bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.04.01.715717

Field and lab phenomics facilitate detection of genetic variation for iron deficiency chlorosis tolerance in sorghum

Abstract

Bioavailability of iron, an essential micronutrient to plants, is low in alkaline or calcareous soils, which are prevalent across semi-arid production regions. Breeding efforts to increase tolerance to iron deficiency chlorosis (IDC) in sorghum, a major crop of semi-arid regions, are confounded by spatial variation of stress severity in field trials. Here we developed and validated two high-throughput phenotyping approaches to address this challenge, with multi-spectral aerial imaging in the field and a controlled-environment assay to isolate the effects of iron bioavailability. In the field, severity and uniformity of stress are highly predictive of genetic signals for IDC tolerance (R2 > 0.6 for soil pH metrics and H2). Plot-level data filtering for stress conditions based on control genotypes successfully addresses field spatial variation (unfiltered H2 = 0.18 vs. filtered H2 = 0.4). The controlled-environment assay proxies field stress using iron sources with differential bioavailability, evidenced by high heritability ( H2 = 0.98) and phenotypic differential for hybrid control genotypes that matches field performance. Finally, we show that assay phenotypes are suitable for genome-wide association studies in global germplasm. Together, these field and lab phenomic approaches can be deployed to understand genetics of IDC tolerance and develop crops resilient to alkaline soils. HIGHLIGHTStress severity and uniformity greatly impact detection of genetic signals underlying iron deficiency chlorosis tolerance in sorghum. A controlled-environment assay reduces spatial heterogeneity and improves assessment of tolerance genetics.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cerimele, G., Kent, M., Miller, M., Best, R., Franks, C., Kakar, N., Felderhoff, T., Sexton-Bowser, S., Morris, G. P.. 2026-04-05. Field and lab phenomics facilitate detection of genetic variation for iron deficiency chlorosis tolerance in sorghum. https://doi.org/10.64898/2026.04.01.715717

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

KEEP EXPLORING

Related preprints

Large language model-based bibliometric evaluation of population descriptors in human genetics

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.

genetics↗

Mitigating biases of rescaling in forward-in-time population genetic simulations

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.

genetics↗

OPA1 controls mitochondrial dysfunction-driven liver fibrosis in MASLD

Progressive hepatic fibrosis is the principal determinant of morbidity and mortality in metabolic dysfunction-associated steatotic liver disease and steatohepatitis (MASLD/MASH). Mitochondrial dysfunction is a hallmark of MASH, and the release of mitochondrial damage-associated molecular patterns (mito-DAMPs) from injured hepatocytes can promote fibrosis. However, how mitochondrial dynamics and quality control shape the fibrotic response in MASLD/MASH remains unclear. Here, through large-scale genomic analyses of mitochondrial genes governing mitophagy, fusion and fission in human MASLD, with a power-equivalent sample size of approximately 700,000 individuals, we identify a strong association between hepatic fibrosis and the mitochondrial fusion factor dynamin-like GTPase optic atrophy 1 (OPA1). OPA1 transcripts and protein abundance in the liver epithelium were progressively dysregulated with advancing fibrosis. In mice, hepatocyte-specific OPA1 loss alone was sufficient to induce hepatic stellate cell activation and fibrosis in zone 3, promoted the release of mito-DAMPs into the circulation and exacerbated fibrosis in experimental MASH. These findings identify OPA1 as a central regulator of the hepatic fibrotic response and connect defective mitochondrial homeostasis to mito-DAMP release, hepatic stellate cell activation and fibrosis in MASLD.

genetics↗