Charles Mathieu, Gaiani Nicolas, Sanchez Marie-Pierre, Boussaha Mekki, Hozé Chris, Boichard Didier, Rocha Dominique, Boulling Arnaud
Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.
INRAE, SIGENAE, 78350, Jouy-en-Josas, France.
Nat Commun. 2025 Apr 24;16(1):3893. doi: 10.1038/s41467-025-58970-5.
GWAS conducted directly on imputed whole genome sequence have led to the identification of numerous genetic variants associated with agronomic traits in cattle. However, such variants are often simply markers in linkage disequilibrium with the actual causal variants, which is a limiting factor for the development of accurate genomic predictions. It is possible to identify causal variants by integrating information on how variants impact gene expression into GWAS output. RNA splicing plays a major role in regulating gene expression. Thus, assessing the effect of variants on RNA splicing may explain their function. Here, we use a high-throughput strategy to functionally analyse putative splice-disrupting variants in the bovine genome. Using GWAS, massively parallel reporter assay and deep learning algorithms designed to predict splice-disrupting variants, we identify 38 splice-disrupting variants associated with complex traits in cattle, three of which could be classified as causal. Our results indicate that splice-disrupting variants are widely found in the quantitative trait loci related to these phenotypes. Using our combined approach, we also assess the validity of splicing predictors originally developed to analyse human variants in the context of the bovine genome.
直接对推算的全基因组序列进行的全基因组关联研究（GWAS）已导致鉴定出许多与牛农艺性状相关的遗传变异。然而，这些变异通常只是与实际因果变异处于连锁不平衡状态的标记，这是准确基因组预测发展的一个限制因素。通过将变异如何影响基因表达的信息整合到GWAS输出中，可以鉴定出因果变异。RNA剪接在调节基因表达中起主要作用。因此，评估变异对RNA剪接的影响可能解释它们的功能。在这里，我们使用一种高通量策略对牛基因组中假定的剪接破坏变异进行功能分析。利用GWAS、大规模平行报告基因检测和旨在预测剪接破坏变异的深度学习算法，我们鉴定出38个与牛复杂性状相关的剪接破坏变异，其中三个可被归类为因果变异。我们的结果表明，剪接破坏变异在与这些表型相关的数量性状位点中广泛存在。使用我们的联合方法，我们还评估了最初为分析人类变异而开发的剪接预测器在牛基因组背景下的有效性。
