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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Plant Physiology and Genetics Research » Research » Publications at this Location » Publication #355123
Title: Genetic associations in four decades of multienvironment trials reveal agronomic trait evolution in common bean

Author
item MACQUEEN, ALICE - University Of Texas
item White, Jeffrey
item LEE, RIAN - North Dakota State University
item OSORNO, JUAN - North Dakota State University
item SCHMUTZ, JEREMY - Hudsonalpha Institute For Biotechnology
item MIKLAS, PHILLIP - North Dakota State University
item MYERS, JAMES - Oregon State University
item MCLEAN, PHILLIP - North Dakota State University
item JUENGER, THOMAS - University Of Texas

Submitted to: Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/12/2020
Publication Date: 3/23/2020
Citation: MacQueen, A., White, J.W., Lee, R., Osorno, J., Schmutz, J., Miklas, P., Myers, J.R., McClean, P., Juenger, T. 2020. Genetic associations in four decades of multienvironment trials reveal agronomic trait evolution in common bean. Genetics. 215(1):267-284. https://doi.org/10.1534/genetics.120.303038.
DOI: https://doi.org/10.1534/genetics.120.303038

Interpretive Summary: Research programs seeking to breed improved cultivars often expend large amounts of resources on multienvironment field trials (METs). The volume of data obtained from METs far exceeds what might be duplicated in trials conducted solely for genetic research. Thus, METs may provide valuable insights into how crop genetics change under the strong artificial selection imposed through plant breeding. Here, we characterized the genetics of breeding lines tested in the Cooperative Dry Bean Nursery (CDBN), a MET for common bean (Phaseolus vulgaris) that has been conducted since the 1950s. The multi-decadal nature of the CDBN dataset allowed us to track the introduction and effects of genomic regions through time. Examining 22 agronomically important traits, the analyses identified multiple genomic regions associated with crop traits that had changed since the 1970s. A region on chromosome one was associated with five phenotypes related to vegetative or reproductive growth, and a region on chromosome eleven was associated with six phenotypes related to disease resistance. The region on chromosome one included a novel candidate that may improve yield in large-seeded Andean bean types. These results demonstrate the value of retrospective analyses of METs and for bean, suggest genomic regions that merit further study based on their associations with agronomically important traits including seed yield.

Technical Abstract: Multienvironment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET data sets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean (Phaseolus vulgaris) grown for . 70 years in the United States and Canada, consisting of 20–50 entries each year at 10–20 locations. The CDBN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association studies (GWAS) using best linear unbiased predictions derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2 million SNPs) for 327 CDBN genotypes. The value of this approach was con'rmed by the discovery of three candidate genes and genomic regions previously identi'ed in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect signi'cant correlated effects, found signi'cant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover signi'cant genetic effects and to de'ne genomic regions associated with crop improvement.