Increasing imputation and prediction accuracy for Chinese Holsteins using joint Chinese–Nordic reference population

This study investigated the effect of including Nordic Holsteins in the reference population on the imputation accuracy and prediction accuracy for Chinese Holsteins. The data used in this study include 85 Chinese Holstein bulls genotyped with both 54K chip and 777K (HD) chip, 2862 Chinese cows genotyped with 54K chip, 510 Nordic Holstein bulls genotyped with HD chip, and 4398 Nordic Holstein bulls genotyped with 54K chip and with deregressed proofs for five milk production traits. Based on these data, the accuracy of imputation from 54K to HD marker data and the accuracy of genomic predictions in Chinese Holstein were assessed. The allele correct rate increased around 2.7 and 1.7% in imputation from the 54K to the HD marker data for Chinese Holstein bulls and cows, respectively, when the Nordic HD‐genotyped bulls were included in the reference data for imputation. However, the prediction accuracy was improved slightly when using the marker data imputed based on the combined HD reference data, compared with using the marker data imputed based on the Chinese HD reference data only. On the other hand, when using the combined reference population including 4398 Nordic Holstein bulls, the accuracy of genomic predictions increased 6.5 percentage points together with a reduction of prediction bias. The HD markers did not outperform the 54K markers in genomic prediction based on the present data. The results indicate that for Chinese Holsteins, it is necessary to genotype more individuals with 54K chip to increase reference population rather than increasing marker density.     

基于不同密度SNP芯片在杜洛克公猪中的全基因组选择效果分析

基因组选择(GS)是近些年发展起来的一项新型育种技术,目前已在动植物育种实践中应用。本研究通过在1 068头杜洛克公猪群体中使用不同密度的SNP芯片进行全基因组选择效果比较分析。结果发现:使用基因型填充后芯片以及高密度SNP芯片所获得的估计基因组育种值(GEBV)之间可以达到99%的相关,并发现个体间亲缘关系的远近对同群体内基因型填充结果的准确率影响不大。由此可见,与目标性状紧密相关的低密度SNP芯片可用于实际育种工作,在降低使用成本的同时并不影响全基因组选择效果,为实质性进行猪分子育种提供了一条可行途径。     

Study on the Strategies of Genotype Imputation

Genomic data is more and more widely used in livestock breeding. Genotype imputation is an important tool to handle missing values in genotypic data, and the quality of imputation results directly affects the subsequent analysis. To obtain good imputation results, a comprehensive imputation strategy needs to be formulated. We studied on the effects of several factors on genotype imputation by simulation. The factors included reference population size, genetic relationship (distance) between the target population and the reference population, the number of target sites (proportion), the minimum allele frequency (MAF), and the imputation algorithm. The results showed that the number of target sites was the main factor affecting the genotype imputation, and it showed significantly positive correlation with the quality of imputation(P<0.05). The reference population size was the main factor affecting the imputation error rate in Beagle5.1. Correspondingly, the number of target sites was the main factor affecting the imputation error rate in Minimac4. Genetic distance between the target population and the reference population had a more significant effect on the imputation quality of Beagle5.1 than Minimac4. In general, the imputation error rate increased as the increases of MAF in a site. When the number of individuals in the reference population was small and the number of target sites was large, the speed of Minimac4 was superior to Beagle5.1, but there was a reverse trend as the reference population size increased. On the premise of ensuring the imputation quality, Beagle5.1 had relatively lower requirements for the above factors. In contrast, when the number of target sites was low and reference population size was large, the imputation effect of Beagle5.1 was better, while Minimac4 was more suitable for the imputation of a small reference population size and a higher number of target sites. In this study, different strategies were formulated for different imputation purposes, and the study results would provide a reference for genotype imputation.     

Application of imputation methods to genomic selection in Chinese Holstein cattle

Missing genotypes are a common feature of high density SNP datasets obtained using SNP chip technology and this is likely to decrease the accuracy of genomic selection. This problem can be circumvented by imputing the missing genotypes with estimated genotypes. When implementing imputation, the criteria used for SNP data quality control and whether to perform imputation before or after data quality control need to consider. In this paper, we compared six strategies of imputation and quality control using different imputation methods, different quality control criteria and by changing the order of imputation and quality control, against a real dataset of milk production traits in Chinese Holstein cattle. The results demonstrated that, no matter what imputation method and quality control criteria were used, strategies with imputation before quality control performed better than strategies with imputation after quality control in terms of accuracy of genomic selection. The different imputation methods and quality control criteria did not significantly influence the accuracy of genomic selection. We concluded that performing imputation before quality control could increase the accuracy of genomic selection, especially when the rate of missing genotypes is high and the reference population is small.     

Pesticide residues in food--acute dietary exposure

Consumer risk assessment is a crucial step in the regulatory approval of pesticide use on food crops. Recently, an additional hurdle has been added to the formal consumer risk assessment process with the introduction of short-term intake or exposure assessment and a comparable short-term toxicity reference, the acute reference dose. Exposure to residues during one meal or over one day is important for short-term or acute intake. Exposure in the short term can be substantially higher than average because the consumption of a food on a single occasion can be very large compared with typical long-term or mean consumption and the food may have a much larger residue than average. Furthermore, the residue level in a single unit of a fruit or vegetable may be higher by a factor (defined as the variability factor, which we have shown to be typically x3 for the 97.5th percentile unit) than the average residue in the lot. Available marketplace data and supervised residue trial data are examined in an investigation of the variability of residues in units of fruit and vegetables. A method is described for estimating the 97.5th percentile value from sets of unit residue data. Variability appears to be generally independent of the pesticide, the crop, crop unit size and the residue level. The deposition of pesticide on the individual unit during application is probably the most significant factor. The diets used in the calculations ideally come from individual and household surveys with enough consumers of each specific food to determine large portion sizes. The diets should distinguish the different forms of a food consumed, eg canned, frozen or fresh, because the residue levels associated with the different forms may be quite different. Dietary intakes may be calculated by a deterministic method or a probabilistic method. In the deterministic method the intake is estimated with the assumptions of large portion consumption of a 'high residue' food (high residue in the sense that the pesticide was used at the highest recommended label rate, the crop was harvested at the smallest interval after treatment and the residue in the edible portion was the highest found in any of the supervised trials in line with these use conditions). The deterministic calculation also includes a variability factor for those foods consumed as units (eg apples, carrots) to allow for the elevated residue in some single units which may not be seen in composited samples. In the probabilistic method the distribution of dietary consumption and the distribution of possible residues are combined in repeated probabilistic calculations to yield a distribution of possible residue intakes. Additional information such as percentage commodity treated and combination of residues from multiple commodities may be incorporated into probabilistic calculations. The IUPAC Advisory Committee on Crop Protection Chemistry has made 11 recommendations relating to acute dietary exposure.     

An association study using imputed whole‐genome sequence data identifies novel significant loci for growth‐related traits in a Duroc × Erhualian F2 population

The average daily gain (ADG) and body weight (BW) are very important traits for breeding programs and for the meat production industry, which have attracted many researchers to delineate the genetic architecture behind these traits. In the present study, single‐ and multi‐trait genome‐wide association studies (GWAS) were performed between imputed whole‐genome sequence data and the traits of the ADG and BW at different stages in a large‐scale White Duroc × Erhualian F₂ population. A bioinformatics annotation analysis was used to assist in the identification of candidate genes that are associated with these traits. Five and seven genome‐wide significant quantitative trait loci (QTLs) were identified by single‐ and multi‐trait GWAS, respectively. Furthermore, more than 40 genome‐wide suggestive loci were detected. On the basis of the whole‐genome sequence association study and the bioinformatics analysis, NDUFAF6, TNS1 and HMGA1 stood out as the strongest candidate genes. The presented single‐ and multi‐trait GWAS analysis using imputed whole‐genome sequence data identified several novel QTLs for pig growth‐related traits. Integrating the GWAS with bioinformatics analysis can facilitate the more accurate identification of candidate genes. Higher imputation accuracy, time‐saving algorithms, improved models and comprehensive databases will accelerate the identification of causal genes or mutations, which will contribute to genomic selection and pig breeding in the future.     

基于点评估方法的渤海湾产区苹果中农药残留膳食暴露风险研究

为评估渤海湾产区苹果中主要农药的残留情况及其产生的风险,在山东、辽宁及河北3个主要省份的150个生产基地进行了苹果样品采集与测定分析,并对我国不同人群的膳食暴露风险进行了评估。结果表明:93.3%的苹果样品检出有低浓度农药残留,经最大残留限量值(MRL)判定后100%合格,82.0%的样品中农药残留种类在3种及以下;共检出17种农药残留,大多为低毒或无毒农药,无禁用和高毒农药。采用点评估方法,选择检出率在20%以上的多菌灵、毒死蜱、啶虫脒和戊唑醇进行不同消费人群暴露点评估。结果显示:4种农药的急性和慢性摄入风险均为儿童高于成年人,绝大多数女性人群的摄入风险高于男性;4种农药急性摄入风险均高于慢性摄入风险,风险水平由高到低为多菌灵毒死蜱戊唑醇啶虫脒,但点评估结果均远低于100%,说明通过食用苹果摄入的农药残留极其微量,不会对人体产生急性或慢性风险。     

Incorporating genomic annotation into single-step genomic prediction with imputed whole-genome sequence data

Single-step genomic best linear unbiased prediction (ssGBLUP) is now intensively investigated and widely used in livestock breeding due to its beneficial feature of combining information from both genotyped and ungenotyped individuals in the single model. With the increasing accessibility of whole-genome sequence (WGS) data at the population level, more attention is being paid to the usage of WGS data in ssGBLUP. The predictive ability of ssGBLUP using WGS data might be improved by incorporating biological knowledge from public databases. Thus, we extended ssGBLUP, incorporated genomic annotation information into the model, and evaluated them using a yellow-feathered chicken population as the examples. The chicken population consisted of 1 338 birds with 23 traits, where imputed WGS data including 5 127 612 single nucleotide polymorphisms (SNPs) are available for 895 birds. Considering different combinations of annotation information and models, original ssGBLUP, haplotype-based ssG_HBLUP, and four extended ssGBLUP incorporating genomic annotation models were evaluated. Based on the genomic annotation (GRCg6a) of chickens, 3 155 524 and 94 837 SNPs were mapped to genic and exonic regions, respectively. Extended ssGBLUP using genic/exonic SNPs outperformed other models with respect to predictive ability in 15 out of 23 traits, and their advantages ranged from 2.5 to 6.1% compared with original ssGBLUP. In addition, to further enhance the performance of genomic prediction with imputed WGS data, we investigated the genotyping strategies of reference population on ssGBLUP in the chicken population. Comparing two strategies of individual selection for genotyping in the reference population, the strategy of evenly selection by family (SBF) performed slightly better than random selection in most situations. Overall, we extended genomic prediction models that can comprehensively utilize WGS data and genomic annotation information in the framework of ssGBLUP, and validated the idea that properly handling the genomic annotation information and WGS data increased the predictive ability of ssGBLUP. Moreover, while using WGS data, the genotyping strategy of maximizing the expected genetic relationship between the reference and candidate population could further improve the predictive ability of ssGBLUP. The results from this study shed light on the comprehensive usage of genomic annotation information in WGS-based single-step genomic prediction.     

猪SNP液相芯片10K~50K基因型填充效果研究

旨在探究低密度液相芯片在生产实践中的实用性,降低育种成本。本试验选用了3 761头约160日龄,110 kg左右健康大白猪,随机抽取100头大白猪,根据10K芯片标记信息,从50K芯片中抽取标记生成10K芯片,作为填充群体。再从剩余群体中,分别随机抽取800、2 000、3 600个个体作为参考群体,使用Beagle 4.1软件对100头填充群体进行基因型填充至50K芯片,重复10次,以基因型一致性和基因型相关系数来评价基因型填充的准确性。结果表明,10K和50K芯片平均连锁不平衡（r²）程度为0.227和0.258,相差不大。最小等位基因频率（MAF）为0.05是基因型填充准确性的拐点,剔除掉MAF<0.05标记后,填充准确性明显升高。填充准确性随参考群体规模增大而上升,参考群由800头扩大到3 600头,填充准确性从0.90提高到0.95,10次重复的标准差也从0.006下降到0.002。对于较小的参考群体规模,染色体基因型填充准确性波动较大,随着参考群体规模增大,每条染色体填充准确性相差不大。本研究结果表明,猪液相芯片从10K填充到50K是可行的,可以大规模用于基因组选择,降低基因组选择育种成本。     

基于分步迁移策略的苹果采摘机械臂轨迹规划方法

针对非结构化自然环境使基于深度强化学习的采摘轨迹规划训练效率低的问题,提出了基于分步迁移策略的深度确定性策略梯度算法(DDPG),并进行了苹果采摘轨迹规划。首先,提出了基于DDPG的渐进空间约束分步训练策略;其次,利用迁移学习思想,将轨迹规划的最优策略由无障碍场景迁移到单一障碍场景、由单一障碍场景迁移到混杂障碍场景;最后,对多自由度苹果采摘机械臂进行了采摘轨迹规划仿真实验,结果表明,分步迁移策略能够提高DDPG算法的训练效率与网络性能,仿真实验验证了本文方法的有效性。