Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery
Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery
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DOI：
10.1186/s13059-020-02105-0
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发表时间：
2020-07-27
期刊：
影响因子：
12.3
通讯作者：
Pausch, Hubert
中科院分区：
文献类型：
--
作者：
Crysnanto, Danang;Pausch, Hubert
背景目前的牛基因组参考序列是从赫里福德牛组装的。所得线性组装缺乏多样性，因为它不包含等位基因变异，这是线性参考的缺点，会导致参考等位基因偏倚。高核苷酸多样性和数百个品种的个体分离使牛非常适合研究变异感知参考的最佳组成。结果：我们用奶牛（瑞士褐牛、荷斯坦牛）和两用牛（弗莱克维牛、原始布朗维牛）品种的等位基因频率筛选的变体扩增牛线性参考序列（ARS-UCD 1.2），使用thevg工具包构建品种特异性或泛基因组参考图。我们发现，如果使用预先选择的变体来构建基因组图，则读段映射对于变异感知比线性参考更准确。包含随机变体的图不会改善线性参考序列上的读段映射。品种特异性扩增和泛基因组图使得与线性参考相比几乎相似的映射准确性提高。我们构建了一个全基因组图，其中包含赫里福德为基础的参考序列和1400万个等位基因，在布朗瑞士牛品种的交替等位基因频率大于0.03。我们的新型变异感知参考有助于SNP和Indel的准确读取映射和无偏序列变异基因分型。结论我们开发了第一个农业动物的变异感知参考图（10.5281/zenodo.3759712）。我们的新型参考结构改进了线性参考的序列读段映射和变体基因分型。我们的工作是第一步，从线性过渡到变异意识的参考结构的物种具有较高的遗传多样性和许多亚种群。
Background The current bovine genomic reference sequence was assembled from a Hereford cow. The resulting linear assembly lacks diversity because it does not contain allelic variation, a drawback of linear references that causes reference allele bias. High nucleotide diversity and the separation of individuals by hundreds of breeds make cattle ideally suited to investigate the optimal composition of variation-aware references. Results We augment the bovine linear reference sequence (ARS-UCD1.2) with variants filtered for allele frequency in dairy (Brown Swiss, Holstein) and dual-purpose (Fleckvieh, Original Braunvieh) cattle breeds to construct either breed-specific or pan-genome reference graphs using thevg toolkit. We find that read mapping is more accurate to variation-aware than linear references if pre-selected variants are used to construct the genome graphs. Graphs that contain random variants do not improve read mapping over the linear reference sequence. Breed-specific augmented and pan-genome graphs enable almost similar mapping accuracy improvements over the linear reference. We construct a whole-genome graph that contains the Hereford-based reference sequence and 14 million alleles that have alternate allele frequency greater than 0.03 in the Brown Swiss cattle breed. Our novel variation-aware reference facilitates accurate read mapping and unbiased sequence variant genotyping for SNPs and Indels. Conclusions We develop the first variation-aware reference graph for an agricultural animal (10.5281/zenodo.3759712). Our novel reference structure improves sequence read mapping and variant genotyping over the linear reference. Our work is a first step towards the transition from linear to variation-aware reference structures in species with high genetic diversity and many sub-populations.