The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, United Kingdom.
Royal (Dick) School of Veterinary Studies, University of Edinburgh, Roslin, UK.
Sci Data. 2022 Oct 13;9(1):618. doi: 10.1038/s41597-022-01684-w.
Structural variants (SV) have been linked to important bovine disease phenotypes, but due to the difficulty of their accurate detection with standard sequencing approaches, their role in shaping important traits across cattle breeds is largely unexplored. Optical mapping is an alternative approach for mapping SVs that has been shown to have higher sensitivity than DNA sequencing approaches. The aim of this project was to use optical mapping to develop a high-quality database of structural variation across cattle breeds from different geographical regions, to enable further study of SVs in cattle. To do this we generated 100X Bionano optical mapping data for 18 cattle of nine different ancestries, three continents and both cattle sub-species. In total we identified 13,457 SVs, of which 1,200 putatively overlap coding regions. This resource provides a high-quality set of optical mapping-based SV calls that can be used across studies, from validating DNA sequencing-based SV calls to prioritising candidate functional variants in genetic association studies and expanding our understanding of the role of SVs in cattle evolution.
结构变异（SV）与重要的牛疾病表型有关，但由于标准测序方法难以准确检测它们，因此它们在塑造不同牛品种重要特征方面的作用在很大程度上仍未得到探索。光学作图是一种用于绘制 SV 的替代方法，已被证明比 DNA 测序方法具有更高的灵敏度。本项目的目的是使用光学作图技术在来自不同地理区域的不同牛品种中开发高质量的结构变异数据库，以进一步研究牛中的 SV。为此，我们为来自三个大洲和两个牛亚种的 9 个不同祖先的 18 头牛生成了 100X Bionano 光学作图数据。总共鉴定出 13457 个 SV，其中 1200 个可能重叠编码区域。该资源提供了一组高质量的基于光学作图的 SV 调用，可用于各种研究，从验证基于 DNA 测序的 SV 调用，到优先考虑遗传关联研究中的候选功能变体，并扩展我们对 SV 在牛进化中的作用的理解。
