Biology subjects

Gore, M. A.

Publications and source records attributed to Gore, M. A..

6 recordsLinked to original sources

bioRxiv2018

Use of Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype x environment (G x E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregon, Mexico over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.

genomics↗

bioRxiv2018

Leveraging mutational burden for complex trait prediction in sorghum

Sorghum (Sorghum bicolor (L.) Moench) is a major staple food cereal for millions of people worldwide. The sorghum genome, like other species, accumulates deleterious mutations, likely impacting its fitness. Though selection keeps deleterious mutations rare, their complete removal from the genome is impeded due to lack of recombination, drift, and their coupling with favorable loci. To study how deleterious mutations impact agronomic phenotypes, we identified putative deleterious mutations among ~5.5M segregating variants of 229 diverse sorghum lines. We provide the whole-genome estimate of the deleterious burden in sorghum, showing that about 33% of nonsynonymous substitutions are putatively deleterious. The pattern of mutation burden varies appreciably among racial groups; the caudatum shows higher mutation burden while the guinea has lower burden. Across racial groups, the mutation burden correlated negatively with biomass, plant height, Specific Leaf Area (SLA), and tissue starch content, suggesting deleterious burden decreases trait fitness. Putatively deleterious variants explain roughly half of the genetic variance. However, there is only moderate improvement in total heritable variance explained for biomass (7.6%) and plant height (5.2%). There is no advantage in total heritable variance for SLA and starch. The contribution of putatively deleterious variants to phenotypic diversity therefore appears to be dependent on the genetic architecture of traits. Overall, our results suggest that including putatively deleterious variants in models do not significantly improve breeding accuracy because of extensive linkage. However, knowledge of deleterious variants could be leveraged for sorghum breeding through genome editing.

genetics↗

bioRxiv2017

Multivariate analysis of the cotton seed ionome reveals integrated genetic signatures of abiotic stress response

To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions.\n\nOne sentence summaryThe cotton seed ionome has a shared genetic basis that provides insight into the physiological status of the plant.

genetics↗

bioRxiv2017

A Century of Guayule: Comprehensive Genetic Characterization of the Guayule (Parthenium argentatum A. Gray) USDA Germplasm Collection

The fragility of a single-source, geographically concentrated supply of natural rubber, a critical material of the modern economy, has brought guayule (Parthenium argentatum A. Gray) to the forefront as an alternative source of natural rubber. The improvement of guayule for commercial-scale production has been limited by the lack of genomic tools and well-characterized genetic resources required for genomics-assisted breeding. To address this issue, we developed nearly 50,000 single nucleotide polymorphism (SNP) genetic markers and genotyped 69 accessions of guayule and its sister taxa mariola (Parthenium incanum Kunth), representing the entire available NALPGRU germplasm collection. We identified multiple interspecific hybrid accessions previously considered guayule, including six guayule-mariola hybrids and non-mariola interspecific hybrid accessions AZ-2 and AZ-3, two commonly used high-yielding cultivars. We dissected genetic diversity within the collection to identify a highly diverse subset of guayule accessions, and showed that wild guayule stands in Big Bend National Park, Texas, USA have the potential to provide hitherto untapped guayule genetic diversity. Together, these results provide the most thorough genetic characterization of guayule germplasm to date and lay the foundation for rapid genetic improvement of commercial guayule germplasm.\n\nKey Results O_LISix guayule accessions are guayule-mariola hybrids\nC_LIO_LIGuayule collections from Big Bend National Park contain novel guayule genotypes not present in collections from Mexico\nC_LIO_LICommonly cultivated accessions AZ2 and AZ3 contain introgressions from other Parthenium species\nC_LIO_LIThe triploid accessions 11591, 11646, N576, N565, N565II, and RICHARDSON are generally indistinguishable from each other with respect to genetic background and likely represent the 4265-I source genotype (Johnson, 1950)\nC_LIO_LIOpen pollinated and purposefully outcrossed tetraploid selections derived from 4265-I incorporate further genetic diversity and form distinct genotypes\nC_LI

plant biology↗

bioRxiv2016

rAmpSeq: Using repetitive sequences for robust genotyping

Repetitive sequences have been used for DNA fingerprinting and genotyping for more than a quarter century. Now, with our knowledge of whole genome sequences, repetitive sequences can be used to identify polymorphisms that can be mapped and scored in a systematic manner. We have developed a simple, robust platform for designing primers, PCR amplification, and high throughput cloning that allows hundreds to thousands of markers to be scored for less than $5 per sample. Conserved regions were used to design PCR primers for amplifying thousands of middle repetitive regions of the maize (Zea mays ssp. mays) genome. Bioinformatic scans were then used to identify DNA sequence polymorphisms in the low copy intervening sequences. When used in conjunction with simple DNA preps, optimized PCR conditions, high multiplex Illumina indexing and a bioinformatic marker calling platform tailored for repetitive sequences, this methodology provides a cost effective genotyping strategy for large-scale genomic selection projects. We show detailed results from four maize primer sets that produced between 1,335-3,225 good coverage loci with 1056 that segregated appropriately in a bi-parental family. This approach could have wide applicability to breeding and conservation biology, where hundreds of thousands of samples need to be genotyped for very minimal cost.

genomics↗

bioRxiv2016

Temporal Genetic Dynamics of an Experimental, Biparental Field Population of Phytophthora capsici

Defining the contributions of dispersal, reproductive mode, and mating system to the population structure of a pathogenic organism is essential to estimating its evolutionary potential. After introduction of the devastating plant pathogen, Phytophthora capsici, into a growers field, a lack of aerial spore dispersal restricts migration. Once established, coexistence of both mating types results in formation of overwintering recombinant oospores, engendering persistent pathogen populations. To mimic these conditions, in 2008, we inoculated a field with two P. capsici isolates of opposite mating type. We analyzed pathogenic isolates collected in 2009-13 from this experimental population, using genome-wide single-nucleotide polymorphism markers. By tracking heterozygosity across years, we show that the population underwent a generational shift; transitioning from exclusively F1 in 2009-10; mixed generational in 2011; and ultimately all inbred in 2012-13. Survival of F1 oospores, characterized by heterozygosity excess, coupled with a low rate of selfing, delayed declines in heterozygosity due to inbreeding and attainment of equilibrium genotypic frequencies. Large allele and haplotype frequency changes in specific genomic regions accompanied the generational shift, representing putative signatures of selection. Finally, we identified an approximately 1.6 Mb region associated with mating type determination, constituting the first detailed genomic analysis of a mating type region (MTR) in Phytophthora. Segregation patterns in the MTR exhibited tropes of sex-linkage, where maintenance of allele frequency differences between isolates of opposite mating type was associated with elevated heterozygosity despite inbreeding. Characterizing the trajectory of this experimental system provides key insights into the processes driving persistent, sexual pathogen populations.

evolutionary biology↗