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低覆盖度全基因组测序有助于在复杂的小鼠杂交中进行准确且经济高效的单倍型重建。

Low-coverage whole-genome sequencing facilitates accurate and cost-effective haplotype reconstruction in complex mouse crosses.

作者信息

Widmayer Samuel J, Wooldridge Lydia K, Swanzey Emily, Barter Mary, Snow Chrystal, Saul Michael, Meng Qingchang, Dumont Beth, Reinholdt Laura, Gatti Daniel M

机构信息

The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA.

The Jackson Laboratory, 600 Main St, Bar Harbor, ME, USA.

出版信息

Mamm Genome. 2025 Jul 1. doi: 10.1007/s00335-025-10148-6.

Abstract

The search for the underlying genetic contributions to complex traits and diseases relies on accurate genetic data from populations of interest. Outbred populations, like the Diversity Outbred (DO), are commonly genotyped using commercial SNP arrays, such as the Giga Mouse Universal Genotyping Array (GigaMUGA). However, array genotypes are expensive to collect, subject to significant ascertainment bias, and too sparse to capture the genetic structure of highly recombined mouse crosses. We investigated the efficacy of sequencing-based genotyping by comparing genotyping results between the GigaMUGA, double-digest restriction-site associated DNA sequencing (ddRADseq), and low-coverage whole-genome sequencing (lcWGS). We aligned reads at ~ 1× coverage and imputed segregating SNPs from the eight DO founder strains onto 48 DO genomes and reconstructed their haplotypes using R/qtl2. Haplotype reconstructions derived from all three methods were highly concordant. However, lcWGS more faithfully recapitulated crossover counts and identified more small (< 1 Mb) haplotype blocks at as low as 0.1× coverage. Over 90% of local expression quantitative trait loci identified in a set of 183 DO-derived embryoid bodies using the GigaMUGA were recalled by lcWGS at coverages as low as 0.1×. We recommend that lcWGS be adopted as the primary method of genotyping complex crosses, and cell-based resources derived from them because they are as accurate as array-based reconstructions, robust to ultra-low sequencing depths, may more accurately model haplotypes of the mouse genome that are difficult to resolve with dense reference data, and cost-effective.

摘要

对复杂性状和疾病潜在遗传贡献的研究依赖于来自目标人群的准确遗传数据。远交群体,如多样性远交系(DO),通常使用商业SNP芯片进行基因分型,如千兆小鼠通用基因分型芯片(GigaMUGA)。然而,芯片基因型的收集成本高昂,存在显著的确定偏差,且过于稀疏,无法捕捉高度重组小鼠杂交的遗传结构。我们通过比较GigaMUGA、双酶切限制性位点关联DNA测序(ddRADseq)和低覆盖度全基因组测序(lcWGS)的基因分型结果,研究了基于测序的基因分型的效果。我们将约1×覆盖度的 reads 进行比对,并将来自八个DO创始菌株的分离SNP 推算到48个DO基因组上,并使用R/qtl2重建它们的单倍型。从所有三种方法获得的单倍型重建高度一致。然而,lcWGS更忠实地概括了交叉计数,并在低至0.1×覆盖度时识别出更多小的(<1 Mb)单倍型块。在一组使用GigaMUGA的183个DO衍生的胚状体中鉴定出的超过90%的局部表达数量性状位点,在低至0.1×覆盖度时可被lcWGS召回。我们建议采用lcWGS作为对复杂杂交进行基因分型的主要方法,以及源自它们的基于细胞的资源,因为它们与基于芯片的重建一样准确,对超低测序深度具有鲁棒性,可能更准确地模拟难以用密集参考数据解析的小鼠基因组单倍型,并且具有成本效益。

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