QTL detection for forage quality and stem histology in four connected mapping populations of the model legume Medicago truncatula

Pearl millet a promising fodder crop for changing climate: a review

The agricultural sector faces colossal challenges amid environmental changes and a burgeoning human population. In this context, crops must adapt to evolving climatic conditions while meeting increasing production demands. The dairy industry is anticipated to hold the highest value in the agriculture sector in future. The rise in the livestock population is expected to result in an increased demand for fodder feed. Consequently, it is crucial to seek alternative options, as crops demand fewer resources and are resilient to climate change. Pearl millet offers an apposite key to these bottlenecks, as it is a promising climate resilience crop with significantly low energy, water and carbon footprints compared to other crops. Numerous studies have explored its potential as a fodder crop, revealing promising performance. Despite its capabilities, pearl millet has often been overlooked. To date, few efforts have been made to document molecular aspects of fodder-related traits. However, several QTLs and candidate genes related to forage quality have been identified in other fodder crops, which can be harnessed to enhance the forage quality of pearl millet. Lately, excellent genomic resources have been developed in pearl millet allowing deployment of cutting-edge genomics-assisted breeding for achieving a higher rate of genetic gains. This review would facilitate a deeper understanding of various aspects of fodder pearl millet in retrospect along with the future challenges and their solution. This knowledge may pave the way for designing efficient breeding strategies in pearl millet thereby supporting sustainable agriculture and livestock production in a changing world.

Identification of Genetic Loci Associated With Crude Protein Content and Fiber Composition in Alfalfa (Medicago sativa L.) Using QTL Mapping

Forage quality determined mainly by protein content and fiber composition has a crucial influence on digestibility and nutrition intake for animal feeding. To explore the genetic basis of quality traits, we conducted QTL mapping based on the phenotypic data of crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and lignin of an F1 alfalfa population generated by crossing of two alfalfa parents with significant difference in quality. In total, 83 QTLs were identified with contribution to the phenotypic variation (PVE) ranging from 1.45 to 14.35%. Among them, 47 QTLs interacted significantly with environment and 12 QTLs were associated with more than one trait. Epistatic effect was also detected for 73 pairs of QTLs with PVE of 1.08-14.06%. The results suggested that the inheritance of quality-related traits was jointly affected by additive, epistasis and environment. In addition, 83.33% of the co-localized QTLs were shared by ADF and NDF with the same genetic direction, while the additive effect of crude protein-associated QTLs was opposite to that fiber composition on the same locus, suggesting that the loci may antagonistically contribute to protein content and fiber composition. Further analysis of a QTL related to all the three traits of fiber composition (qNDF1C, qADF1C-2, and qlignin1C-2) showed that five candidate genes were homologs of cellulose synthase-like protein A1 in Medicago truncatula, indicating the potential role in fiber synthesis. For the protein-associated loci we identified, qCP4C-1 was located in the shortest region (chr 4.3 39.3-39.4 Mb), and two of the seven corresponding genes in this region were predicted to be E3 ubiquitin-protein ligase in protein metabolism. Therefore, our results provide some reliable regions significantly associated with alfalfa quality, and identification of the key genes would facilitate marker-assisted selection for favorable alleles in breeding program of alfalfa quality improvement.

Harnessing the Potential of Forage Legumes, Alfalfa, Soybean, and Cowpea for Sustainable Agriculture and Global Food Security

Substantial improvements in access to food and increased purchasing power are driving many people toward consuming nutrition-rich foods causing an unprecedented demand for protein food worldwide, which is expected to rise further. Forage legumes form an important source of feed for livestock and have potential to provide a sustainable solution for food and protein security. Currently, alfalfa is a commercially grown source of forage and feed in many countries. However, soybean and cowpea also have the potential to provide quality forage and fodder for animal use. The cultivation of forage legumes is under threat from changing climatic conditions, indicating the need for breeding cultivars that can sustain and acclimatize to the negative effects of climate change. Recent progress in genetic and genomic tools have facilitated the identification of quantitative trait loci and genes/alleles that can aid in developing forage cultivars through genomics-assisted breeding. Furthermore, transgenic technology can be utilized to manipulate the genetic makeup of plants to improve forage digestibility for better animal performance. In this article, we assess the genetic potential of three important legume crops, alfalfa, soybean, and cowpea in supplying quality fodder and feed for livestock. In addition, we examine the impact of climate change on forage quality and discuss efforts made in enhancing the adaptation of the plant to the abiotic stress conditions. Subsequently, we suggest the application of integrative approaches to achieve adequate forage production amid the unpredictable climatic conditions.

Medicago truncatula: Genetic and Genomic Resources

Medicago truncatula was chosen by the legume community, along with Lotus japonicus, as a model plant to study legume biology. Since then, numerous resources and tools have been developed for M. truncatula. These include, for example, its genome sequence, core ecotype collections, transformation/regeneration methods, extensive mutant collections, and a gene expression atlas. This review aims to describe the different genetic and genomic tools and resources currently available for M. truncatula. We also describe how these resources were generated and provide all the information necessary to access these resources and use them from a practical point of view. © 2017 by John Wiley & Sons, Inc.

Genome-Wide Association Mapping and Genomic Selection for Alfalfa (Medicago sativa) Forage Quality Traits

Genetic progress for forage quality has been poor in alfalfa (Medicago sativa L.), the most-grown forage legume worldwide. This study aimed at exploring opportunities for marker-assisted selection (MAS) and genomic selection of forage quality traits based on breeding values of parent plants. Some 154 genotypes from a broadly-based reference population were genotyped by genotyping-by-sequencing (GBS), and phenotyped for leaf-to-stem ratio, leaf and stem contents of protein, neutral detergent fiber (NDF) and acid detergent lignin (ADL), and leaf and stem NDF digestibility after 24 hours (NDFD), of their dense-planted half-sib progenies in three growing conditions (summer harvest, full irrigation; summer harvest, suspended irrigation; autumn harvest). Trait-marker analyses were performed on progeny values averaged over conditions, owing to modest germplasm × condition interaction. Genomic selection exploited 11,450 polymorphic SNP markers, whereas a subset of 8,494 M. truncatula-aligned markers were used for a genome-wide association study (GWAS). GWAS confirmed the polygenic control of quality traits and, in agreement with phenotypic correlations, indicated substantially different genetic control of a given trait in stems and leaves. It detected several SNPs in different annotated genes that were highly linked to stem protein content. Also, it identified a small genomic region on chromosome 8 with high concentration of annotated genes associated with leaf ADL, including one gene probably involved in the lignin pathway. Three genomic selection models, i.e., Ridge-regression BLUP, Bayes B and Bayesian Lasso, displayed similar prediction accuracy, whereas SVR-lin was less accurate. Accuracy values were moderate (0.3-0.4) for stem NDFD and leaf protein content, modest for leaf ADL and NDFD, and low to very low for the other traits. Along with previous results for the same germplasm set, this study indicates that GBS data can be exploited to improve both quality traits (by genomic selection or MAS) and forage yield.

Ploidy effect and genetic architecture exploration of stalk traits using DH and its corresponding haploid populations in maize

BACKGROUND

Doubled haploid (DH) lines produced via in vivo haploid induction have become indispensable in maize research and practical breeding, so it is important to understand traits characteristics in DH and its corresponding haploids which derived from each DH lines. In this study, a DH population derived from Zheng58 × Chang7-2 and a haploid population, were developed, genotyped and evaluated to investigate genetic architecture of eight stalk traits, especially rind penetrometer resistance (RPR) and in vitro dry matter digestion (IVDMD), which affecting maize stalk lodging-resistance and feeding values, respectively.

RESULTS

Phenotypic correlation coefficients ranged from 0.38 to 0.69 between the two populations for eight stalk traits. Heritability values of all stalk traits ranged from 0.49 to 0.81 in the DH population, and 0.58 to 0.89 in the haploid population. Quantitative trait loci (QTL) mapping study showed that a total of 47 QTL for all traits accounting for genetic variations ranging from 1.6 to 36.5% were detected in two populations. One or more QTL sharing common region for each trait were detected between two different ploidy populations. Potential candidate genes predicated from the four QTL support intervals for RPR and IVDMD were indirectly or directly involved with cellulose and lignin biosynthesis, which participated in cell wall formation. The increased expression levels of lignin and cellulose synthesis key genes in the haploid situation illustrated that dosage compensation may account for genome dosage effect in our study.

CONCLUSIONS

The current investigation extended understanding about the genetic basis of stalk traits and correlations between DH and its haploid populations, which showed consistence and difference between them in phenotype, QTL characters, and gene expression. The higher heritabilities and partly higher QTL detection power were presented in haploid population than in DH population. All of which described above could lay a preliminary foundation for genetic architecture study with haploid population and may benefit selection in haploid-stage to reduce cost in DH breeding.

Genetic variability and identification of quantitative trait loci affecting plant growth and chlorophyll fluorescence parameters in the model legume Medicago truncatula under control and salt stress conditions

Salinity is one of the major stresses that limits crop production worldwide and affects most physiological activities in plants. In order to study the genetic control of salt stress in the model legume Medicago truncatula Gaertn., an experiment was undertaken to determine the genetic variability and to identify quantitative trait loci (QTLs) controlling several traits related to plant growth and physiology in a population of recombinant inbred lines. Shoot and root DW, relative water content, leaf area, chlorophyll content, chlorophyll fluorescence parameters, and Na+ and K+ in shoots and roots were measured. The experiment was carried out with three replications. ANOVA showed a large genetic variation and transgressive segregation for the traits studied, suggesting putative complex tolerance mechanisms. A total of 21 QTLs were detected under control conditions and 19 QTLs were identified under 100mm salt stress conditions, with three QTLs being common to both situations. The percentage of total phenotypic variance explained by the QTLs ranged from 4.6% to 23.01%. Overlapping QTLs for different traits were also observed, which enables us to discriminate independent traits from linked ones. The results should be helpful information for further functional analysis of salt tolerance in M. truncatula.

Clusthaplo: a plug-in for MCQTL to enhance QTL detection using ancestral alleles in multi-cross design

We enhance power and accuracy of QTL mapping in multiple related families, by clustering the founders of the families on their local genomic similarity. MCQTL is a linkage mapping software application that allows the joint QTL mapping of multiple related families. In its current implementation, QTLs are modeled with one or two parameters for each parent that is a founder of the multi-cross design. The higher the number of parents, the higher the number of model parameters which can impact the power and the accuracy of the mapping. We propose to make use of the availability of denser and denser genotyping information on the founders to lessen the number of MCQTL parameters and thus boost the QTL discovery. We developed clusthaplo, an R package ( http://cran.r-project.org/web/packages/clusthaplo/index.html ), which aims to cluster haplotypes using a genomic similarity that reflects the probability of sharing the same ancestral allele. Computed in a sliding window along the genome and followed by a clustering method, the genomic similarity allows the local clustering of the parent haplotypes. Our assumption is that the haplotypes belonging to the same class transmit the same ancestral allele. So their putative QTL allelic effects can be modeled with the same parameter, leading to a parsimonious model, that is plugged in MCQTL. Intensive simulations using three maize data sets showed the significant gain in power and in accuracy of the QTL mapping with the ancestral allele model compared to the classical MCQTL model. MCQTL_LD (clusthaplo outputs plug in MCQTL) is a versatile and powerful tool for QTL mapping in multiple related families that makes use of linkage and linkage disequilibrium (web site http://carlit.toulouse.inra.fr/MCQTL/ ).