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Lipka, Alexander E.

Publications and source records attributed to Lipka, Alexander E..

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Introgressions of novel diseases resistance genes from Miscanthus into energycane

Sugarcane (Saccharum) is among the world’s leading bioenergy crops. However, modern sugarcane cultivars are derived from a relatively small set of founder genotypes, which has contributed to cultivar susceptibility to diseases. Miscanthus is a close relative of sugarcane that is genetically diverse and a potential source of genes for improving sugarcane. We found that Miscanthus is a source of resistance to four major diseases of sugarcane and we crossed these disease-resistance genes into a predominantly sugarcane genetic background (BC1 generation). Additionally, we found that Miscanthus could also confer genes for chilling-tolerant photosynthesis to sugarcane, which would be highly advantageous for production of energycane in subtropical environments, like the southern coastal plain of the US. Using advanced modeling techniques, we found that standard marker-assisted selection could be effective for breeding resistance conferred by a small number of genes each with large effect (i.e. vertical resistance), but to breed for many genes each of small effect (i.e. horizontal resistance), genomic selection would be the better strategy. Lastly, we learned that Miscanthus can be induced to flower sooner by giving the plants short days (long nights) but that the ideal day length for a given accession depends on its adaptation to its latitude of origin, with tropical accessions requiring shorter days to flower than accessions from high latitudes. This information will facilitate plant breeders’ ability to make crosses between Miscanthus and sugarcane, and enable greater use of Miscanthus as a genetic resource to improve sugarcane.

09 BIOMASS FUELS↗
DOE OSTI2023

Impact of genotype‐calling methodologies on genome‐wide association and genomic prediction in polyploids

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum , and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Inhibition of ethylene involved in resistance to E. turcicum in an exotic-derived double haploid maize population

Northern corn leaf blight (NCLB) is an economically important disease of maize. While the genetic architecture of NCLB has been well characterized, the pathogen is known to overcome currently deployed resistance genes, and the role of hormones in resistance to NCLB is an area of active research. The objectives of the study were (i) to identify significant markers associated with resistance to NCLB, (ii) to identify metabolic pathways associated with NCLB resistance, and (iii) to examine role of ethylene in resistance to NCLB. We screened 252 lines from the exotic-derived double haploid BGEM maize population for resistance to NCLB in both field and greenhouse environments. We used a genome wide association study (GWAS) and stepwise regression to identify four markers associated with resistance, followed by a pathway association study tool (PAST) to identify important metabolic pathways associated with disease severity and incubation period. The ethylene synthesis pathway was significant for disease severity and incubation period. We conducted a greenhouse assay in which we inhibited ethylene to examine the role of ethylene in resistance to NCLB. We observed a significant increase in incubation period and a significant decrease in disease severity between plants treated with the ethylene inhibitor and mock-treated plants. Our study confirms the potential of the BGEM population as a source of novel alleles for resistance. We also confirm the role of ethylene in resistance to NCLB and contribute to the growing body of literature on ethylene and disease resistance in monocots.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Genome‐wide association and genomic prediction for yield and component traits of Miscanthus sacchariflorus

Abstract Accelerating biomass improvement is a major goal of Miscanthus breeding. The development and implementation of genomic‐enabled breeding tools, like marker‐assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of Miscanthus breeding. The present study conducted genome‐wide association (GWA) and genomic prediction of biomass yield and 14 yield‐components traits in Miscanthus sacchariflorus . We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single‐nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported Miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield‐component traits in M. sacchariflorus that may facilitate marker‐assisted breeding for biomass yield. Genomic prediction accuracy for the yield‐related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia ( N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus . This study indicated that MAS and genomic prediction will likely be beneficial for conducting population‐improvement of M. sacchariflorus .

09 BIOMASS FUELS↗
DOE OSTI2022

Genetic mapping of sorghum resistance to an Illinois isolate of Colletotrichum sublineola

Anthracnose leaf blight (ALB) is an economically important disease of sorghum [Sorghum bicolor (L.) Moench] caused by the fungal pathogen Colletotrichum sublineola Henn. ex Sacc. & Trotter. Although qualitative and quantitative resistance have been identified for ALB, the usefulness of resistance loci differs depending on the pathogen pathotype. Identifying resistance effective against unique pathogen pathotypes is critical to managing ALB, as the disease is managed primarily through the deployment of host resistance. We isolated C. sublineola from ALB-infected leaves collected in Illinois and found that the strain was a novel pathotype, as it produced a unique combination of virulence against a set of differential lines. Using this isolate, we inoculated 579 temperate-adapted sorghum conversion lines in 2019 and 2020. We then conducted a genome-wide association study (GWAS) and a metabolic pathway analysis using the Pathway Associated Study Tool (PAST). We identified 47 significant markers distributed across all chromosomes except chromosome 8. We identified 32 candidate genes based on physical proximity with significant markers, some of which have a known role in host defense. We identified 47 pathways associated with ALB resistance, indicating a role for secondary metabolism in defense to ALB. Our results are important to improve the understanding of the genetic basis of ALB resistance in sorghum and highlight the importance of developing durable resistance to ALB.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Training Population Optimization for Genomic Selection in Miscanthus

Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M×g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M×g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train these GS models. To overcome the drawback of having only one interspecific M×g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M×g F2 panels derived from different sets of Msi and diploid Msa parents. The results revealed that genetic architectures with common causal mutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M×g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible.

59 BASIC BIOLOGICAL SCIENCES↗