Discovery Of Quantitative Trait Loci Research Articles

Advancements in Lentil Genomics for Enhanced Crop Breeding: A Review

Lentil (Lens culinaris Medik) is an essential pulse crop that is widely grown for its high nutritional value, notably its high protein content, making it an important dietary component for vegetarians and vegans. Despite being the world's fifth most produced pulse, with large contributions from Canada and India, lentil production confronts obstacles such as poor productivity due to limited genetic improvement against biotic and abiotic stresses under rainfed cultivation conditions. Recent advances in lentil genetics and genomics, such as the discovery of genes related to yield, disease resistance, and nutritional content, have boosted breeding efforts to generate improved lentil varieties. The use of contemporary genomic techniques like molecular markers, marker-assisted selection (MAS), genomic selection (GS), and next-generation sequencing (NGS) technology has sped up the discovery of quantitative trait loci (QTLs) and the production of novel cultivars with superior agronomic characteristics. Databases such as NCBI and ENA, as well as specialized resources like KnowPulse, provide critical genomic data, while the creation of lentil genome assemblies, notably the CDC Redberry variety, has improved our understanding of lentil genetics. These resources help to solve the constraints of traditional breeding, particularly for complex characteristics impacted by genotype-environment interactions, opening the way for more robust and productive lentil varieties. Although the application of advanced tools such as genetic engineering, cisgenesis, and genome editing has moved more slowly in lentils than in other crops, their potential to improve lentil output is encouraging. Recent studies on lentil genomes, together with the creation of increased genetic resources and cutting-edge techniques, offer the ability to overcome production constraints and dramatically increase lentil production and quality throughout the world.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms.

Understanding the genetic causes for variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from diverse distinct projects and laboratories. We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1,454 unique donor individuals across 653 studies from public databases, we catalog 23,381 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often strongly linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility and diverse complex human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes. Our work provides a proof of principle for caQTL calling from previously ungenotyped samples, and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

Mining Candidate Genes for Leaf Angle in Brassica napus L. by Combining QTL Mapping and RNA Sequencing Analysis.

Leaf angle (LA) is an important trait of plant architecture, and individuals with narrow LA can better capture canopy light under high-density planting, which is beneficial for increasing the overall yield per unit area. To study the genetic basis and molecular regulation mechanism of leaf angle in rapeseed, we carried out a series of experiments. Quantitative trait loci (QTL) mapping was performed using the RIL population, and seven QTLs were identified. Transcriptome analysis showed that the cell wall formation/biogenesis processes and biosynthesis/metabolism of cell wall components were the most enrichment classes. Most differentially expressed genes (DEGs) involved in the synthesis of lignin, xylan, and cellulose showed down-regulated expression in narrow leaf material. Microscopic analysis suggested that the cell size affected by the cell wall in the junction area of the stem and petiole was the main factor in leaf petiole angle (LPA) differences. Combining QTL mapping and RNA sequencing, five promising candidate genes BnaA01G0125600ZS, BnaA01G0135700ZS, BnaA01G0154600ZS, BnaA10G0154200ZS, and BnaC03G0294200ZS were identified in rapeseed, and most of them were involved in cell wall biogenesis and the synthesis/metabolism of cell wall components. The results of QTL, transcriptome analysis, and cytological analysis were highly consistent, collectively revealing that genes related to cell wall function played a crucial role in regulating the LA trait in rapeseed. The study provides further insights into LA traits, and the discovery of new QTLs and candidate genes is highly beneficial for genetic improvement.

QTL discovery for agronomic and quality traits in diploid potato clones using PotatoMASH amplicon sequencing.

We genotyped a population of 618 diploid potato clones derived from six independent potato-breeding programmes from NW-Europe. The diploids were phenotyped for 23 traits, using standardised protocols and common check varieties, enabling us to derive whole population estimators for most traits. We subsequently performed a Genome-Wide Association Study (GWAS) to identify quantitative trait loci (QTL) for all traits with SNPs and short-read haplotypes derived from read-backed phasing. In this study, we used a marker platform called PotatoMASH (Potato Multi-Allele Scanning Haplotags); a pooled multiplex amplicon sequencing based approach. Through this method, neighbouring SNPs within an amplicon can be combined to generate multi-allelic short-read haplotypes (haplotags) that capture recombination history between the constituent SNPs, and reflect the allelic diversity of a given locus in a different way than single bi-allelic SNPs. We found a total of 37 unique QTL across both marker types. A core of 10 QTL were detected with SNPs as well as with haplotags. Haplotags allowed to detect an additional 14 QTL not found based on the SNP set. Conversely, the bi-allelic SNP set also found 13 QTL not detectable using the haplotag set. We conclude that both marker types should routinely be used in parallel to maximize the QTL detection power. We report 19 novel QTL for nine traits: Skin Smoothness, Sprout Dormancy, Total Tuber Number, Tuber Length, Yield, Chipping Colour, After-cooking Blackening, Cooking Type and Eye depth.

Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes

BackgroundBody conformation, including withers height, is a major selection criterion in horse breeding and is associated with other important traits, such as health and performance. However, little is known about the genomic background of equine conformation. Therefore, the aim of this study was to use imputed sequence-level genotypes from up to 4891 German Warmblood horses to identify genomic regions associated with withers height and linear conformation traits. Furthermore, the traits were genetically characterised and putative causal variants for withers height were detected.ResultsA genome-wide association study (GWAS) for withers height confirmed the presence of a previously known quantitative trait locus (QTL) on Equus caballus (ECA) chromosome 3 close to the LCORL/NCAPG locus, which explained 16% of the phenotypic variance for withers height. An additional significant association signal was detected on ECA1. Further investigations of the region on ECA3 identified a few promising candidate causal variants for withers height, including a nonsense mutation in the coding sequence of the LCORL gene. The estimated heritability for withers height was 0.53 and ranged from 0 to 0.34 for the conformation traits. GWAS identified significantly associated variants for more than half of the investigated conformation traits, among which 13 showed a peak on ECA3 in the same region as withers height. Genetic parameter estimation revealed high genetic correlations between these traits and withers height for the QTL on ECA3.ConclusionsThe use of imputed sequence-level genotypes from a large study cohort led to the discovery of novel QTL associated with conformation traits in German Warmblood horses. The results indicate the high relevance of the QTL on ECA3 for various conformation traits, including withers height, and contribute to deciphering causal mutations for body size in horses.

YQTL Pipeline: A structured computational workflow for large scale quantitative trait loci discovery and downstream visualization.

Quantitative trait loci (QTL) denote regions of DNA whose variation is associated with variations in quantitative traits. QTL discovery is a powerful approach to understand how changes in molecular and clinical phenotypes may be related to DNA sequence changes. However, QTL discovery analysis encompasses multiple analytical steps and the processing of multiple input files, which can be laborious, error prone, and hard to reproduce if performed manually. To facilitate and automate large-scale QTL analysis, we developed the yQTL Pipeline, where the 'y' indicates the dependent quantitative variable being modeled. Prior to the association test, the pipeline supports the calculation or the direct input of pre-defined genome-wide principal components and genetic relationship matrix when applicable. User-specified covariates can also be provided. Depending on whether familial relatedness exists among the subjects, genome-wide association tests will be performed using either a linear mixed-effect model or a linear model. The options to run an ANOVA model or testing the interaction with a covariate are also available. Using the workflow management tool Nextflow, the pipeline parallelizes the analysis steps to optimize run-time and ensure results reproducibility. In addition, a user-friendly R Shiny App is developed to facilitate result visualization. It can generate Manhattan and Miami plots of phenotype traits, genotype-phenotype boxplots, and trait-QTL connection networks. We applied the yQTL Pipeline to analyze metabolomics profiles of blood serum from the New England Centenarians Study (NECS) participants. A total of 9.1M SNPs and 1,052 metabolites across 194 participants were analyzed. Using a p-value cutoff 5e-8, we found 14,983 mQTLs associated with 312 metabolites. The built-in parallelization of our pipeline reduced the run time from ~90 min to ~26 min. Visualization using the R Shiny App revealed multiple mQTLs shared across multiple metabolites. The yQTL Pipeline is available with documentation on GitHub at https://github.com/montilab/yQTLpipeline.

QTL Mapping for Agronomic Important Traits in Well-Adapted Wheat Cultivars

Wheat (Triticum aestivum L.) is one of the most important food crops worldwide and provides the staple food for 40% of the world’s population. Increasing wheat production has become an important goal to ensure global food security. The grain yield of wheat is a complex trait that is usually influenced by multiple agronomically important traits. Thus, the genetic dissection and discovery of quantitative trait loci (QTL) of wheat-yield-related traits are very important to develop high-yield cultivars to improve wheat production. To analyze the genetic basis and discover genes controlling important agronomic traits in wheat, a recombinant inbred lines (RILs) population consisting of 180 RILs derived from a cross between Xinong822 (XN822) and Yannong999 (YN999), two well-adapted cultivars, was used to map QTL for plant height (PH), spike number per spike (SNS), spike length (SL), grain number per spike (GNS), spike number per plant (SN), 1000- grain weight (TGW), grain length (GL), grain width (GW), length/width of grain (GL/GW), perimeter of grain (Peri), and surface area of grains (Sur) in three environments. A total of 64 QTL were detected and distributed on all wheat chromosomes except 3A and 5A. The identified QTL individually explained 2.24–38.24% of the phenotypic variation, with LOD scores ranging from 2.5 to 29. Nine of these QTL were detected in multiple environments, and seven QTL were associated with more than one trait. Additionally, Kompetitive Allele Specific PCR (KASP) assays for five major QTL QSns-1A.2 (PVE = 6.82), QPh-2D.1 (PVE = 37.81), QSl-2D (PVE = 38.24), QTgw-4B (PVE = 8.78), and QGns-4D (PVE = 13.54) were developed and validated in the population. The identified QTL and linked markers are highly valuable in improving wheat yield through marker-assisted breeding, and the large-effect QTL can be fine-mapped for further QTL cloning of yield-related traits in wheat.

THE CONTRIBUTION OF TRANSGENIC RICE TO ENHANCE GRAIN YIELD

Breeders' main goal is to increase the proportion of high-quality rice produced overall. To create effective rice breeding strategies, possible yield-related loci have been mined. many researchers are using transgenic strategies as cutting-edge methods to increase rice productivity. Quantitative trait loci (QTLs) play a pivotal role in governing grain yield in Oryza sativa L., commonly known as rice. The genes contributing to QTLs that determine grain size, length, and weight have been successfully identified. Numerous genes are upregulated to enhance the overall yield of rice. Recent advancements have led to the discovery of genes and QTLs specifically associated with rice yield. Through an in-depth analysis of various yield characteristics, including grain weight, thousand grain weight, grain length, grain width, grain yield per plant, grain number per panicle, and panicles per plant, we conducted a comprehensive review using extensive literature research and public domain databases. Additionally, we explored the progress made in transgenic technology and advanced genomic techniques. The compiled information on genes and QTLs related to yield enhancement aims to provide a valuable resource. The integrated analysis of existing data on genes and/or QTLs provide evidence on potential combinations for creating superior genotypes that combine high yield across multiple traits. Integration of molecular markers, transgenic techniques and conventional breeding as discussed in this extensive review opens up the prospect of developing high yielding rice varieties

An integrative pipeline for circular RNA quantitative trait locus discovery with application in human T cells.

Molecular quantitative trait locus (QTL) mapping has proven to be a powerful approach for prioritizing genetic regulatory variants and causal genes identified by Genome-wide association studies (GWAS). Recently, this success has been extended to circular RNA (circRNA), a potential group of RNAs that can serve as markers for the diagnosis, prognosis, or therapeutic targets of various human diseases. However, a well-developed computational pipeline for circRNA QTL (circQTL) discovery is still lacking. We introduce an integrative method for circQTL mapping and implement it as an automated pipeline based on Nextflow, named cscQTL. The proposed method has two main advantages. Firstly, cscQTL improves the specificity by systematically combining outputs of multiple circRNA calling algorithms to obtain highly confident circRNA annotations. Secondly, cscQTL improves the sensitivity by accurately quantifying circRNA expression with the help of pseudo references. Compared to the single method approach, cscQTL effectively identifies circQTLs with an increase of 20-100% circQTLs detected and recovered all circQTLs that are highly supported by the single method approach. We apply cscQTL to a dataset of human T cells and discover genetic variants that control the expression of 55 circRNAs. By colocalization tests, we further identify circBACH2 and circYY1AP1 as potential candidates for immune disease regulation. cscQTL is freely available at: https://github.com/datngu/cscQTL and https://doi.org/10.5281/zenodo.7851982. Supplementary data are available at Bioinformatics online.

QTL discovery for resistance to black spot and cercospora leaf spot, and defoliation in two interconnected F1 bi-parental tetraploid garden rose populations.

Garden roses are an economically important horticultural crop worldwide, and two major fungal pathogens, black spot (Diplocarpon rosae F.A. Wolf) and cercospora leaf spot of rose (Rosisphaerella rosicola Pass.), affect both the health and ornamental value of the plant. Most studies on black spot disease resistance have focused on diploid germplasm, and little work has been performed on cercospora leaf spot resistance. With the use of newly developed software tools for autopolyploid genetics, two interconnected tetraploid garden rose F1 populations (phenotyped over the course of 3 years) were used for quantitative trait locus (QTL) analysis of black spot and cercospora leaf spot resistance as well as plant defoliation. QTLs for black spot resistance were mapped to linkage groups (LGs) 1-6. QTLs for cercospora resistance and susceptibility were found in LGs 1, 4, and 5 and for defoliation in LGs 1, 3, and 5. The major locus on LG 5 for black spot resistance coincides with the previously discovered Rdr4 locus inherited from Rosa L. 'Radbrite' (Brite Eyes™), the common parent used in these mapping populations. This work is the first report of any QTL for cercospora resistance/susceptibility in tetraploid rose germplasm and the first report of defoliation QTL in roses. A major QTL for cercospora susceptibility coincides with the black spot resistance QTL on LG 5 (Rdr4). A major cercospora resistance QTL was found on LG 1. These populations provide a genetic resource that will further the knowledge base of rose genetics as more traits are studied. Studying more traits from these populations will allow for the stacking of various QTLs for desirable traits.

Ecological genetics of local adaptation in Arabidopsis: An 8-year field experiment.

There is considerable evidence for local adaptation in nature, yet important questions remain regarding its genetic basis. How many loci are involved? What are their effect sizes? What is the relative importance of conditional neutrality versus genetic trade-offs? Here we address these questions in the self-pollinating, annual plant Arabidopsis thaliana. We used 400 recombinant inbred lines (RILs) derived from two locally adapted populations in Italy and Sweden, grew the RILs and parents at the parental locations, and mapped quantitative trait loci (QTL) for mean fitness (fruits/seedling planted). We previously published results from the first 3 years of the study, and here add five additional years, providing a unique opportunity to assess how temporal variation in selection might affect QTL detection and classification. We found 10 adaptive and one maladaptive QTL in Italy, and six adaptive and four maladaptive QTL in Sweden. The discovery of maladaptive QTL at both sites suggests that even locally adapted populations are not always at their genotypic optimum. Mean effect sizes for adaptive QTL, 0.97 and 0.55 fruits in Italy and Sweden, respectively, were large relative to the mean fitness of the RILs (approximately 8 fruits/seedling planted at both sites). Both genetic trade-offs (four cases) and conditional neutrality (seven cases) contribute to local adaptation in this system. The 8-year dataset provided greater power to detect QTL and to estimate their locations compared to our previous 3-year study, identifying one new genetic trade-off and resolving one genetic trade-off into two conditionally adaptive QTL.

Advances in deciphering salt tolerance mechanism in maize

Maize (Zea mays L.) is a global cereal crop whose demand is projected to double by 2050. Along with worsening of farmland salinization, salt stress has become a major environmental threat to the sustainability of maize production worldwide. Accordingly, there is an urgent need to decipher salt-tolerant mechanisms and facilitate the breeding of salt-tolerant maize. As salt tolerance is a complex trait regulated by multiple genes, and maize germplasm varies widely in salt tolerance, efforts have been devoted to the identification and application of quantitative-trait loci (QTL) for salt tolerance. QTL associated with ion regulation, osmotic tolerance, and other aspects of salt tolerance have been discovered using genome-wide association studies (GWAS), linkage mapping, and omics-based approaches. This review highlights recent advances in the molecular-level understanding of salt stress response in maize, in particular in (a) the discovery of salt-tolerance QTL, (b) the mechanisms of salt tolerance, (c) the development of salt-tolerant maize cultivars, and (d) current challenges and future prospects.

High density linkage maps, genetic architecture, and genomic prediction of growth and wood properties in Pinus radiata

BackgroundThe growing availability of genomic resources in radiata pine paves the way for significant advances in fundamental and applied genomic research. We constructed robust high-density linkage maps based on exome-capture genotyping in two F1 populations, and used these populations to perform quantitative trait locus (QTL) scans, genomic prediction and quantitative analyses of genetic architecture for key traits targeted by tree improvement programmes.ResultsOur mapping approach used probabilistic error correction of the marker data, followed by an iterative approach based on stringent parameters. This approach proved highly effective in producing high-density maps with robust marker orders and realistic map lengths (1285–4674 markers per map, with sizes ranging from c. 1643–2292 cM, and mean marker intervals of 0.7–2.1 cM). Colinearity was high between parental linkage maps, although there was evidence for a large chromosomal rearrangement (affecting ~ 90 cM) in one of the parental maps. In total, 28 QTL were detected for growth (stem diameter) and wood properties (wood density and fibre properties measured by Silviscan) in the QTL discovery population, with 1–3 QTL of small to moderate effect size detected per trait in each parental map. Four of these QTL were validated in a second, unrelated F1 population. Results from genomic prediction and analyses of genetic architecture were consistent with those from QTL scans, with wood properties generally having moderate to high genomic heritabilities and predictive abilities, as well as somewhat less complex genetic architectures, compared to growth traits.ConclusionsDespite the economic importance of radiata pine as a plantation forest tree, robust high-density linkage maps constructed from reproducible, sequence-anchored markers have not been published to date. The maps produced in this study will be a valuable resource for several applications, including the selection of marker panels for genomic prediction and anchoring a recently completed de novo whole genome assembly. We also provide the first map-based evidence for a large genomic rearrangement in radiata pine. Finally, results from our QTL scans, genomic prediction, and genetic architecture analyses are informative about the genomic basis of variation in important phenotypic traits.

A SNP‐based genetic dissection of versatile traits in bread wheat (Triticum aestivum L.)

SUMMARYThe continuous increase in global population prompts increased wheat production. Future wheat (Triticum aestivum L.) breeding will heavily rely on dissecting molecular and genetic bases of wheat yield and related traits which is possible through the discovery of quantitative trait loci (QTLs) in constructed populations, such as recombinant inbred lines (RILs). Here, we present an evaluation of 92 RILs in a bi‐parental RIL mapping population (the International Triticeae Mapping Initiative Mapping Population [ITMI/MP]) using newly generated phenotypic data in 3‐year experiments (2015), older phenotypic data (1997–2009), and newly created single nucleotide polymorphism (SNP) marker data based on 92 of the original RILs to search for novel and stable QTLs. Our analyses of more than 15 unique traits observed in multiple experiments included analyses of 46 traits in three environments in the USA, 69 traits in eight environments in Germany, 149 traits in 10 environments in Russia, and 28 traits in four environments in India (292 traits in 25 environments) with 7584 SNPs (292 × 7584 = 2 214 528 data points). A total of 874 QTLs were detected with limit of detection (LOD) scores of 2.01–3.0 and 432 QTLs were detected with LOD > 3.0. Moreover, 769 QTLs could be assigned to 183 clusters based on the common markers and relative proximity of related QTLs, indicating gene‐rich regions throughout the A, B, and D genomes of common wheat. This upgraded genotype–phenotype information of ITMI/MP can assist breeders and geneticists who can make crosses with suitable RILs to improve or investigate traits of interest.

Genome-wide SNP discovery, linkage mapping, and analysis of QTL for morpho-physiological traits in rice during vegetative stage under drought stress.

The online version contains supplementary material available at 10.1007/s12298-021-01095-y.

QTL mapping in outbred tetraploid (and diploid) diallel populations.

Over the last decade, multiparental populations have become a mainstay of genetics research in diploid species. Our goal was to extend this paradigm to autotetraploids by developing software for quantitative trait locus (QTL) mapping in connected F1 populations derived from a set of shared parents. For QTL discovery, phenotypes are regressed on the dosage of parental haplotypes to estimate additive effects. Statistical properties of the model were explored by simulating half-diallel diploid and tetraploid populations with different population sizes and numbers of parents. Across scenarios, the number of progeny per parental haplotype (pph) largely determined the statistical power for QTL detection and accuracy of the estimated haplotype effects. Multiallelic QTL with heritability 0.2 were detected with 90% probability at 25 pph and genome-wide significance level 0.05, and the additive haplotype effects were estimated with over 90% accuracy. Following QTL discovery, the software enables a comparison of models with multiple QTL and nonadditive effects. To illustrate, we analyzed potato tuber shape in a half-diallel population with three tetraploid parents. A well-known QTL on chromosome 10 was detected, for which the inclusion of digenic dominance lowered the Deviance Information Criterion (DIC) by 17 points compared to the additive model. The final model also contained a minor QTL on chromosome 1, but higher-order dominance and epistatic effects were excluded based on the DIC. In terms of practical impacts, the software is already being used to select offspring based on the effect and dosage of particular haplotypes in breeding programs.

A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery.

Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31-year follow-up of the Northern Finland Birth Cohort 1966 (NFBC66). These metabolites, as with many multivariate phenotypes produced by high-throughput biomarker technology, exhibit strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate QTL analysis generally ignore phenotypic correlations or make restrictive assumptions about the associations between phenotypes and genetic loci. We present a computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional data, with cell-sparse variable selection and sparse graphical structure for covariance selection. Cell sparsity allows different phenotype responses to be associated with different genetic predictors and the graphical structure is used to represent the conditional dependencies between phenotype variables. To achieve feasible computation of the large model space, we exploit a factorisation of the covariance matrix. Applying the model to the NFBC66 data with 9000 directly genotyped single nucleotide polymorphisms, we are able to simultaneously estimate genotype–phenotype associations and the residual dependence structure among the metabolites. The R package BayesSUR with full documentation is available at https://cran.r-project.org/web/packages/BayesSUR/

Improved Genetic Map Identified Major QTLs for Drought Tolerance- and Iron Deficiency Tolerance-Related Traits in Groundnut.

A deep understanding of the genetic control of drought tolerance and iron deficiency tolerance is essential to hasten the process of developing improved varieties with higher tolerance through genomics-assisted breeding. In this context, an improved genetic map with 1205 loci was developed spanning 2598.3 cM with an average 2.2 cM distance between loci in the recombinant inbred line (TAG 24 × ICGV 86031) population using high-density 58K single nucleotide polymorphism (SNP) “Axiom_Arachis” array. Quantitative trait locus (QTL) analysis was performed using extensive phenotyping data generated for 20 drought tolerance- and two iron deficiency tolerance-related traits from eight seasons (2004–2015) at two locations in India, one in Niger, and one in Senegal. The genome-wide QTL discovery analysis identified 19 major main-effect QTLs with 10.0–33.9% phenotypic variation explained (PVE) for drought tolerance- and iron deficiency tolerance- related traits. Major main-effect QTLs were detected for haulm weight (20.1% PVE), SCMR (soil plant analytical development (SPAD) chlorophyll meter reading, 22.4% PVE), and visual chlorosis rate (33.9% PVE). Several important candidate genes encoding glycosyl hydrolases; malate dehydrogenases; microtubule-associated proteins; and transcription factors such as MADS-box, basic helix-loop-helix (bHLH), NAM, ATAF, and CUC (NAC), and myeloblastosis (MYB) were identified underlying these QTL regions. The putative function of these genes indicated their possible involvement in plant growth, development of seed and pod, and photosynthesis under drought or iron deficiency conditions in groundnut. These genomic regions and candidate genes, after validation, may be useful to develop molecular markers for deploying genomics-assisted breeding for enhancing groundnut yield under drought stress and iron-deficient soil conditions.

Manipulating Amino Acid Metabolism to Improve Crop Nitrogen Use Efficiency for a Sustainable Agriculture.

In a context of a growing worldwide food demand coupled to the need to develop a sustainable agriculture, it is crucial to improve crop nitrogen use efficiency (NUE) while reducing field N inputs. Classical genetic approaches based on natural allelic variations existing within crops have led to the discovery of quantitative trait loci controlling NUE under low nitrogen conditions; however, the identification of candidate genes from mapping studies is still challenging. Amino acid metabolism is the cornerstone of plant N management, which involves N uptake, assimilation, and remobilization efficiencies, and it is finely regulated during acclimation to low N conditions and other abiotic stresses. Over the last two decades, biotechnological engineering of amino acid metabolism has led to promising results for the improvement of crop NUE, and more recently under low N conditions. This review summarizes current work carried out in crops and provides perspectives on the identification of new candidate genes and future strategies for crop improvement.

The role of gene discovery, QTL analyses and gene expression in reproductive traits in the pig

The reproductive performance of the sow is one of the key factors affecting production profitability of the pig industry. Reproductive traits are in general, lowly heritable, and with reliable markers, they can be used to enhance current selection procedures for improvement of these traits. To find potential markers, large scale quantitative trait loci (QTL) and candidate gene studies have been conducted for reproductive traits. The present review discusses QTL and candidate gene discovery, large scale SNP association studies, gene expression profiling and discovery of miRNA regulation of pig reproductive tissues. Many QTL have been found for reproduction traits and a limited number of useful genes (e.g.: ESR1, PRLR, FSHB, EPOR and RBP4) have been found to have significant associations with reproductive traits. Expression studies with reproductive tissues have revealed differential expression within a few gene networks which need further mapping and association analyses to select prospective gene markers. The near completion of the pig genome sequence and the development of high density SNP chips will allow for large scale SNP association studies for pig reproductive traits in the future. Collection of appropriate phenotypes in large numbers and in broad populations representative of the swine industry are required if such genomic studies will ultimately be successful.