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Functionally prioritised whole-genome sequence variants improve the accuracy of genomic prediction for heat tolerance
Genetics Selection Evolution ( IF 3.6 ) Pub Date : 2022-02-19 , DOI: 10.1186/s12711-022-00708-8
Evans K Cheruiyot 1, 2 , Mekonnen Haile-Mariam 2 , Benjamin G Cocks 1, 2 , Iona M MacLeod 2 , Raphael Mrode 3, 4 , Jennie E Pryce 1, 2
Affiliation  

Heat tolerance is a trait of economic importance in the context of warm climates and the effects of global warming on livestock production, reproduction, health, and well-being. This study investigated the improvement in prediction accuracy for heat tolerance when selected sets of sequence variants from a large genome-wide association study (GWAS) were combined with a standard 50k single nucleotide polymorphism (SNP) panel used by the dairy industry. Over 40,000 dairy cattle with genotype and phenotype data were analysed. The phenotypes used to measure an individual’s heat tolerance were defined as the rate of decline in milk production traits with rising temperature and humidity. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance. The prioritised sequence variants were the most significant SNPs passing a GWAS p-value threshold selected based on sliding 100-kb windows along each chromosome. We used a bull reference set to develop the genomic prediction equations, which were then validated in an independent set of Holstein, Jersey, and crossbred cows. Prediction analyses were performed using the BayesR, BayesRC, and GBLUP methods. The accuracy of genomic prediction for heat tolerance improved by up to 0.07, 0.05, and 0.10 units in Holstein, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holstein cows were added to the 50k SNP panel. However, in some scenarios, the prediction accuracy decreased unexpectedly with the largest drop of − 0.10 units for the heat tolerance fat yield trait observed in Jersey cows when 50k plus pre-selected SNPs from Holstein cows were used. Using pre-selected SNPs discovered on a combined set of Holstein and Jersey cows generally improved the accuracy, especially in the Jersey validation. In addition, combining Holstein and Jersey bulls in the reference set generally improved prediction accuracy in most scenarios compared to using only Holstein bulls as the reference set. Informative sequence markers can be prioritised to improve the genomic prediction of heat tolerance in different breeds. In addition to providing biological insight, these variants could also have a direct application for developing customized SNP arrays or can be used via imputation in current industry SNP panels.

中文翻译:

功能优先的全基因组序列变异提高了基因组预测耐热性的准确性

在温暖的气候和全球变暖对牲畜生产、繁殖、健康和福祉的影响的背景下,耐热性是一种具有经济重要性的特征。本研究调查了当从大型全基因组关联研究 (GWAS) 中选择的一组序列变体与乳制品行业使用的标准 50k 单核苷酸多态性 (SNP) 面板相结合时,耐热性预测准确性的提高。分析了超过 40,000 头具有基因型和表型数据的奶牛。用于测量个体耐热性的表型被定义为产奶量随温度和湿度升高而下降的速率。我们使用荷斯坦奶牛和泽西奶牛来选择与耐热性相关的序列变体。优先序列变体是最重要的 SNP,通过基于沿每条染色体滑动 100-kb 窗口选择的 GWAS p 值阈值。我们使用公牛参考集来开发基因组预测方程,然后在一组独立的荷斯坦奶牛、泽西奶牛和杂交奶牛中进行验证。使用 BayesR、BayesRC 和 GBLUP 方法进行预测分析。当将来自荷斯坦奶牛的选定序列标记集添加到 50k SNP 面板中时,荷斯坦奶牛、泽西奶牛和杂交奶牛的耐热性基因组预测的准确性分别提高了 0.07、0.05 和 0.10 个单位。然而,在某些情况下,预测精度意外下降,最大下降为 - 0。当使用来自荷斯坦奶牛的 50k 加预选 SNP 时,在泽西奶牛中观察到的耐热脂肪产量性状为 10 个单位。使用在一组荷斯坦奶牛和泽西奶牛上发现的预选 SNP 通常会提高准确性,特别是在泽西验证中。此外,与仅使用荷斯坦公牛作为参考集相比,在大多数情况下,在参考集中结合荷斯坦公牛和泽西公牛通常会提高预测准确性。可以优先考虑信息序列标记,以改善不同品种耐热性的基因组预测。除了提供生物学见解外,这些变体还可以直接应用于开发定制的 SNP 阵列,或者可以通过插补在当前行业 SNP 面板中使用。使用在一组荷斯坦奶牛和泽西奶牛上发现的预选 SNP 通常会提高准确性,特别是在泽西验证中。此外,与仅使用荷斯坦公牛作为参考集相比,在大多数情况下,在参考集中结合荷斯坦公牛和泽西公牛通常会提高预测准确性。可以优先考虑信息序列标记,以改善不同品种耐热性的基因组预测。除了提供生物学见解外,这些变体还可以直接应用于开发定制的 SNP 阵列,或者可以通过插补在当前行业 SNP 面板中使用。使用在一组荷斯坦奶牛和泽西奶牛上发现的预选 SNP 通常会提高准确性,特别是在泽西验证中。此外,与仅使用荷斯坦公牛作为参考集相比,在大多数情况下,在参考集中结合荷斯坦公牛和泽西公牛通常会提高预测准确性。可以优先考虑信息序列标记,以改善不同品种耐热性的基因组预测。除了提供生物学见解外,这些变体还可以直接应用于开发定制的 SNP 阵列,或者可以通过插补在当前行业 SNP 面板中使用。与仅使用荷斯坦公牛作为参考集相比,在大多数情况下,在参考集中结合荷斯坦公牛和泽西公牛通常会提高预测准确性。可以优先考虑信息序列标记,以改善不同品种耐热性的基因组预测。除了提供生物学见解外,这些变体还可以直接应用于开发定制的 SNP 阵列,或者可以通过插补在当前行业 SNP 面板中使用。与仅使用荷斯坦公牛作为参考集相比,在大多数情况下,在参考集中结合荷斯坦公牛和泽西公牛通常会提高预测准确性。可以优先考虑信息序列标记,以改善不同品种耐热性的基因组预测。除了提供生物学见解外,这些变体还可以直接应用于开发定制的 SNP 阵列,或者可以通过插补在当前行业 SNP 面板中使用。
更新日期:2022-02-21
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