Identification of nodulation‐related genes in Medicago truncatula using genome‐wide association studies and co‐expression networks

Abstract

Genome‐wide association studies (GWAS) have proven to be a valuable approach for identifying genetic intervals associated with phenotypic variation in Medicago truncatula. These intervals can vary in size, depending on the historical local recombination. Typically, significant intervals span numerous gene models, limiting the ability to resolve high‐confidence candidate genes underlying the trait of interest. Additional genomic data, including gene co‐expression networks, can be combined with the genetic mapping information to successfully identify candidate genes. Co‐expression network analysis provides information about the functional relationships of each gene through its similarity of expression patterns to other well‐defined clusters of genes. In this study, we integrated data from GWAS and co‐expression networks to pinpoint candidate genes that may be associated with nodule‐related phenotypes in M. truncatula. We further investigated a subset of these genes and confirmed that several had existing evidence linking them nodulation, including MEDTR2G101090 (PEN3‐like), a previously validated gene associated with nodule number.

cageminer: an R/Bioconductor package to prioritize candidate genes by integrating GWAS and gene coexpression networks

“…First, most GWAS-derived SNPs are located in non-coding portions of the genome, which can be regulatory regions very far from a causative gene (Peat et al , 2020). Further, causative variants can be in strong linkage disequilibrium (LD) with non-causative ones, leading to large LD blocks with dozens of putative candidates (Michno et al , 2020).…”

“…To address this issue, integrating GWAS with the vast amounts of RNA-seq data in public repositories has become a promising solution, particularly using gene coexpression network (GCN)-based approaches (Michno et al , 2020; Yao et al , 2020; Guo et al , 2020). Currently, the only statistical framework that automates such integration is Camoco, a Python library that identifies sets of densely connected genes for a given sliding window relative to each SNP (Schaefer et al , 2018).…”

“…Currently, the only statistical framework that automates such integration is Camoco, a Python library that identifies sets of densely connected genes for a given sliding window relative to each SNP (Schaefer et al , 2018). However, as sliding windows are expanded ( e.g., 50 kb), Camoco loses the ability to discover candidate genes because of background noise (Michno et al , 2020). This is a major limitation, as SNPs can be up to 2 Mb away from the causative genes if they are in distal regions (Brodie et al , 2016).…”

“…To address this issue, integrating GWAS with the vast amounts of RNA-seq data in public repositories has become a promising solution, particularly using gene coexpression network (GCN)-based approaches (Michno et al, 2020;Yao et al, 2020;Guo et al, 2020).…”

Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

“…Nevertheless, GWAS often fail to accurately pinpoint the causative genes (Baxter, 2020). GWAS limitations are particularly challenging for selfpollinating plants (e.g., soybean) because of limited recombination and strong linkage disequilibrium between causative and non-causative variants (Michno et al, 2020). Such limitations ultimately lead to large genetic intervals with several genes, hindering causative gene identification.…”