Andrea Dixon1,2, David Comont1, Gancho T Slavov1,3, Paul Neve1,4. 1. Rothamsted Research, West Common, Harpenden, Hertfordshire, UK. 2. College of Veterinary Medicine, Kansas State University, Manhattan, KS, USA. 3. Scion, Rotorua, New Zealand. 4. Agriculture & Horticulture Development Board, Warwickshire, UK.
Abstract
BACKGROUND: Alopecurus myosuroides (blackgrass) is a major weed in Europe with known resistance to multiple herbicide modes of action. In the UK, there is evidence that blackgrass has undergone a range expansion. In this paper, genotyping-by-sequencing and population-level herbicide resistance phenotypes are used to explore spatial patterns of selectively neutral genetic variation and resistance. We also perform a preliminary genome-wide association study (GWAS) and genomic prediction analysis to evaluate the potential of these approaches for investigating nontarget site herbicide resistance. RESULTS: Blackgrass was collected from 47 fields across the British Isles and up to eight plants per field population (n = 369) were genotyped by Restriction site-associated DNA (RAD)-sequencing. A total of 20 426 polymorphic loci were identified and used for population genetic analyses. Phenotypic assays revealed significant variation in herbicide resistance between populations. Population structure was weak (FST = 0.024-0.048), but spatial patterns were consistent with an ongoing westward and northward range expansion. We detected strong and consistent Wahlund effects (FIS = 0.30). There were no spatial patterns of herbicide resistance or evidence for confounding with population structure. Using a combination of population-level GWAS and genomic prediction we found that the top 20, 200, and 2000 GWAS loci had higher predictive abilities for fenoxaprop resistance compared to all markers. CONCLUSION: There is likely extensive human-mediated gene flow between field populations of the weed blackgrass at a national scale. The lack of confounding of adaptive and neutral genetic variation can enable future, more extensive GWAS analyses to identify the genetic architecture of evolved herbicide resistance.
BACKGROUND:Alopecurus myosuroides (blackgrass) is a major weed in Europe with known resistance to multiple herbicide modes of action. In the UK, there is evidence that blackgrass has undergone a range expansion. In this paper, genotyping-by-sequencing and population-level herbicide resistance phenotypes are used to explore spatial patterns of selectively neutral genetic variation and resistance. We also perform a preliminary genome-wide association study (GWAS) and genomic prediction analysis to evaluate the potential of these approaches for investigating nontarget site herbicide resistance. RESULTS: Blackgrass was collected from 47 fields across the British Isles and up to eight plants per field population (n = 369) were genotyped by Restriction site-associated DNA (RAD)-sequencing. A total of 20 426 polymorphic loci were identified and used for population genetic analyses. Phenotypic assays revealed significant variation in herbicide resistance between populations. Population structure was weak (FST = 0.024-0.048), but spatial patterns were consistent with an ongoing westward and northward range expansion. We detected strong and consistent Wahlund effects (FIS = 0.30). There were no spatial patterns of herbicide resistance or evidence for confounding with population structure. Using a combination of population-level GWAS and genomic prediction we found that the top 20, 200, and 2000 GWAS loci had higher predictive abilities for fenoxaprop resistance compared to all markers. CONCLUSION: There is likely extensive human-mediated gene flow between field populations of the weed blackgrass at a national scale. The lack of confounding of adaptive and neutral genetic variation can enable future, more extensive GWAS analyses to identify the genetic architecture of evolved herbicide resistance.
Authors: Anna Wenda-Piesik; Agnieszka Synowiec; Katarzyna Marcinkowska; Barbara Wrzesińska; Cezary Podsiadło; Krzysztof Domaradzki; Piotr Kuc; Ewa Kwiecińska-Poppe Journal: Sci Rep Date: 2022-05-24 Impact factor: 4.996
Authors: Karl Ravet; Crystal D Sparks; Andrea L Dixon; Anita Küpper; Eric P Westra; Dean J Pettinga; Patrick J Tranel; Joel Felix; Don W Morishita; Prashant Jha; Andrew Kniss; Phillip W Stahlman; Paul Neve; Eric L Patterson; Philip Westra; Todd A Gaines Journal: Mol Ecol Date: 2021-10-21 Impact factor: 6.622
Authors: David Comont; Dana R MacGregor; Laura Crook; Richard Hull; Lieselot Nguyen; Robert P Freckleton; Dylan Z Childs; Paul Neve Journal: Pest Manag Sci Date: 2022-05-09 Impact factor: 4.462