Association study of crude seed protein and fat concentration in a USDA pea diversity panel.

Pea (Pisum sativum L.) is a key rotational crop and is increasingly important in the food processing sector for its protein. This study focused on identifying diverse high seed protein concentration (SPC) lines in pea plant genetic resources. Objectives included identifying high-protein pea lines, exploring genetic architecture across environments, pinpointing genes and metabolic pathways associated with high protein, and documenting information for single nucleotide polymorphism (SNP)-based marker-assisted selection. From 2019 to 2021, a 487-accession pea diversity panel, More protein, More pea, More profit, was evaluated in a randomized complete block design. DNA was extracted for genomic analysis via genotype-by-sequencing. Phenotypic analysis included protein and fat measurements in seeds and flower color. Genome-wide association study (GWAS) used multiple models, and the Pathways Association Study Tool was used for metabolic pathway analysis. Significant associations were found between SNPs and pea seed protein and fat concentration. Gene Psat7g216440 on chromosome 7, which targets proteins to cellular destinations, including seed storage proteins, was identified as associated with SPC. Genes Psat4g009200, Psat1g199800, Psat1g199960, and Psat1g033960, all involved in lipid metabolism, were associated with fat concentration. GWAS also identified genes annotated for storage proteins associated with fat concentration, indicating a complex relationship between fat and protein. Metabolic pathway analysis identified 20 pathways related to fat and seven to protein concentration, involving fatty acids, amino acid and protein metabolism, and the tricarboxylic acid cycle. These findings will assist in breeding of high-protein, diverse pea cultivars, and SNPs that can be converted to breeder-friendly molecular marker assays are identified for genes associated with high protein.

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts.

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures), as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence-absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium-a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts.

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures) as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence-absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium - a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

BUZZ: an essential gene for postinitiation root hair growth and a mediator of root architecture in Brachypodium distachyon.

Here, we discover a player in root development. Recovered from a forward-genetic screen in Brachypodium distachyon, the buzz mutant initiates root hairs but they fail to elongate. In addition, buzz roots grow twice as fast as wild-type roots. Also, lateral roots show increased sensitivity to nitrate, whereas primary roots are less sensitive to nitrate. Using whole-genome resequencing, we identified the causal single nucleotide polymorphism as occurring in a conserved but previously uncharacterized cyclin-dependent kinase (CDK)-like gene. The buzz mutant phenotypes are rescued by the wild-type B. distachyon BUZZ coding sequence and by an apparent homolog in Arabidopsis thaliana. Moreover, T-DNA mutants in A. thaliana BUZZ have shorter root hairs. BUZZ mRNA localizes to epidermal cells and develops root hairs and, in the latter, partially colocalizes with the NRT1.1A nitrate transporter. Based on qPCR and RNA-Seq, buzz overexpresses ROOT HAIRLESS LIKE SIX-1 and -2 and misregulates genes related to hormone signaling, RNA processing, cytoskeletal, and cell wall organization, and to the assimilation of nitrate. Overall, these data demonstrate that BUZZ is required for tip growth after root hair initiation and root architectural responses to nitrate.

Nanopore Sequencing with GraphMap for Comprehensive Pathogen Detection in Potato Field Soil.

Early detection of causal pathogens is important to prevent crop loss from diseases. However, some diseases, such as soilborne diseases, are difficult to diagnose due to the absence of visible or characteristic symptoms. In the present study, the use of the Oxford Nanopore MinION sequencer as a molecular diagnostic tool was assessed due to its long-read sequencing capabilities and portability. Nucleotide samples (DNA or RNA) from potato field soils were sequenced and analyzed using a locally curated pathogen database, followed by identification via sequence mapping. We performed computational speed tests of three commonly used mapping/annotation tools (BLAST, BWA-BLAST, and BWA-GraphMap) and found BWA-GraphMap to be the fastest tool for local searching against our curated pathogen database. The data collected demonstrate the high potential of Nanopore sequencing as a minimally biased diagnostic tool for comprehensive pathogen detection in soil from potato fields. Our GraphMap-based MinION sequencing method could be useful as a predictive approach for disease management by identifying pathogens present in field soil prior to planting. Although this method still needs further experimentation with a larger sample size for practical use, the data analysis pipeline presented can be applied to other cropping systems and diagnostics for detecting multiple pathogens.

There and back again; historical perspective and future directions for Vaccinium breeding and research studies.

The genus Vaccinium L. (Ericaceae) contains a wide diversity of culturally and economically important berry crop species. Consumer demand and scientific research in blueberry (Vaccinium spp.) and cranberry (Vaccinium macrocarpon) have increased worldwide over the crops' relatively short domestication history (~100 years). Other species, including bilberry (Vaccinium myrtillus), lingonberry (Vaccinium vitis-idaea), and ohelo berry (Vaccinium reticulatum) are largely still harvested from the wild but with crop improvement efforts underway. Here, we present a review article on these Vaccinium berry crops on topics that span taxonomy to genetics and genomics to breeding. We highlight the accomplishments made thus far for each of these crops, along their journey from the wild, and propose research areas and questions that will require investments by the community over the coming decades to guide future crop improvement efforts. New tools and resources are needed to underpin the development of superior cultivars that are not only more resilient to various environmental stresses and higher yielding, but also produce fruit that continue to meet a variety of consumer preferences, including fruit quality and health related traits.

CottonGen: The Community Database for Cotton Genomics, Genetics, and Breeding Research.

Over the last eight years, the volume of whole genome, gene expression, SNP genotyping, and phenotype data generated by the cotton research community has exponentially increased. The efficient utilization/re-utilization of these complex and large datasets for knowledge discovery, translation, and application in crop improvement requires them to be curated, integrated with other types of data, and made available for access and analysis through efficient online search tools. Initiated in 2012, CottonGen is an online community database providing access to integrated peer-reviewed cotton genomic, genetic, and breeding data, and analysis tools. Used by cotton researchers worldwide, and managed by experts with crop-specific knowledge, it continuous to be the logical choice to integrate new data and provide necessary interfaces for information retrieval. The repository in CottonGen contains colleague, gene, genome, genotype, germplasm, map, marker, metabolite, phenotype, publication, QTL, species, transcriptome, and trait data curated by the CottonGen team. The number of data entries housed in CottonGen has increased dramatically, for example, since 2014 there has been an 18-fold increase in genes/mRNAs, a 23-fold increase in whole genomes, and a 372-fold increase in genotype data. New tools include a genetic map viewer, a genome browser, a synteny viewer, a metabolite pathways browser, sequence retrieval, BLAST, and a breeding information management system (BIMS), as well as various search pages for new data types. CottonGen serves as the home to the International Cotton Genome Initiative, managing its elections and serving as a communication and coordination hub for the community. With its extensive curation and integration of data and online tools, CottonGen will continue to facilitate utilization of its critical resources to empower research for cotton crop improvement.

The Breeding Information Management System (BIMS): an online resource for crop breeding.

In this era of big data, breeding programs are producing ever larger amounts of data. This necessitates access to efficient management systems to keep track of cross, performance, pedigree, geographical and image-based data, as well as genotyping data. In this article, we report the progress on the Breeding Information Management System (BIMS), a free, secure and online breeding management system that allows breeders to store, manage, archive and analyze their private breeding data. BIMS is the first publicly available database system that enables individual breeders to integrate their private phenotypic and genotypic data with public data and, at the same time, have complete control of their own breeding data along with access to tools such as data import/export, data analysis and data archiving. The integration of breeding data with publicly available genomic and genetic data enhances genetic understanding of important traits and maximizes the marker-assisted breeding utility for breeders and allied scientists. BIMS incorporates the use of the Android App Field Book, open-source phenotype data collection software for phones and tablets that allows breeders to replace hard copy field books, thus alleviating the possibility of transcription errors while providing faster access to the collected data. BIMS comes with training materials and support for individual or small group training and is currently implemented in the Genome Database for Rosaceae, CottonGEN, the Citrus Genome Database, the Pulse Crop Database, and the Genome Database for Vaccinium. Database URLs: (https://www.rosaceae.org/), (https://www.cottongen.org/), (https://www.citrusgenomedb.org/), (https://www.pulsedb.org/) and (https://www.vaccinium.org/).

Tripal, a community update after 10 years of supporting open source, standards-based genetic, genomic and breeding databases.

Online, open access databases for biological knowledge serve as central repositories for research communities to store, find and analyze integrated, multi-disciplinary datasets. With increasing volumes, complexity and the need to integrate genomic, transcriptomic, metabolomic, proteomic, phenomic and environmental data, community databases face tremendous challenges in ongoing maintenance, expansion and upgrades. A common infrastructure framework using community standards shared by many databases can reduce development burden, provide interoperability, ensure use of common standards and support long-term sustainability. Tripal is a mature, open source platform built to meet this need. With ongoing improvement since its first release in 2009, Tripal provides full functionality for searching, browsing, loading and curating numerous types of data and is a primary technology powering at least 31 publicly available databases spanning plants, animals and human data, primarily storing genomics, genetics and breeding data. Tripal software development is managed by a shared, inclusive governance structure including both project management and advisory teams. Here, we report on the most important and innovative aspects of Tripal after 11 years development, including integration of diverse types of biological data, successful collaborative projects across member databases, and support for implementing FAIR principles.

Tripal MegaSearch: a tool for interactive and customizable query and download of big data.

Tripal MegaSearch is a Tripal module for querying and downloading biological data stored in Chado. This module allows site users to select data types, restrict the dataset by applying various filters and then customizing fields to view and download through a single interface. Set by site administrators, example data types include gene, germplasm, marker, map, QTL, genotype, phenotype and expression data. When querying for genes, users can restrict the gene dataset using various filters such as name, chromosome position and functional annotation. They can then customize fields to download, such as name, organism, type, chromosome position, various functional annotations such as BLAST, KEGG, InterPro and GO term. FASTA files can also be downloaded for the sequence data. Site administrators can choose from two different data sources to serve data: Tripal MegaSearch materialized views or Chado tables. If neither data source is desired, administrators may also create their own materialized views and serve them through the flexible dynamic Tripal MegaSearch query form. Tripal MegaSearch is currently implemented in several databases including the Genome Database for Rosaceae www.rosaceae.org and TreeGenes www.https://treegenesdb.org/.

Defining the 'HoneySweet' insertion event utilizing NextGen sequencing and a de novo genome assembly of plum (Prunus domestica).

'HoneySweet' plum (Prunus domestica) is resistant to Plum pox potyvirus, through an RNAi-triggered mechanism. Determining the precise nature of the transgene insertion event has been complicated due to the hexaploid genome of plum. DNA blots previously indicated an unintended hairpin arrangement of the Plum pox potyvirus coat protein gene as well as a multicopy insertion event. To confirm the transgene arrangement of the insertion event, 'HoneySweet' DNA was subjected to whole genome sequencing using Illumina short-read technology. Results indicated two different insertion events, one containing seven partial copies flanked by putative plum DNA sequence and a second with the predicted inverted repeat of the coat protein gene driven by a double 35S promoter on each side, flanked by plum DNA. To determine the locations of the two transgene insertions, a phased plum genome assembly was developed from the commercial plum 'Improved French'. A subset of the scaffolds (2447) that were >10 kb in length and representing, >95% of the genome were annotated and used for alignment against the 'HoneySweet' transgene reads. Four of eight matching scaffolds spanned both insertion sites ranging from 157,704 to 654,883 bp apart, however we were unable to identify which scaffold(s) represented the actual location of the insertion sites due to potential sequence differences between the two plum cultivars. Regardless, there was no evidence of any gene(s) being interrupted as a result of the insertions. Furthermore, RNA-seq data verified that the insertions created no new transcriptional units and no dramatic expression changes of neighboring genes.

Harnessing Genetic Diversity in the USDA Pea Germplasm Collection Through Genomic Prediction.

Phenotypic evaluation and efficient utilization of germplasm collections can be time-intensive, laborious, and expensive. However, with the plummeting costs of next-generation sequencing and the addition of genomic selection to the plant breeder's toolbox, we now can more efficiently tap the genetic diversity within large germplasm collections. In this study, we applied and evaluated genomic prediction's potential to a set of 482 pea (Pisum sativum L.) accessions-genotyped with 30,600 single nucleotide polymorphic (SNP) markers and phenotyped for seed yield and yield-related components-for enhancing selection of accessions from the USDA Pea Germplasm Collection. Genomic prediction models and several factors affecting predictive ability were evaluated in a series of cross-validation schemes across complex traits. Different genomic prediction models gave similar results, with predictive ability across traits ranging from 0.23 to 0.60, with no model working best across all traits. Increasing the training population size improved the predictive ability of most traits, including seed yield. Predictive abilities increased and reached a plateau with increasing number of markers presumably due to extensive linkage disequilibrium in the pea genome. Accounting for population structure effects did not significantly boost predictive ability, but we observed a slight improvement in seed yield. By applying the best genomic prediction model (e.g., RR-BLUP), we then examined the distribution of genotyped but nonphenotyped accessions and the reliability of genomic estimated breeding values (GEBV). The distribution of GEBV suggested that none of the nonphenotyped accessions were expected to perform outside the range of the phenotyped accessions. Desirable breeding values with higher reliability can be used to identify and screen favorable germplasm accessions. Expanding the training set and incorporating additional orthogonal information (e.g., transcriptomics, metabolomics, physiological traits, etc.) into the genomic prediction framework can enhance prediction accuracy.

RosBREED: bridging the chasm between discovery and application to enable DNA-informed breeding in rosaceous crops.

The Rosaceae crop family (including almond, apple, apricot, blackberry, peach, pear, plum, raspberry, rose, strawberry, sweet cherry, and sour cherry) provides vital contributions to human well-being and is economically significant across the U.S. In 2003, industry stakeholder initiatives prioritized the utilization of genomics, genetics, and breeding to develop new cultivars exhibiting both disease resistance and superior horticultural quality. However, rosaceous crop breeders lacked certain knowledge and tools to fully implement DNA-informed breeding-a "chasm" existed between existing genomics and genetic information and the application of this knowledge in breeding. The RosBREED project ("Ros" signifying a Rosaceae genomics, genetics, and breeding community initiative, and "BREED", indicating the core focus on breeding programs), addressed this challenge through a comprehensive and coordinated 10-year effort funded by the USDA-NIFA Specialty Crop Research Initiative. RosBREED was designed to enable the routine application of modern genomics and genetics technologies in U.S. rosaceous crop breeding programs, thereby enhancing their efficiency and effectiveness in delivering cultivars with producer-required disease resistances and market-essential horticultural quality. This review presents a synopsis of the approach, deliverables, and impacts of RosBREED, highlighting synergistic global collaborations and future needs. Enabling technologies and tools developed are described, including genome-wide scanning platforms and DNA diagnostic tests. Examples of DNA-informed breeding use by project participants are presented for all breeding stages, including pre-breeding for disease resistance, parental and seedling selection, and elite selection advancement. The chasm is now bridged, accelerating rosaceous crop genetic improvement.

Limited divergent adaptation despite a substantial environmental cline in wild pea.

Isolation by environment (IBE) is a widespread phenomenon in nature. It is commonly expected that the degree of difference among environments is proportional to the level of divergence between populations in their respective environments. It is therefore assumed that a species' genetic diversity displays a pattern of IBE in the presence of a strong environmental cline if gene flow does not mitigate isolation. We tested this common assumption by analysing the genetic diversity and demographic history of Pisum fulvum, which inhabits contrasting habitats in the southern Levant and is expected to display only minor migration rates between populations, making it an ideal test case. Ecogeographical and subpopulation structure were analysed and compared. The correlation of genetic with environmental distances was calculated to test the effect of isolation by distance and IBE and detect the main drivers of these effects. Historical effective population size was estimated using stairway plot. Limited overlap of ecogeographical and genetic clustering was observed, and correlation between genetic and environmental distances was statistically significant but small. We detected a sharp decline of effective population size during the last glacial period. The low degree of IBE may be the result of genetic drift due to a past bottleneck. Our findings contradict the expectation that strong environmental clines cause IBE in the absence of extensive gene flow.

Environmental and genetic determinants of amphicarpy in Pisum fulvum, a wild relative of domesticated pea.

Pisum fulvum is an annual legume native to Syria, Lebanon, Israel and Jordan. In certain locations, P. fulvum individuals were documented to display a reproductive dimorphism - amphicarpy, with both above and below ground flowers and pods. Herein we aimed to study the possible role of soil texture on amphicarpy in P. fulvum, to investigate the possible bio-climatic associations of P. fulvum amphicarpy and to identify genetic markers associated with this phenotype. A set of 127 germplasm accessions sampled across the Israeli distribution range of the species was phenotyped in two common garden nurseries. Land use and bioclimatic data were used to delineate the eco-geographic clustering of accession's sampling sites. Single nucleotide polymorphism (SNP) markers were employed in genome-wide association study to identify associated loci. Amphicarpy was subject to strong experimental site x genotype interaction with higher phenotypic expression in fine textured soil relative to sandy loam. Amphicarpy was more prevalent among accessions sampled in eastern Judea and Samaria and was weakly associated with early phenology and relatively modest above ground biomass production. Twelve SNP markers were significantly associated with amphicarpy, each explaining between 8 and 12 % of the phenotypic variation. In P. fulvum amphicarpy seems to be a polygenetic trait controlled by an array of genes that is likely to be affected by environmental stimuli. The probable selective advantage of the association between amphicarpy and early flowering is in line with its relative prevalence in drought prone territories subject to heavy grazing.

A Rosaceae Family-Level Approach To Identify Loci Influencing Soluble Solids Content in Blackberry for DNA-Informed Breeding.

A Rosaceae family-level candidate gene approach was used to identify genes associated with sugar content in blackberry (Rubus subgenus Rubus). Three regions conserved among apple (Malus × domestica), peach (Prunus persica), and alpine strawberry (Fragaria vesca) were identified that contained previously detected sweetness-related quantitative trait loci (QTL) in at least two of the crops. Sugar related genes from these conserved regions and 789 sugar-associated apple genes were used to identify 279 Rubus candidate transcripts. A Hyb-Seq approach was used in conjunction with PacBio sequencing to generate haplotype level sequence information of sugar-related genes for 40 cultivars with high and low soluble solids content from the University of Arkansas and USDA blackberry breeding programs. Polymorphisms were identified relative to the 'Hillquist' blackberry (R. argutus) and ORUS 4115-3 black raspberry (R. occidentalis) genomes and tested for their association with soluble solids content (SSC). A total of 173 alleles were identified that were significantly (α = 0.05) associated with SSC. KASP genotyping was conducted for 92 of these alleles on a validation set of blackberries from each breeding program and 48 markers were identified that were significantly associated with SSC. One QTL, qSSC-Ruh-ch1.1, identified in both breeding programs accounted for an increase of 1.5 °Brix and the polymorphisms were detected in the intron space of a sucrose synthase gene. This discovery represents the first environmentally stable sweetness QTL identified in blackberry. The approach demonstrated in this study can be used to develop breeding tools for other crops that have not yet benefited directly from the genomics revolution.

Tripal and Galaxy: supporting reproducible scientific workflows for community biological databases.

Online biological databases housing genomics, genetic and breeding data can be constructed using the Tripal toolkit. Tripal is an open-source, internationally developed framework that implements FAIR data principles and is meant to ease the burden of constructing such websites for research communities. Use of a common, open framework improves the sustainability and manageability of such as site. Site developers can create extensions for their site and in turn share those extensions with others. One challenge that community databases often face is the need to provide tools for their users that analyze increasingly larger datasets using multiple software tools strung together in a scientific workflow on complicated computational resources. The Tripal Galaxy module, a 'plug-in' for Tripal, meets this need through integration of Tripal with the Galaxy Project workflow management system. Site developers can create workflows appropriate to the needs of their community using Galaxy and then share those for execution on their Tripal sites via automatically constructed, but configurable, web forms or using an application programming interface to power web-based analytical applications. The Tripal Galaxy module helps reduce duplication of effort by allowing site developers to spend time constructing workflows and building their applications rather than rebuilding infrastructure for job management of multi-step applications.

Developmental Mechanisms of Fleshy Fruit Diversity in Rosaceae.

Rosaceae (the rose family) is an economically important family that includes species prized for high-value fruits and ornamentals. The family also exhibits diverse fruit types, including drupe (peach), pome (apple), drupetum (raspberry), and achenetum (strawberry). Phylogenetic analysis and ancestral fruit-type reconstruction suggest independent evolutionary paths of multiple fleshy fruit types from dry fruits. A recent whole genome duplication in the Maleae/Pyreae tribe (with apple, pear, hawthorn, and close relatives; referred to as Maleae here) may have contributed to the evolution of pome fruit. MADS-box genes, known to regulate floral organ identity, are emerging as important regulators of fruit development. The differential competence of floral organs to respond to fertilization signals may explain the different abilities of floral organs to form fleshy fruit. Future comparative genomics and functional studies in closely related Rosaceae species with distinct fruit types will test hypotheses and provide insights into mechanisms of fleshy fruit diversity. These efforts will be facilitated by the wealth of genome data and resources in Rosaceae.

The cherry 6+9K SNP array: a cost-effective improvement to the cherry 6K SNP array for genetic studies.

Cherry breeding and genetic studies can benefit from genome-wide genetic marker assays. Currently, a 6K SNP array enables genome scans in cherry; however, only a third of these SNPs are informative, with low coverage in many genomic regions. Adding previously detected SNPs to this array could provide a cost-efficient upgrade with increased genomic coverage across the 670 cM/352.9 Mb cherry whole genome sequence. For sweet cherry, new SNPs were chosen following a focal point strategy, grouping six to eight SNPs within 10-kb windows with an average of 0.6 cM (627 kb) between focal points. Additional SNPs were chosen to represent important regions. Sweet cherry, the fruticosa subgenome of sour cherry, and cherry organellar genomes were targeted with 6942, 2020, and 38 new SNPs, respectively. The +9K add-on provided 2128, 1091, and 70 new reliable, polymorphic SNPs for sweet cherry and the avium and the fruticosa subgenomes of sour cherry, respectively. For sweet cherry, 1241 reliable polymorphic SNPs formed 237 informative focal points, with another 2504 SNPs in-between. The +9K SNPs increased genetic resolution and genome coverage of the original cherry SNP array and will help increase understanding of the genetic control of key traits and relationships among individuals in cherry.

Dissecting the Genetic Architecture of Aphanomyces Root Rot Resistance in Lentil by QTL Mapping and Genome-Wide Association Study.

Lentil (Lens culinaris Medikus) is an important source of protein for people in developing countries. Aphanomyces root rot (ARR) has emerged as one of the most devastating diseases affecting lentil production. In this study, we applied two complementary quantitative trait loci (QTL) analysis approaches to unravel the genetic architecture underlying this complex trait. A recombinant inbred line (RIL) population and an association mapping population were genotyped using genotyping by sequencing (GBS) to discover novel single nucleotide polymorphisms (SNPs). QTL mapping identified 19 QTL associated with ARR resistance, while association mapping detected 38 QTL and highlighted accumulation of favorable haplotypes in most of the resistant accessions. Seven QTL clusters were discovered on six chromosomes, and 15 putative genes were identified within the QTL clusters. To validate QTL mapping and genome-wide association study (GWAS) results, expression analysis of five selected genes was conducted on partially resistant and susceptible accessions. Three of the genes were differentially expressed at early stages of infection, two of which may be associated with ARR resistance. Our findings provide valuable insight into the genetic control of ARR, and genetic and genomic resources developed here can be used to accelerate development of lentil cultivars with high levels of partial resistance to ARR.