University of Zürich, Department of Evolutionary Biology and Environmental Studies, Zürich, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
PLoS Comput Biol. 2020 Aug 13;16(8):e1008082. doi: 10.1371/journal.pcbi.1008082. eCollection 2020 Aug.
We study the genotype-phenotype maps of 80 quantitative phenotypes in the model plant Arabidopsis thaliana, by representing the genotypes affecting each phenotype as a genotype network. In such a network, each vertex or node corresponds to an individual's genotype at all those genomic loci that affect a given phenotype. Two vertices are connected by an edge if the associated genotypes differ in exactly one nucleotide. The 80 genotype networks we analyze are based on data from genome-wide association studies of 199 A. thaliana accessions. They form connected graphs whose topography differs substantially among phenotypes. We focus our analysis on the incidence of epistasis (non-additive interactions among mutations) because a high incidence of epistasis can reduce the accessibility of evolutionary paths towards high or low phenotypic values. We find epistatic interactions in 67 phenotypes, and in 51 phenotypes every pairwise mutant interaction is epistatic. Moreover, we find phenotype-specific differences in the fraction of accessible mutational paths to maximum phenotypic values. However, even though epistasis affects the accessibility of maximum phenotypic values, the relationships between genotypic and phenotypic change of our analyzed phenotypes are sufficiently smooth that some evolutionary paths remain accessible for most phenotypes, even where epistasis is pervasive. The genotype network representation we use can complement existing approaches to understand the genetic architecture of polygenic traits in many different organisms.
我们通过将影响每个表型的基因型表示为基因型网络,研究了模式植物拟南芥中 80 个定量表型的基因型-表型图谱。在这样的网络中,每个顶点或节点对应于在影响给定表型的所有基因组位点上具有个体基因型的个体。如果相关基因型在一个核苷酸上恰好不同,则两个顶点通过边连接。我们分析的 80 个基因型网络基于 199 个拟南芥品系的全基因组关联研究数据。它们形成了连接的图,其拓扑结构在表型之间有很大的不同。我们将分析重点放在上位性(突变之间的非加性相互作用)的发生率上,因为上位性的高发率会降低进化路径到达高或低表型值的可及性。我们在 67 个表型中发现了上位性相互作用,在 51 个表型中,每个双突变相互作用都是上位性的。此外,我们还发现了可达最大表型值的突变路径的可及分数在表型之间存在差异。然而,即使上位性影响了最大表型值的可及性,但我们分析的表型的基因型和表型变化之间的关系足够平滑,即使上位性普遍存在,大多数表型仍有一些进化路径是可及的。我们使用的基因型网络表示可以补充现有的方法,以理解许多不同生物体中多基因性状的遗传结构。