AUTHOR=Chamarthi Siva K. , Purcell Larry C. , Fritschi Felix B. , Ray Jeffery D. , Smith James R. , Kaler Avjinder S. , King C. Andy , Gillman Jason D. TITLE=Association mapping for water use efficiency in soybean identifies previously reported and novel loci and permits genomic prediction JOURNAL=Frontiers in Plant Science VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1486736 DOI=10.3389/fpls.2024.1486736 ISSN=1664-462X ABSTRACT=

Soybean is a major legume crop cultivated globally due to the high quality and quantity of its seed protein and oil. However, drought stress is the most significant factor that decreases soybean yield, and more than 90% of US soybean acreage is dependent on rainfall. Water use efficiency (WUE) is positively correlated with the carbon isotopic ratio 13C/12C (C13 ratio) and selecting soybean varieties for high C13 ratio may enhance WUE and help improve tolerance to drought. Our study objective was to identify genetic loci associated with C13 ratio using a diverse set of 205 soybean maturity group IV accessions, and to examine the genomic prediction accuracy of C13 ratio across a range of environments. An accession panel was grown and assessed across seven distinct combinations of site, year and treatment, with five site-years under irrigation and two site-years under drought stress. Genome-wide association mapping (GWAM) analysis identified 103 significant single nucleotide polymorphisms (SNPs) representing 93 loci associated with alterations to C13 ratio. Out of these 93 loci, 62 loci coincided with previous studies, and 31 were novel. Regions tagged by 96 significant SNPs overlapped with 550 candidate genes involved in plant stress responses. These confirmed genomic loci could serve as a valuable resource for marker-assisted selection to enhance WUE and drought tolerance in soybean. This study also demonstrated that genomic prediction can accurately predict C13 ratio across different genotypes and environments and by examining only significant SNPs identified by GWAM analysis, higher prediction accuracies (P ≤ 0.05; 0.51 ≤ r ≤ 0.65) were observed. We generated genomic estimated breeding values for each genotype in the entire USDA-GRIN germplasm collection for which there was marker data. This information was used to identify the top ten extreme genotypes for each soybean maturity group, which could serve as valuable genetic and physiological resources for future breeding and physiological studies.