A Semi-Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses
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ABSTRACT:
SUBMITTER: Martins F
PROVIDER: S-EPMC8569613 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Drought severely limits forage productivity of C4 grasses across the tropics. The avoidance of water deficit by increasing the capacity for water uptake or by controlling water loss are common responses in forage C4 grasses. Napier grass (Pennisetum purpureum) and Brachiaria hybrid cv. Mulato II are tropical C4 grasses used for livestock production due to their reputed resistance to drought conditions. However, there is scant information on the mechanisms used by these grasses to overcome water-limited conditions. Therefore, assessments of cumulative transpired water, shoot growth, leaf rolling, leaf gas exchange, dry mass production and a number of morpho-physiological traits were recorded over a period of 21 days under well-watered or drought conditions. Drought reduced shoot dry mass of both grasses by 35 %, yet each grass exhibited contrasting strategies to cope with water shortage. Napier grass transpired most available water by the end of the drought treatment, whereas a significant amount of water was still available for Mulato II. Napier grass maintained carbon assimilation until the soil was fairly dry, whereas Mulato II restricted water loss by early stomatal closure at relatively wet soil conditions. Our results suggest that Napier grass exhibits a 'water-spending' behaviour that might be targeted to areas with intermittent drought stress, whereas Mulato II displays a 'water-saving' nature that could be directed to areas with longer dry periods.
| S-EPMC4595746 | biostudies-literature
Project description:Background and aimsThe C4Urochloa species (syn. Brachiaria) and Megathyrsus maximus (syn. Panicum maximum) are used as pasture for cattle across vast areas in tropical agriculture systems in Africa and South America. A key target for variety improvement is forage quality: enhanced digestibility could decrease the amount of land required per unit production, and enhanced lipid content could decrease methane emissions from cattle. For these traits, loss-of-function (LOF) alleles in known gene targets are predicted to improve them, making a reverse genetics approach of allele mining feasible. We therefore set out to look for such alleles in diverse accessions of Urochloa species and Megathyrsus maximus from the genebank collection held at the CIAT.MethodsWe studied allelic diversity of 20 target genes (11 for digestibility, nine for lipid content) in 104 accessions selected to represent genetic diversity and ploidy levels of U. brizantha, U. decumbens, U. humidicola, U. ruziziensis and M. maximum. We used RNA sequencing and then bait capture DNA sequencing to improve gene models in a U. ruziziensis reference genome to assign polymorphisms with high confidence.Key resultsWe found 953 non-synonymous polymorphisms across all genes and accessions; within these, we identified seven putative LOF alleles with high confidence, including those in the non-redundant SDP1 and BAHD01 genes present in diploid and tetraploid accessions. These LOF alleles could respectively confer increased lipid content and digestibility if incorporated into a breeding programme.ConclusionsWe demonstrated a novel, effective approach to allele discovery in diverse accessions using a draft reference genome from a single species. We used this to find gene variants in a collection of tropical grasses that could help reduce the environmental impact of cattle production.
| S-EPMC8422886 | biostudies-literature
Project description:BACKGROUND:Our group studies the interactions between cells of the brain and the neurotropic parasite Toxoplasma gondii. Using an in vivo system that allows us to permanently mark and identify brain cells injected with Toxoplasma protein, we have identified that Toxoplasma-injected neurons (TINs) are heterogeneously distributed throughout the brain. Unfortunately, standard methods to quantify and map heterogeneous cell populations onto a reference brain atlas are time consuming and prone to user bias. NEW METHOD:We developed a novel MATLAB-based semi-automated quantification and mapping program to allow the rapid and consistent mapping of heterogeneously distributed cells on to the Allen Institute Mouse Brain Atlas. The system uses two-threshold background subtraction to identify and quantify cells of interest. RESULTS:We demonstrate that we reliably quantify and neuroanatomically localize TINs with low intra- or inter-observer variability. In a follow up experiment, we show that specific regions of the mouse brain are enriched with TINs. COMPARISON WITH EXISTING METHODS:The procedure we use takes advantage of simple immunohistochemistry labeling techniques, use of a standard microscope with a motorized stage, and low cost computing that can be readily obtained at a research institute. To our knowledge there is no other program that uses such readily available techniques and equipment for mapping heterogeneous populations of cells across the whole mouse brain. CONCLUSION:The quantification method described here allows reliable visualization, quantification, and mapping of heterogeneous cell populations in immunolabeled sections across whole mouse brains.
| S-EPMC6357967 | biostudies-literature
Project description:Poaceae, among the most abundant plant families, includes many economically important polyploid species, such as forage grasses and sugarcane (Saccharum spp.). These species have elevated genomic complexities and limited genetic resources, hindering the application of marker-assisted selection strategies. Currently, the most promising approach for increasing genetic gains in plant breeding is genomic selection. However, due to the polyploidy nature of these polyploid species, more accurate models for incorporating genomic selection into breeding schemes are needed. This study aims to develop a machine learning method by using a joint learning approach to predict complex traits from genotypic data. Biparental populations of sugarcane and two species of forage grasses (Urochloa decumbens, Megathyrsus maximus) were genotyped, and several quantitative traits were measured. High-quality markers were used to predict several traits in different cross-validation scenarios. By combining classification and regression strategies, we developed a predictive system with promising results. Compared with traditional genomic prediction methods, the proposed strategy achieved accuracy improvements exceeding 50%. Our results suggest that the developed methodology could be implemented in breeding programs, helping reduce breeding cycles and increase genetic gains.
| S-EPMC9304331 | biostudies-literature
Project description:Intense structuring of plant breeding populations challenges the design of the training set (TS) in genomic selection (GS). An important open question is how the TS should be constructed from multiple related or unrelated small biparental families to predict progeny from individual crosses. Here, we used a set of five interconnected maize (Zea mays L.) populations of doubled-haploid (DH) lines derived from four parents to systematically investigate how the composition of the TS affects the prediction accuracy for lines from individual crosses. A total of 635 DH lines genotyped with 16,741 polymorphic SNPs were evaluated for five traits including Gibberella ear rot severity and three kernel yield component traits. The populations showed a genomic similarity pattern, which reflects the crossing scheme with a clear separation of full sibs, half sibs, and unrelated groups. Prediction accuracies within full-sib families of DH lines followed closely theoretical expectations, accounting for the influence of sample size and heritability of the trait. Prediction accuracies declined by 42% if full-sib DH lines were replaced by half-sib DH lines, but statistically significantly better results could be achieved if half-sib DH lines were available from both instead of only one parent of the validation population. Once both parents of the validation population were represented in the TS, including more crosses with a constant TS size did not increase accuracies. Unrelated crosses showing opposite linkage phases with the validation population resulted in negative or reduced prediction accuracies, if used alone or in combination with related families, respectively. We suggest identifying and excluding such crosses from the TS. Moreover, the observed variability among populations and traits suggests that these uncertainties must be taken into account in models optimizing the allocation of resources in GS.
| S-EPMC3664858 | biostudies-other
Project description:One of the most important applications of genomic selection in maize breeding is to predict and identify the best untested lines from biparental populations, when the training and validation sets are derived from the same cross. Nineteen tropical maize biparental populations evaluated in multienvironment trials were used in this study to assess prediction accuracy of different quantitative traits using low-density (~200 markers) and genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs), respectively. An extension of the Genomic Best Linear Unbiased Predictor that incorporates genotype × environment (GE) interaction was used to predict genotypic values; cross-validation methods were applied to quantify prediction accuracy. Our results showed that: (1) low-density SNPs (~200 markers) were largely sufficient to get good prediction in biparental maize populations for simple traits with moderate-to-high heritability, but GBS outperformed low-density SNPs for complex traits and simple traits evaluated under stress conditions with low-to-moderate heritability; (2) heritability and genetic architecture of target traits affected prediction performance, prediction accuracy of complex traits (grain yield) were consistently lower than those of simple traits (anthesis date and plant height) and prediction accuracy under stress conditions was consistently lower and more variable than under well-watered conditions for all the target traits because of their poor heritability under stress conditions; and (3) the prediction accuracy of GE models was found to be superior to that of non-GE models for complex traits and marginal for simple traits.
| S-EPMC4815582 | biostudies-other
Project description:Urochloa humidicola (Koronivia grass) is a polyploid (6x to 9x) species that is used as forage in the tropics. Facultative apospory apomixis is present in most of the genotypes of this species, although one individual has been described as sexual. Molecular studies have been restricted to molecular marker approaches for genetic diversity estimations and linkage map construction. The objectives of the present study were to describe and compare the leaf transcriptome of two important genotypes that are highly divergent in terms of their phenotypes and reproduction modes: the sexual BH031 and the aposporous apomictic cultivar BRS Tupi.We sequenced the leaf transcriptome of Koronivia grass using an Illumina GAIIx system, which produced 13.09 Gb of data that consisted of 163,575,526 paired-end reads between the two libraries. We de novo-assembled 76,196 transcripts with an average length of 1,152 bp and filtered 35,093 non-redundant unigenes. A similarity search against the non-redundant National Center of Biotechnology Information (NCBI) protein database returned 65 % hits. We annotated 24,133 unigenes in the Phytozome database and 14,082 unigenes in the UniProtKB/Swiss-Prot database, assigned 108,334 gene ontology terms to 17,255 unigenes and identified 5,324 unigenes in 327 known metabolic pathways. Comparisons with other grasses via a reciprocal BLAST search revealed a larger number of orthologous genes for the Panicum species. The unigenes were involved in C4 photosynthesis, lignocellulose biosynthesis and flooding stress responses. A search for functional molecular markers revealed 4,489 microsatellites and 560,298 single nucleotide polymorphisms (SNPs). A quantitative real-time PCR analysis validated the RNA-seq expression analysis and allowed for the identification of transcriptomic differences between the two evaluated genotypes. Moreover, 192 unannotated sequences were classified as containing complete open reading frames, suggesting that the new, potentially exclusive genes should be further investigated.The present study represents the first whole-transcriptome sequencing of U. humidicola leaves, providing an important public information source of transcripts and functional molecular markers. The qPCR analysis indicated that the expression of certain transcripts confirmed the differential expression observed in silico, which demonstrated that RNA-seq is useful for identifying differentially expressed and unique genes. These results corroborate the findings from previous studies and suggest a hybrid origin for BH031.
| S-EPMC5106776 | biostudies-literature
Project description:BackgroundWhole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploid's parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIO was made simple to set up, run and interpret, and its implementation ensures reproducibility by including both package management and containerization.ResultsWe evaluated ARPEGGIO in two ways. First, we tested EAGLE-RC's performance with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. Second, using the same initial dataset, we show agreement between ARPEGGIO's output and published results. Compared to other similar workflows, ARPEGGIO is the only one supporting polyploid data.ConclusionsThe goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation. ARPEGGIO is available at https://github.com/supermaxiste/ARPEGGIO .
| S-EPMC8285871 | biostudies-literature
Project description:Cassava improvement using traditional breeding strategies is slow due to the species' long breeding cycle. However, the use of genomic selection can lead to a shorter breeding cycle. This study aimed to estimate genetic parameters for productive traits based on pedigree (pedigree and phenotypic information) and genomic (markers and phenotypic information) analyses using biparental crosses at different stages of selection. A total of 290 clones were genotyped and phenotyped for fresh root yield (FRY), dry matter content (DMC), dry yield (DY), fresh shoot yield (FSY) and harvest index (HI). The clones were evaluated in clonal evaluation trials (CET), preliminary yield trials (PYT), advanced yield trials (AYT) and uniform yield trials (UYT), from 2013 to 2018 in ten locations. The breeding stages were analyzed as follows: one stage (CET), two stages (CET and PYT), three stages (CET, PYT and AYT) and four stages (CET, PYT, AYT and UYT). The genomic predictions were analyzed via k-fold cross-validation based on the genomic best linear unbiased prediction (GBLUP) considering a model with genetic additive effects and genotype × location interactions. Genomic and pedigree accuracies were moderate to high (0.56-0.72 and 0.62-0.78, respectively) for important starch-related traits such as DY and FRY; when considering one breeding stage (CET) with the aim of early selection, the genomic accuracies ranged from 0.60 (DMC) to 0.71 (HI). Moreover, the correlations between the genomic estimation breeding values of one-stage genomic analysis and the estimated breeding values of the four-stage (full data set) pedigree analysis were high for all traits as well as for a selection index including all traits. The results indicate great possibilities for genomic selection in cassava, especially for selection early in the breeding cycle (saving time and effort).
| S-EPMC6658084 | biostudies-literature
Project description:Modern neuroscientific research stands on the shoulders of countless giants. PubMed alone contains more than 21 million peer-reviewed articles with 40-50,000 more published every month. Understanding the human brain, cognition, and disease will require integrating facts from dozens of scientific fields spread amongst millions of studies locked away in static documents, making any such integration daunting, at best. The future of scientific progress will be aided by bridging the gap between the millions of published research articles and modern databases such as the Allen brain atlas (ABA). To that end, we have analyzed the text of over 3.5 million scientific abstracts to find associations between neuroscientific concepts. From the literature alone, we show that we can blindly and algorithmically extract a "cognome": relationships between brain structure, function, and disease. We demonstrate the potential of data-mining and cross-platform data-integration with the ABA by introducing two methods for semi-automated hypothesis generation. By analyzing statistical "holes" and discrepancies in the literature we can find understudied or overlooked research paths. That is, we have added a layer of semi-automation to a part of the scientific process itself. This is an important step toward fundamentally incorporating data-mining algorithms into the scientific method in a manner that is generalizable to any scientific or medical field.
| S-EPMC3376233 | biostudies-literature