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下一代 GWAS:全二维上位性互作图谱可获取部分缺失的遗传力并提高表型预测能力。

Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction.

机构信息

BionomeeX, Montpellier, France.

IMAG, Univ. Montpellier, CNRS, Montpellier, France.

出版信息

Genome Biol. 2024 Mar 25;25(1):76. doi: 10.1186/s13059-024-03202-0.

Abstract

The problem of missing heritability requires the consideration of genetic interactions among different loci, called epistasis. Current GWAS statistical models require years to assess the entire combinatorial epistatic space for a single phenotype. We propose Next-Gen GWAS (NGG) that evaluates over 60 billion single nucleotide polymorphism combinatorial first-order interactions within hours. We apply NGG to Arabidopsis thaliana providing two-dimensional epistatic maps at gene resolution. We demonstrate on several phenotypes that a large proportion of the missing heritability can be retrieved, that it indeed lies in epistatic interactions, and that it can be used to improve phenotype prediction.

摘要

遗传力缺失问题需要考虑不同基因座之间的遗传相互作用,称为上位性。目前的 GWAS 统计模型需要数年时间来评估单个表型的整个组合上位性空间。我们提出了 Next-Gen GWAS(NGG),它可以在数小时内评估超过 600 亿个单核苷酸多态性组合一阶相互作用。我们将 NGG 应用于拟南芥,提供了基因分辨率的二维上位性图谱。我们在几个表型上证明了很大一部分遗传力缺失可以被找回,它确实存在于上位性相互作用中,并且可以用于改善表型预测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d5b/10962106/fb782b59c693/13059_2024_3202_Fig1_HTML.jpg

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