Ni Guiyan, Cavero David, Fangmann Anna, Erbe Malena, Simianer Henner
Animal Breeding and Genetics Group, Georg-August-Universität, Göttingen, Germany.
Lohmann Tierzucht GmbH, Cuxhaven, Germany.
Genet Sel Evol. 2017 Jan 16;49(1):8. doi: 10.1186/s12711-016-0277-y.
With the availability of next-generation sequencing technologies, genomic prediction based on whole-genome sequencing (WGS) data is now feasible in animal breeding schemes and was expected to lead to higher predictive ability, since such data may contain all genomic variants including causal mutations. Our objective was to compare prediction ability with high-density (HD) array data and WGS data in a commercial brown layer line with genomic best linear unbiased prediction (GBLUP) models using various approaches to weight single nucleotide polymorphisms (SNPs).
A total of 892 chickens from a commercial brown layer line were genotyped with 336 K segregating SNPs (array data) that included 157 K genic SNPs (i.e. SNPs in or around a gene). For these individuals, genome-wide sequence information was imputed based on data from re-sequencing runs of 25 individuals, leading to 5.2 million (M) imputed SNPs (WGS data), including 2.6 M genic SNPs. De-regressed proofs (DRP) for eggshell strength, feed intake and laying rate were used as quasi-phenotypic data in genomic prediction analyses. Four weighting factors for building a trait-specific genomic relationship matrix were investigated: identical weights, -(log P) from genome-wide association study results, squares of SNP effects from random regression BLUP, and variable selection based weights (known as BLUP|GA). Predictive ability was measured as the correlation between DRP and direct genomic breeding values in five replications of a fivefold cross-validation.
Averaged over the three traits, the highest predictive ability (0.366 ± 0.075) was obtained when only genic SNPs from WGS data were used. Predictive abilities with genic SNPs and all SNPs from HD array data were 0.361 ± 0.072 and 0.353 ± 0.074, respectively. Prediction with -(log P) or squares of SNP effects as weighting factors for building a genomic relationship matrix or BLUP|GA did not increase accuracy, compared to that with identical weights, regardless of the SNP set used.
Our results show that little or no benefit was gained when using all imputed WGS data to perform genomic prediction compared to using HD array data regardless of the weighting factors tested. However, using only genic SNPs from WGS data had a positive effect on prediction ability.
随着新一代测序技术的出现，基于全基因组测序（WGS）数据的基因组预测在动物育种方案中已变得可行，并且有望带来更高的预测能力，因为此类数据可能包含所有基因组变异，包括因果突变。我们的目标是使用各种加权单核苷酸多态性（SNP）的方法，通过基因组最佳线性无偏预测（GBLUP）模型，比较商业褐壳蛋鸡品系中高密度（HD）芯片数据和WGS数据的预测能力。
对来自商业褐壳蛋鸡品系的892只鸡进行基因分型，使用包含15.7万个基因SNP（即基因内或基因周围的SNP）的33.6万个分离SNP（芯片数据）。对于这些个体，基于25个个体的重测序数据估算全基因组序列信息，从而得到520万个估算SNP（WGS数据），其中包括260万个基因SNP。蛋壳强度、采食量和产蛋率的去回归证明（DRP）被用作基因组预测分析中的准表型数据。研究了构建性状特异性基因组关系矩阵的四个加权因子：相同权重、全基因组关联研究结果中的 -（log P）、随机回归BLUP的SNP效应平方以及基于变量选择的权重（称为BLUP|GA）。预测能力通过五倍交叉验证的五次重复中DRP与直接基因组育种值之间的相关性来衡量。
在这三个性状上平均而言，仅使用WGS数据中的基因SNP时获得了最高的预测能力（0.366±0.075）。HD芯片数据中基因SNP和所有SNP的预测能力分别为0.361±0.072和0.353±0.074。与使用相同权重相比，使用 -（log P）或SNP效应平方作为构建基因组关系矩阵或BLUP|GA的加权因子进行预测，无论使用何种SNP集，都不会提高准确性。
我们的结果表明，与使用HD芯片数据相比，无论测试何种加权因子，使用所有估算的WGS数据进行基因组预测几乎没有益处或根本没有益处。然而，仅使用WGS数据中的基因SNP对预测能力有积极影响。
