Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.02.02.429409

INCORPORATING A DYNAMIC GENE-BASED PROCESS MODULE INTO A CROP SIMULATION MODEL

Abstract

Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as "genetic coefficients." These GSPs are estimated using phenotypic observations and may not represent "true" genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study conducted in 2011 and 2012 included 187 recombinant inbred lines (RILs) from a bi-parental bean family to measure the effects of quantitative trait loci (QTL), environment (E), and QTLxE interactions across five sites. The dynamic mixed linear model from Vallejos et al. (2020) was modified in this study to create a dynamic module that was then integrated into the CSM-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic makeup of each genotype being simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and GxE effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Oliveira, F. A. A., Jones, J. W., Pavan, W., Bhakta, M., Vallejos, C. E., Correll, M. J., Boote, K. J., Fernandes, J. M. C., Hölbig, C. A., Hoogenboom, G.. 2021-02-03. INCORPORATING A DYNAMIC GENE-BASED PROCESS MODULE INTO A CROP SIMULATION MODEL. https://doi.org/10.1101/2021.02.02.429409

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Genome-wide diversity across 130 years reveals drastic post-bottleneck genetic erosion in a recovering population of golden eagles (Aquila chrysaetos)

Many wild populations have experienced severe human-induced declines, but the genetic consequences of such bottlenecks are often poorly understood. In Finland, as well as in many other countries, golden eagles were heavily persecuted during the 19th-20th centuries, disappearing from southern regions and reaching their lowest population size in the 1950-1960s. Although conservation efforts facilitated demographic recovery, the long-term genomic consequences of the bottleneck remained unknown. Here, we used whole-genome sequencing data spanning 136 years to evaluate patterns of genetic diversity in 26 Finnish golden eagles before, during, and after the mid-20th century bottleneck. We found substantial loss of genetic variation, including an 82% reduction in genome-wide heterozygosity, a 42% decline in nucleotide diversity, a 67% reduction in Watterson's theta, and a substantial loss of variable sites. Analyses of the site frequency spectrum and Tajima's D confirmed historical population contraction, and runs of homozygosity analysis indicated elevated post-bottleneck inbreeding. These results indicate strong genetic erosion in Finnish golden eagles despite the census size recovery. Therefore, incorporating genomic indicators in conservation monitoring and performing regional assessments are necessary to guide management and preserve biodiversity in line with the Global Biodiversity Framework's goal of maintaining genetic variation.

genomics↗

Long-read analysis of complete human 5S rDNA arrays reveals a zoned organization with two-state unit methylation linked to chromatin architecture

Despite the fundamental importance of the 5S ribosomal DNA (rDNA) to all cellular life, the landscape of its units by position along its array has remained poorly understood. Here, recent telomere-to-telomere long-read sequencing is used to resolve 5S rDNA arrays from flank to flank and to follow the state of each unit along single molecules. The array is zoned at both the sequence and the epigenetic level. Notably, methylation is bimodal at the level of the unit, and the combination of low-methylation and high-methylation units gives rise to the overall methylation pattern of the array. Moreover, the average number of low-methylation units remains relatively constant regardless of array length. Chromatin fiber sequencing (Fiber-seq) data further show that low-methylation units are more accessible, have lower nucleosome occupancy and are more frequently footprinted at the TFIIIB-binding site, consistent with analyses of ATAC-seq and ChIP-seq data from cell lines. Across species, this pattern is shared among apes, whereas other species show diverse patterns. These results provide a unit-resolved view of the human 5S rDNA array and reveal a zone-specific organization of unit methylation that is associated with chromatin architecture.

genomics↗

Using Oyster Genomics to Understand Human Health

As sessile filter feeders that experience continuous environmental exposure, oysters, particularly Crassostrea virginica, offer a unique opportunity to understand how environmental exposure alters the genome and influence health. Furthermore, oysters must be sorted by size for market value for premium size and taste. However, it requires intensive labor, specialized equipment (tumblers), and is susceptible to environmental risks like pollution. Therefore, it is desirable to produce uniform oysters without machines. However, limitations in the previous reference genome [2] have constrained our understanding of how genes are organized and maintained in oyster populations. To this end, we harvested 240 adult oysters in same location, 120 extremely large size and 120 extremely small, controlled with all confounding factors such as age and water temperature. We perform a genome wise associate study (GWAS) using next generation whole genome sequencing, aiming to discover genomic regions associated with oyster size. Using PacBio HiFi, Illumina, and Hi-C technologies, we generated a 99.7% complete chromosome-level genome assembly for C. virginica (NCBI RefSeq GCF_053477285.1) [75]. We identified a ~13 Mb region on chromosome 1 uniquely expanded in C. virginica and enriched for innate immune and stress-response genes. To evaluate whether these structural features are reflected at the population level, we then conducted a k-mer analysis. This revealed differential sequence patterns by size, including enrichment of bacterial DNA in smaller individuals and host variation at the RNF213 locus within this region. By highlighting genome structure as a mediator of immune resilience, this work supports the use of oysters as sentinels to advance out understanding of how the dynamic reassembly of the genome due to environmental exposures influence health in human populations.

genomics↗