Genetic association between different criteria to define sexual precocious heifers with growth,carcass, reproductive and feed efficiency indicator traits in Nellore cattle using genomic information

The aim of this study was to estimate genetic parameters for different precocious calving criteria and their relationship with reproductive, growth, carcass and feed efficiency in Nellore cattle using the single‐step genomic BLUP. The reproductive traits used were probability of precocious calving (PPC) at 24 (PPC24), 26 (PPC26), 28 (PPC28) and 30 (PPC30) months of age, stayability (STAY) and scrotal circumference at 455 days of age (SC455). Growth traits such as weights at 240 (W240) and 455 (W455) days of age and adult weight (AW) were used. Rib eye area (REA), subcutaneous fat thickness (SFT), rump fat thickness (RFT) and residual feed intake (RFI) were included in the analyses. The estimation of genetic parameters was performed using a bi‐trait threshold model including genomic information in a single‐step approach. Heritability for PPC traits was moderate to high (0.29–0.56) with highest estimates for PPC24 (0.56) and PPC26 (0.50). Genetic correlation estimates between PPC and STAY weakened as a function of calving age. Correlation with SC455, growth and carcass traits were low (0.25–0.31; ?0.22 to 0.04; ?0.09 to 0.18, respectively), the same occurs with RFI (?0.09 to 0.08), this suggests independence between female sexual precocity and feed efficiency traits. The results of this study encourage the use of PPC traits in Nellore cattle because the selection for such trait would not have a negative impact on reproductive, growth, carcass and feed efficiency indicator traits. Stayability for sexual precocious heifers (PPC24 and PPC26) must be redefined to avoid incorrectly phenotype assignment.     

一种适用于单胎动物多品种选择的基因组关系矩阵

旨在提出一种新型基因组关系矩阵并验证其在多品种联合群体中的模拟应用效果。本研究利用QMsim软件模拟牛的表型数据和基因型数据;利用Gmatrix软件构建常规G阵;利用R语言构建新型G阵,新型G阵在常规G阵的基础上,将多品种联合群体的非哈代-温伯格平衡位点考虑在内;利用DMU软件使用“一步”法模型计算基因组估计育种值（estimated genomic breeding value,GEBV）;比较不同情况下使用两种G阵的GEBV预测准确性。结果表明,在不同遗传力及QTL数下,不对新型G阵使用A₂₂阵加权就能达到常规G阵使用A₂₂阵加权时的GEBV预测准确性。在系谱部分缺失时,新型G阵不加权较常规G阵加权时GEBV预测准确性高。证明,在系谱有部分缺失时,新型G阵对多品种GEBV的预测有一定优势。     

Genome‐wide association study and genomic evaluation of feed efficiency traits in Japanese Black cattle using single‐step genomic best linear unbiased prediction method

The objectives of this study were to better understand the genetic architecture and the possibility of genomic evaluation for feed efficiency traits by (i) performing genome‐wide association studies (GWAS), and (ii) assessing the accuracy of genomic evaluation for feed efficiency traits, using single‐step genomic best linear unbiased prediction (ssGBLUP)‐based methods. The analyses were performed in residual feed intake (RFI), residual body weight gain (RG), and residual intake and body weight gain (RIG) during three different fattening periods. The phenotypes from 4,578 Japanese Black steers, which were progenies of 362 progeny‐tested bulls and the genotypes from the bulls were used in this study. The results of GWAS showed that a total of 16, 8, and 12 gene ontology terms were related to RFI, RG, and RIG, respectively, and the candidate genes identified in RFI and RG were involved in olfactory transduction and the phosphatidylinositol signaling system, respectively. The realized reliabilities of genomic estimated breeding values were low to moderate in the feed efficiency traits. In conclusion, ssGBLUP‐based method can lead to understand some biological functions related to feed efficiency traits, even with small population with genotypes, however, an alternative strategy will be needed to enhance the reliability of genomic evaluation.     

Reducing bias in the dairy cattle single‐step genomic evaluation by ignoring bulls without progeny

The number of genotyped animals has increased rapidly creating computational challenges for genomic evaluation. In animal model BLUP, candidate animals without progeny and phenotype do not contribute information to the evaluation and can be discarded. In theory, genotyped candidate animal without progeny can bring information into single‐step BLUP (ssGBLUP) and affect the estimation of other breeding values. We studied the effect of including or excluding genomic information of culled bull calves on genomic breeding values (GEBV) from ssGBLUP. In particular, GEBVs of genotyped bulls with daughters and GEBVs of young bulls selected into AI to be progeny tested (test bulls) were studied. The ssGBLUP evaluation was computed using Nordic test day (TD) model and TD data for the Nordic Red Dairy Cattle. The results indicate that genomic information of culled bull calves does not affect the GEBVs of progeny tested reference animals, but if genotypes of the culled bulls are used in the TD ssGBLUP, the genetic trend in the test bulls is considerably higher compared to the situation when genomic information of the culled bull calves is excluded. It seems that by discarding genomic information of culled bull calves without progeny, upward bias of GEBVs of test bulls is reduced.     

A method to approximate the inverse of a part of the additive relationship matrix

Single‐step genomic predictions need the inverse of the part of the additive relationship matrix between genotyped animals ( A ₂₂). Gains in computing time are feasible with an algorithm that sets up the sparsity pattern of (SP algorithm) using pedigree searches, when is close to sparse. The objective of this study is to present a modification of the SP algorithm (RSP algorithm) and to assess its use in approximating when the actual is dense. The RSP algorithm sets up a restricted sparsity pattern of by limiting the pedigree search to a maximum number of searched branches. We have tested its use on four different simulated genotyped populations, from 10 000 to 75 000 genotyped animals. Accuracy of approximation is tested by replacing the actual by its approximation in an equivalent mixed model including only genotyped animals. Results show that limiting the pedigree search to four branches is enough to provide accurate approximations of , which contain approximately 80% of zeros. Computing approximations is not expensive in time but may require a great amount of memory (at maximum, approximately 81 min and approximately 55 Gb of RAM for 75 000 genotyped animals using parallel processing on four threads).     

猪群体一步法基因组选择应用效果评估

【背景】在提高畜牧生产效率中,遗传育种的贡献率占比最高。通过育种可使畜牧企业提高生产效率,获得最大的经济效益。目前,基因组选择已经成为动植物育种中广泛应用的技术手段。基因组选择能够利用覆盖全基因组的高密度标记对育种值进行估计,与系谱信息相比,利用这些标记得到的个体间平均亲缘关系更加准确,从而能更准确地估计育种值（Estimate breeding values, EBV）,对个体进行选育。在实际育种中,对所有个体进行基因分型是不现实的,尤其是猪这种个体经济价值较小的物种,这限制了基因组选择在猪育种中的应用。一步法（single-step genomic best linear unbiased prediction,ssGBLUP）能够同时利用系谱和基因型信息,允许只测定部分个体的基因型,在保持较高预测准确性的同时,大大降低基因分型成本。目前,已经有很多研究表明,在猪育种中使用基因组选择方法能够提高预测准确性,但在实际育种中,育种成本也是畜牧企业考虑的一个重要问题。因此,如何经济有效地实施育种方案,具有重大的研究价值。【目的】通过对一步法基因组选择在杜洛克猪群体评估效果的研究,为基因组选择育种方案提供依据。【方法】以福建某猪场2009—2018年出生的杜洛克猪群体的3个重要经济性状为研究对象,比较了BLUP、GBLUP和一步法等方法在杜洛克猪生长性状上的基因组预测准确性与估计育种值预测可靠性,探究了当参考群中具有不同比例的基因型个体时,一步法预测准确性的变化规律。【结果】（1）达100 kg日龄、背膘厚和眼肌面积的遗传力分别为0.257±0.038、0.250±0.039和0.399±0.040;（2）ssGBLUP相比于BLUP准确性提升14.7%—51.1%;相比于GBLUP准确性提升13.4%—45.7%;（3）10%—30%的个体有基因型时,ssGBLUP预测的准确性超过BLUP;在40%—60%的个体有基因型时,准确性提升速度降低,趋于平缓。【结论】（1）与BLUP相比,一步法能提高各性状估计育种值的准确性和可靠性;与GBLUP方法相比,只有无基因型个体的系谱信息时一步法略低于GBLUP,但在加入无基因型个体的表型信息后,一步法表现优于GBLUP。（2）随着参考群中测定基因型个体的比例逐渐提高,不管使用哪种筛选测定基因型个体的方式（随机选取和筛选关键个体）,一步法预测效果都逐渐提高。表明,在企业育种预算有限时,即使只测定部分个体基因型,一步法可提高基因组选择的预测效果。     

Comparison between haplotype-based and individual snp-based genomic predictions for beef fatty acid profile in Nelore cattle

The aim of this study was to evaluate the genomic predictions using the single-step genomic best linear unbiased predictor (ssGBLUP) method based on SNPs and haplotype markers associated with beef fatty acids (FAs) profile in Nelore cattle. The data set contained records from 963 Nelore bulls finished in feedlot (±90 days) and slaughtered with approximately 24 months of age. Meat samples from the Longissimus dorsi muscle were taken for FAs profile measurement. FAs were quantified by gas chromatography using a SP-2560 capillary column. Animals were genotyped with the high-density SNP panel (BovineHD BeadChip assay) containing 777,962 markers. SNPs with a minor allele frequency and a call rate lower than 0.05 and 0.90, respectively, monomorphic, located on sex chromosomes, and with unknown position were removed from the data set. After genomic quality control, a total of 469,981 SNPs and 892 samples were available for subsequent analyses. Missing genotypes were imputed and phased using the FImpute software. Haplotype blocks were defined based on linkage disequilibrium using the Haploview software. The model to estimate variance components and genetic parameters and to predict the genomic values included the random genetic additive effects, fixed effects of the contemporary group and the age at slaughter as a linear covariate. Accuracies using the haplotype-based approach ranged from 0.07 to 0.31, and those SNP-based ranged from 0.06 to 0.33. Regression coefficients ranged from 0.07 to 0.74 and from 0.08 to 1.45 using the haplotype- and SNP-based approaches, respectively. Despite the low to moderate accuracies for the genomic values, it is possible to obtain genetic progress trough selection using genomic information based either on SNPs or haplotype markers. The SNP-based approach allows less biased genomic evaluations, and it is more feasible when taking into account the computational and operational cost underlying the haplotypes inference.     

Estimation of total genetic effects for survival time in crossbred laying hens showing cannibalism,using pedigree or genomic information

Mortality of laying hens due to cannibalism is a major problem in the egg‐laying industry. Survival depends on two genetic effects: the direct genetic effect of the individual itself (DGE) and the indirect genetic effects of its group mates (IGE). For hens housed in sire‐family groups, DGE and IGE cannot be estimated using pedigree information, but the combined effect of DGE and IGE is estimated in the total breeding value (TBV). Genomic information provides information on actual genetic relationships between individuals and might be a tool to improve TBV accuracy. We investigated whether genomic information of the sire increased TBV accuracy compared with pedigree information, and we estimated genetic parameters for survival time. A sire model with pedigree information (BLUP) and a sire model with genomic information (ssGBLUP) were used. We used survival time records of 7290 crossbred offspring with intact beaks from four crosses. Cross‐validation was used to compare the models. Using ssGBLUP did not improve TBV accuracy compared with BLUP which is probably due to the limited number of sires available per cross (~50). Genetic parameter estimates were similar for BLUP and ssGBLUP. For both BLUP and ssGBLUP, total heritable variance (T²), expressed as a proportion of phenotypic variance, ranged from 0.03 ± 0.04 to 0.25 ± 0.09. Further research is needed on breeding value estimation for socially affected traits measured on individuals kept in single‐family groups.     

Implications of SNP weighting on single‐step genomic predictions for different reference population sizes

We investigated the importance of SNP weighting in populations with 2,000 to 25,000 genotyped animals. Populations were simulated with two effective sizes (20 or 100) and three numbers of QTL (10, 50 or 500). Pedigree information was available for six generations; phenotypes were recorded for the four middle generations. Animals from the last three generations were genotyped for 45,000 SNP. Single‐step genomic BLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used to estimate genomic EBV using a genomic relationship matrix ( G ). The WssGBLUP performed better in small genotyped populations; however, any advantage for WssGBLUP was reduced or eliminated when more animals were genotyped. WssGBLUP had greater resolution for genome‐wide association (GWA) as did increasing the number of genotyped animals. For few QTL, accuracy was greater for WssGBLUP than ssGBLUP; however, for many QTL, accuracy was the same for both methods. The largest genotyped set was used to assess the dimensionality of genomic information (number of effective SNP). The number of effective SNP was considerably less in weighted G than in unweighted G . Once the number of independent SNP is well represented in the genotyped population, the impact of SNP weighting becomes less important.