We aren't good at picking candidate genes, and it's slowing us down

Prioritization of candidate genes for major QTLs governing yield traits employing integrated multi-omics approach in rice (Oryza sativa L.)

Rapidly identifying candidate genes underlying major QTLs is crucial for improving rice (Oryza sativa L.). In this study, we developed a workflow to rapidly prioritize candidate genes underpinning 99 major QTLs governing yield component traits. This workflow integrates multiomics databases, including sequence variation, gene expression, gene ontology, co-expression analysis, and protein-protein interaction. We predicted 206 candidate genes for 99 reported QTLs governing ten economically important yield-contributing traits using this approach. Among these, transcription factors belonging to families of MADS-box, WRKY, helix-loop-helix, TCP, MYB, GRAS, auxin response factor, and nuclear transcription factor Y subunit were promising. Validation of key prioritized candidate genes in contrasting rice genotypes for sequence variation and differential expression identified Leucine-Rich Repeat family protein (LOC_Os03g28270) and cytochrome P450 (LOC_Os02g57290) as candidate genes for the major QTLs GL1 and pl2.1, which govern grain length and panicle length, respectively. In conclusion, this study demonstrates that our workflow can significantly narrow down a large number of annotated genes in a QTL to a very small number of the most probable candidates, achieving approximately a 21-fold reduction. These candidate genes have potential implications for enhancing rice yield.

Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry

Two decades have passed since the strawberry (Fragaria x ananassa) disease caused by Macrophomina phaseolina, a necrotrophic soilborne fungal pathogen, began surfacing in California, Florida, and elsewhere. This disease has since become one of the most common causes of plant death and yield losses in strawberry. The Macrophomina problem emerged and expanded in the wake of the global phase-out of soil fumigation with methyl bromide and appears to have been aggravated by an increase in climate change-associated abiotic stresses. Here we show that sources of resistance to this pathogen are rare in gene banks and that the favorable alleles they carry are phenotypically unobvious. The latter were exposed by transgressive segregation and selection in populations phenotyped for resistance to Macrophomina under heat and drought stress. The genetic gains were immediate and dramatic. The frequency of highly resistant individuals increased from 1% in selection cycle 0 to 74% in selection cycle 2. Using GWAS and survival analysis, we found that phenotypic selection had increased the frequencies of favorable alleles among 10 loci associated with resistance and that favorable alleles had to be accumulated among four or more of these loci for an individual to acquire resistance. An unexpectedly straightforward solution to the Macrophomina disease resistance breeding problem emerged from our studies, which showed that highly resistant cultivars can be developed by genomic selection per se or marker-assisted stacking of favorable alleles among a comparatively small number of large-effect loci.

Machine learning applications to improve flavor and nutritional content of horticultural crops through breeding and genetics

Over the last decades, significant strides were made in understanding the biochemical factors influencing the nutritional content and flavor profile of fruits and vegetables. Product differentiation in the produce aisle is the natural consequence of increasing consumer power in the food industry. Cotton-candy grapes, specialty tomatoes, and pineapple-flavored white strawberries provide a few examples. Given the increased demand for flavorful varieties, and pressing need to reduce micronutrient malnutrition, we expect breeding to increase its prioritization toward these traits. Reaching this goal will, in part, necessitate knowledge of the genetic architecture controlling these traits, as well as the development of breeding methods that maximize their genetic gain. Can artificial intelligence (AI) help predict flavor preferences, and can such insights be leveraged by breeding programs? In this Perspective, we outline both the opportunities and challenges for the development of more flavorful and nutritious crops, and how AI can support these breeding initiatives.

Discovering and prioritizing candidate resistance genes against soybean pests by integrating GWAS and gene coexpression networks

Soybean is one of the most important legume crops worldwide. Soybean pests have a considerable impact on crop yield. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate resistance genes against the insects Aphis glycines and Spodoptera litura, and the nematode Heterodera glycines. We identified 171, 7, and 228 high-confidence candidate resistance genes against A. glycines, S. litura, and H. glycines, respectively. We found some overlap of candidate genes between insect species, but not between insects and H. glycines. Although 15% of the prioritized candidate genes encode proteins of unknown function, the vast majority of the candidates are related to plant immunity processes, such as transcriptional regulation, signaling, oxidative stress, recognition, and physical defense. Based on the number of resistance alleles, we selected the ten most promising accessions against each pest species in the soybean USDA germplasm. The most resistant accessions do not reach the maximum theoretical resistance potential, indicating that they might be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression networks we inferred in this work are available in a user-friendly web application (https://soypestgcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyPestGCN) that serve as a public resource to explore soybean-pest interactions at the transcriptional level.

Identification of growth regulators using cross-species network analysis in plants

With the need to increase plant productivity, one of the challenges plant scientists are facing is to identify genes that play a role in beneficial plant traits. Moreover, even when such genes are found, it is generally not trivial to transfer this knowledge about gene function across species to identify functional orthologs. Here, we focused on the leaf to study plant growth. First, we built leaf growth transcriptional networks in Arabidopsis (Arabidopsis thaliana), maize (Zea mays), and aspen (Populus tremula). Next, known growth regulators, here defined as genes that when mutated or ectopically expressed alter plant growth, together with cross-species conserved networks, were used as guides to predict novel Arabidopsis growth regulators. Using an in-depth literature screening, 34 out of 100 top predicted growth regulators were confirmed to affect leaf phenotype when mutated or overexpressed and thus represent novel potential growth regulators. Globally, these growth regulators were involved in cell cycle, plant defense responses, gibberellin, auxin, and brassinosteroid signaling. Phenotypic characterization of loss-of-function lines confirmed two predicted growth regulators to be involved in leaf growth (NPF6.4 and LATE MERISTEM IDENTITY2). In conclusion, the presented network approach offers an integrative cross-species strategy to identify genes involved in plant growth and development.

Capturing Wheat Phenotypes at the Genome Level

Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.

Genetics as a key to improving crop photosynthesis

Since the basic biochemical mechanisms of photosynthesis are remarkably conserved among plant species, genetic modification approaches have so far been the main route to improve the photosynthetic performance of crops. Yet, phenotypic variation observed in wild species and between varieties of crop species implies there is standing natural genetic variation for photosynthesis, offering a largely unexplored resource to use for breeding crops with improved photosynthesis and higher yields. The reason this has not yet been explored is that the variation probably involves thousands of genes, each contributing only a little to photosynthesis, making them hard to identify without proper phenotyping and genetic tools. This is changing, though, and increasingly studies report on quantitative trait loci for photosynthetic phenotypes. So far, hardly any of these quantitative trait loci have been used in marker assisted breeding or genomic selection approaches to improve crop photosynthesis and yield, and hardly ever have the underlying causal genes been identified. We propose to take the genetics of photosynthesis to a higher level, and identify the genes and alleles nature has used for millions of years to tune photosynthesis to be in line with local environmental conditions. We will need to determine the physiological function of the genes and alleles, and design novel strategies to use this knowledge to improve crop photosynthesis through conventional plant breeding, based on readily available crop plant germplasm. In this work, we present and discuss the genetic methods needed to reveal natural genetic variation, and elaborate on how to apply this to improve crop photosynthesis.

Fruit Fly Larval Survival in Picked and Unpicked Tomato Fruit of Differing Ripeness and Associated Gene Expression Patterns

The larvae of frugivorous tephritid fruit flies feed within fruit and are global pests of horticulture. With the reduced use of pesticides, alternative control methods are needed, of which fruit resistance is one. In the current study, we explicitly tested for phenotypic evidence of induced fruit defences by running concurrent larval survival experiments with fruit on or off the plant, assuming that defence induction would be stopped or reduced by fruit picking. This was accompanied by RT-qPCR analysis of fruit defence and insect detoxification gene expression. Our fruit treatments were picking status (unpicked vs. picked) and ripening stage (colour break vs. fully ripe), our fruit fly was the polyphagous Bactrocera tryoni, and larval survival was assessed through destructive fruit sampling at 48 and 120 h, respectively. The gene expression study targeted larval and fruit tissue samples collected at 48 h and 120 h from picked and unpicked colour-break fruit. At 120 h in colour-break fruit, larval survival was significantly higher in the picked versus unpicked fruit. The gene expression patterns in larval and plant tissue were not affected by picking status, but many putative plant defence and insect detoxification genes were upregulated across the treatments. The larval survival results strongly infer an induced defence mechanism in colour-break tomato fruit that is stronger/faster in unpicked fruits; however, the gene expression patterns failed to provide the same clear-cut treatment effect. The lack of conformity between these results could be related to expression changes in unsampled candidate genes, or due to critical changes in gene expression that occurred during the unsampled periods.

Meta-Analysis as a Tool to Identify Candidate Genes Involved in the Fagus sylvatica L. Abiotic Stress Response

In this study, we aimed to evaluate whether candidate genes for abiotic stresses in Fagus sylvatica L. are also candidate genes for herbaceous plants, with the purpose of better defining the abiotic stress response model of F. sylvatica. Therefore, a meta-analysis was performed on published papers related to abiotic stress. Firstly, we carried out a systematic review regarding the activity of 24 candidate genes selected for F. sylvatica under abiotic stress reported in 503 articles. After choosing the inclusion criteria, 73 articles out of 503, regarding 12 candidate genes, were included in this analysis. We performed an exploratory meta-analysis based on the random-effect model and the combined effect-size approach (Cohen’s d). The results obtained through Forest and Funnel plots indicate that the candidate genes for F. sylvatica are considered to be candidate genes in other herbaceous species. These results allowed us to set up models of plants’ response to abiotic stresses implementing the stress models in forest species. The results of this study will serve to bridge knowledge gaps regarding the pathways of response to abiotic stresses in trees based on the meta-analysis. The study approach used could be extended to observe larger gene databases and different species.

Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

Soybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application ( https://soyfungigcn.venanciogroup.uenf.br/ ) and an R/Shiny package ( https://github.com/almeidasilvaf/SoyFungiGCN ) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

Co-expression networks in Chlamydomonas reveal significant rhythmicity in batch cultures and empower gene function discovery

The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis and chloroplast metabolism, cilium assembly and function, lipid and starch metabolism, and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes. We extracted several hundred high-confidence genes at the intersection of multiple curated lists involved in cilia, cell division, and photosynthesis, illustrating the power of our method. Surprisingly, Chlamydomonas experiments retained a significant rhythmic component across the transcriptome, suggesting an underappreciated variable during sample collection, even in samples collected in constant light. Our results therefore document substantial residual synchronization in batch cultures, contrary to assumptions of asynchrony. We provide step-by-step protocols for the analysis of co-expression across transcriptome data sets from Chlamydomonas and other species to help foster gene function discovery.

Genome-wide association study and gene network analyses reveal potential candidate genes for high night temperature tolerance in rice

High night temperatures (HNT) are shown to significantly reduce rice (Oryza sativa L.) yield and quality. A better understanding of the genetic architecture of HNT tolerance will help rice breeders to develop varieties adapted to future warmer climates. In this study, a diverse indica rice panel displayed a wide range of phenotypic variability in yield and quality traits under control night (24 °C) and higher night (29 °C) temperatures. Genome-wide association analysis revealed 38 genetic loci associated across treatments (18 for control and 20 for HNT). Nineteen loci were detected with the relative changes in the traits between control and HNT. Positive phenotypic correlations and co-located genetic loci with previously cloned grain size genes revealed common genetic regulation between control and HNT, particularly grain size. Network-based predictive models prioritized 20 causal genes at the genetic loci based on known gene/s expression under HNT in rice. Our study provides important insights for future candidate gene validation and molecular marker development to enhance HNT tolerance in rice. Integrated physiological, genomic, and gene network-informed approaches indicate that the candidate genes for stay-green trait may be relevant to minimizing HNT-induced yield and quality losses during grain filling in rice by optimizing source-sink relationships.

Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.

A curated list of genes that affect the plant ionome

Understanding the mechanisms underlying plants' adaptation to their environment will require knowledge of the genes and alleles underlying elemental composition. Modern genetics is capable of quickly, and cheaply indicating which regions of DNA are associated with particular phenotypes in question, but most genes remain poorly annotated, hindering the identification of candidate genes. To help identify candidate genes underlying elemental accumulations, we have created the known ionome gene (KIG) list: a curated collection of genes experimentally shown to change uptake, accumulation, and distribution of elements. We have also created an automated computational pipeline to generate lists of KIG orthologs in other plant species using the PhytoMine database. The current version of KIG consists of 176 known genes covering 5 species, 23 elements, and their 1588 orthologs in 10 species. Analysis of the known genes demonstrated that most were identified in the model plant Arabidopsis thaliana, and that transporter coding genes and genes altering the accumulation of iron and zinc are overrepresented in the current list.

Functional contexts of adipose and gluteal muscle tissue gene co-expression networks in the domestic horse

A gene's response to an environment is tightly bound to the underlying genetic variation present in an individual's genome and varies greatly depending on the tissue it is being expressed in. Gene co-expression networks provide a mechanism to understand and interpret the collective transcriptional responses of genes. Here, we use the Camoco co-expression network framework to characterize the transcriptional landscape of adipose and gluteal muscle tissue in 83 domestic horses (Equus caballus) representing 5 different breeds. In each tissue, gene expression profiles, capturing transcriptional response due to variation across individuals, were used to build two separate, tissue-focused, genotypically-diverse gene co-expression networks. The aim of our study was to identify significantly co-expressed clusters of genes in each tissue, then compare the clusters across networks to quantify the extent that clusters were found in both networks as well as to identify clusters found in a single network. The known and unknown functions for each network were quantified using complementary, supervised and unsupervised approaches. First, supervised ontological enrichment was utilized to quantify biological functions represented by each network. Curated ontologies (GO and KEGG) were used to measure the known biological functions present in each tissue. Overall, a large percentage of terms (40.3% of GO and 41% of KEGG) were co-expressed in at least one tissue. Many terms were co-expressed in both tissues, however a small proportion of terms exhibited single tissue co-expression suggesting functional differentiation based on curated, functional annotation. To complement this, an unsupervised approach not relying on ontologies was employed. Strongly co-expressed sets of genes defined by Markov clustering identified sets of unannotated genes showing similar patterns of co-expression within a tissue. We compared gene sets across tissues and identified clusters of genes the either segregate in co-expression by tissue or exhibit high levels of co-expression in both tissues. Clusters were also integrated with GO and KEGG ontologies to identify gene sets containing previously curated annotations versus unannotated gene sets indicating potentially novel biological function. Coupling together these transcriptional datasets, we mapped the transcriptional landscape of muscle and adipose setting up a generalizable framework for interpreting gene function for additional tissues in the horse and other species.