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Dive into the research topics where Judith M. Kolkman is active.

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Featured researches published by Judith M. Kolkman.


Proceedings of the National Academy of Sciences of the United States of America | 2011

Multivariate analysis of maize disease resistances suggests a pleiotropic genetic basis and implicates a GST gene

Randall J. Wisser; Judith M. Kolkman; Megan E. Patzoldt; James B. Holland; Jianming Yu; Matthew D. Krakowsky; Rebecca J. Nelson; Peter J. Balint-Kurti

Plants are attacked by pathogens representing diverse taxonomic groups, such that genes providing multiple disease resistance (MDR) are expected to be under positive selection pressure. To address the hypothesis that naturally occurring allelic variation conditions MDR, we extended the framework of structured association mapping to allow for the analysis of correlated complex traits and the identification of pleiotropic genes. The multivariate analytical approach used here is directly applicable to any species and set of traits exhibiting correlation. From our analysis of a diverse panel of maize inbred lines, we discovered high positive genetic correlations between resistances to three globally threatening fungal diseases. The maize panel studied exhibits rapidly decaying linkage disequilibrium that generally occurs within 1 or 2 kb, which is less than the average length of a maize gene. The positive correlations therefore suggested that functional allelic variation at specific genes for MDR exists in maize. Using a multivariate test statistic, a glutathione S-transferase (GST) gene was found to be associated with modest levels of resistance to all three diseases. Resequencing analysis pinpointed the association to a histidine (basic amino acid) for aspartic acid (acidic amino acid) substitution in the encoded protein domain that defines GST substrate specificity and biochemical activity. The known functions of GSTs suggested that variability in detoxification pathways underlie natural variation in maize MDR.


Genetics | 2007

Single Nucleotide Polymorphisms and Linkage Disequilibrium in Sunflower

Judith M. Kolkman; Simon Berry; Alberto Leon; Mary B. Slabaugh; Shunxue Tang; Wenxiang Gao; David K. Shintani; John M. Burke; Steven J. Knapp

Genetic diversity in modern sunflower (Helianthus annuus L.) cultivars (elite oilseed inbred lines) has been shaped by domestication and breeding bottlenecks and wild and exotic allele introgression−the former narrowing and the latter broadening genetic diversity. To assess single nucleotide polymorphism (SNP) frequencies, nucleotide diversity, and linkage disequilibrium (LD) in modern cultivars, alleles were resequenced from 81 genic loci distributed throughout the sunflower genome. DNA polymorphisms were abundant; 1078 SNPs (1/45.7 bp) and 178 insertions-deletions (INDELs) (1/277.0 bp) were identified in 49.4 kbp of DNA/genotype. SNPs were twofold more frequent in noncoding (1/32.1 bp) than coding (1/62.8 bp) sequences. Nucleotide diversity was only slightly lower in inbred lines (θ = 0.0094) than wild populations (θ = 0.0128). Mean haplotype diversity was 0.74. When extraploted across the genome (∼3500 Mbp), sunflower was predicted to harbor at least 76.4 million common SNPs among modern cultivar alleles. LD decayed more slowly in inbred lines than wild populations (mean LD declined to 0.32 by 5.5 kbp in the former, the maximum physical distance surveyed), a difference attributed to domestication and breeding bottlenecks. SNP frequencies and LD decay are sufficient in modern sunflower cultivars for very high-density genetic mapping and high-resolution association mapping.


Plant Physiology | 2008

Deregulation of Maize C4 Photosynthetic Development in a Mesophyll Cell-Defective Mutant

Sarah Covshoff; Wojciech Majeran; Peng Liu; Judith M. Kolkman; Klaas J. van Wijk; Thomas P. Brutnell

During maize (Zea mays) C4 differentiation, mesophyll (M) and bundle sheath (BS) cells accumulate distinct sets of photosynthetic enzymes, with very low photosystem II (PSII) content in BS chloroplasts. Consequently, there is little linear electron transport in the BS and ATP is generated by cyclic electron flow. In contrast, M thylakoids are very similar to those of C3 plants and produce the ATP and NADPH that drive metabolic activities. Regulation of this differentiation process is poorly understood, but involves expression and coordination of nuclear and plastid genomes. Here, we identify a recessive allele of the maize high chlorophyll fluorescence (Hcf136) homolog that in Arabidopsis (Arabidopsis thaliana) functions as a PSII stability or assembly factor located in the thylakoid lumen. Proteome analysis of the thylakoids and electron microscopy reveal that Zmhcf136 lacks PSII complexes and grana thylakoids in M chloroplasts, consistent with the previously defined Arabidopsis function. Interestingly, hcf136 is also defective in processing the full-length psbB-psbT-psbH-petB-petD polycistron specifically in M chloroplasts. To determine whether the loss of PSII in M cells affects C4 differentiation, we performed cell-type-specific transcript analysis of hcf136 and wild-type seedlings. The results indicate that M and BS cells respond uniquely to the loss of PSII, with little overlap in gene expression changes between data sets. These results are discussed in the context of signals that may drive differential gene expression in C4 photosynthesis.


Genetics | 2008

Selection Mapping of Loci for Quantitative Disease Resistance in a Diverse Maize Population

Randall J. Wisser; Seth C. Murray; Judith M. Kolkman; Hernán Ceballos; Rebecca J. Nelson

The selection response of a complex maize population improved primarily for quantitative disease resistance to northern leaf blight (NLB) and secondarily for common rust resistance and agronomic phenotypes was investigated at the molecular genetic level. A tiered marker analysis with 151 simple sequence repeat (SSR) markers in 90 individuals of the population indicated that on average six alleles per locus were available for selection. An improved test statistic for selection mapping was developed, in which quantitative trait loci (QTL) are identified through the analysis of allele-frequency shifts at mapped multiallelic loci over generations of selection. After correcting for the multiple tests performed, 25 SSR loci showed evidence of selection. Many of the putatively selected loci were unlinked and dispersed across the genome, which was consistent with the diffuse distribution of previously published QTL for NLB resistance. Compelling evidence for selection was found on maize chromosome 8, where several putatively selected loci colocalized with published NLB QTL and a race-specific resistance gene. Analysis of F2 populations derived from the selection mapping population suggested that multiple linked loci in this chromosomal segment were, in part, responsible for the selection response for quantitative resistance to NLB.


Genetics | 2014

Unraveling Genomic Complexity at a Quantitative Disease Resistance Locus in Maize

Tiffany M. Jamann; Jesse Poland; Judith M. Kolkman; Laurie G. Smith; Rebecca J. Nelson

Multiple disease resistance has important implications for plant fitness, given the selection pressure that many pathogens exert directly on natural plant populations and indirectly via crop improvement programs. Evidence of a locus conditioning resistance to multiple pathogens was found in bin 1.06 of the maize genome with the allele from inbred line “Tx303” conditioning quantitative resistance to northern leaf blight (NLB) and qualitative resistance to Stewart’s wilt. To dissect the genetic basis of resistance in this region and to refine candidate gene hypotheses, we mapped resistance to the two diseases. Both resistance phenotypes were localized to overlapping regions, with the Stewart’s wilt interval refined to a 95.9-kb segment containing three genes and the NLB interval to a 3.60-Mb segment containing 117 genes. Regions of the introgression showed little to no recombination, suggesting structural differences between the inbred lines Tx303 and “B73,” the parents of the fine-mapping population. We examined copy number variation across the region using next-generation sequencing data, and found large variation in read depth in Tx303 across the region relative to the reference genome of B73. In the fine-mapping region, association mapping for NLB implicated candidate genes, including a putative zinc finger and pan1. We tested mutant alleles and found that pan1 is a susceptibility gene for NLB and Stewart’s wilt. Our data strongly suggest that structural variation plays an important role in resistance conditioned by this region, and pan1, a gene conditioning susceptibility for NLB, may underlie the QTL.


Nature Genetics | 2017

A gene encoding maize caffeoyl-CoA O -methyltransferase confers quantitative resistance to multiple pathogens

Qin Yang; Yijian He; Mercy Kabahuma; Timothy Chaya; Amy Kelly; Eli Borrego; Yang Bian; Farid El Kasmi; Li Yang; Paulo José Pereira Lima Teixeira; Judith M. Kolkman; Rebecca J. Nelson; Michael V. Kolomiets; Jeffery L. Dangl; Randall J. Wisser; Jeffrey L. Caplan; Xu Li; Nick Lauter; Peter J. Balint-Kurti

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.


Theoretical and Applied Genetics | 2016

A remorin gene is implicated in quantitative disease resistance in maize

Tiffany M. Jamann; Xingyu Luo; Laura Morales; Judith M. Kolkman; Chia-Lin Chung; Rebecca J. Nelson

Key messageQuantitative disease resistance is used by plant breeders to improve host resistance. We demonstrate a role for a maize remorin (ZmREM6.3) in quantitative resistance against northern leaf blight using high-resolution fine mapping, expression analysis, and mutants. This is the first evidence of a role for remorins in plant-fungal interactions.AbstractQuantitative disease resistance (QDR) is important for the development of crop cultivars and is particularly useful when loci also confer multiple disease resistance. Despite its widespread use, the underlying mechanisms of QDR remain largely unknown. In this study, we fine-mapped a known quantitative trait locus (QTL) conditioning disease resistance on chromosome 1 of maize. This locus confers resistance to three foliar diseases: northern leaf blight (NLB), caused by the fungus Setosphaeria turcica; Stewart’s wilt, caused by the bacterium Pantoea stewartii; and common rust, caused by the fungus Puccinia sorghi. The Stewart’s wilt QTL was confined to a 5.26-Mb interval, while the rust QTL was reduced to an overlapping 2.56-Mb region. We show tight linkage between the NLB QTL locus and the loci conferring resistance to Stewart’s wilt and common rust. Pleiotropy cannot be excluded for the Stewart’s wilt and the common rust QTL, as they were fine-mapped to overlapping regions. Four positional candidate genes within the 243-kb NLB interval were examined with expression and mutant analysis: a gene with homology to an F-box gene, a remorin gene (ZmREM6.3), a chaperonin gene, and an uncharacterized gene. The F-box gene and ZmREM6.3 were more highly expressed in the resistant line. Transposon tagging mutants were tested for the chaperonin and ZmREM6.3, and the remorin mutant was found to be more susceptible to NLB. The putative F-box is a strong candidate, but mutants were not available to test this gene. Multiple lines of evidence strongly suggest a role for ZmREM6.3 in quantitative disease resistance.


Nature Communications | 2017

The effect of artificial selection on phenotypic plasticity in maize

Joseph L. Gage; Diego Jarquin; Cinta Romay; Aaron J. Lorenz; Edward S. Buckler; Shawn M. Kaeppler; Naser Alkhalifah; M. Bohn; Darwin A. Campbell; Jode W. Edwards; David Ertl; Sherry Flint-Garcia; Jack M. Gardiner; Byron Good; Candice N. Hirsch; James B. Holland; David C. Hooker; Joseph E. Knoll; Judith M. Kolkman; Greg R. Kruger; Nick Lauter; Carolyn J. Lawrence-Dill; E. A. Lee; Jonathan P. Lynch; Seth C. Murray; Rebecca J. Nelson; Jane Petzoldt; Torbert Rocheford; James C. Schnable; Brian T. Scully

Remarkable productivity has been achieved in crop species through artificial selection and adaptation to modern agronomic practices. Whether intensive selection has changed the ability of improved cultivars to maintain high productivity across variable environments is unknown. Understanding the genetic control of phenotypic plasticity and genotype by environment (G × E) interaction will enhance crop performance predictions across diverse environments. Here we use data generated from the Genomes to Fields (G2F) Maize G × E project to assess the effect of selection on G × E variation and characterize polymorphisms associated with plasticity. Genomic regions putatively selected during modern temperate maize breeding explain less variability for yield G × E than unselected regions, indicating that improvement by breeding may have reduced G × E of modern temperate cultivars. Trends in genomic position of variants associated with stability reveal fewer genic associations and enrichment of variants 0–5000 base pairs upstream of genes, hypothetically due to control of plasticity by short-range regulatory elements.Breeding has increased crop productivity, but whether it has also changed phenotypic plasticity is unclear. Here, the authors find maize genomic regions selected for high productivity show reduced contribution to genotype by environment variation and provide evidence for regulatory control of phenotypic stability.


Phytopathology | 2012

Multivariate Mixed Linear Model Analysis of Longitudinal Data:An Information-Rich Statistical Technique for Analyzing Plant Disease Resistance

Yogasudha Veturi; Kristen L. Kump; Ellie Walsh; Oliver Ott; Jesse Poland; Judith M. Kolkman; Peter J. Balint-Kurti; James B. Holland; Randall J. Wisser

ABSTRACT The mixed linear model (MLM) is an advanced statistical technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.


Genetics | 2005

Distribution of Activator (Ac) Throughout the Maize Genome for Use in Regional Mutagenesis

Judith M. Kolkman; Liza J. Conrad; Phyllis R. Farmer; Kristine Hardeman; Kevin R. Ahern; Paul E. Lewis; Ruairidh J. H. Sawers; Sara Lebejko; Paul Chomet; Thomas P. Brutnell

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Peter J. Balint-Kurti

North Carolina State University

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James B. Holland

North Carolina State University

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Jesse Poland

Kansas State University

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Thomas P. Brutnell

Donald Danforth Plant Science Center

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