Pangenome-based association testing between a structural variant located upstream of the KIT gene and head depigmentation across a diverse panel of cattle breeds

基于泛基因组的关联性检测,分析了位于KIT基因上游的结构变异与不同牛品种头部色素脱失之间的关系。

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Abstract

Coat color variation is a key phenotypic trait in domestic animals. Among the genetic factors involved, the KIT gene has frequently been associated with pigmentation diversity across species. In cattle, spotting or piebald phenotypes have been linked to variation in the genomic region encompassing KIT, but the identification of causal variants was not always possible. This is largely due to the regulatory nature of the underlying variants and the structural complexity of this genomic region, which remains difficult to investigate with linear reference genome-based approaches. In the present study, we used a local pangenome strategy to investigate a genomic region on chromosome 6 encompassing KIT which was recently suggested to be associated with head depigmentation in white-headed cattle breeds. We constructed a 2 Mb pangenome graph encompassing the associated region using 79 assemblies from 20 cattle breeds. Through the evaluation of the coverage at the node level on this pangenome graph, we identified a ~ 7 kb structural variant which was supported by 21 assemblies only from breeds exhibiting a white-headed phenotype. To validate these findings, we aligned 564 short-read sequencing data to a local graph of 30 kb, spanning the structural variant identified, and computed normalized coverage across the region. White-headed cattle breeds consistently exhibited higher coverage values, while color-headed breeds displayed nearly zero coverage. Together, these results confirm the association between a structural variant upstream of KIT with the white-headed phenotype. More broadly, our study demonstrates how targeted local pangenome graphs can efficiently resolve complex structural variants (SVs) with phenotypic impact, offering an interesting and computationally feasible alternative to whole-genome graph approaches.

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