Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast.
Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast.
复制标题
条形码批量QTL定位揭示了酵母复杂性状的高度多基因和上位性结构。
DOI：
10.7554/elife.73983
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发表时间：
2022-02-11
期刊：
影响因子：
7.7
通讯作者：
Desai MM
中科院分区：
文献类型：
--
作者：
Nguyen Ba AN;Lawrence KR;Rego-Costa A;Gopalakrishnan S;Temko D;Michor F;Desai MM
绘制复杂性状的遗传基础对于揭示疾病和其他表型的生物学机制至关重要。人类全基因组关联研究（GWAS）和模式生物中的数量性状基因座（QTL）定位现在可以解释许多性状中观察到的遗传力，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研究中较小的样本量对功效的限制仍然限制了我们解决许多小效应变体的能力，将它们映射到因果基因，识别多个性状之间的多效性效应，并推断基因座之间的非加性相互作用（上位性）。在这里，我们介绍了条形码批量数量性状基因座（BB-QTL）定位，它允许我们构建，基因型和表型100，000个芽殖酵母杂交后代，比以前的技术水平大两个数量级。我们使用这个面板来定位18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密集分布在整个基因组中的小效应基因座，许多基因座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，成千上万的相互作用提供了对遗传网络的洞察。BB-QTL作图通过显著增加样本量，证明了高效能QTL研究中自然变异体的潜力，以揭示复杂性状的高度多基因、多效性和上位性结构。
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.