@article{daba_tyagi_brown-guedira_mohammadi_2020, title={Genome-wide association study in historical and contemporary U.S. winter wheats identifies height-reducing loci}, volume={8}, ISSN={["2214-5141"]}, DOI={10.1016/j.cj.2019.09.005}, abstractNote={Plant height has been a major target for selection of high-yielding varieties in wheat. Two height-reducing loci (Rht-B1 and Rht-D1) have been widely used since the Green Revolution. However, these genes also negatively affect other agronomic traits such as kernel weight. Identifying alternative height-reducing loci could benefit wheat improvement. This study focused on the genetics of plant height in 260 historical and contemporary winter wheat accessions via genome-wide association studies using 38,693 single nucleotide polymorphism (SNP) markers generated through genotyping by sequencing, two Kompetitive Allele Specific Polymorphism markers, and phenotypic data recorded in two seasons (2016 and 2018). The 260 accessions showed wide variation in plant height. Most accessions developed after 1960 were shorter than earlier accessions. The broad-sense heritability for plant height was high (H2 = 0.82), which was also supported by a high correlation (r = 0.82, P < 0.0001) between heights from the two years. We detected a total of 16 marker–trait associations (MTAs) for plant height at –lg (P) ≥ 4.0 on chromosomes 1A, 2B, 2D, 3B, 4D, 5A, 5D, 6A, 6B, 7A, and 7D. We detected three of the MTAs (QPLH-2D, QPLH-4B.2, and QPLH-4D) consistently in individual-year and combined-year analyses. These MTAs individually explained 10%–16% of phenotypic variation. The height-reducing alleles at these three MTAs appeared after 1960 and increased in frequency thereafter. Among the genes near these loci were gibberellic acid insensitive (GAI) and GRAS transcription factor (GIBBERELLIC-ACID INSENSITIVE (GAI), REPRESSOR of GAI (RGA), and SCARECROW (SCR)). The evidence from this study and previous reports suggests that QPLH-2D is Rht8. A gene encoding a GRAS transcription factor is located near QPLH-2D. Validation of the QPLH-2D locus and associated candidate genes awaits further study.}, number={2}, journal={CROP JOURNAL}, author={Daba, Sintayehu D. and Tyagi, Priyanka and Brown-Guedira, Gina and Mohammadi, Mohsen}, year={2020}, month={Apr}, pages={243–251} }
 @article{dewitt_guedira_lauer_sarinelli_tyagi_fu_hao_murphy_marshall_akhunova_et al._2020, title={Sequence-based mapping identifies a candidate transcription repressor underlying awn suppression at the B1 locus in wheat}, volume={225}, ISSN={["1469-8137"]}, DOI={10.1111/nph.16152}, abstractNote={Summary}, number={1}, journal={NEW PHYTOLOGIST}, author={DeWitt, Noah and Guedira, Mohammed and Lauer, Edwin and Sarinelli, Martin and Tyagi, Priyanka and Fu, Daolin and Hao, QunQun and Murphy, J. Paul and Marshall, David and Akhunova, Alina and et al.}, year={2020}, month={Jan}, pages={326–339} }
 @article{hemshrot_poets_tyagi_lei_carter_hirsch_li_brown-guedira_morrell_muehlbauer_et al._2019, title={Development of a Multiparent Population for Genetic Mapping and Allele Discovery in Six-Row Barley}, volume={213}, ISSN={["1943-2631"]}, DOI={10.1534/genetics.119.302046}, abstractNote={Abstract}, number={2}, journal={GENETICS}, author={Hemshrot, Alex and Poets, Ana M. and Tyagi, Priyanka and Lei, Li and Carter, Corey K. and Hirsch, Candice N. and Li, Lin and Brown-Guedira, Gina and Morrell, Peter L. and Muehlbauer, Gary J. and et al.}, year={2019}, month={Oct}, pages={595–613} }
 @article{ward_brown-guedira_kolb_van sanford_tyagi_sneller_griffey_2019, title={Genome-wide association studies for yield-related traits in soft red winter wheat grown in Virginia}, volume={14}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0208217}, abstractNote={Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and increasing affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 322 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship estimation multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate a number of promising candidate genes contributing to grain yield, including an ortholog of the rice aberrant panicle organization (APO1) protein and a gibberellin oxidase protein (GA2ox-A1) affecting the trait grains per square meter, an ortholog of the Arabidopsis thaliana mother of flowering time and terminal flowering 1 (MFT) gene affecting the trait seeds per square meter, and a B2 heat stress response protein affecting the trait seeds per head.}, number={2}, journal={PLOS ONE}, author={Ward, Brian P. and Brown-Guedira, Gina and Kolb, Frederic L. and Van Sanford, David A. and Tyagi, Priyanka and Sneller, Clay H. and Griffey, Carl A.}, year={2019}, month={Feb} }
 @article{beyer_daba_tyagi_bockelman_brown-guedira_mohammadi_2019, title={Loci and candidate genes controlling root traits in wheat seedlingsa wheat root GWAS}, volume={19}, ISSN={["1438-7948"]}, DOI={10.1007/s10142-018-0630-z}, abstractNote={Two hundred one hexaploid wheat accessions, representing 200 years of selection and breeding history, were sampled from the National Small Grains Collection in Aberdeen, ID, and evaluated for five root traits at the seedling stage. A paper roll-supported hydroponic system was used for seedling growth. Replicated roots samples were analyzed by WinRHIZO. We observed accessions with nearly no branching and accessions with up to 132 cm of branching. Total seminal root length ranged from 70 to 248 cm, a 3.5-fold difference. Next-generation sequencing was used to produce single-nucleotide polymorphism (SNP) markers and genomic libraries that were aligned to the wheat reference genome IWGSCv1 and were called single-nucleotide polymorphism (SNP) markers. After filtering and imputation, a total of 20,881 polymorphic sites were used to perform association mapping in TASSEL. Gene annotations were conducted for identified marker-trait associations (MTAs) with - log 10 P > 3.5 (p value < 0.003). In total, we identified 63 MTAs with seven for seminal axis root length (SAR), 24 for branching (BR), four for total seminal root length (TSR), eight for root dry matter (RDM), and 20 for root diameter (RD). Putative proteins of interest that we identified include chalcone synthase, aquaporin, and chymotrypsin inhibitor for SAR, MYB transcription factor and peroxidase for BR, zinc fingers and amino acid transporters for RDM, and cinnamoyl-CoA reductase for RD. We evaluated the effects of height-reducing Rht alleles and the 1B/1R translocation event on root traits and found presence of the Rht-B1b allele decreased RDM, while presence of the Rht-D1b allele increased TSR and decreased RD.}, number={1}, journal={FUNCTIONAL & INTEGRATIVE GENOMICS}, author={Beyer, Savannah and Daba, Sintayehu and Tyagi, Priyanka and Bockelman, Harold and Brown-Guedira, Gina and Mohammadi, Mohsen}, year={2019}, month={Jan}, pages={91–107} }
 @article{ward_brown-guedira_tyagi_kolb_van sanford_sneller_griffey_2019, title={Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early-Generation Wheat Yield Trials}, volume={59}, ISSN={["1435-0653"]}, DOI={10.2135/cropsci2018.03.0189}, abstractNote={The majority of studies evaluating genomic selection (GS) for plant breeding have used single‐trait, single‐site models that ignore genotype × environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study's goal was to test GS methods for prediction in scenarios that simulate early‐generation yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across‐environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low‐heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments.}, number={2}, journal={CROP SCIENCE}, author={Ward, Brian P. and Brown-Guedira, Gina and Tyagi, Priyanka and Kolb, Frederic L. and Van Sanford, David A. and Sneller, Clay H. and Griffey, Carl A.}, year={2019}, pages={491–507} }
 @article{sarinelli_murphy_tyagi_holland_johnson_mergoum_mason_babar_harrison_sutton_et al._2019, title={Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel}, volume={132}, ISSN={["1432-2242"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85060724945&partnerID=MN8TOARS}, DOI={10.1007/s00122-019-03276-6}, abstractNote={The optimization of training populations and the use of diagnostic markers as fixed effects increase the predictive ability of genomic prediction models in a cooperative wheat breeding panel. Plant breeding programs often have access to a large amount of historical data that is highly unbalanced, particularly across years. This study examined approaches to utilize these data sets as training populations to integrate genomic selection into existing pipelines. We used cross-validation to evaluate predictive ability in an unbalanced data set of 467 winter wheat (Triticum aestivum L.) genotypes evaluated in the Gulf Atlantic Wheat Nursery from 2008 to 2016. We evaluated the impact of different training population sizes and training population selection methods (Random, Clustering, PEVmean and PEVmean1) on predictive ability. We also evaluated inclusion of markers associated with major genes as fixed effects in prediction models for heading date, plant height, and resistance to powdery mildew (caused by Blumeria graminis f. sp. tritici). Increases in predictive ability as the size of the training population increased were more evident for Random and Clustering training population selection methods than for PEVmean and PEVmean1. The selection methods based on minimization of the prediction error variance (PEV) outperformed the Random and Clustering methods across all the population sizes. Major genes added as fixed effects always improved model predictive ability, with the greatest gains coming from combinations of multiple genes. Maximum predictabilities among all prediction methods were 0.64 for grain yield, 0.56 for test weight, 0.71 for heading date, 0.73 for plant height, and 0.60 for powdery mildew resistance. Our results demonstrate the utility of combining unbalanced phenotypic records with genome-wide SNP marker data for predicting the performance of untested genotypes.}, number={4}, journal={THEORETICAL AND APPLIED GENETICS}, author={Sarinelli, J. Martin and Murphy, J. Paul and Tyagi, Priyanka and Holland, James B. and Johnson, Jerry W. and Mergoum, Mohamed and Mason, Richard E. and Babar, Ali and Harrison, Stephen and Sutton, Russell and et al.}, year={2019}, month={Apr}, pages={1247–1261} }
 @article{case_bhavani_macharia_pretorius_coetzee_kloppers_tyagi_brown-guedira_steffenson_2018, title={Mapping adult plant stem rust resistance in barley accessions Hietpas-5 and GAW-79}, volume={131}, ISSN={["1432-2242"]}, DOI={10.1007/s00122-018-3149-8}, abstractNote={Key message Major stem rust resistance QTLs proposed to be Rpg2 from Hietpas-5 and Rpg3 from GAW-79 were identified in chromosomes 2H and 5H, respectively, and will enhance the diversity of stem rust resistance in barley improvement programs. Stem rust is a devastating disease of cereal crops worldwide. In barley (Hordeum vulgare ssp. vulgare), the disease is caused by two pathogens: Puccinia graminis f. sp. secalis (Pgs) and Puccinia graminis f. sp. tritici (Pgt). In North America, the stem rust resistance gene Rpg1 has protected barley from serious losses for more than 60 years; however, widely virulent Pgt races from Africa in the Ug99 group threaten the crop. The accessions Hietpas-5 (CIho 7124) and GAW-79 (PI 382313) both possess moderate-to-high levels of adult plant resistance to stem rust and are the sources of the resistance genes Rpg2 and Rpg3, respectively. To identify quantitative trait loci (QTL) for stem rust resistance in Hietpas-5 and GAW-79, two biparental populations were developed with Hiproly (PI 60693), a stem rust-susceptible accession. Both populations were phenotyped to the North American Pgt races of MCCFC, QCCJB, and HKHJC in St. Paul, Minnesota, and to African Pgt races (predominately TTKSK in the Ug99 group) in Njoro, Kenya. In the Hietpas-5/Hiproly population, a major effect QTL was identified in chromosome 2H, which is proposed as the location for Rpg2. In the GAW-79/Hiproly population, a major effect QTL was identified in chromosome 5H and is the proposed location for Rpg3. These QTLs will enhance the diversity of stem rust resistance in barley improvement programs.}, number={10}, journal={THEORETICAL AND APPLIED GENETICS}, author={Case, Austin J. and Bhavani, Sridhar and Macharia, Godwin and Pretorius, Zacharias and Coetzee, Vicky and Kloppers, Frederik and Tyagi, Priyanka and Brown-Guedira, Gina and Steffenson, Brian J.}, year={2018}, month={Oct}, pages={2245–2266} }
 @article{case_bhavani_macharia_pretorius_coetzee_kloppers_tyagi_brown-guedira_steffenson_2018, title={Mapping adult plant stem rust resistance in barley accessions Hietpas-5 and GAW-79 (vol 131, pg 2245, 2018)}, volume={131}, ISSN={["1432-2242"]}, DOI={10.1007/s00122-018-3170-y}, abstractNote={Unfortunately, one co-author name was incorrectly published in the original publication. The complete correct name should read as follows.}, number={10}, journal={THEORETICAL AND APPLIED GENETICS}, author={Case, Austin J. and Bhavani, Sridhar and Macharia, Godwin and Pretorius, Zacharias and Coetzee, Vicky and Kloppers, Frederik and Tyagi, Priyanka and Brown-Guedira, Gina and Steffenson, Brian J.}, year={2018}, month={Oct}, pages={2267–2267} }
 @article{huang_ward_griffey_van sanford_mckendry_brown-guedira_tyagi_sneller_2018, title={The Accuracy of Genomic Prediction between Environments and Populations for Soft Wheat Traits}, volume={58}, ISSN={["1435-0653"]}, DOI={10.2135/cropsci2017.10.0638}, abstractNote={Genomic selection (GS) uses training population (TP) data to estimate the value of lines in a selection population. In breeding, the TP and selection population are often grown in different environments, which can cause low prediction accuracy when the correlation of genetic effects between the environments is low. Subsets of TP data may be more predictive than using all TP data. Our objectives were (i) to evaluate the effect of using subsets of TP data on GS accuracy between environments, and (ii) to assess the accuracy of models incorporating marker × environment interaction (MEI). Two wheat (Triticum aestivum L.) populations were phenotyped for 11 traits in independent environments and genotyped with single‐nucleotide polymorphism markers. Within each population–trait combination, environments were clustered. Data from one cluster were used as the TP to predict the value of the same lines in the other cluster(s) of environments. Models were built using all TP data or subsets of markers selected for their effect and stability. The GS accuracy using all TP data was >0.25 for 9 of 11 traits. The between‐environment accuracy was generally greatest using a subset of stable and significant markers; accuracy increased up to 48% relative to using all TP data. We also assessed accuracy using each population as the TP and the other as the selection population. Using subsets of TP data or the MEI models did not improve accuracy between populations. Using optimized subsets of markers within a population can improve GS accuracy by reducing noise in the prediction data set.}, number={6}, journal={CROP SCIENCE}, author={Huang, Mao and Ward, Brian and Griffey, Carl and Van Sanford, David and McKendry, Anne and Brown-Guedira, Gina and Tyagi, Priyanka and Sneller, Clay}, year={2018}, pages={2274–2288} }
 @article{kaur_tyagi_kuraparthy_2017, title={Genetic Diversity and Population Structure in the Landrace Accessions of Gossypium hirsutum}, volume={57}, ISSN={["1435-0653"]}, DOI={10.2135/cropsci2016.12.0999}, abstractNote={In this study, genetic diversity and population structure was assessed in a set of 185 Gossypium hirsutum L. landrace accessions, collected mainly from Central America during the mid‐1900s using genomewide simple sequence repeat (SSR) markers. Genotyping the diversity panel using 122 SSRs detected 143 marker loci. A total of 819 alleles were identified across 143 markers loci, and out of these, 23.3% were unique alleles, observed only in one accession. Average genetic distance between accessions was 0.36, suggesting higher levels of genetic variation present in the cotton tropical landrace germplasm. Using Bayesian model‐based structure analysis, five major subgroups were identified that roughly corresponded to the geographical origins of accessions. Substantial admixture was observed as accessions from different geographical locations were grouped together. Results from phylogenetic analysis, principal component analysis, and analysis of molecular variance supported clustering based on STRUCTURE analysis. Pairwise kinship estimates suggested that most of the accessions were unrelated. Finally, core sets representing various levels of allelic richness were identified using POWERMARKER. Assessing genetic diversity, population structure, and identifying the core sets in the landraces will facilitate the utilization of unexploited tropical genetic diversity towards developing improved cotton cultivars.}, number={5}, journal={CROP SCIENCE}, author={Kaur, Baljinder and Tyagi, Priyanka and Kuraparthy, Vasu}, year={2017}, pages={2457–2470} }
 @article{sallam_tyagi_brown-guedira_muehlbauer_hulse_steffenson_2017, title={Genome-Wide Association Mapping of Stem Rust Resistance in Hordeum vulgare subsp spontaneum}, volume={7}, ISSN={["2160-1836"]}, DOI={10.1534/g3.117.300222}, abstractNote={Abstract}, number={10}, journal={G3-GENES GENOMES GENETICS}, author={Sallam, Ahmad H. and Tyagi, Priyanka and Brown-Guedira, Gina and Muehlbauer, Gary J. and Hulse, Alex and Steffenson, Brian J.}, year={2017}, month={Oct}, pages={3491–3507} }
 @article{tollenaar_fridgen_tyagi_stackhouse_kumudini_2017, title={The contribution of solar brightening to the US maize yield trend}, volume={7}, ISSN={["1758-6798"]}, DOI={10.1038/nclimate3234}, abstractNote={Gains in maize yield from the US Corn Belt have been attributed to agricultural technologies. A study now shows that solar brightening was responsible for approximately 27% of yield growth from 1984 to 2013. Predictions of crop yield under future climate change are predicated on historical yield trends1,2,3, hence it is important to identify the contributors to historical yield gains and their potential for continued increase. The large gains in maize yield in the US Corn Belt have been attributed to agricultural technologies4, ignoring the potential contribution of solar brightening (decadal-scale increases in incident solar radiation) reported for much of the globe since the mid-1980s. In this study, using a novel biophysical/empirical approach, we show that solar brightening contributed approximately 27% of the US Corn Belt yield trend from 1984 to 2013. Accumulated solar brightening during the post-flowering phase of development of maize increased during the past three decades, causing the yield increase that previously had been attributed to agricultural technology. Several factors are believed to cause solar brightening, but their relative importance and future outlook are unknown5,6,7,8,9, making prediction of continued solar brightening and its future contribution to yield gain uncertain. Consequently, results of this study call into question the implicit use of historical yield trends in predicting yields under future climate change scenarios.}, number={4}, journal={NATURE CLIMATE CHANGE}, author={Tollenaar, Matthijs and Fridgen, Jon and Tyagi, Priyanka and Stackhouse, Paul W., Jr. and Kumudini, Saratha}, year={2017}, month={Apr}, pages={275-+} }