@article{mcneece_gillenwater_li_mian_2021, title={Assessment of soybean test weight among genotypes, environments, agronomic and seed compositional traits}, ISSN={["1435-0645"]}, DOI={10.1002/agj2.20665}, abstractNote={Abstract}, journal={AGRONOMY JOURNAL}, author={McNeece, Brant T. and Gillenwater, Jay H. and Li, Zenglu and Mian, M. A. Rouf}, year={2021}, month={May} }
 @article{gillenwater_mcneece_taliercio_mian_2021, title={QTL mapping of seed protein and oil traits in two recombinant inbred line soybean populations}, ISSN={["1542-7536"]}, DOI={10.1080/15427528.2021.1985028}, abstractNote={ABSTRACT Seed oil and seed protein contents are commercially important components of soybean (Glycine max (L.) Merr.) that are inversely correlated. The objectives of this study were to identify novel quantitative trait loci (QTL) and validate existing QTL associated with seed oil, seed protein, and seed weight in soybean. Two mapping populations, Pop 201 and Pop 202, consisting of 180 and 170 recombinant inbred lines (RILs), respectively, were used in this study. The phenotypic data for each population were collected from four environments. The linkage maps of Pop 201 and Pop 202 consisted of 421 and 416 polymorphic single nucleotide polymorphism (SNP) markers, respectively. Multiple QTL Mapping (MQM) analyses identified a total of 13 QTL for seed oil, 7 QTL for seed protein, and 6 for seed weight (SDWT). QTL for seed oil content not co-located with protein QTL were found on chromosomes 17 and 18 in multiple environments in Pop 201 and Pop 202, respectively. These QTL can be useful in reducing the inverse correlation between seed protein and seed oil contents. Most QTL found in this study are in previously reported genomic regions, and thus provide additional evidence for the stability of those QTL across genetic and environmental backgrounds. The findings of this study provide additional insight into the genetic control of these traits and potentially enable breeders to utilize the QTL-linked SNPs in marker-assisted selection (MAS).}, journal={JOURNAL OF CROP IMPROVEMENT}, author={Gillenwater, Jay H. and McNeece, Brant T. and Taliercio, Earl and Mian, M. A. Rouf}, year={2021}, month={Oct} }