Biology subjects

Ozias-Akins, P.

Publications and source records attributed to Ozias-Akins, P..

11 recordsLinked to original sources

bioRxiv2026

BABYBOOM-like expression in the cowpea egg and central cellenables parthenogenesis, endosperm development, and viable haploid seed formation

Introductory Paragraph (Nature Plants format)Parthenogenesis or fertilization-independent embryogenesis occurs at low frequency in sexual plants. Expression of BABYBOOM-like (BBML) and PARTHENOGENESIS (PAR) genes in the egg cell of several diploid dicot crops induce parthenogenesis at varying frequency; however, recovery of viable haploid seeds has rarely been reported, perhaps due to a lack of viable endosperm formation. In the legume cowpea (Vigna unguiculata L. Walp), ectopic egg cell expression of the endogenous BBML homolog (VuBBML1) and PAR from Taraxacum officinale induces parthenogenesis; however, seeds abort as endosperm formation is blocked following self-pollination. Expression of VuBBML1 in both the egg cell and central cell, together with central cell fertilization following self-pollination, results in viable seeds that germinate and give rise to haploid plants. VuBBML1 has a functional role in the formation of cowpea embryo and endosperm seed compartments. This finding opens possibilities for establishing double haploid production during homozygous parental breeding, and asexual seed induction for fixing hybrid vigor in cowpea.

plant biology↗

bioRxiv2025

Scaling up orphan crop research: A global genetic perspective of cowpea (Vigna unguiculata) diversity from 10,617 accessions

Cowpea (Vigna unguiculata L. Walp) is a dryland legume crop, providing essential food and nutritional security for millions of people across the semi-arid tropics, in Africa, Asia, and Latin America. However, as a typical "orphan crop", cowpea has long remained underrepresented in global genomic research to support crop improvement. Here, we conducted the largest genetic diversity analysis of cowpea to date, comprising 10,617 accessions sourced from seven international collections. Using genotyping-by-sequencing, we characterized the global patterns of genetic diversity, assessed redundancy within and across collections, and examined the geographic structure of the cowpea global allele pool. Our results revealed nine distinct genetic groups with clear geographic associations and fine-scale population differentiation, reflecting dispersal history, regional adaptation and the influence of modern breeding. Duplication across collections was detected, highlighting the need for improved curation and integration of germplasm resources. Landraces from sub-Saharan Africa do not fully capture the genetic diversity present in several other geographic regions, indicating the existence of abundant and untapped genetic resources worldwide. These findings not only provide insights into the genetic structure and evolutionary history of cowpea but also offer a valuable foundation for harnessing global germplasm diversity to enhance breeding potential and accelerate crop improvement.

plant biology↗

bioRxiv2025

Long-Read Low-Pass Sequencing for High-Resolution Trait Mapping

Accelerating crop improvement is critical to meeting food security demands in a changing climate. Long-read sequencing offers advantages over short-reads in resolving structural variations (SVs) and aligning to complex genomes, but its high cost has limited adoption in breeding programs. Here we develop a high-throughput, scalable approach for long-read low-pass (LRLP) sequencing and variant analysis with PacBio HiFi reads, and apply it to trait mapping in a complex tetraploid peanut (Arachis hypogaea) genome multi-parent advanced generation intercross. We analyze LRLP using both a single reference genome and a pangraph, using both proprietary and open-source tools to analyze SVs and coverage. An increased number of variants are consistently called for LRLP data compared to short-read data. At 1.63x average depth, LRLP sequencing covered 55% of the genome and 58% of gene space, outperforming 1.68x depth short-read low-pass sequencing, which achieved only 17% and 11%, respectively. Enhanced data retention after filtering for probabilistic misalignment and an [~]8.5x decrease in cost per value further demonstrated LRLPs efficacy. Our results highlight LRLP sequencing as a scalable, cost-effective tool for high-resolution trait mapping, with transformative potential for plant breeding and broader genomic applications.

genetics↗

bioRxiv2021

Genome-wide approaches delineate the additive, epistatic, and pleiotropic nature of variants controlling fatty acid composition in peanut (Arachis hypogaea L.).

The fatty acid composition of seed oil is a major determinant of the flavor, shelf-life, and nutritional quality of peanuts. Major QTLs controlling high oil content, high oleic content, and low linoleic content have been characterized in several seed oil crop species. Here we employ genome-wide association approaches on a recently genotyped collection of 787 plant introduction accessions in the USDA peanut core collection, plus selected improved cultivars, to discover markers associated with the natural variation in fatty acid composition, and to explain the genetic control of fatty acid composition in seed oils. Overall, 251 single nucleotide polymorphisms (SNPs) had significant trait associations with the measured fatty acid components. Twelve SNPs were associated with two or three different traits. Of these loci with apparent pleiotropic effects, 10 were associated with both oleic (C18:1) and linoleic acid (C18:2) content at different positions in the genome. In all 10 cases, the favorable allele had an opposite effect - increasing and lowering the concentration, respectively, of oleic and linoleic acid. The other traits with pleiotropic variant control were palmitic (C16:0), behenic (C22:0), lignoceric (C24:0), gadoleic (C20:1), total saturated, and total unsaturated fatty acid content. One hundred (100) of the significantly associated SNPs were located within 1000 kbp of 55 genes with fatty acid biosynthesis functional annotations. These genes encoded, among others: ACCase carboxyl transferase subunits, and several fatty acid synthase II enzymes. With the exception of gadoleic (C20:1) and lignoceric (C24:0) acid content, which occur at relatively low abundance in cultivated peanut, all traits had significant SNP interactions exceeding a stringent Bonferroni threshold ( = 1%). We detected 7,682 pairwise SNP interactions affecting the relative abundance of fatty acid components in the seed oil. Of these, 627 SNP pairs had at least one SNP within 1000 kbp of a gene with fatty acid biosynthesis functional annotation. We evaluated 168 candidate genes underlying these SNP interactions. Functional enrichment and protein-to-protein interactions supported significant interactions (p- value < 1.0E-16) among the genes evaluated. These results show the complex nature of the biology and genes underlying the variation in seed oil fatty acid composition and contribute to an improved genotype-to-phenotype map for fatty acid variation in peanut seed oil. Key phrasesSNP Genotyping, Genome-wide Association Study (GWAS), GWAS of interacting SNPs (GWASi), Pleiotropy, Seed fatty acid composition, Oleic-Linoleic acid ratio.

genomics↗

bioRxiv2021

Identification of a Major Locus for Flowering Pattern Sheds Light on Plant Architecture Diversification in Cultivated Peanut

Flowering pattern is a major taxonomic characteristic differentiating the two main subspecies of cultivated peanut (Arachis hypogaea L.). subsp. fastigiata possessing flowers on the mainstem (MSF) and a sequential flowering pattern, whereas subsp. hypogaea lacks flowers on the mainstem and exhibits an alternate flowering pattern. This character is considered the main contributor to plant architecture and the adaptability of each subgroup to specific growing conditions. Evidence indicates that flowering pattern differentiation occurred during the several thousand years of domestication and diversification in South America. However, the exact genetic mechanism that controls flowering pattern and the molecular changes that led to its historical diversification in peanut are unknown. We investigated the genetics of the flowering pattern in a recombinant inbred population of 259 lines (RILs), derivatives of an A. hypogaea and A. fastigiata cross. RILs segregated 1:1 in both the sequential/alternative and the MSF-plus/MSF-minus traits, indicating a single gene effect. Using the Axiom_Arachis2 SNP-array, MSF was mapped to a 1.7 Mbp segment on chromosome B02 of the cultivated A. hypogaea. Significant haplotype conservation was found for this locus in the USA peanut mini core collection, suggesting a possible selection upon hypogaea/fastigiata speciation. Furthermore, a candidate Terminal Flowering 1-like (AhTFL1) gene was identified within the MSF region, in which a 1492 bp deletion occurred in the fastigiata line that leads to a truncated protein product. Remapping MSF in the RIL population with the AhTFL1 deletion as a marker increased the LOD score from 53.3 to 158.8 with no recombination. The same deletion was also found to co-segregate with the phenotype in two EMS-mutagenized M2 families, suggesting a hotspot for large mutational deletion or gene conversion that may play a role in evolution. BLASTX analysis showed that the most similar homologous gene for TFL1-like in soybean is Det1, which previously was shown to control shoot determination. Sequence analysis of the TFL-1 in a series of domesticated lines showed that TFL1 was subjected to gain/loss events of the deletion, partly explaining the evolution of MSF in Arachis. Altogether, these results support the role of AhTFL-1 in peanut speciation during domestication and modern cultivation.

genetics↗

bioRxiv2021

Accurate analysis of short read sequencing in complex genomes: A case study using QTL-seq to target blanchability in peanut (Arachis hypogaea)

Next Generation sequencing was a step change for molecular genetics and genomics. Illumina sequencing in particular still provides substantial value to animal and plant genomics. A simple yet powerful technique, referred to as QTL sequencing (QTL-seq) is susceptible to high levels of noise due to ambiguity of alignment of short reads in complex regions of the genome. This noise is particularly high when working with polyploid and/or outcrossing crop species, which impairs the efficacy of QTL-seq in identifying functional variation. By filtering loci based on the optimal alignment of short reads, we have developed a pipeline, named Khufu, that substantially improves the accuracy of QTL-seq analysis in complex genomes, allowing de novo variant discovery directly from bulk sequence. We first demonstrate the pipeline by identifying and validating loci contributing to blanching percentage in peanut using lines from multiple related populations. Using other published datasets in peanut, Brassica rapa, Hordeum volgare, Lactua satvia, and Felis catus, we demonstrate that Khufu produces more accurate results straight from bulk sequence. Khufu works across species, genome ploidy level, and data types. In cases where identified QTL were fine mapped, the fine mapped region corresponds to the top of the peak identified by Khufu. The accuracy of Khufu allows the analysis of population sequencing at very low coverage (<3x), greatly decreasing the amount of sequence needed to genotype even the most complex genomes.

genomics↗

bioRxiv2021

AhNGE : A Database for Arachis hypogaea Nodule Developmental Gene Expression

Symbiotic nitrogen fixation (SNF) inside root-nodules is a primary and sustainable source of soil nitrogen. Understanding nodule development and metabolism in crop legumes may lead to more effective SNF in agriculture. Peanut (Arachis hypogaea) is an economically important allotetraploid legume with non-canonical nodule developmental features. Recent genome sequencing of peanut has opened the possibility of making peanut a model for studying atypical nodule development. To help the community of nodule biologists, we have developed a database called AhNGE (Arachis hypogaea Nodule Developmental Gene Expression: http://nipgr.ac.in/AhNGE/index.php). AhNGE contains RNAseq data from six data points of nodule development in A. hypogagea cv. Tifrunner. This data represents a dynamic view of gene expression during peanut nodule development. Research in model legumes has generated a huge knowledgebase in the last twenty years. To streamline comparative genomics among legumes, we performed ortholog analysis among four legumes (Cicer, Glycine, Lotus, and Medicago) and one non-legume (Arabidopsis). This will facilitate the integration of existing knowledge in nodule development with the Arachis transcriptome. The available data can be retrieved using a single or batch query or searching using gene ID, from above mentioned five species. The output displays the gene expression pattern in graphical as well as tabular form, along with further options to download the sequence data. The database is linked with PeanutBase, the main genomic resource of peanut. Additionally, the expression level of different splicing variants can be retrieved from the database. In summary, AhNGE serves as an important resource for the scientific community working on nodule development.

bioinformatics↗

bioRxiv2019

Developing a rapid and highly efficient cowpea regeneration and transformation system using embryonic axis explants

Cowpea is one of the most important legume crops planted worldwide, especially in Sub-Saharan Africa and Asia. Despite decades of effort, genetic engineering of cowpea is still challenging due to inefficient in vitro shoot regeneration, Agrobacterium-mediated T-DNA delivery and transgenic selection. Here, we report a rapid and highly efficient cowpea transformation system using embryonic axis explants isolated from imbibed mature seeds. We found that removal of the shoot apical meristem by cutting through the middle of the epicotyl stimulated direct multiple shoot organogenesis from the cotyledonary node tissue. Furthermore, the application of a ternary transformation vector system using an optimized pVIR accessory plasmid provided high levels of Agrobacterium-mediated gene delivery. The utilization of spectinomycin as the selection agent enabled more efficient transgenic selection and plant recovery. Transgenic cowpea shoots developed exclusively from the cotyledonary nodes at high frequencies of 4.5 to 37% across a wide range of cowpea genotypes. We believe that the transformation principles established in this study could also be applied to other legumes to increase transformation efficiencies.

plant biology↗

bioRxiv2019

Genetic analysis of seed and pod traits in a set of Recombinant Inbred Lines (RILs) in peanut (Arachis hypogaea L.)

Although seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explorethe distribution of recombination and potential chromosomal rearrangements. These traits were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.

plant biology↗

bioRxiv2019

Major QTLs for resistance to early and late leaf spot diseases are identified on chromosomes 3 and 5 in peanut (Arachis hypogaea)

Early and late leaf spots are the major foliar diseases of peanut responsible for severely decreased yield in the absence of intensive fungicide spray programs. Pyramiding host resistance to leaf spots in elite cultivars is a sustainable solution to mitigate the diseases. In order to determine the genetic control of leaf spot disease resistance in peanut, a recombinant inbred line population (Florida-07 x GP-NC WS16) segregating for resistance to both diseases was used to construct a SNP-based linkage map consisting of 855 loci. QTL mapping revealed three resistance QTLs for late leaf spot qLLSA05 (phenotypic variation explained, PVE=7-10%), qLLSB03 (PVE=5-7%), and qLLSB05 (PVE=15-41%) that were consistently expressed over multi-year analysis. Two QTL, qLLSA05 and qLLSB05, confirmed our previously published QTL-seq results. For early leaf spot, three resistance QTLs were identified in multiple years, two on chromosome A03 (PVE=8-12%) and one on chromosome B03 (PVE=13-20%), with the locus qELSA03_1.1 coinciding with the previously published genomic region for LLS resistance in GPBD4. Comparative analysis of the genomic regions spanning the QTLs suggests that resistance to early and late leaf spots are largely genetically independent. In addition, QTL analysis on yield showed that the presence of resistance allele in qLLSB03 and qLLSB05 loci might result in protection from yield loss caused by LLS disease damage. Finally, post hoc analysis of the RIL subpopulation that was not utilized in the QTL mapping revealed that the flanking markers for these QTLs can successfully select for resistant and susceptible lines, confirming the effectiveness of pyramiding these resistance loci to improve host-plant resistance in peanut breeding programs using marker-assisted selection.

genetics↗

bioRxiv2018

Machine learning as an effective method for identifying true SNPs in polyploid plants

Single Nucleotide Polymorphisms (SNPs) have many advantages as molecular markers since they are ubiquitous and co-dominant. However, the discovery of true SNPs especially in polyploid species is difficult. Peanut is an allopolyploid, which has a very low rate of true SNP calling. A large set of true and false SNPs identified from the Arachis 58k Affymetrix array was leveraged to train machine learning models to select true SNPs straight from sequence data. These models achieved accuracy rates of above 80% using real peanut RNA-seq and whole genome shotgun (WGS) re-sequencing data, which is higher than previously reported for polyploids. A 48K SNP array, Axiom Arachis2, was designed using the approach which revealed 75% accuracy of calling SNPs from different tetraploid peanut genotypes. Using the method to simulate SNP variation in peanut, cotton, wheat, and strawberry, we show that models built with our parameter sets achieve above 98% accuracy in selecting true SNPs. Additionally, models built with simulated genotypes were able to select true SNPs at above 80% accuracy using real peanut data, demonstrating that our model can be used even if real data are not available to train the models. This work demonstrates an effective approach for calling highly reliable SNPs from polyploids using machine learning. A novel tool was developed for predicting true SNPs from sequence data, designated as SNP-ML (SNP-Machine Learning, pronounced \"snip mill\"), using the described models. SNP-ML additionally provides functionality to train new models not included in this study for customized use, designated SNP-MLer (SNP-Machine Learner, pronounced \"snip miller\"). SNP-ML is freely available for public use.

bioinformatics↗