vital traits, seeds propagation and truth identification of commercial varieties have provided more opportunities for further development of the seed industry of foxtail millet. However, challenges of seed industry development still exist in China, including deficiency of excellent germplasms, backward breeding approaches, inharmonious of yield and quality characters, unclear criterion of seed propagation and market supervision problems. Future direction of original innovation studies related to foxtail millet seed industry were as follows: 1) Large scale breeding systems constructed from utilizations of crop phenomics and genomic modification technologies, double haploid breeding and genome selection tools, identification and pyramiding of superior haplotypes; 2) Seeds production, store and quality supervision systems constructed from techniques of development and water content of commercial seeds, and establishment of molecular fingerprints and efficient utilization of heterosis in foxtail millet; 3) Construction of innovation system through integrating education, research and promotion sectors of seed industry and stimulate personnel training in China.