Biology subjects

Cannon, S. B.

Publications and source records attributed to Cannon, S. B..

9 recordsLinked to original sources

bioRxiv2024

Legume genome structures and histories inferred from Cercis canadensis and Chamaecrista fasciculata genomes

O_LIThe legume family originated ca. 70 million years ago and soon diversified into at least six lineages (now extant subfamilies). The signal of whole genome duplications (WGD) is apparent in species sampled from all six subfamilies. The early diversification has posed difficulties for resolving the legume backbone structure and the timing of WGDs. C_LIO_LIIn this study, we report the genome sequences and annotations for Cercis canadensis (Cercidoideae) and Chamaecrista fasciculata (Caesalpinoideae) to help resolve the relative taxonomic placements along the legume backbone, the timings of WGDs relative to subfamily origins, and the ancestral legume karyotype. C_LIO_LIAnalyses of genome assemblies from four subfamilies within Fabaceae show that the last common ancestor of all legumes likely had seven chromosomes, with a genome structure similar to the extant Cercis genome. Our analysis supports an allopolyploid origin of the subfamily Caesalpinoideae, with progenitors involving lineages along the backbone of the legume phylogeny. C_LIO_LIA probable allopolyploid origin of Caesalpinoideae subfamily provides a partial explanation for the difficulty in resolving the structure of the legume backbone. The retained karyotype structure and lack of a WGD in the last 100+ Mya, underscore the utility of the Cercis genome as an ancestral reference for the legume family. C_LI

genomics↗

bioRxiv2024

Complex Reticulation in Backbone Subfamily Relationships in Leguminosae

Contradictory lines of evidence have made it difficult to resolve the phylogenetic history of the legume diversification era; this is true for the backbone topology, and for the number and timing of whole genome duplications (WGDs). By analyzing the transcriptomic data for 473 gene families in 76 species covering all six accepted legume subfamilies, we assessed the phylogenetic relationships of the legume backbone and uncovered evidence of independent whole genome duplications in each of the six legume subfamilies. Three subfamilies -- Cercidoideae, Dialioideae, and Caesalpinioideae -- bear evidence of an allopolyploid duplication pattern suggestive of ancient hybridization. In Cercidoideae and Dialioideae, the hybridization appears to be within-subfamily, with the genera Cercis and Poeppigia apparently unduplicated descendants of one of the parental lineages; in Caesalpinioideae, the hybridization appears to involve a member of the Papilionoideae lineage, and some other lineage, potentially extinct. Three independent lines of evidence, consisting of a concatenated superalignment, concordance factor analysis of the set of gene family alignments and topologies, and direct inference of reticulation events via maximum pseudo-likelihood implemented by PhyloNet, converged on a single backbone hypothesis and the above hypotheses of reticulate evolution. Significance StatementIn a hybridization event, genes that have already been evolving separately for potentially millions of years become sister chromosomes, yet remain related to one another not at the moment of hybridization, but at the speciation node of the hybrids parents. Methodologies based on counts of bursts of duplicated genes, can therefore be fundamentally vulnerable to incorrect and contradictory conclusions about the number and timing of WGD events, unless interpreted carefully and in combination with data from gene trees discordant with the consensus backbone. Our assessment of the legume backbone in that light, resolves previous contradictory findings by concluding that three legume subfamilies are allopolyploid relative to the ur-legume.

evolutionary biology↗

bioRxiv2024

Assembly, comparative analysis, and utilization of a single haplotype reference genome for soybean

Cultivar Williams 82 has served as the reference genome for the soybean research community since 2008, but is known to have areas of genomic heterogeneity among different sub-lines. This work provides an updated assembly (version Wm82.a6) derived from a specific sub-line known as Wm82-ISU-01 (seeds available under USDA accession PI 704477). The genome was assembled using Pacific BioSciences HiFi reads and integrated into chromosomes using HiC. The 20 soybean chromosomes assembled into a genome of 1.01Gb, consisting of 36 contigs. The genome annotation identified 48,387 gene models, named in accordance with previous assembly versions Wm82.a2 and Wm82.a4. Comparisons of Wm82.a6 with other near-gapless assemblies of Williams 82 reveal large regions of genomic heterogeneity, including regions of differential introgression from the genotype Kingwa within approximately 30 Mb and 25 Mb segments on chromosomes 03 and 07, respectively. Additionally, our analysis revealed a previously unknown large ([~]20 Mb) heterogeneous region in the pericentromeric region of chromosome 12, where Wm82.a6 matches the Williams haplotype while the other two near-gapless assemblies do not match the haplotype of either parent of Williams 82. In addition to the Wm82.a6 assembly, we also assembled the genome of soybean line Fiskeby III, a rich resource for abiotic stress resistance genes. A genome comparison of Wm82.a6 with Fiskeby III revealed the nucleotide and structural polymorphisms between the two genomes within a QTL region for iron deficiency chlorosis resistance. The Wm82.a6 and Fiskeby III genomes described here will enhance comparative and functional genomics capacities and applications in the soybean community.

plant biology↗

bioRxiv2022

CAPG: Comprehensive Allopolyploid Genotyper

MotivationGenotyping by sequencing is a powerful tool for investigating genetic variation in plants, but many economically important plants are allopolyploids, where homoeologous similarity obscures the subgenomic origin of reads and confounds allelic and homoeologous SNPs. Recent polyploid genotyping methods use allelic frequencies, rate of heterozygosity, parental cross or other information to resolve read assignment, but good subgenomic references offer the most direct information. The typical strategy aligns reads to the joint reference, performs diploid genotyping within each subgenome, and filters the results, but persistent read misassignment results in an excess of false heterozygous calls. ResultsWe introduce the Comprehensive Allopolyploid Genotyper (CAPG), which formulates an explicit likelihood to weight read alignments against both subgenomic references and genotype individual allopolyploids from whole genome resequencing (WGS) data. We demonstrate CAPG in allotetraploids, where it performs better than GATKs HaplotypeCaller applied to reads aligned to the combined subgenomic references. AvailabilityCode and tutorials are available at https://github.com/Kkulkarni1/CAPG.git.

bioinformatics↗

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↗

bioRxiv2020

Applications of spatial models to ordinal data: Geospatial Statistics for Soybean Iron Deficiency Chlorosis

Models have been developed to account for heterogeneous spatial variation in field trials. These spatial models have been shown to successfully increase the quality of phenotypic data resulting in improved effectiveness of selection by plant breeders. The models were developed for continuous data types such as grain yield and plant height, but data for most traits, such as in iron deficiency chlorosis (IDC), are recorded on ordinal scales. Is it reasonable to make spatial adjustments to ordinal data by simply applying methods developed for continuous data? The objective of the research described herein is to evaluate methods for spatial adjustment on ordinal data, using soybean IDC as an example. Spatial adjustment models are classified into three different groups: group I, moving average grid adjustment; group II, geospatial autoregressive regression (SAR) models; and group III, tensor product penalized P-splines. Comparisons of eight models sampled from these three classes demonstrate that spatial adjustments depend on severity of field heterogeneity, the irregularity of the spatial patterns, and the model used. SAR models generally produce better performance metrics than other classes of models. However, none of the eight evaluated models fully removed spatial patterns indicating that there is a need to either adjust existing models or develop novel models for spatial adjustments of ordinal data collected in fields exhibiting discontinuous transitions between heterogeneous patches.

plant biology↗

bioRxiv2020

Genotypic characterization of the U.S. peanut core collection

Cultivated peanut (Arachis hypogaea) is an important oil, food, and feed crop worldwide. The USDA peanut germplasm collection currently contains 8,982 accessions. In the 1990s, 812 accessions were selected as a core collection on the basis of phenotype and country of origin. The present study reports genotyping results for the entire available core collection. Each accession was genotyped with the Arachis_Axiom2 SNP array, yielding 14,430 high-quality, informative SNPs across the collection. Additionally, a subset of 253 accessions was replicated, using between two and five seeds per accession, to assess heterogeneity within these accessions. the genotypic diversity of the core is mostly captured in five genotypic clusters, which have some correspondence with botanical variety and market type. There is little genetic clustering by country of origin, reflecting peanuts rapid global dispersion in the 18th and 19th centuries. A genetic cluster associated with the hypogaea/aequatoriana/peruviana varieties, with accessions coming primarily from Bolivia, Peru, and Ecuador, is consistent with these having been the earliest landraces. The genetics, phenotypic characteristics, and biogeography are all consistent with previous reports of tetraploid peanut originating in Southeast Bolivia. Analysis of the genotype data indicates an early genetic radiation, followed by regional distribution of major genetic classes through South America, and then a global dissemination that retains much of the early genetic diversity in peanut. Comparison of the genotypic data relative to alleles from the diploid progenitors also indicates that subgenome exchanges, both large and small, have been major contributors to the genetic diversity in peanut. All data is available at the National Ag Library: https://doi.org/10.15482/USDA.ADC/1518508 and at PeanutBase: https://peanutbase.org/data/public/Arachis_hypogaea/mixed.esm.KNWV

genomics↗

bioRxiv2020

A Sequence-Pair-Classification-Based Method For Detecting And Correcting Under-Clustered Gene Families

Gene families are groups of genes that have descended from a common ancestral gene present in the species under study. Current, widely used gene family building algorithms can produce family clusters that may be fragmented or missing true family sequences (under-clustering). Here we present a classification method based on sequence pairs that, first, inspects given families for under-clustering and then predicts the missing sequences for the families using family-specific alignment score cutoffs. We have tested this method on a set of curated, gold-standard ("true") families from the Yeast Gene Order Browser (YGOB) database, including 20 yeast species, as well as a test set of intentionally under-clustered ("deficient") families derived from the YGOB families. For 83% of the modified yeast families, our pair-classification method was able to reliably detect under-clustering in "deficient" families that were missing 20% of sequences relative to the full/" true" families. We also attempted to predict back the missing sequences using the family-specific alignment score cutoffs obtained during the detection phase. In the case of "pure" under-clustered families (under-clustered families with no "wrong"/unrelated sequences), for 78% of families the prediction precision and recall was [≥]0.75, with mean precision = 0.928 and mean recall = 0.859. For "impure" under-clustered families, (under-clustered families containing closest sequences from outside the family, in addition to missing true family sequences), the prediction precision and recall was [≥]0.75 for 63% of families with mean precision = 0.790 and mean recall = 0.869. To check if our method can detect and correct incomplete families obtained using existing family building methods, we attempted to correct 374 under-clustered yeast families produced using the OrthoFinder tool. We were able to predict missing sequences for at least 19 yeast families with mean precision of 0.9 and mean recall of 0.65. We also analyzed 14,663 legume families built using the OrthoFinder program, with 14 legume species. We were able to identify 1,665 OrthoFinder families that were missing one or more sequences - sequences which were previously un-clustered or clustered into unusually small families. Further, using a simple merging strategy, we were able to merge 2,216 small families into 933 under-clustered families using the predicted missing sequences. Out of the 933 merged families, we could confirm correct mergings in at least 534 families using the maximum-likelihood phylogenies of the merged families. We also provide recommendations on different types of family-specific alignment score cutoffs that can be used for predicting the missing sequences based on the "purity" of under-clustered families and the chosen precision and recall for prediction. Finally, we provide the containerized version of the pair-classification method that can be applied on any given set of gene families.

bioinformatics↗

bioRxiv2019

The genome of cowpea (Vigna unguiculata Walp.)

Cowpea (Vigna unguiculata [L.] Walp.) is a major crop for worldwide food and nutritional security, especially in sub-Saharan Africa, that is resilient to hot and drought-prone environments. A high-quality assembly of the single-haplotype inbred genome of cowpea IT97K-499-35 was developed by exploiting the synergies between single molecule real-time sequencing, optical and genetic mapping, and a novel assembly reconciliation algorithm. A total of 519 Mb is included in the assembled sequences. Nearly half of the assembled sequence is composed of repetitive elements, which are enriched within recombination-poor pericentromeric regions. A comparative analysis of these elements suggests that genome size differences between Vigna species are mainly attributable to changes in the amount of Gypsy retrotransposons. Conversely, genes are more abundant in more distal, high-recombination regions of the chromosomes; there appears to be more duplication of genes within the NBS-LRR and the SAUR-like auxin superfamilies compared to other warm-season legumes that have been sequenced. A surprising outcome of this study is the identification of a chromosomal inversion of 4.2 Mb among landraces and cultivars, which includes a gene that has been associated in other plants with interactions with the parasitic weed Striga gesnerioides. The genome sequence also facilitated the identification of a putative syntelog for multiple organ gigantism in legumes. A new numbering system has been adopted for cowpea chromosomes based on synteny with common bean (Phaseolus vulgaris).

genomics↗