The genetic architecture and genomic context of glyphosate resistance inAmaranthus tuberculatus

Abstract

Although much of what we know about the genetic basis of herbicide resistance has come from detailed investigations of monogenic adaptation at known target-sites, the importance of polygenic resistance has been increasingly recognized. Despite this, little work has been done to characterize the genomic basis of herbicide resistance, including the number and distribution of involved genes, their effect sizes, allele frequencies, and signatures of selection. Here we implement genome-wide association (GWA) and population genomic approaches to examine the genetic architecture of glyphosate resistance in the problematic agricultural weed, Amaranthus tuberculatus. GWA correctly identifies the gene targeted by glyphosate, and additionally finds more than 100 genes across all 16 chromosomes associated with resistance. The encoded proteins have relevant non-target-site resistance and stress-related functions, with potential for pleiotropic roles in resistance to other herbicides and diverse life history traits. Resistance-related alleles are enriched for large effects and intermediate frequencies, implying that strong selection has shaped the genetic architecture of resistance despite potential pleiotropic costs. The range of common and rare allele involvement implies a partially shared genetic basis of non-target-site resistance across populations, complemented by population-specific alleles. Resistance-related alleles show evidence of balancing selection, and suggest a long-term maintenance of standing variation at stress-response loci that have implications for plant performance under herbicide pressure. By our estimates, genome-wide SNPs explain a near comparable amount of the total variation in glyphosate resistance to monogenic mechanisms, indicating the potential for an underappreciated polygenic contribution to the evolution of herbicide resistance in weed populations.

Non-target-Site Resistance in Lolium spp. Globally: A Review

“…Limited weed science literature is available where GWAS was used to detect NTSR. Kreiner et al (2020) conducted a GWAS in glyphosate-resistant A. tuberculatus that exhibited increased EPSPS duplication in the majority of the populations tested and, as expected, found that the genomic regions containing the EPSPS coding sequences were related to the resistance phenotype. The authors also found > 100 genes across the weed genome involved in the glyphosate resistance, and were identified as involved in stressresponse and NTSR.…”

Genetic architecture underlying HPPD‐inhibitor resistance in a Nebraska Amaranthus tuberculatus population

“…Efforts to investigate the evolutionary origins of herbicide resistance have utilized population genomics screens in unstructured populations. 19,39,40 In the case of Ipomoea purpurea L. Roth, five genomic regions under selection to glyphosate were identified through the comparison of 80 samples from eight populations, while Kreiner et al investigated 162 samples across 21 collection sites and several natural areas. 19 In each case, the genetic architecture underlying the resistance traits was complex, and numerous candidate genes were proposed.…”

Repeated origins, gene flow, and allelic interactions of herbicide resistance mutations in a widespread agricultural weed

“…Population genomic approaches can greatly help to understand the origin and spread of herbicide resistance. Genomic methods have tested for differences in population structure among resistant and susceptible agricultural populations (Kuester et al, 2017), reconstructed complex genomic regions associated with resistance (Molin et al, 2017), and investigated patterns of selection on and the extent of convergence between loci conferring non-target site resistance (Kreiner et al, 2021;Van Etten et al, 2019). But even for validated resistance mutations that occur within the gene whose product is targeted by the herbicide (target-site resistance [TSR] mutations), investigations of their recent evolutionary history are sparse (but see Flood et al (2016); Kreiner et al (2019)).…”