Search bioRxiv⌕ Search

bioRxiv · 10.64898/2026.02.20.707111

Life, the universe, and everything for $42: ultra-low pass sequencing of maize for genotyping, mapping, and pedigree analysis

Abstract

Convenient and economical genotyping methods and simplified bioinformatic workflows are critical for genetic studies and breeding. The declining cost of sequencing library construction, sample multiplexing, and the advent of skim sequencing has reduced costs and enabled large-scale genetic and genomic experiments. Here, we present a simple skim sequencing and bioinformatics pipeline sufficient for various genotyping applications. Our low-depth skim sequencing method costs 21 USD per sample and provides an average of 144k reads. Our approach uses a double-stranded DNA sample to prepare libraries for genome sequencing. We demonstrate various uses for this strategy in maize, a complex and large (2.5 Gbp) genome. DNA from multiple pedigreed populations, including advanced backcrossed progenies, bi-parental populations, near-isogenic lines, and recombinant inbred lines, were used to map loci, detect donor introgressions, and determine introgression haplotypes. Read counts at known polymorphic positions detected donor genotypes even when derived from parents of unknown origin and could localize mutations of phenotypic impact via bulked segregant analysis. Remarkably, the small amount of sequencing data produced were sufficient to identify the haplotypes of introgressions of unknown origin from by comparison to known genotypes. Correct haplotype identification enabled more accurate allele frequencies to be calculated when mapping loci. This is of exceptional value in maize, where a rich collection of mutants from the 20th century are of unknown pedigree. One sentence summaryEconomical and efficient whole genome ultra-low pass sequencing of DNA samples for numerous genetic and genomic applications.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Khangura, R. S., Kaur, A., San Miguel, P. J., Dilkes, B. P.. 2026-02-23. Life, the universe, and everything for $42: ultra-low pass sequencing of maize for genotyping, mapping, and pedigree analysis. https://doi.org/10.64898/2026.02.20.707111

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↗