Mancin Enrico, Sosa-Madrid Bolívar Samuel, Blasco Agustín, Ibáñez-Escriche Noelia
Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell'Università 16, 35020 Legnaro, PD, Italy.
Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 Valencia, Spain.
Animals (Basel). 2021 Mar 13;11(3):803. doi: 10.3390/ani11030803.
Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits. However, genotyping costs in rabbits are still too high to enable genomic prediction in selective breeding programs. One method for decreasing genotyping costs is the genotype imputation, where parents are genotyped at high SNP-density (HD) and the progeny are genotyped at lower SNP-density, followed by imputation to HD. The aim of this study was to disentangle the best imputation strategies with a trade-off between genotyping costs and the accuracy of breeding values for litter size. A selection process, mimicking a commercial breeding rabbit selection program for litter size, was simulated. Two different Quantitative Trait Nucleotide (QTN) models (QTN_5 and QTN_44) were generated 36 times each. From these simulations, seven different scenarios (S1-S7) and a further replicate of the third scenario (S3_A) were created. Scenarios consist of a different combination of genotyping strategies. In these scenarios, ancestors and progeny were genotyped with a mix of three different platforms, containing 200,000, 60,000, and 600 SNPs under a cost of EUR 100, 50 and 11 per animal, respectively. Imputation accuracy (IA) was measured as a Pearson's correlation between true genotype and imputed genotype, whilst the accuracy of gEBVs was the correlation between true breeding value and the estimated one. The relationships between IA, the accuracy of gEBVs, genotyping costs, and response to selection were examined under each QTN model. QTN_44 presented better performance, according to the results of genomic prediction, but the same ranks between scenarios remained in both QTN models. The highest IA (0.99) and the accuracy of gEBVs (0.26; QTN_44, and 0.228; QTN_5) were observed in S1 where all ancestors were genotyped at HD and progeny at medium SNP-density (MD). Nevertheless, this was the most expensive scenario compared to the others in which the progenies were genotyped at low SNP-density (LD). Scenarios with low average costs presented low IA, particularly when female ancestors were genotyped at LD (S5) or non-genotyped (S7). The S3_A, imputing whole-genomes, had the lowest accuracy of gEBVs (0.09), even worse than Best Linear Unbiased Prediction (BLUP). The best trade-off between genotyping costs and the accuracy of gEBVs (0.234; QTN_44 and 0.199) was in S6, in which dams were genotyped with MD whilst grand-dams were non-genotyped. However, this relationship would depend mainly on the distribution of QTN and SNP across the genome, suggesting further studies on the characterization of the rabbit genome in the Spanish lines. In summary, genomic selection with genotype imputation is feasible in the rabbit industry, considering only genotyping strategies with suitable IA, accuracy of gEBVs, genotyping costs, and response to selection.
基因组选择利用遗传标记信息来预测基因组育种值（gEBVs），并且可以成为选择低遗传力性状（如兔产仔数）的合适工具。然而，兔的基因分型成本仍然过高，无法在选择性育种计划中进行基因组预测。降低基因分型成本的一种方法是基因型填充，即对亲本进行高SNP密度（HD）基因分型，对后代进行较低SNP密度基因分型，然后填充到HD。本研究的目的是在基因分型成本与产仔数育种值准确性之间进行权衡，找出最佳的填充策略。模拟了一个类似于商业育种兔产仔数选择计划的选择过程。两种不同的数量性状核苷酸（QTN）模型（QTN_5和QTN_44）各生成36次。从这些模拟中，创建了七种不同的情景（S1 - S7）以及第三种情景的另一次重复（S3_A）。情景由不同的基因分型策略组合构成。在这些情景中，祖先和后代使用三种不同平台的组合进行基因分型，分别包含200,000、60,000和600个SNP，每只动物的成本分别为100欧元、50欧元和11欧元。填充准确性（IA）通过真实基因型与填充基因型之间的皮尔逊相关性来衡量，而gEBVs的准确性是真实育种值与估计育种值之间的相关性。在每个QTN模型下，研究了IA、gEBVs准确性、基因分型成本和选择反应之间的关系。根据基因组预测结果，QTN_44表现更好，但两种QTN模型中情景之间的相同排名仍然存在。在S1中观察到最高的IA（0.99）和gEBVs准确性（QTN_44为0.26，QTN_5为0.228），其中所有祖先均进行HD基因分型，后代进行中等SNP密度（MD）基因分型。然而，与后代进行低SNP密度（LD）基因分型的其他情景相比，这是最昂贵的情景。平均成本较低的情景呈现出较低的IA，特别是当雌性祖先进行LD基因分型（S5）或未进行基因分型（S7）时。S3_A，即全基因组填充，具有最低的gEBVs准确性（0.09），甚至比最佳线性无偏预测（BLUP）还要差。基因分型成本与gEBVs准确性（QTN_44为0.234，QTN_5为0.199）之间的最佳权衡出现在S6中，其中母兔进行MD基因分型，而祖母兔未进行基因分型。然而，这种关系将主要取决于QTN和SNP在基因组中的分布，这表明需要对西班牙品系兔基因组的特征进行进一步研究。总之，考虑到仅具有合适的IA、gEBVs准确性、基因分型成本和选择反应的基因分型策略，采用基因型填充的基因组选择在兔产业中是可行的。
