Biology subjects

Kolkman, J. M.

Publications and source records attributed to Kolkman, J. M..

5 recordsLinked to original sources

bioRxiv2026

Genome wide association analysis of resistance to scald in an adapted multiparent winter malting barley population

Scald, caused by the fungus Rhynchosporium graminicola Heinsen 1897, is a major foliar disease in winter malting barley (Hordeum vulgare L). Resistance to scald in winter malting barley is controlled by major and minor resistance genes. We used a population of 377 lines derived from biparental crosses among five winter malting barley parents to analyze resistance to scald and associated agronomic traits. Increased winter survival and later heading dates were negatively correlated with increased resistance, whereas increased height was positively correlated with resistance. A genome-wide association study (GWAS) for resistance to scald was analyzed with multiple models, using 14,789 SNP and 374 lines. The similarities and differences between the models were identified in SNP trait associations and phenotypic effect sizes. SNP associations identified a large region on chromosome 3H across models. FarmCPU identified additional associations on chromosomes 2H, 3H, 4H and 7H. Linkage disequilibrium on chromosome 3H and GWAS for resistance to scald using the Rrs1-linked marker, HVS3, as a covariate confirmed Rrs1 was segregating in this population. GWAS for winter survival, heading date and plant height identified associations across the genome, with chromosome 2H showing SNP-trait colocalizations between resistance to scald, winter survival, heading date and plant height. Breeding for durable resistance to scald in winter malting barley can include pyramiding major resistance loci, such as Rrs1, as well as QTL for disease resistance and agronomic traits. PLAIN LANGUAGE SUMMARYO_ST_ABSGenetic architecture of resistance to scald in winter malting barleyC_ST_ABSScald is an important foliar pathogen in winter malting barley, affecting both grain yield and quality. While resistance to scald is controlled by major and minor resistance genes, agronomic traits are also known to limit the spread of scald in barley. We determined the genetic architecture using a large multiparent population of winter malting barley. The FarmCPU genome-wide association model proved optimal for defining the resistance genes, with the major resistance gene, Rrs1, conferring 29% of the variation in this population. Fewer days to heading and taller plants contributed to plant avoidance of scald. Reduced canopy coverage in plants with low winter survival led to less scald severity. A region of the genome contributing a minor resistance effect was co-localized with a region for plant height, heading date and winter survival. Core IdeasO_LIResistance to scald in a large multiparent population was derived from a major resistance gene (Rrs1) and several smaller effect QTLs C_LIO_LIRrs1 resistance was derived from Lightning and is located within a large linkage block on Chromosome 3H C_LIO_LIFewer days to heading and taller plants were correlated with less disease in the multiparent winter malting barley population in NY state C_LIO_LIA QTL for resistance to scald co-localized on chromosome 2H with winter survival, heading date, and plant height C_LIO_LIFarmCPU was an optimal model for association analysis for resistance to scald in the multiparent unbalanced diallel population. C_LI

plant biology↗

bioRxiv2024

Regulatory architecture of disease resistance in maize revealed by multi-omic systems genetics

Complex traits such as disease resistance have been traditionally studied using quantitative genetics. Here, we use systems genetics to integrate disease severity and multi-omic quantitate trait loci (QTL) to uncover biological networks underlying interaction with northern leaf blight (NLB), a yield-limiting disease of corn. Specifically, we integrated transcriptome, (phospho)proteome, and metabolome measurements to map molecular QTL and build predictive regulatory networks following NLB infection. These inferred networks identified a critical signaling module that was genetically validated comprised of a kinase termed NLB SUSCEPTIBLE KINASE 1, a bHLH transcription factor, and the lignin biosynthesis enzyme BROWN MIDRIB 2. Our results demonstrate the feasibility of high-throughput mapping of genetic determinants of gene- product levels and demonstrates the power of systems genetics to identify upstream regulatory genes that confer resistance to NLB that can inform future strategies for crop protection.

plant biology↗

bioRxiv2022

Yield Prediction Through Integration of Genetic, Environment, and Management Data Through Deep Learning

Accurate prediction of the phenotypic outcomes produced by different combinations of genotypes, environments, and management interventions remains a key goal in biology with direct applications to agriculture, research, and conservation. The past decades have seen an expansion of new methods applied towards this goal. Here we predict maize yield using deep neural networks, compare the efficacy of two model development methods, and contextualize model performance using linear models, which are the conventional method for this task, and machine learning models We examine the usefulness of incorporating interactions between disparate data types. We find a deep learning model with interactions has the best average performance. Optimizing submodules for each datatype improved model performance relative to optimizing the whole model for all data types at once. Examining the effect of interactions in the best performing model revealed that including interactions altered the models sensitivity to weather and management features, including a reduction of the importance scores for timepoints expected to have limited physiological basis for influencing yield - those at the extreme end of the season, nearly 200 days post planting. Based on these results, deep learning provides a promising avenue for phenotypic prediction of complex traits in complex environments and a potential mechanism to better understand the influence of environmental and genetic factors.

plant biology↗

bioRxiv2022

Uncovering the roles of lignin-related genes in disease resistance in maize using mutant and association analysis

Brown midrib (BMR) maize harbors mutations that result in lower lignin levels and higher feed digestibility, making it a desirable silage market class for ruminant nutrition. Northern leaf blight (NLB) epidemics in upstate NY highlighted the disease susceptibility of commercially grown BMR maize hybrids. We found the bm1, bm2, bm3 and bm4 mutants in a W64A genetic background to be more susceptible to foliar fungal (NLB, gray leaf spot, and anthracnose leaf blight) and bacterial (Stewarts wilt) diseases. The bm1, bm2 and bm3 mutants showed enhanced susceptibility to anthracnose stalk rot, and the bm1 and bm3 mutants were more susceptible to Gibberella ear rot. Co-localization of quantitative trait loci and correlations between stalk strength and disease traits in recombinant inbred line families suggest possible pleiotropies. The role of lignin in plant defense was explored using high-resolution, genome-wide association analysis for resistance to NLB in the Goodman diversity panel. Association analysis identified 100 single and clustered SNP associations for resistance to NLB, but did not implicate natural functional variation at bm1-bm5. Strong associations implicated a suite of diverse candidate genes, including lignin-related genes such as a {beta}-glucosidase gene cluster, hct11, knox1, knox2, zim36, lbd35, CASP-like protein 8, and xat3. The candidate genes are targets for breeding quantitative resistance to NLB in maize for use in silage and non-silage purposes. Core IdeasO_LIBrown midrib mutants (bm1-bm4) in maize, characterized as mutations with reduced lignin content, are susceptible to a range of foliar pathogens. C_LIO_LISeveral brown midrib mutants in maize show enhanced susceptibility to stalk rot and ear rot pathogens. C_LIO_LIGenome wide association analysis identified a number of diverse candidate genes associated with resistance to northern leaf blight in maize, including lignin-related genes involved in the monolignol pathway, developmental genes and transcription factors. C_LIO_LIAssociation analysis for resistance to NLB implicated a {beta}-glucosidase gene cluster, including Zmbglu24 and Zmbglu25 known for targeting monolignols and contributing to [~]15% phenotypic variation. C_LIO_LIhct11, which plays an important role in the monolignol pathway, was implicated for resistance to northern leaf blight in maize with potential for multiple stress tolerance. C_LI

genetics↗

bioRxiv2019

Maize nested introgression library provides evidence for the involvement of liguleless1 in resistance to northern leaf blight

Plant disease resistance is largely governed by complex genetic architecture. In maize, few disease resistance loci have been characterized. Near-isogenic lines (NILs) are a powerful genetic tool to dissect quantitative trait loci (QTL). We analyzed an introgression library of maize near-isogenic lines (NILs), termed a nested NIL (nNIL) library for resistance to northern leaf blight (NLB) caused by the fungal pathogen Setosphaeria turcica. The nNIL library was comprised of 412 BC5F4 NILs that originated from 18 diverse donor parents and a common recurrent parent, B73. Single nucleotide polymorphisms identified through genotyping by sequencing (GBS) were used to define introgressions and for association analysis. NILs that conferred resistance and susceptibility to NLB were comprised of introgressions that overlapped known NLB QTL. Genome-wide association analysis and stepwise regression further resolved five QTL regions, and implicated several candidate genes, including Liguleless1 (Lg1), a key determinant of leaf architecture in cereals. Two independently-derived mutant alleles of lg1 inoculated with S. turcica showed enhanced susceptibility to NLB. In the maize nested association mapping population, leaf angle was positively correlated with NLB in five recombinant inbred line (RIL) populations, and negatively correlated with NLB in four RIL populations. This study demonstrates the power of a nNIL library combined with high density SNP coverage to resolve QTLs. Furthermore, the role of lg1 in leaf architecture and in resistance to NLB has important applications in crop improvement.\n\nSignificance Statement (120 words)Understanding the genetic basis of disease resistance is important for crop improvement. We analyzed response to northern leaf blight (NLB) in a maize population consisting of 412 near-isogenic lines (NILs) derived from 18 diverse donor parents backcrossed to a recurrent parent, B73. NILs were genotyped by sequencing to detect introgressed segments. We identified NILs with greater resistance or susceptibility to NLB than B73. Genome-wide association analysis, coupled with stepwise regression, identified 5 candidate loci for NLB resistance, including the liguleless1 gene. The LIGULELESS1 transcription factor is critical in development of the leaf ligular region and influences leaf angle. We found that liguleless1 mutants are significantly more susceptible to NLB, uncovering a pleiotropic role for liguleless1 in development and disease resistance.

plant biology↗