Laboratoire de Recherche en Sciences Végétales (LRSV), Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), Université Paul Sabatier (UPS), Castanet-Tolosan, France.
Institute of Plant Sciences-Paris Saclay (IPS2), Centre National de la Recherche Scientifique, Univ Paris-Sud, Univ Paris-Diderot, Univ d'Evry, Institut National de la Recherche Agronomique, Université Paris-Saclay, 91192, Gif-sur-Yvette, France.
Heredity (Edinb). 2021 Jan;126(1):77-91. doi: 10.1038/s41437-020-0349-1. Epub 2020 Jul 30.
The quest for signatures of selection using single nucleotide polymorphism (SNP) data has proven efficient to uncover genes involved in conserved and/or adaptive molecular functions, but none of the statistical methods were designed to identify interacting alleles as targets of selective processes. Here, we propose a statistical test aimed at detecting epistatic selection, based on a linkage disequilibrium (LD) measure accounting for population structure and heterogeneous relatedness between individuals. SNP-based ([Formula: see text]) and window-based ([Formula: see text]) statistics fit a Student distribution, allowing to test the significance of correlation coefficients. As a proof of concept, we use SNP data from the Medicago truncatula symbiotic legume plant and uncover a previously unknown gene coadaptation between the MtSUNN (Super Numeric Nodule) receptor and the MtCLE02 (CLAVATA3-Like) signaling peptide. We also provide experimental evidence supporting a MtSUNN-dependent negative role of MtCLE02 in symbiotic root nodulation. Using human HGDP-CEPH SNP data, our new statistical test uncovers strong LD between SLC24A5 (skin pigmentation) and EDAR (hairs, teeth, sweat glands development) world-wide, which persists after correction for population structure and relatedness in Central South Asian populations. This result suggests that epistatic selection or coselection could have contributed to the phenotypic make-up in some human populations. Applying this approach to genome-wide SNP data will facilitate the identification of coadapted gene networks in model or non-model organisms.
利用单核苷酸多态性（SNP）数据寻找选择信号的研究已经证明了在发现参与保守和/或适应性分子功能的基因方面是有效的，但没有一种统计方法是专门为识别作为选择过程目标的相互作用等位基因而设计的。在这里，我们提出了一种基于连锁不平衡（LD）度量的统计检验方法，用于检测上位性选择，该方法考虑了群体结构和个体间异质的亲缘关系。基于 SNP 的 ([Formula: see text]) 和基于窗口的 ([Formula: see text]) 统计量符合学生分布，允许检验相关系数的显著性。作为概念验证，我们使用 Medicago truncatula 共生豆科植物的 SNP 数据，揭示了 MtSUNN（超数值结节）受体和 MtCLE02（CLAVATA3-Like）信号肽之间以前未知的基因共适应。我们还提供了支持 MtCLE02 在共生根结瘤中依赖 MtSUNN 的负调控的实验证据。使用人类 HGDP-CEPH SNP 数据，我们的新统计检验揭示了 SLC24A5（皮肤色素沉着）和 EDAR（毛发、牙齿、汗腺发育）之间在全球范围内存在强烈的 LD，在对中南亚人群的群体结构和亲缘关系进行校正后，这种相关性仍然存在。这一结果表明，上位性选择或共选择可能对一些人群的表型构成产生了影响。将这种方法应用于全基因组 SNP 数据将有助于识别模型或非模型生物中的共适应基因网络。
