@article{yang_he_kabahuma_chaya_kelly_borrego_bian_el kasmi_yang_teixeira_et al._2017, title={A gene encoding maize caffeoyl-CoA O-methyltransferase confers quantitative resistance to multiple pathogens}, volume={49}, ISSN={1061-4036 1546-1718}, url={http://dx.doi.org/10.1038/ng.3919}, DOI={10.1038/ng.3919}, abstractNote={Alleles that confer multiple disease resistance (MDR) are valuable in crop improvement, although the molecular mechanisms underlying their functions remain largely unknown. A quantitative trait locus, qMdr9.02, associated with resistance to three important foliar maize diseases-southern leaf blight, gray leaf spot and northern leaf blight-has been identified on maize chromosome 9. Through fine-mapping, association analysis, expression analysis, insertional mutagenesis and transgenic validation, we demonstrate that ZmCCoAOMT2, which encodes a caffeoyl-CoA O-methyltransferase associated with the phenylpropanoid pathway and lignin production, is the gene within qMdr9.02 conferring quantitative resistance to both southern leaf blight and gray leaf spot. We suggest that resistance might be caused by allelic variation at the level of both gene expression and amino acid sequence, thus resulting in differences in levels of lignin and other metabolites of the phenylpropanoid pathway and regulation of programmed cell death.}, number={9}, journal={Nature Genetics}, publisher={Springer Science and Business Media LLC}, author={Yang, Qin and He, Yijian and Kabahuma, Mercy and Chaya, Timothy and Kelly, Amy and Borrego, Eli and Bian, Yang and El Kasmi, Farid and Yang, Li and Teixeira, Paulo and et al.}, year={2017}, month={Jul}, pages={1364–1372} }
 @article{bian_holland_2017, title={Enhancing genomic prediction with genome-wide association studies in multiparental maize populations}, volume={118}, ISSN={["1365-2540"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85012882558&partnerID=MN8TOARS}, DOI={10.1038/hdy.2017.4}, abstractNote={Genome-wide association mapping using dense marker sets has identified some nucleotide variants affecting complex traits that have been validated with fine-mapping and functional analysis. However, many sequence variants associated with complex traits in maize have small effects and low repeatability. In contrast to genome-wide association study (GWAS), genomic prediction (GP) is typically based on models incorporating information from all available markers, rather than modeling effects of individual loci. We considered methods to integrate results of GWASs into GP models in the context of multiple interconnected families. We compared association tests based on a biallelic additive model constraining the effect of a single-nucleotide polymorphism (SNP) to be equal across all families in which it segregates to a model in which the effect of a SNP can vary across families. Association SNPs were then included as fixed effects into a GP model that also included the random effects of the whole genome background. Simulation studies revealed that the effectiveness of this joint approach depends on the extent of polygenicity of the traits. Congruent with this finding, cross-validation studies indicated that GP including the fixed effects of the most significantly associated SNPs along with the polygenic background was more accurate than the polygenic background model alone for moderately complex but not highly polygenic traits measured in the maize nested association mapping population. Individual SNPs with strong and robust association signals can effectively improve GP. Our approach provides a new integrative modeling approach for both reliable gene discovery and robust GP.}, number={6}, journal={HEREDITY}, author={Bian, Y. and Holland, J. B.}, year={2017}, month={Jun}, pages={585–593} }
 @article{olukolu_bian_de vries_tracy_wisser_holland_balint-kurti_2016, title={The Genetics of Leaf Flecking in Maize and Its Relationship to Plant Defense and Disease Resistance}, volume={172}, ISSN={0032-0889 1532-2548}, url={http://dx.doi.org/10.1104/pp.15.01870}, DOI={10.1104/pp.15.01870}, abstractNote={Leaf flecking in maize may be related to disease resistance and to a diverse set of metabolic pathways. Physiological leaf spotting, or flecking, is a mild-lesion phenotype observed on the leaves of several commonly used maize (Zea mays) inbred lines and has been anecdotally linked to enhanced broad-spectrum disease resistance. Flecking was assessed in the maize nested association mapping (NAM) population, comprising 4,998 recombinant inbred lines from 25 biparental families, and in an association population, comprising 279 diverse maize inbreds. Joint family linkage analysis was conducted with 7,386 markers in the NAM population. Genome-wide association tests were performed with 26.5 million single-nucleotide polymorphisms (SNPs) in the NAM population and with 246,497 SNPs in the association population, resulting in the identification of 18 and three loci associated with variation in flecking, respectively. Many of the candidate genes colocalizing with associated SNPs are similar to genes that function in plant defense response via cell wall modification, salicylic acid- and jasmonic acid-dependent pathways, redox homeostasis, stress response, and vesicle trafficking/remodeling. Significant positive correlations were found between increased flecking, stronger defense response, increased disease resistance, and increased pest resistance. A nonlinear relationship with total kernel weight also was observed whereby lines with relatively high levels of flecking had, on average, lower total kernel weight. We present evidence suggesting that mild flecking could be used as a selection criterion for breeding programs trying to incorporate broad-spectrum disease resistance.}, number={3}, journal={Plant Physiology}, publisher={Oxford University Press (OUP)}, author={Olukolu, Bode A. and Bian, Yang and De Vries, Brian and Tracy, William F. and Wisser, Randall J. and Holland, James B. and Balint-Kurti, Peter J.}, year={2016}, month={Sep}, pages={1787–1803} }
 @article{bian_holland_2015, title={Ensemble Learning of QTL Models Improves Prediction of Complex Traits}, volume={5}, ISSN={["2160-1836"]}, url={https://doi.org/10.1534/g3.115.021121}, DOI={10.1534/g3.115.021121}, abstractNote={Abstract}, number={10}, journal={G3-GENES GENOMES GENETICS}, publisher={Genetics Society of America}, author={Bian, Yang and Holland, James B.}, year={2015}, month={Oct}, pages={2073–2084} }
 @article{ogut_bian_bradbury_holland_2015, title={Joint-multiple family linkage analysis predicts within-family variation better than single-family analysis of the maize nested association mapping population}, volume={114}, ISSN={["1365-2540"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84922607318&partnerID=MN8TOARS}, DOI={10.1038/hdy.2014.123}, abstractNote={Quantitative trait locus (QTL) mapping has been used to dissect the genetic architecture of complex traits and predict phenotypes for marker-assisted selection. Many QTL mapping studies in plants have been limited to one biparental family population. Joint analysis of multiple biparental families offers an alternative approach to QTL mapping with a wider scope of inference. Joint-multiple population analysis should have higher power to detect QTL shared among multiple families, but may have lower power to detect rare QTL. We compared prediction ability of single-family and joint-family QTL analysis methods with fivefold cross-validation for 6 diverse traits using the maize nested association mapping population, which comprises 25 biparental recombinant inbred families. Joint-family QTL analysis had higher mean prediction abilities than single-family QTL analysis for all traits at most significance thresholds, and was always better at more stringent significance thresholds. Most robust QTL (detected in >50% of data samples) were restricted to one family and were often not detected at high frequency by joint-family analysis, implying substantial genetic heterogeneity among families for complex traits in maize. The superior predictive ability of joint-family QTL models despite important genetic differences among families suggests that joint-family models capture sufficient smaller effect QTL that are shared across families to compensate for missing some rare large-effect QTL.}, number={6}, journal={HEREDITY}, author={Ogut, F. and Bian, Y. and Bradbury, P. J. and Holland, J. B.}, year={2015}, month={Jun}, pages={552–563} }
 @article{bian_yang_balint-kurti_wisser_holland_2014, title={Limits on the reproducibility of marker associations with southern leaf blight resistance in the maize nested association mapping population}, volume={15}, ISSN={["1471-2164"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84924290940&partnerID=MN8TOARS}, DOI={10.1186/1471-2164-15-1068}, abstractNote={A previous study reported a comprehensive quantitative trait locus (QTL) and genome wide association study (GWAS) of southern leaf blight (SLB) resistance in the maize Nested Association Mapping (NAM) panel. Since that time, the genomic resources available for such analyses have improved substantially. An updated NAM genetic linkage map has a nearly six-fold greater marker density than the previous map and the combined SNPs and read-depth variants (RDVs) from maize HapMaps 1 and 2 provided 28.5 M genomic variants for association analysis, 17 fold more than HapMap 1. In addition, phenotypic values of the NAM RILs were re-estimated to account for environment-specific flowering time covariates and a small proportion of lines were dropped due to genotypic data quality problems. Comparisons of original and updated QTL and GWAS results confound the effects of linkage map density, GWAS marker density, population sample size, and phenotype estimates. Therefore, we evaluated the effects of changing each of these parameters individually and in combination to determine their relative impact on marker-trait associations in original and updated analyses.Of the four parameters varied, map density caused the largest changes in QTL and GWAS results. The updated QTL model had better cross-validation prediction accuracy than the previous model. Whereas joint linkage QTL positions were relatively stable to input changes, the residual values derived from those QTL models (used as inputs to GWAS) were more sensitive, resulting in substantial differences between GWAS results. The updated NAM GWAS identified several candidate genes consistent with previous QTL fine-mapping results.The highly polygenic nature of resistance to SLB complicates the identification of causal genes. Joint linkage QTL are relatively stable to perturbations of data inputs, but their resolution is generally on the order of tens or more Mbp. GWAS associations have higher resolution, but lower power due to stringent thresholds designed to minimize false positive associations, resulting in variability of detection across studies. The updated higher density linkage map improves QTL estimation and, along with a much denser SNP HapMap, greatly increases the likelihood of detecting SNPs in linkage with causal variants. We recommend use of the updated genetic resources and results but emphasize the limited repeatability of small-effect associations.}, number={1}, journal={BMC GENOMICS}, publisher={Springer Science \mathplus Business Media}, author={Bian, Yang and Yang, Qin and Balint-Kurti, Peter J. and Wisser, Randall J. and Holland, James B.}, year={2014}, month={Dec} }
 @article{bian_ballington_raja_brouwer_reid_burke_wang_rowland_bassil_brown_2014, title={Patterns of simple sequence repeats in cultivated blueberries (Vaccinium section Cyanococcus spp.) and their use in revealing genetic diversity and population structure}, volume={34}, ISSN={["1572-9788"]}, DOI={10.1007/s11032-014-0066-7}, number={2}, journal={MOLECULAR BREEDING}, author={Bian, Yang and Ballington, James and Raja, Archana and Brouwer, Cory and Reid, Robert and Burke, Mark and Wang, Xinguo and Rowland, Lisa J. and Bassil, Nahla and Brown, Allan}, year={2014}, month={Aug}, pages={675–689} }