@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{ogut_maltecca_whetten_mckeand_isik_2014, title={Genetic Analysis of Diallel Progeny Test Data Using Factor Analytic Linear Mixed Models}, volume={60}, ISSN={["1938-3738"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84893424508&partnerID=MN8TOARS}, DOI={10.5849/forsci.12-108}, abstractNote={Multienvironmental trials are commonly used in plant breeding programs to select superior genotypes for specific sites or across multiple sites for breeding and deployment decisions. We compared the efficiency of factor analytic (FA) and other covariance structures for genetic analysis of height growth in Pinus taeda L. diallel progeny trials to account for heterogeneity in variances and covariances among different environments. Among the models fitted, FA models produced the smallest Akaike information criterion (AIC) model fit statistic. An unstructured (US) variance-covariance matrix produced a log likelihood value similar to that for the FA model but had a large number of parameters. As a result, some models with US covariance failed to converge. FA models captured both variance and covariance at the genetic level better than simpler models and provided more accurate predictions of breeding values. Narrow-sense heritability estimates for height from 10 different sites were about 0.20 when more complex variance structures were used, compared with 0.13 when simpler variance structures such as identity and block-diagonal variance structures were used. FA models are robust for modeling genotype × environment interaction, and they reduce the computational requirements of mixed-model analysis. On average, all 10 environments had additive genetic correlation of 0.83 and dominance genetic correlation of 0.91, suggesting that genotype × environment interaction should not be a concern for this specific population in the environments in which the genotypes were tested.}, number={1}, journal={FOREST SCIENCE}, publisher={Oxford University Press (OUP)}, author={Ogut, Funda and Maltecca, Christian and Whetten, Ross and McKeand, Steven and Isik, Fikret}, year={2014}, month={Feb}, pages={119–127} }
 @article{zila_ogut_romay_gardner_buckler_holland_2014, title={Genome-wide association study of Fusarium ear rot disease in the USA maize inbred line collection}, volume={14}, ISSN={["1471-2229"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84964314102&partnerID=MN8TOARS}, DOI={10.1186/s12870-014-0372-6}, abstractNote={Resistance to Fusarium ear rot of maize is a quantitative and complex trait. Marker-trait associations to date have had small additive effects and were inconsistent between previous studies, likely due to the combined effects of genetic heterogeneity and low power of detection of many small effect variants. The complexity of inheritance of resistance hinders the use marker-assisted selection for ear rot resistance. We conducted a genome-wide association study (GWAS) for Fusarium ear rot resistance in a panel of 1687 diverse inbred lines from the USDA maize gene bank with 200,978 SNPs while controlling for background genetic relationships with a mixed model and identified seven single nucleotide polymorphisms (SNPs) in six genes associated with disease resistance in either the complete inbred panel (1687 lines with highly unbalanced phenotype data) or in a filtered inbred panel (734 lines with balanced phenotype data). Different sets of SNPs were detected as associated in the two different data sets. The alleles conferring greater disease resistance at all seven SNPs were rare overall (below 16%) and always higher in allele frequency in tropical maize than in temperate dent maize. Resampling analysis of the complete data set identified one robust SNP association detected as significant at a stringent p-value in 94% of data sets, each representing a random sample of 80% of the lines. All associated SNPs were in exons, but none of the genes had predicted functions with an obvious relationship to resistance to fungal infection. GWAS in a very diverse maize collection identified seven SNP variants each associated with between 1% and 3% of trait variation. Because of their small effects, the value of selection on these SNPs for improving resistance to Fusarium ear rot is limited. Selection to combine these resistance alleles combined with genomic selection to improve the polygenic background resistance might be fruitful. The genes associated with resistance provide candidate gene targets for further study of the biological pathways involved in this complex disease resistance.}, number={1}, journal={BMC PLANT BIOLOGY}, publisher={Springer Science \mathplus Business Media}, author={Zila, Charles T. and Ogut, Funda and Romay, Maria C. and Gardner, Candice A. and Buckler, Edward S. and Holland, James B.}, year={2014}, month={Dec} }