Genomic selection for morphological and yield-related traits using genome-wide SNPs in oil palm.

Oil palm is the most important oil crop worldwide. Colombia is the fourth largest producer, primarily relying on production from interspecific hybrids, derived from crosses between Elaeis oleifera and Elaeis guineensis (OxG). However, conventional breeding can take up to 20 years to generate a new variety. Therefore, reducing the breeding cycle while improving the genetic gain for complex traits is desirable. Genomic selection (GS) is an approach with the potential to achieve this goal. In this study, we evaluated 431 F1 interspecific hybrids (OxG) and 444 backcrosses (BC1) for morphological and yield-related traits. Genomic predictions were performed with the G-BLUP model using three different population datasets for training the model: the same population (TRN1), the other population (TRN2), and both populations (TRN1+2). Higher multi-family prediction accuracies were obtained for foliar area (0.3 in OxG) and trunk height (0.47 in BC1) when the model was trained with TRN1. Single-family prediction accuracies were lower in the OxG compared to BC1 families for traits such as trunk diameter, trunk height, bunch number, and yield using TRN1. Conversely, lower prediction accuracies were obtained for most traits when the model was trained using TRN2 (< 0.1). Multi-trait models showed a substantial increase of the predictions for traits such as yield (0.22 for OxG and 0.44 for BC1), because of the genetic correlations between traits. The results herein highlighted the potential of GS for parental selection in OxG and BC1 populations, but further studies are required to improve the models to select individuals by their genetic value. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-022-01341-5.

Genome-wide association study (GWAS) for morphological and yield-related traits in an oil palm hybrid (Elaeis oleifera x Elaeis guineensis) population.

BACKGROUND: The genus Elaeis has two species of economic importance for the oil palm agroindustry: Elaeis oleifera (O), native to the Americas, and Elaeis guineensis (G), native to Africa. This work provides to our knowledge, the first association mapping study in an interspecific OxG oil palm population, which shows tolerance to pests and diseases, high oil quality, and acceptable fruit bunch production. RESULTS: Using genotyping-by-sequencing (GBS), we identified a total of 3776 single nucleotide polymorphisms (SNPs) that were used to perform a genome-wide association analysis (GWAS) in 378 OxG hybrid population for 10 agronomic traits. Twelve genomic regions (SNPs) were located near candidate genes implicated in multiple functional categories, such as tissue growth, cellular trafficking, and physiological processes. CONCLUSIONS: We provide new insights on genomic regions that mapped on candidate genes involved in plant architecture and yield. These potential candidate genes need to be confirmed for future targeted functional analyses. Associated markers to the traits of interest may be valuable resources for the development of marker-assisted selection in oil palm breeding.

The Physalis peruviana leaf transcriptome: assembly, annotation and gene model prediction.

BACKGROUND: Physalis peruviana commonly known as Cape gooseberry is a member of the Solanaceae family that has an increasing popularity due to its nutritional and medicinal values. A broad range of genomic tools is available for other Solanaceae, including tomato and potato. However, limited genomic resources are currently available for Cape gooseberry. RESULTS: We report the generation of a total of 652,614 P. peruviana Expressed Sequence Tags (ESTs), using 454 GS FLX Titanium technology. ESTs, with an average length of 371 bp, were obtained from a normalized leaf cDNA library prepared using a Colombian commercial variety. De novo assembling was performed to generate a collection of 24,014 isotigs and 110,921 singletons, with an average length of 1,638 bp and 354 bp, respectively. Functional annotation was performed using NCBI's BLAST tools and Blast2GO, which identified putative functions for 21,191 assembled sequences, including gene families involved in all the major biological processes and molecular functions as well as defense response and amino acid metabolism pathways. Gene model predictions in P. peruviana were obtained by using the genomes of Solanum lycopersicum (tomato) and Solanum tuberosum (potato). We predict 9,436 P. peruviana sequences with multiple-exon models and conserved intron positions with respect to the potato and tomato genomes. Additionally, to study species diversity we developed 5,971 SSR markers from assembled ESTs. CONCLUSIONS: We present the first comprehensive analysis of the Physalis peruviana leaf transcriptome, which will provide valuable resources for development of genetic tools in the species. Assembled transcripts with gene models could serve as potential candidates for marker discovery with a variety of applications including: functional diversity, conservation and improvement to increase productivity and fruit quality. P. peruviana was estimated to be phylogenetically branched out before the divergence of five other Solanaceae family members, S. lycopersicum, S. tuberosum, Capsicum spp, S. melongena and Petunia spp.

Genetic architecture of variation in Arabidopsis thaliana rosettes.

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

Identifying genes associated with abiotic stress tolerance suitable for CRISPR/Cas9 editing in upland rice cultivars adapted to acid soils.

Five genes of large phenotypic effect known to confer abiotic stress tolerance in rice were selected to characterize allelic variation in commercial Colombian tropical japonica upland rice cultivars adapted to drought-prone acid soil environments (cv. Llanura11 and Porvenir12). Allelic variants of the genes ART1, DRO1, SUB1A, PSTOL1, and SPDT were characterized by PCR and/or Sanger sequencing in the two upland cultivars and compared with the Nipponbare and other reference genomes. Two genes were identified as possible targets for gene editing: SUB1A (Submergence 1A), to improve tolerance to flooding, and SPDT (SULTR3;4) (SULTR-like Phosphorus Distribution Transporter), to improve phosphorus utilization efficiency and grain quality. Based on technical and regulatory considerations, SPDT was targeted for editing. The two upland cultivars were shown to carry the SPDT wild-type (nondesirable) allele based on sequencing, RNA expression, and phenotypic evaluations under hydroponic and greenhouse conditions. A gene deletion was designed using the CRISPR/Cas9 system, and specialized reagents were developed for SPDT editing, including vectors targeting the gene and a protoplast transfection transient assay. The desired edits were confirmed in protoplasts and serve as the basis for ongoing plant transformation experiments aiming to improve the P-use efficiency of upland rice grown in acidic soils.

Combining transcriptome analysis and GWAS for identification and validation of marker genes in the Physalis peruviana-Fusarium oxysporum pathosystem.

Vascular wilt, caused by the pathogen Fusarium oxysporum f. sp. physali (Foph), is a major disease of cape gooseberry (Physalis peruviana L.) in Andean countries. Despite the economic losses caused by this disease, there are few studies related to molecular mechanisms in the P. peruviana-Foph pathosystem as a useful tool for crop improvement. This study evaluates eight candidate genes associated with this pathosystem, using real-time quantitative PCR (RT-qPCR). The genes were identified and selected from 1,653 differentially expressed genes (DEGs) derived from RNA-Seq analysis and from a previous genome-wide association study (GWAS) of this plant-pathogen interaction. Based on the RT-qPCR analysis, the tubuline (TUB) reference gene was selected for its highly stable expression in cape gooseberry. The RT-qPCR validation of the candidate genes revealed the biological variation in their expression according to their known biological function. Three genes related to the first line of resistance/defense responses were highly expressed earlier during infection in a susceptible genotype, while three others were overexpressed later, mostly in the tolerant genotype. These genes are mainly involved in signaling pathways after pathogen recognition, mediated by hormones such as ethylene and salicylic acid. This study provided the first insight to uncover the molecular mechanism from the P. peruviana-Foph pathosystem. The genes validated here have important implications in the disease progress and allow a better understanding of the defense response in cape gooseberry at the molecular level. Derived molecular markers from these genes could facilitate the identification of tolerant/susceptible genotypes for use in breeding schemes.

Optimization of the genotyping-by-sequencing SNP calling for diversity analysis in cape gooseberry (Physalis peruviana L.) and related taxa.

A robust Genotyping-By-Sequencing (GBS) pipeline platform was examined to provide accurate discovery of Single Nucleotide Polymorphisms (SNPs) in a cape gooseberry (Physalis peruviana L.) and related taxa germplasm collection. A total of 176 accessions representing, wild, weedy, and commercial cultivars as well as related taxa from the Colombian germplasm bank and other world repositories were screened using GBS. The pipeline parameters mnLCov of 0.5 and a mnScov of 0.7, tomato and potato genomes, and cape gooseberry transcriptome for read alignments, were selected to better assess diversity and population structure in cape gooseberry and related taxa. A total of 7,425 SNPs, derived from P. peruviana common tags (unique 64 bp sequences shared between selected species), were used. Within P. peruviana, five subpopulations with a high genetic diversity and allele fixation (HE: 0.35 to 0.36 and FIS: -0.11 to -0.01, respectively) were detected. Conversely, low genetic differentiation (FST: 0.01 to 0.05) was also observed, indicating a high gene flow among subpopulations. These results contribute to the establishment of adequate conservation and breeding strategies for Cape gooseberry and closely related Physalis species.

DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis.

BACKGROUND: High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addressed. Advances in sensor technology have led to the increasing use of imaging to monitor and measure a range of plants including the model Arabidopsis thaliana. These extensive datasets contain diverse trait information, but feature extraction is often still implemented using approaches requiring substantial manual input. RESULTS: The computational detection and segmentation of individual fruits from images is a challenging task, for which we have developed DeepPod, a patch-based 2-phase deep learning framework. The associated manual annotation task is simple and cost-effective without the need for detailed segmentation or bounding boxes. Convolutional neural networks (CNNs) are used for classifying different parts of the plant inflorescence, including the tip, base, and body of the siliques and the stem inflorescence. In a post-processing step, different parts of the same silique are joined together for silique detection and localization, whilst taking into account possible overlapping among the siliques. The proposed framework is further validated on a separate test dataset of 2,408 images. Comparisons of the CNN-based prediction with manual counting (R2 = 0.90) showed the desired capability of methods for estimating silique number. CONCLUSIONS: The DeepPod framework provides a rapid and accurate estimate of fruit number in a model system widely used by biologists to investigate many fundemental processes underlying growth and reproduction.

Workflow and web application for annotating NCBI BioProject transcriptome data.

Abstract: The volume of transcriptome data is growing exponentially due to rapid improvement of experimental technologies. In response, large central resources such as those of the National Center for Biotechnology Information (NCBI) are continually adapting their computational infrastructure to accommodate this large influx of data. New and specialized databases, such as Transcriptome Shotgun Assembly Sequence Database (TSA) and Sequence Read Archive (SRA), have been created to aid the development and expansion of centralized repositories. Although the central resource databases are under continual development, they do not include automatic pipelines to increase annotation of newly deposited data. Therefore, third-party applications are required to achieve that aim. Here, we present an automatic workflow and web application for the annotation of transcriptome data. The workflow creates secondary data such as sequencing reads and BLAST alignments, which are available through the web application. They are based on freely available bioinformatics tools and scripts developed in-house. The interactive web application provides a search engine and several browser utilities. Graphical views of transcript alignments are available through SeqViewer, an embedded tool developed by NCBI for viewing biological sequence data. The web application is tightly integrated with other NCBI web applications and tools to extend the functionality of data processing and interconnectivity. We present a case study for the species Physalis peruviana with data generated from BioProject ID 67621. Database: URL: http://www.ncbi.nlm.nih.gov/projects/physalis/.

Genetic diversity and population structure in Physalis peruviana and related taxa based on InDels and SNPs derived from COSII and IRG markers.

The genus Physalis is common in the Americas and includes several economically important species, among them Physalis peruviana that produces appetizing edible fruits. We studied the genetic diversity and population structure of P. peruviana and characterized 47 accessions of this species along with 13 accessions of related taxa consisting of 222 individuals from the Colombian Corporation of Agricultural Research (CORPOICA) germplasm collection, using Conserved Orthologous Sequences (COSII) and Immunity Related Genes (IRGs). In addition, 642 Single Nucleotide Polymorphism (SNPs) markers were identified and used for the genetic diversity analysis. A total of 121 alleles were detected in 24 InDels loci ranging from 2 to 9 alleles per locus, with an average of 5.04 alleles per locus. The average number of alleles in the SNP markers was two. The observed heterozygosity for P. peruviana with InDel and SNP markers was higher (0.48 and 0.59) than the expected heterozygosity (0.30 and 0.41). Interestingly, the observed heterozygosity in related taxa (0.4 and 0.12) was lower than the expected heterozygosity (0.59 and 0.25). The coefficient of population differentiation FST was 0.143 (InDels) and 0.038 (SNPs), showing a relatively low level of genetic differentiation among P. peruviana and related taxa. Higher levels of genetic variation were instead observed within populations based on the AMOVA analysis. Population structure analysis supported the presence of two main groups and PCA analysis based on SNP markers revealed two distinct clusters in the P. peruviana accessions corresponding to their state of cultivation. In this study, we identified molecular markers useful to detect genetic variation in Physalis germplasm for assisting conservation and crossbreeding strategies.