Efficiency of genomic prediction for boar taint reduction in Danish Landrace pigs

Genetic selection against boar taint, which is caused by high skatole and androstenone concentrations in fat, is a more acceptable alternative than is the current practice of castration. Genomic predictors offer an opportunity to overcome the limitations of such selection caused by the phenotype being expressed only in males at slaughter, and this study evaluated different approaches to obtain such predictors. Samples from 1000 pigs were included in a design which was dominated by 421 sib pairs, each pair having one animal with high and one with low skatole concentration (≥0.3 μg/g). All samples were measured for both skatole and androstenone and genotyped using the Illumina SNP60 porcine BeadChip for 62 153 single nucleotide polymorphisms. The accuracy of predicting phenotypes was assessed by cross‐validation using six different genomic evaluation methods: genomic best linear unbiased prediction (GBLUP) and five Bayesian regression methods. In addition, this was compared to the accuracy of predictions using only QTL that showed genome‐wide significance. The range of accuracies obtained by different prediction methods was narrow for androstenone, between 0.29 (Bayes Lasso) and 0.31 (Bayes B), and wider for skatole, between 0.21 (GBLUP) and 0.26 (Bayes SSVS). Relative accuracies, corrected for h², were 0.54–0.56 and 0.75–0.94 for androstenone and skatole respectively. The whole‐genome evaluation methods gave greater accuracy than using only the QTL detected in the data. The results demonstrate that GBLUP for androstenone is the simplest genomic technology to implement and was also close to the most accurate method. More specialised models may be preferable for skatole.     

Impact of genotyping strategy on the accuracy of genomic prediction in simulated populations of purebred swine

Single-step genomic BLUP (ssGBLUP) has been widely used in genomic evaluation due to relatively higher prediction accuracy and simplicity of use. The prediction accuracy from ssGBLUP depends on the amount of information available concerning both genotype and phenotype. This study investigated how information on genotype and phenotype that had been acquired from previous generations influences the prediction accuracy of ssGBLUP, and thus we sought an optimal balance about genotypic and phenotypic information to achieve a cost-effective and computationally efficient genomic evaluation. We generated two genetically correlated traits (h² = 0.35 for trait A, h² = 0.10 for trait B and genetic correlation 0.20) as well as two distinct populations mimicking purebred swine. Phenotypic and genotypic information in different numbers of previous generations and different genotyping rates for each litter were set to generate different datasets. Prediction accuracy was evaluated by correlating genomic estimated breeding values with true breeding values for genotyped animals in the last generation. The results revealed a negligible impact of previous generations that lacked genotyped animals on the prediction accuracy. Phenotypic and genotypic data, including the most recent three to four generations with a genotyping rate of 40% or 50% for each litter, could lead to asymptotic maximum prediction accuracy for genotyped animals in the last generation. Single-step genomic best linear unbiased prediction yielded an optimal balance about genotypic and phenotypic information to ensure a cost-effective and computationally efficient genomic evaluation of populations of polytocous animals such as purebred pigs.     

基因组选择在猪杂交育种中的应用

基因组选择是指在全基因组范围内通过基因组中大量的标记信息估计出个体全基因组范围的育种值,可进一步提升育种效率和准确性,目前在猪纯繁育种中得到广泛应用。但有研究表明,现有的基因组选择方法在猪杂交育种上的应用效果并不理想,在跨群体条件下预测准确性极低。杂交作为养猪业中最为广泛的育种手段之一,通过结合基因组选择理论进一步提升猪的生产性能,具有重要的经济和研究价值。本文综述了基因组选择的发展及其在猪育种中的应用现状,并结合国内外猪杂交育种的方式,分析了目前基因组选择方法在猪杂交育种应用方面的不足,旨在为未来基因组选择在猪杂交育种中的合理应用提供参考。     

Partition of the genetic trend to validate multiple selection decisions

In this note, a procedure to partition the genetic trend of a selected population is presented. Each part of the genetic gain accounts for the Mendelian sampling terms of different groups of animals, which can be sometimes assigned to different selection policies. The method is based on a simple transformation of the predicted breeding values. The procedure was illustrated with two simulated examples. In the first example, the genetic trend is partitioned into two pieces, one coming from the selection on sires and the other coming from the selection on dams. The second example shows how the impact of an artificial insemination center in the genetic gain of the whole population can be evaluated.     

Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction

Genomic best linear unbiased prediction (BLUP) is a statistical method that uses relationships between individuals calculated from single-nucleotide polymorphisms (SNPs) to capture relationships at quantitative trait loci (QTL). We show that genomic BLUP exploits not only linkage disequilibrium (LD) and additive-genetic relationships, but also cosegregation to capture relationships at QTL. Simulations were used to study the contributions of those types of information to accuracy of genomic estimated breeding values (GEBVs), their persistence over generations without retraining, and their effect on the correlation of GEBVs within families. We show that accuracy of GEBVs based on additive-genetic relationships can decline with increasing training data size and speculate that modeling polygenic effects via pedigree relationships jointly with genomic breeding values using Bayesian methods may prevent that decline. Cosegregation information from half sibs contributes little to accuracy of GEBVs in current dairy cattle breeding schemes but from full sibs it contributes considerably to accuracy within family in corn breeding. Cosegregation information also declines with increasing training data size, and its persistence over generations is lower than that of LD, suggesting the need to model LD and cosegregation explicitly. The correlation between GEBVs within families depends largely on additive-genetic relationship information, which is determined by the effective number of SNPs and training data size. As genomic BLUP cannot capture short-range LD information well, we recommend Bayesian methods with t-distributed priors.     

Domestic estimated breeding values and genomic enhanced breeding values of bulls in comparison with their foreign genomic enhanced breeding values

Estimated breeding values (EBVs) and genomic enhanced breeding values (GEBVs) for milk production of young genotyped Holstein bulls were predicted using a conventional BLUP – Animal Model, a method fitting regression coefficients for loci (RRBLUP), a method utilizing the realized genomic relationship matrix (GBLUP), by a single-step procedure (ssGBLUP) and by a one-step blending procedure. Information sources for prediction were the nation-wide database of domestic Czech production records in the first lactation combined with deregressed proofs (DRP) from Interbull files (August 2013) and domestic test-day (TD) records for the first three lactations. Data from 2627 genotyped bulls were used, of which 2189 were already proven under domestic conditions. Analyses were run that used Interbull values for genotyped bulls only or that used Interbull values for all available sires. Resultant predictions were compared with GEBV of 96 young foreign bulls evaluated abroad and whose proofs were from Interbull method GMACE (August 2013) on the Czech scale. Correlations of predictions with GMACE values of foreign bulls ranged from 0.33 to 0.75. Combining domestic data with Interbull EBVs improved prediction of both EBV and GEBV. Predictions by Animal Model (traditional EBV) using only domestic first lactation records and GMACE values were correlated by only 0.33. Combining the nation-wide domestic database with all available DRP for genotyped and un-genotyped sires from Interbull resulted in an EBV correlation of 0.60, compared with 0.47 when only Interbull data were used. In all cases, GEBVs had higher correlations than traditional EBVs, and the highest correlations were for predictions from the ssGBLUP procedure using combined data (0.75), or with all available DRP from Interbull records only (one-step blending approach, 0.69). The ssGBLUP predictions using the first three domestic lactation records in the TD model were correlated with GMACE predictions by 0.69, 0.64 and 0.61 for milk yield, protein yield and fat yield, respectively.     

SSR heterogenic patterns of parents for marking and predicting heterosis in rice breeding

The most important concerns of hybrid rice breeders are selection of donors to improve parental lines and prediction of hybrid performance. In this study, SSR molecular marker technology and a half-diallel method were used to address these related hybrid production issues. The results show that genetic diversity among the parental lines is certainly related to heterosis. The heterozygosity of each parental pair is significantly associated with the general combining ability, not with the specific combining ability. However, neither genetic diversity nor heterozygosity is a good indicator for predicting heterosis. From these results, it is suggested that donors for improving parents of hybrids be selected from the improved inbred lines by conventional breeding programs. In this investigation, we also discovered that four favorable alleles and six favorable heterogenic patterns on the parental lines significantly contribute to the heterosis of their hybrids in grain yield, whereas six unfavorable alleles and six unfavorable heterogenic patterns significantly reduce heterosis. These noticeable findings could be, in practice, useful for hybrid rice breeding programs with SSR marker-assisted selection. It is suggested that the optimal combinations with the superior grain yield could be bred out by assembling those favorable alleles into their parental lines and by removing the unfavorable alleles from the parental lines. This study also indicates that there is still a great heterosis potential to be exploited in indica/indica hybrids by the same strategy. In indica/japonica hybrid breeding programs, it may also be important to remove unfavorable alleles rather than broaden genetic diversity or heterozygosity of the parents.     

Optimal properties of the conditional mean as a selection criterion

Summary Rules for selection that maximize the expected merit of selected candidates are discussed. When the proportion selected is constant, selection based on conditional means of merit given the observations is optimum in the above sense, regardless of the distribution. This does not hold if the proportion selected is random. When the expected value of the observations is a linear function of a set of unknown parameters, selection can be based on a vector of corrected records, w. It is shown that under normality, the conditional mean of merit given w is the best linear unbiased predictor (BLUP), provided that the expected value of the merit function is the same in all candidates. A Bayesian argument is given to justify the use of BLUP as a selection rule when the expected merit differs from candidate to candidate.     

Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle

Genomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) in farm animals. In this study, we conducted genomic prediction for 20 economically important traits including growth, carcass and meat quality traits in Chinese Simmental beef cattle. Five approaches (GBLUP, BayesA, BayesB, BayesCπ and BayesR) were used to estimate the genomic breeding values. The predictive accuracies ranged from 0.159 (lean meat percentage estimated by BayesCπ) to 0.518 (striploin weight estimated by BayesR). Moreover, we found that the average predictive accuracies across 20 traits were 0.361, 0.361, 0.367, 0.367 and 0.378, and the averaged regression coefficients were 0.89, 0.86, 0.89, 0.94 and 0.95 for GBLUP, BayesA, BayesB, BayesCπ and BayesR respectively. The genomic prediction accuracies were mostly moderate and high for growth and carcass traits, whereas meat quality traits showed relatively low accuracies. We concluded that Bayesian regression approaches, especially for BayesR and BayesCπ, were slightly superior to GBLUP for most traits. Increasing with the sizes of reference population, these two approaches are feasible for future application of genomic selection in Chinese beef cattle.     

A rapid method for computing the inverse of the gametic covariance matrix between relatives for a marked Quantitative Trait Locus

The inverse of the gametic covariance matrix between relatives, G^-1, for a marked quantitative trait locus (QTL) is required in best linear unbiased prediction (BLUP) of breeding values if marker data are available on a QTL. A rapid method for computing the inverse of a gametic relationship matrix for a marked QTL without building G itself is presented. The algorithm is particularly useful due to the approach taken in computing inbreeding coefficients by having to compute only few elements of G. Numerical techniques for determining, storing, and computing the required elements of G and the nonzero elements of the inverse are discussed. We show that the subset of G required for computing the inbreeding coefficients and hence the inverse is a tiny proportion of the whole matrix and can be easily stored in computer memory using sparse matrix storage techniques. We also introduce an algorithm to determine the maximum set of nonzero elements that can be found in G^-1 and a strategy to efficiently store and access them. Finally, we demonstrate that the inverse can be efficiently built using the present techniques for very large and inbred populations.     

A whole‐genome SNP array (RICE6K) for genomic breeding in rice

The advances in genotyping technology provide an opportunity to use genomic tools in crop breeding. As compared to field selections performed in conventional breeding programmes, genomics‐based genotype screen can potentially reduce number of breeding cycles and more precisely integrate target genes for particular traits into an ideal genetic background. We developed a whole‐genome single nucleotide polymorphism (SNP) array, RICE6K, based on Infinium technology, using representative SNPs selected from more than four million SNPs identified from resequencing data of more than 500 rice landraces. RICE6K contains 5102 SNP and insertion–deletion (InDel) markers, about 4500 of which were of high quality in the tested rice lines producing highly repeatable results. Forty‐five functional markers that are located inside 28 characterized genes of important traits can be detected using RICE6K. The SNP markers are evenly distributed on the 12 chromosomes of rice with the average density of 12 SNPs per 1 Mb and can provide information for polymorphisms between indica and japonica subspecies as well as varieties within indica and japonica groups. Application tests of RICE6K showed that the array is suitable for rice germplasm fingerprinting, genotyping bulked segregating pools, seed authenticity check and genetic background selection. These results suggest that RICE6K provides an efficient and reliable genotyping tool for rice genomic breeding.     

5种红树林植物叶片SPAD值比较

以5种红树林植物为研究材料,分析测试其叶片相对叶绿素含量(SPAD值),并比较红树林树种、样区对其叶片SPAD值的影响。结果表明,各样区、各树种以及单株之间SPAD值均存差异,对不同样区进行多重比较,可将其分为7类,5个红树林树种在叶绿素含量上差异较明显。各树种中以秋茄Kandelia obvolata最大(70.4),其次是红海榄Rhizophora stylosa (65.9)和木榄Bruguiera gymnorrhiza (62.9),白骨壤Avicennia marina为51.9,最小为桐花树Aegiceras corniculatum(48.6),树种平均为60.5。试验分析表明,树种因子效应极显著(P<0.01),强于样区因子效应,说明红树林树种是影响其叶片SPAD值的重要因子,同时也表明不同的立地环境条件对叶绿素含量存在影响。5种红树林植物叶片SPAD值均与其树高、基径生长性状存在显著正相关。     

Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program

Recent genomic evaluation studies using real data and predicting genetic gain by modeling breeding programs have reported moderate expected benefits from the replacement of classic selection schemes by genomic selection (GS) in small ruminants. The objectives of this study were to compare the cost, monetary genetic gain and economic efficiency of classic selection and GS schemes in the meat sheep industry. Deterministic methods were used to model selection based on multi-trait indices from a sheep meat breeding program. Decisional variables related to male selection candidates and progeny testing were optimized to maximize the annual monetary genetic gain (AMGG), that is, a weighted sum of meat and maternal traits annual genetic gains. For GS, a reference population of 2000 individuals was assumed and genomic information was available for evaluation of male candidates only. In the classic selection scheme, males breeding values were estimated from own and offspring phenotypes. In GS, different scenarios were considered, differing by the information used to select males (genomic only, genomic+own performance, genomic+offspring phenotypes). The results showed that all GS scenarios were associated with higher total variable costs than classic selection (if the cost of genotyping was 123 euros/animal). In terms of AMGG and economic returns, GS scenarios were found to be superior to classic selection only if genomic information was combined with their own meat phenotypes (GS-Pheno) or with their progeny test information. The predicted economic efficiency, defined as returns (proportional to number of expressions of AMGG in the nucleus and commercial flocks) minus total variable costs, showed that the best GS scenario (GS-Pheno) was up to 15% more efficient than classic selection. For all selection scenarios, optimization increased the overall AMGG, returns and economic efficiency. As a conclusion, our study shows that some forms of GS strategies are more advantageous than classic selection, provided that GS is already initiated (i.e. the initial reference population is available). Optimizing decisional variables of the classic selection scheme could be of greater benefit than including genomic information in optimized designs.     

Association of missense variants in GDF9 with litter size in Entlebucher Mountain dogs

In the past two decades, average litter size (ALS) in Entlebucher Mountain dogs decreased by approximately 0.8 puppies. We conducted a GWAS for ALS using the single-step methodology to take advantage of 1632 pedigree records, 892 phenotypes and 372 genotypes (173 662 markers) for which only 12% of the dogs had both phenotypes and genotypes available. Our analysis revealed associations towards the growth differentiation factor 9 gene (GDF9), which is known to regulate oocyte maturation. The trait heritability was estimated at 43.1%, from which approximately 15% was accountable by the GDF9 locus alone. Therefore, markers flanking GDF9 explained approximately 6.5% of the variance in ALS. Analysis of WGSs revealed two missense substitutions in GDF9, one of which (g.11:21147009G>A) affected a highly conserved nucleotide in vertebrates. The derived allele A was validated in 111 dogs and shown to be associated with decreased ALS (−0.75 ± 0.22 puppies per litter). The variant was further predicted to cause a proline to serine substitution. The affected residue was immediately followed by a six-residue deletion that is fixed in the canine species but absent in non-canids. We further confirmed that the deletion is prevalent in the Canidae family by sequencing three species of wild canids. Since canids uniquely ovulate oocytes at the prophase stage of the first meiotic division, requiring maturation in the oviduct, we conjecture that the amino acid substitution and the six-residue deletion of GDF9 may serve as a model for insights into the dynamics of oocyte maturation in canids.     

萍乡显性雄性核不育水稻超微结构研究

利用电镜技术对萍乡显性核不育水稻可育株和不育株花粉形成及发育过程和药壁组织的基本结构及其发育进行了研究。导致了其不育花粉败育的主要原因有：（1）绒毡层细胞表现为延迟解体,乌氏体不能与小孢子细胞壁的形成,特别是纯合不育株绒毡层细胞存在乌氏体的异位分布;（2）药隔维管束异常,导致营养物质运输缺乏,薄壁细胞液泡化,排列紊乱,杂合不育株薄壁细胞互相融合,出现核转移的独特现象。     

Characterization and Use of Male Sterility in Hybrid Rice Breeding

The hybrid rice （Oryza sativa L.） breeding that was Initiated In China in the 1970s led to a great improvement in rice productivity. In general, It increases the grain yield by over 20% to the inbred rice varieties, and now hybrid rice has been widely introduced into Africa, Southern Asia and America. These hybrid varieties are generated through either three-line hybrid and two-line hybrid systems; the former is derived from cytoplasmic male sterility （CMS） and the latter derived from genlc male sterility （GMS）. There are three major types of CMS （HL, BT and WA） and two types of GMS （photoperlod-sensitlve （PGMS） and temperature-sensitive （TGMS））. The BT- and HL-type CMS genes are characterized as orf79 and orfH79, which are chimeric toxic genes derived from mltochondrial rearrangement. Rf3 for CMS-WA Is located on chromosome 1, while Rf1, Rf4, Rf5 and Rf6 correspond to CMS-BT, CMSoWA and CMS- HL, located on chromosome 10. The Rfl gene for BT-CMS has been cloned recently, and encodes a mltochondriatargeted PPR protein. PGMS Is thought to be controlled by two recessive loci on chromosomes 7 and 12, whereas nine recessive alleles have been identified for TGMS and mapped on different chromosomes. Attention Is still urgently needed to resolve the molecular complexity of male sterility to assist rice breeding.     

A model for marker-assisted selection among single crosses with multiple genetic markers

 Trait means of marker genotypes are often inconsistent across experiments, thereby hindering the use of regression techniques in marker-assisted selection. Best linear unbiased prediction based on trait and marker data (TM-BLUP) does not require prior information on the mean effects associated with specific marker genotypes and, consequently, may be useful in applied breeding programs. The objective of this paper is to present a flanking-marker, TM-BLUP model that is applicable to interpopulation single crosses that characterize maize (Zea mays L.) breeding programs. The performance of a single cross is modeled as the sum of testcross additive and dominance effects at unmarked quantitative trait loci (QTL) and at marked QTL (MQTL). The TM-BLUP model requires information on the recombination frequencies between flanking markers and the MQTL and on MQTL variances. A tabular method is presented for calculating the conditional probability that MQTL alleles in two inbreds are identical by descent given the observed marker genotypes (G ^k _obs) at the kth MQTL. Information on identity by descent of MQTL alleles can then be used to calculate the conditional covariance of MQTL effects between single crosses given G ^k _obs. The inverse of the covariance matrix for dominance effects at unmarked QTL and MQTL can be written directly from the inverse of the covariance matrices of the corresponding testcross additive effects. In practice, the computations required in TM-BLUP may be prohibitive. The computational requirements may be reduced with simplified TM-BLUP models wherein dominance effects at MQTL are excluded, only the single crosses that have been tested are included, or information is pooled across several MQTL. Received: 22 June 1997 / Accepted: 25 February 1998     

Structural analysis and identification of cis-elements of rice osRACD gene

Sexual reproduction in many plants is sensitive to thechange of day-long and photoperiod. It was discoveredby Shi et al. [1] in 1973 that the natural mutant Nongken58S is infertile under long day while being fertile undershort day. In recent two decades, much work has beendone to probe into its molecular mechanism from variousperspectives, such as physiology, genetics as well asmolecular biology [2,3]. The previous study showed thatphysiological changes of 58S rice could be dependent oncombi…