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条码化 bulk QTL 作图揭示了酵母中复杂性状的高度多基因和上位性结构。

Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast.

机构信息

Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States.

NSF-Simons Center for Mathematical and Statistical Analysis of Biology, Harvard University, Cambridge, United States.

出版信息

Elife. 2022 Feb 11;11:e73983. doi: 10.7554/eLife.73983.

Abstract

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.

摘要

解析复杂性状的遗传基础对于揭示疾病和其他表型背后的生物学机制至关重要。人类全基因组关联研究(GWAS)和模式生物的数量性状位点(QTL)作图现在可以解释许多性状中大部分可观察到的遗传率,使我们能够从基因型预测表型。然而,由于大型 GWAS 中的统计混杂因素和 QTL 研究中的样本量较小,对功效的限制仍然限制了我们解析众多小效应变体、将它们映射到因果基因、识别多个性状中的多效性效应以及推断基因座之间非加性相互作用(上位性)的能力。在这里,我们引入了带有条形码的批量数量性状位点(BB-QTL)作图,它使我们能够构建、基因分型并表型分析酵母杂交的 100,000 个后代,这比以前的技术水平大两个数量级。我们使用这个面板来映射十八个复杂性状的遗传基础,发现这些性状的遗传结构涉及数百个小效应位点,这些位点在基因组中密集分布,许多位点在多个性状中具有广泛的多效性效应。上位性起着核心作用,有成千上万的相互作用提供了对遗传网络的深入了解。通过显著增加样本量,BB-QTL 作图展示了在高功效 QTL 研究中利用自然变体揭示复杂性状高度多基因、多效性和上位性结构的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d578/8979589/bd5f94d3da6b/elife-73983-fig1.jpg

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