参考群筛选方法及规模对基因型填充准确性的影响

为探究基于A矩阵期望遗传关系最大化（maximizing the expected genetic relationship for matrix A,RELA）、基于A矩阵目标群体遗传方差最小化（minimized the target population genetic variance for matrix A,MCA）、平均亲缘关系最大化（the highest mean kinship coefficients,KIN）、随机选择（random selection,RAN）、共同祖先筛选（common ancestor,CA）等不同参考群筛选方法及参考群规模对基因型填充准确性的影响。本研究使用矮小型黄羽肉鸡作为试验群体,采用鸡600K SNP芯片（Affymetrix Axion HD genotyping array）进行基因分型,测定435羽子代公鸡45、56、70、84、91日龄体重。利用Beagle软件将低密度SNP芯片填充为高密度SNP芯片数据,比较不同参考群筛选方法、参考群规模对基因型填充准确性的影响,以及填充芯片基因组预测准确性。结果表明,使用Beagle 4.0结合系谱信息进行填充效果最佳,其次为Beagle 4.0,而Beagle 5.1填充效果最差。使用MCA方法筛选参考群进行基因型填充准确性最高,使用RAN方法筛选参考群进行基因型填充准确性最低,MCA、RELA、CA 3种方法基因型填充准确性差别较小。相比其他方法,使用MCA方法筛选个体作为参考群将低密度SNP芯片填充至高密度SNP芯片进行基因组选择的预测准确性较高,与真实高密度SNP芯片的基因组预测准确性相差甚微。随着参考群规模增大,基因型填充准确性也随之增加,但增速逐渐下降,最后趋于平缓。综上所述,可以通过参考群筛选方法构建参考群以及控制参考群规模,以保证基因型填充和基因组预测准确性并节省成本,本研究为基因型填充在畜禽遗传育种中的应用提供技术参考。     

Accuracy of imputation of single nucleotide polymorphism marker genotypes from low‐density panels in Japanese Black cattle

Using target and reference fattened steer populations, the performance of genotype imputation using lower‐density marker panels in Japanese Black cattle was evaluated. Population imputation was performed using BEAGLE software. Genotype information for approximately 40 000 single nucleotide polymorphism (SNP) markers by Illumina BovineSNP50 BeadChip was available, and imputation accuracy was assessed based on the average concordance rates of the genotypes, varying equally spaced SNP densities, and the number of individuals in the reference population. Two additional statistics were also calculated as indicators of imputation performance. The concordance rates tended to be lower for SNPs with greater minor allele frequencies, or those located near the ends of the chromosomes. Longer autosomes yielded greater imputation accuracies than shorter ones. When SNPs were selected based on linkage disequilibrium information, relative imputation accuracy was slightly improved. When 3000 and 10 000 equally spaced SNPs were used, the imputation accuracies were greater than 90% and approximately 97%, respectively. These results indicate that combining genotyping using a lower‐density SNP chip with genotype imputation based on a population of individuals genotyped using a higher‐density SNP chip is a cost‐effective and valid approach for genomic prediction.     

Accuracy of genomic prediction using imputed whole‐genome sequence data in white layers

There is an increasing interest in using whole‐genome sequence data in genomic selection breeding programmes. Prediction of breeding values is expected to be more accurate when whole‐genome sequence is used, because the causal mutations are assumed to be in the data. We performed genomic prediction for the number of eggs in white layers using imputed whole‐genome resequence data including ~4.6 million SNPs. The prediction accuracies based on sequence data were compared with the accuracies from the 60 K SNP panel. Predictions were based on genomic best linear unbiased prediction (GBLUP) as well as a Bayesian variable selection model (BayesC). Moreover, the prediction accuracy from using different types of variants (synonymous, non‐synonymous and non‐coding SNPs) was evaluated. Genomic prediction using the 60 K SNP panel resulted in a prediction accuracy of 0.74 when GBLUP was applied. With sequence data, there was a small increase (~1%) in prediction accuracy over the 60 K genotypes. With both 60 K SNP panel and sequence data, GBLUP slightly outperformed BayesC in predicting the breeding values. Selection of SNPs more likely to affect the phenotype (i.e. non‐synonymous SNPs) did not improve the accuracy of genomic prediction. The fact that sequence data were based on imputation from a small number of sequenced animals may have limited the potential to improve the prediction accuracy. A small reference population (n = 1004) and possible exclusion of many causal SNPs during quality control can be other possible reasons for limited benefit of sequence data. We expect, however, that the limited improvement is because the 60 K SNP panel was already sufficiently dense to accurately determine the relationships between animals in our data.     

Community-based alternative breeding plans for indigenous sheep breeds in four agro-ecological zones of Ethiopia

Based on the results of participatory approaches to define traits in the breeding objectives, four scenarios of ram selection and ram use were compared via deterministic modelling of breeding plans for community-based sheep breeding programmes in four diverse agro-ecological regions of Ethiopia. The regions (and production systems) were Afar (pastoral/agro-pastoral), Bonga and Horro (both mixed crop-livestock) and Menz (sheep-barley). The schemes or scenarios differed in terms of selection intensity and duration of ram use. The predicted genetic gains per year in yearling weight (kilograms) were comparable across the schemes but differed among the breeds and ranged from 0.399 to 0.440 in Afar, 0.813 to 0.894 in Bonga, 0.850 to 0.940 in Horro, and 0.616 to 0.699 in Menz. The genetic gains per year in number of lambs born per ewe bred ranged from 0.009 to 0.010 in both Bonga and Horro. The predicted genetic gain in the proportion of lambs weaned per ewe joined was nearly comparable in all breeds ranging from 0.008 to 0.011. The genetic gain per year in milk yield of Afar breed was in the order of 0.018 to 0.020 kg, while the genetic gain per generation for greasy fleece weight (kg) ranged from 0.016 to 0.024 in Menz. Generally, strong selection and shorter duration of ram use for breeding were the preferred options. The expected genetic gains are satisfactory but largely rely on accurate and continuous pedigree and performance recording.     

Nineteenth century narratives reveal historic catch rates for Australian snapper (Pagrus auratus)

Snapper (Pagrus auratus) is widely distributed throughout subtropical and temperate southern oceans and forms a significant recreational and commercial fishery in Queensland, Australia. Using data from government reports, media sources, popular publications and a government fisheries survey carried out in 1910, we compiled information on individual snapper fishing trips that took place prior to the commencement of fisherywide organized data collection, from 1871 to 1939. In addition to extracting all available quantitative data, we translated qualitative information into bounded estimates and used multiple imputation to handle missing values, forming 287 records for which catch rate (snapper fisher^?1 h^?1) could be derived. Uncertainty was handled through a parametric maximum likelihood framework (a transformed trivariate Gaussian), which facilitated statistical comparisons between data sources. No statistically significant differences in catch rates were found among media sources and the government fisheries survey. Catch rates remained stable throughout the time series, averaging 3.75 snapper fisher^?1 h^?1 (95% confidence interval, 3.42–4.09) as the fishery expanded into new grounds. In comparison, a contemporary (1993–2002) south‐east Queensland charter fishery produced an average catch rate of 0.4 snapper fisher^?1 h^?1 (95% confidence interval, 0.31–0.58). These data illustrate the productivity of a fishery during its earliest years of development and represent the earliest catch rate data globally for this species. By adopting a formalized approach to address issues common to many historical records – missing data, a lack of quantitative information and reporting bias – our analysis demonstrates the potential for historical narratives to contribute to contemporary fisheries management.     

A multiple-phenotype imputation procedure as a method for prediction of cheese-making efficiency in Spanish Assaf sheep

Sheep milk is mainly intended to manufacture a wide variety of high-quality cheeses. The ovine cheese industry would benefit from an improvement, through genetic selection, of traits related to the milk coagulation properties (MCPs) and cheese yield-related traits, broadly denoted as “cheese-making traits.” Considering that routine measurements of these traits needed for genetic selection are expensive and time-consuming, this study aimed to evaluate the accuracy of a cheese-making phenotype imputation method based on the information from official milk control records combined with the pH of the milk. For this study, we analyzed records of milk production traits, milk composition traits, and measurements of cheese-making traits available from a total of 1,145 dairy ewes of the Spanish Assaf sheep breed. Cheese-making traits included five related to the MCPs and two cheese yield-related traits. The milk and cheese-making phenotypes were adjusted for significant effects based on a general linear model. The adjusted phenotypes were used to define a multiple-phenotype imputation procedure for the cheese-making traits based on multivariate normality and Markov chain Monte Carlo sampling. Five of the seven cheese-making traits considered in this study achieved a prediction accuracy of 0.60 computed as the correlation between the adjusted phenotypes and the imputed phenotypes. Particularly the logarithm of curd-firming time since rennet addition (logK₂₀) (0.68), which has been previously suggested as a potential candidate trait to improve the cheese ability in this breed, and the logarithm of the ratio between the rennet clotting time and the curd firmness at 60 min (logRCT/A60) (0.65), which has been defined by other studies as an indicator trait of milk coagulation efficiency. This study represents a first step toward the possible use of the phenotype imputation of cheese-making traits to develop a practical methodology for the dairy sheep industry to impute cheese-making traits only based on the analysis of a milk sample without the need of pedigree information. This information could be also used in future planning of specific breeding programs considering the importance of the cheese-making efficiency in dairy sheep and highlights the potential of phenotype imputation to leverage sample size on expensive, hard-to-measure phenotypes.     

An imputation‐based genome‐wide association study on traits related to male reproduction in a White Duroc × Erhualian F2 population

Boar reproductive traits are economically important for the pig industry. Here we conducted a genome‐wide association study (GWAS) for 13 reproductive traits measured on 205 F₂ boars at day 300 using 60 K single nucleotide polymorphism (SNP) data imputed from a reference panel of 1200 pigs in a White Duroc × Erhualian F₂ intercross population. We identified 10 significant loci for seven traits on eight pig chromosomes (SSC). Two loci surpassed the genome‐wide significance level, including one for epididymal weight around 60.25 Mb on SSC7 and one for semen temperature around 43.69 Mb on SSC4. Four of the 10 significant loci that we identified were consistent with previously reported quantitative trait loci for boar reproduction traits. We highlighted several interesting candidate genes at these loci, including APN, TEP1, PARP2, SPINK1 and PDE1C. To evaluate the imputation accuracy, we further genotyped nine GWAS top SNPs using PCR restriction fragment length polymorphism or Sanger sequencing. We found an average of 91.44% of genotype concordance, 95.36% of allelic concordance and 0.85 of r² correlation between imputed and real genotype data. This indicates that our GWAS mapping results based on imputed SNP data are reliable, providing insights into the genetic basis of boar reproductive traits.     

基因型填充策略研究

基因组数据在畜禽遗传育种中的应用越来越广泛，基因型填充作为基因组数据处理的重要工具，填充结果的好坏直接影响后续分析，为了得到好的填充结果，需要制定完善的填充策略。本研究通过模拟数据探讨参考群体大小、目标群体与参考群体间遗传关系（距离）远近、目标位点数目（比例）、最小等位基因频率以及填充算法等因素对基因型填充效果的影响。结果表明，目标位点数目与填充效果呈显著的正相关（P<0.05），是影响基因型填充准确性的主要因素；参考群体大小是影响Beagle5.1填充错误率的主要因素，目标位点数目是影响Minimac4填充错误率的主要因素；目标群体和参考群体的遗传距离对Beagle5.1填充效果的影响较Minimac4更为显著；一般情况下，最小等位基因频率越高的位点填充错误率越高；在参考群体个体数量少且目标位点数目多的情况下，Minimac4的填充速度优于Beagle5.1，但随参考群体个体数目增加有逆趋势。在保证填充质量的前提下，Beagle5.1对本研究中几种因素的标准要求相对较低。相对地，当目标群体位点数目较低，参考群体个体数目较多时，Beagle5.1的填充效果更好，而Minimac4更适合参考群体个体数目较少，目标群体位点数目较高的填充中。本研究针对不同的填充目的制定了不同策略，为基因型填充标准提供了参考。