Biology subjects

Shook, J. M.

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

4 recordsLinked to original sources

bioRxiv2025

Genetic and environmental factors regulating soybean reproductive stages and their transitions

The reproductive stage of soybean is influenced by the effect of genotype, environment, and their interactions. While days to flowering and days to full maturity have been studied, a systematic and comprehensive study that investigates the variation in days to each stage and the role of maturity-related genes and environmental variables is lacking. Therefore, we studied 508 unique accessions from the USDA germplasm collection from maturity group 0-IV, and a set of 67 near-isogenic lines differing for maturity-related genes. Field experiments and evaluations were conducted in central Iowa, USA. The days to each of the reproductive stages, R1-R8, were recorded. We report considerable variation in the duration of reproductive growth stages between flowering and maturity, which is largely explainable by known flowering and maturity genes as well as environmental variables, day length, and growing degree days. Besides the known maturity-related genes E1, E2, and Dt1, we identified two novel SNPs, such as Glyma.01G180600 and Glyma.10G221300, as potential targets for genetic regulation of reproductive stages. We also captured two other loci, Glyma.08G216800 and Glyma.04G088100 for day length and growing degree days, respectively, that revealed dynamic regulation of environmental gradients on the reproductive stages. Furthermore, we developed a random forest-based genetic maturity model that can predict genetic and environmental effects across a wide range of genotypes. This study broadens the understanding of the factors that contribute to reproductive development, which will help to develop cultivars that combine the optimal combinations of stage durations for a higher seed yield and enhanced resilience.

genetics↗

bioRxiv2020

Meta-GWAS for quantitative trait loci identification in soybean

We report a meta-Genome Wide Association Study involving 73 published studies in soybean (Glycine max L. [Merr.]) covering 17,556 unique accessions, with improved statistical power for robust detection of loci associated with a broad range of traits. De novo GWAS and meta-analysis were conducted for composition traits including fatty acid and amino acid composition traits, disease resistance traits, and agronomic traits including seed yield, plant height, stem lodging, seed weight, seed mottling, seed quality, flowering timing, and pod shattering. To examine differences in detectability and test statistical power between single- and multi-environment GWAS, comparison of meta-GWAS results to those from the constituent experiments were performed. Using meta-GWAS analysis and the analysis of individual studies, we report 483 quantitative trait loci (QTL) at 393 unique loci. Using stringent criteria to detect significant marker trait associations, 66 candidate genes were identified, including 17 candidate genes for agronomic traits, 19 for seed related traits, and 33 for disease reaction traits. This study identified potentially valuable candidate genes that affect multiple traits. The success in narrowing down the genomic region for some loci through overlapping mapping results of multiple studies is a promising avenue for community-based studies and plant breeding applications.

genetics↗

bioRxiv2020

PATRIOT: A pipeline for tracing identical-by-descent chromosome segments to improve genomic prediction in self-pollinating crop species

The lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene. This prevents parsing out genetic effects associated with the underlying allele of that gene among the set of ancestral haplotypes. We present Parental Allele Tracing, Recombination Identification, and Optimal predicTion (i.e., PATRIOT) approach that utilizes marker data to allow for a rapid identification of lines carrying specific alleles, increases the accuracy of genomic relatedness and diversity estimates, and improves genomic prediction. Leveraging identity by descent, PATRIOT showed an improvement in GP accuracy by 16.6% compared to the traditional rrBLUP method. This approach will help to increase the rate of genetic gain and allow available information to be more effectively utilized within breeding programs.

genomics↗

bioRxiv2018

Integrating genotype and weather variables for soybean yield prediction using deep learning

Realized performance of complex traits is dependent on both genetic and environmental factors, which can be difficult to dissect due to the requirement for multiple replications of many genotypes in diverse environmental conditions. To mediate these problems, we present a machine learning framework in soybean (Glycine max (L.) Merr.) to analyze historical performance records from Uniform Soybean Tests (UST) in North America, with an aim to dissect and predict genotype response in multiple envrionments leveraging pedigree and genomic relatedness measures along with weekly weather parameters. The ML framework of Long Short Term Memory - Recurrent Neural Networks works by isolating key weather events and genetic interactions which affect yield, seed oil, seed protein and maturity enabling prediction of genotypic responses in unseen environments. This approach presents an exciting avenue for genotype x environment studies and enables prediction based systems. Our approaches can be applied in plant breeding programs with multi-environment and multi-genotype data, to identify superior genotypes through selection for commercial release as well as for determining ideal locations for efficient performance testing.

bioinformatics↗