Individual Quantitative Trait Loci Research Articles

Grain albumin content is affected by 1BL/1RS translocation and alters the processing quality of wheat (Triticum aestivum L.)

Grain albumin is highly nutritious and closely related to the processing quality of wheat. However, few studies have explored the grain albumin content (GAC) in wheat. This study aims to uncover quantitative trait loci (QTLs) linked to wheat GAC by analyzing a doubled haploid (DH) population derived from common wheat cultivars ShanNong23 and ZhouMai17. We detected six QTLs controlling GAC on chromosomes 1B, 5A, and 6D, with individual QTL explaining 5.78% to 22.29% of the GAC variation. The effect of QGac.cau-1B.1 on GAC is attributed to the presence of the 1BL/1RS translocation, indicating that the 1BL/1RS translocation increase of GAC compared with the non-1BL/1RS translocation lines. The higher GAC observed in 1BL/1RS lines could be primarily attributed to the increased accumulation of omega-secalin, omega-gliadin, low molecular weight glutenin subunit and ribosomal protein content. Additionally, we also found that the SDS-sedimentation value of whole wheat flour was decreased by adding albumin solution. These results advance our understanding of the genetic basis of GAC and offer novel perspectives for enhancing wheat quality through genetic enhancements.

Linkage mapping of root shape traits in two carrot populations.

This study investigated the genetic basis of carrot root shape traits using composite interval mapping in two biparental populations (n = 119 and n = 128). The roots of carrot F2:3 progenies were grown over 2 years and analyzed using a digital imaging pipeline to extract root phenotypes that compose market class. Broad-sense heritability on an entry-mean basis ranged from 0.46 to 0.80 for root traits. Reproducible quantitative trait loci (QTL) were identified on chromosomes 2 and 6 on both populations. Colocalization of QTLs for phenotypically correlated root traits was also observed and coincided with previously identified QTLs in published association and linkage mapping studies. Individual QTLs explained between 14 and 27% of total phenotypic variance across traits, while four QTLs for length-to-width ratio collectively accounted for up to 73% of variation. Predicted genes associated with the OFP-TRM (OVATE Family Proteins-TONNEAU1 Recruiting Motif) and IQD (IQ67 domain) pathway were identified within QTL support intervals. This observation raises the possibility of extending the current regulon model of fruit shape to include carrot storage roots. Nevertheless, the precise molecular mechanisms through which this pathway operates in roots characterized by secondary growth originating from cambium layers remain unknown.

Progress in the Management of Rice Blast Disease: The Role of Avirulence and Resistance Genes through Gene-for-Gene Interactions

Rice is a vital component in the diets of many people worldwide, supplying necessary calories for subsistence. Nevertheless, the yield of this crucial agricultural crop is consistently hindered by a range of biotic stresses. Out of these, rice blast, claused mainly by the fungus Magnaporthe oryzae, poses a significant menace to worldwide rice cultivation as well as yield in recent years. The consequences are particularly crucial given the current climate change challenges. In recent decades, substantial progress has been achieved in the development of efficient ways to manage rice blast disease. These procedures entail using a variety of rice genetic resources to find, map, clone, and functionally validate individual resistance (R) genes and quantitative trait loci (QTLs) that provide long-lasting resistance to rice blast disease. Moreover, the replication and practical confirmation of homologous avirulence (Avr) genes in various M. oryzae strains have been crucial in comprehending the fundamental molecular mechanisms of host–pathogen interactions. This article offers a thorough examination of the cloning and functional verification of different R genes and QTLs linked to resistance against rice blast disease. The complex interplay between R–Avr pairings, which contributes to the development of resistance against rice blast throughout a wide range, is thoroughly explained. Finally, this study explores the most recent progress in next-generation sequencing (NGS) and genome editing technologies (GETs), examining their potential uses in improving the treatment of rice blast disease.

Unraveling the genetic basis of grain number-related traits in a wheat-Agropyron cristatum introgressed line through high-resolution linkage mapping

BackgroundGrain number per spike (GNS) is a pivotal determinant of grain yield in wheat. Pubing 3228 (PB3228), a wheat-Agropyron cristatum germplasm, exhibits a notably higher GNS.ResultsIn this study, we developed a recombinant inbred line (RIL) population derived from PB3228/Gao8901 (PG-RIL) and constructed a high-density genetic map comprising 101,136 loci, spanning 4357.3 cM using the Wheat 660 K SNP array. The genetic map demonstrated high collinearity with the wheat assembly IWGSC RefSeq v1.0. Traits related to grain number and spikelet number per spike were evaluated in seven environments for quantitative trait locus (QTL) analysis. Five environmentally stable QTLs were detected in at least three environments. Among these, two major QTLs, QGns-4A.2 and QGns-1A.1, associated with GNS, exhibited positive alleles contributed by PB3228. Further, the conditional QTL analysis revealed a predominant contribution of PB3228 to the GNS QTLs, with both grain number per spikelet (GNSL) and spikelet number per spike (SNS) contributing to the overall GNS trait. Four kompetitive allele-specific PCR (KASP) markers that linked to QGns-4A.2 and QGns-1A.1 were developed and found to be effective in verifying the QTL effect within a diversity panel. Compared to previous studies, QGns-4A.2 exhibited stability across different trials, while QGns-1A.1 represents a novel QTL. The results from unconditional and conditional QTL analyses are valuable for dissecting the genetic contribution of the component traits to GNS at the individual QTL level and for understanding the genetic basis of the superior grain number character in PB3228. The KASP markers can be utilized in marker-assisted selection for enhancing GNS.ConclusionsFive environmentally stable QTLs related to grain number and spikelet number per spike were identified. PB3228 contributed to the majority of the QTLs associated with GNS.

QTL mapping and analysis for drought tolerance in rice by genome-wide association study.

Rice drought resistance is a complicated quantitative feature involving a range of biological and agronomic variables, but little is known about the underlying genetics and regulatory mechanisms that regulate drought tolerance. This study used 120 recombinant inbred lines (RILs), derived from a cross between drought tolerant Lvhan 1 and susceptible Aixian 1. The RILs were subjected to drought stress at the first ear stage, and phenotypic data of 16 agronomic and physiological traits under varying conditions were investigated. Genome-wide association study (GWAS) on the drought resistance index of traits was carried out. A total of 9 quantitative trait loci (QTLs) associated with drought-related traits were identified on chromosomes 2, 6, 7, 8, 9, and 10, which includes QTLs for plant height (PH) qPH10.1, effective panicles number (EPN) qEPN6.1, panicle length (PL) qPL9.1, thousand-grain weight (TGW) qTGW2.1, qTGW6.1, qTGW8.1, leaf length (LL) qLL7.1, leaf width (LW) qLW7.1, and leaf area (LA) qLA7.1. The fraction of phenotypic variation explained by individual QTL varied from 10.6% to 13.9%. Except for days to flowering (DTF), the mean values of all traits under normal water management conditions were considerably higher than those under drought conditions. Except for the DTF, the drought resistance index of all rice traits was less than 1, indicating that drought treatment reduced the EPN, FGPP, SSR, PH, and LA, which affected the growth and development of rice. The drought resistance index of DTF was 1.02, indicating that drought prolonged the heading time of rice and diminish the yield parameters. Along with identifying QTLs, the results also predicted ten candidate genes, which are directly or indirectly involved in various metabolic functioning related to drought stress. The identification of these genomic sites or QTLs that effectively respond to water scarcity will aid in the quest of understanding the drought tolerance mechanisms. This study will facilitate the marker-assisted rice breeding and handy in the breeding of drought-tolerant rice varieties.

Construction of a genetic linkage map and QTL mapping of fruit quality traits in guava (Psidium guajava L.).

Guava (Psidium guajava L.) is an important fruit crop of the Indian sub-continent, with potential for improvements in quality and yield. The goal of the present study was to construct a genetic linkage map in an intraspecific cross between the elite cultivar 'Allahabad Safeda' and the Purple Guava landrace to identify the genomic regions responsible for important fruit quality traits, viz., total soluble solids, titratable acidity, vitamin C, and sugars. This population was phenotyped in field trials (as a winter crop) for three consecutive years, and showed moderate-to-high values of heterogeneity coefficients along with higher heritability (60.0%-97.0%) and genetic-advance-over-mean values (13.23%-31.17%), suggesting minimal environmental influence on the expression of fruit-quality traits and indicating that these traits can be improved by phenotypic selection methods. Significant correlations and strong associations were also detected among fruit physico-chemical traits in segregating progeny. The constructed linkage map consisted of 195 markers distributed across 11 chromosomes, spanning a length of 1,604.47 cM (average inter-loci distance of 8.80 markers) and with 88.00% coverage of the guava genome. Fifty-eight quantitative trait loci (QTLs) were detected in three environments with best linear unbiased prediction (BLUP) values using the composite interval mapping algorithm of the BIP (biparental populations) module. The QTLs were distributed on seven different chromosomes, explaining 10.95%-17.77% of phenotypic variance, with the highest LOD score being 5.96 for qTSS.AS.pau-6.2. Thirteen QTLs detected across multiple environments with BLUPs indicate stability and utility in a future breeding program for guava. Furthermore, seven QTL clusters with stable or common individual QTLs affecting two or more different traits were located on six linkage groups (LGs), explaining the correlation among fruit-quality traits. Thus, the multiple environmental evaluations conducted here have increased our understanding of the molecular basis of phenotypic variation, providing the basis for future high-resolution fine-mapping and paving the way for marker-assisted breeding of fruit-quality traits.

QTL Analysis of Yield and End-Use Quality Traits in Texas Hard Red Winter Wheat

Genetic dissection of complex traits by quantitative trait locus (QTL) analysis permits the understanding of the genotypic effects of QTL, interactions between QTLs, and QTL-by-environment interactions in wheat. This study aimed to identify the QTL linked to yield, its components, end-use quality traits including kernel, flour, and dough rheology, and related agronomic traits under dryland and irrigated conditions. A mapping population of 179 F2:6 recombinant inbred lines (RILs) derived from ‘TAM 111’/‘TX05A001822’ was evaluated for these traits to investigate their genetic stability and phenotypic plasticity using 2658 single nucleotide polymorphisms (SNPs) with 35 linkage groups. Traits associated with chromosome regions were detected for individual and across-environment QTL by inclusive composite interval mapping. A total of 30 QTL regions were identified, including 14 consistent QTLs mapped on 11 chromosomes and six pleiotropic QTLs mapped on 5 chromosomes. Three consistent QTLs in chromosomes 1A, 3B, and 6D might be novel. Three major QTLs with both consistent and pleiotropic effects were co-localized with known genes. The first QTL for dough mixing properties was physically clustered around Glu-D1 and had an phenotypic variation explained (PVE) up to 31.3%. The second QTL for kernel-related traits was physically close to the TaCWI-4A (cell wall invertase) gene, which influences the thousand kernel weight, heading date, and harvest index, with a PVE of up to 12.3%. The third QTL, which was colocalized with the TaCWI-5D gene for kernel traits, was identified with a PVE of 6.7%. Epistasis was also detected, but major QTLs were not involved in significant epistasis or interactions with environmental effects. The current study provided new information that is useful for enhanced wheat breeding, which will benefit from the deployment of the favorable alleles for end-use quality, yield, and other agronomic traits in wheat-breeding programs through marker-assisted selection.

Novel loci for hyperglycemia identified by QTL mapping of longitudinal phenotypes and congenic analysis

We previously reported that four hyperglycemia loci are located on three chromosomes in the Nagoya-Shibata-Yasuda (NSY) mouse model, commonly used to study type 2 diabetes. However, we did not search for hyperglycemia loci across all chromosomes. In this study, we performed quantitative trait loci (QTLs) mapping of longitudinal phenotypes from crosses between NSY (hyperglycemic) and C3H (normoglycemic) mice. We identified four new QTLs for hyperglycemia, namely Nidd5nsy, Nidd6nsy, Nidd1c3h, and Nidd2c3h, on Chromosome 1, 4, 10, and 13, respectively. These QTLs were associated with hyperglycemia in young mice and had attenuated effects in older mice. Nidd5nsy and Nidd6nsy were hyperglycemic with NSY alleles, and Nidd1c3h and Nidd2c3h were hyperglycemic with C3H alleles. We further bred Nidd5nsy congenic mice and demonstrated that Nidd5nsy has a strong effect on hyperglycemia when young, accompanied by insulin resistance and visceral fat accumulation. These results showed that the effects of individual QTLs strengthened or weakened with age, and that the sum of the effects of QTLs captured the age-related deterioration of glucose tolerance in individuals. Our results support the importance of longitudinal phenotypes in the genetic analysis of polygenic traits and have implications for the genetic basis and pathogenesis of type 2 diabetes in humans.

Pinb-D1p is an elite allele for improving end-use quality in wheat (Triticum aestivum L.).

We identified ten QTLs controlling SDS-SV trait in a RIL population derived from ND3331 and Zang1817. Pinb-D1p is an elite allele from Tibetan semi‑wild wheat for good end-use quality. Gluten strength is an important factor for wheat processing and end-product quality and is commonly characterized using the sodium dodecyl sulfate-sedimentation volume (SDS-SV) test. The objective of this study was to identify quantitative trait loci (QTLs) associated with wheat SDS-SV traits using a recombinant inbred line (RIL) population derived from common wheat line NongDa3331 (ND3331) and Tibetan semi-wild wheat accession Zang1817. We detected 10 QTLs controlling SDS-SV on chromosomes 1A, 1B, 3A, 4A, 4B, 5A, 5D, 6B and 7A, with individual QTLs explaining 2.02% to 15.53% of the phenotypic variation. They included four major QTLs, Qsdss-1A, Qsdss-1B.1, Qsdss-1B.2, and Qsdss-5D, whose effects on SDS-SV were due to the Glu-A1 locus encoding the high-molecular-weight glutenin subunit 1Ax1, the 1B/1R translocation, 1Bx7 + 1By8 at the Glu-B1 locus, and the hardness-controlling loci Pina-D1 and Pinb-D1, respectively. We developed KASP markers for the Glu-A1, Glu-B1, and Pinb-D1 loci. Importantly, we showed for the first time that the hardness allele Pinb-D1p positively affects SDS-SV, making it a good candidate for wheat quality improvement. These results broaden our understanding of the genetic characterization of SDS-SV, and the QTLs identified are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

A network modeling approach provides insights into the environment-specific yield architecture of wheat.

Wheat (Triticum aestivum) yield is impacted by a diversity of developmental processes which interact with the environment during plant growth. This complex genetic architecture complicates identifying quantitative trait loci that can be used to improve yield. Trait data collected on individual processes or components of yield have simpler genetic bases and can be used to model how quantitative trait loci generate yield variation. The objectives of this experiment were to identify quantitative trait loci affecting spike yield, evaluate how their effects on spike yield proceed from effects on component phenotypes, and to understand how the genetic basis of spike yield variation changes between environments. A 358 F5:6 recombinant inbred line population developed from the cross of LA-95135 and SS-MPV-57 was evaluated in 2 replications at 5 locations over the 2018 and 2019 seasons. The parents were 2 soft red winter wheat cultivars differing in flowering, plant height, and yield component characters. Data on yield components and plant growth were used to assemble a structural equation model to characterize the relationships between quantitative trait loci, yield components, and overall spike yield. The effects of major quantitative trait loci on spike yield varied by environment, and their effects on total spike yield were proportionally smaller than their effects on component traits. This typically resulted from contrasting effects on component traits, where an increase in traits associated with kernel number was generally associated with a decrease in traits related to kernel size. In all, the complete set of identified quantitative trait loci was sufficient to explain most of the spike yield variation observed within each environment. Still, the relative importance of individual quantitative trait loci varied dramatically. Path analysis based on coefficients estimated through structural equation model demonstrated that these variations in effects resulted from both different effects of quantitative trait loci on phenotypes and environment-by-environment differences in the effects of phenotypes on one another, providing a conceptual model for yield genotype-by-environment interactions in wheat.

Berry Anthocyanin, Acid, and Volatile Trait Analyses in a Grapevine-Interspecific F2 Population Using an Integrated GBS and rhAmpSeq Genetic Map.

Increased map density and transferability of markers are essential for the genetic analysis of fruit quality and stress tolerance in interspecific grapevine populations. We used 1449 GBS and 2000 rhAmpSeq markers to develop a dense map for an interspecific F2 population (VRS-F2) that was derived by selfing a single F1 from a Vitis riparia x ‘Seyval blanc’ cross. The resultant map contained 2519 markers spanning 1131.3 cM and was highly collinear with the Vitis vinifera ‘PN40024’ genome. Quantitative trait loci (QTL) for berry skin color and flower type were used to validate the map. Four rhAmpSeq transferable markers were identified that can be used in pairs (one pistillate and one hermaphroditic) to predict pistillate and hermaphrodite flower type with ≥99.7% accuracy. Total and individual anthocyanin diglucoside QTL mapped to chromosome 9 near a 5-O-GLUCOSYLTRANSFERASE candidate gene. Malic acid QTL were observed on chromosome 1 and 6 with two MALATE DEHYRDROGENASE CYTOPLASMIC 1 and ALUMINUM-ACTIVATED MALATE TRANSPORTER 2-LIKE (ALMT) candidate genes, respectively. Modeling malic acid identified a potential QTL on chromosome 8 with peak position in proximity of another ALMT. A first-ever reported QTL for the grassy smelling volatile (E)-2-hexenal was found on chromosome 2 with a PHOSPHOLIPID HYDROPEROXIDE GLUTATHIONE PEROXIDASE candidate gene near peak markers.

Quantitative trait loci underlying flooding tolerance in soybean (Glycine max)

AbstractFlooding stress typically causes oxygen deprivation (hypoxia) in plants, which damages roots and substantially affects soybean growth, survival and yield. Identifying quantitative trait loci (QTLs) and understanding the inheritance of flooding tolerance will help in developing soybean cultivars with tolerance to flooding. The objective of this study was to map QTLs responsible for flooding tolerance using an F5‐derived recombinant inbred line (RIL) population derived from a ‘Benning’ × PI 416937 cross. RILs, along with the parents ‘Benning’ and PI 416937, were grown in 2012, 2014 and 2015 at Stuttgart, AR, and phenotyped for flooding tolerance using a visual determination of plant health and the survival rate of plants in the field. The population was genotyped with the SoySNP6K Infinium BeadChips. Analysis of variance revealed significant effects of genotype, environment and genotype × environment interaction (P < .0001) on flooding tolerance. QTL analysis detected nine significant QTL (logarithm of the odds [LOD] > 3.0) for flooding tolerance on chromosomes (Chrs) 1, 4, 5, 16 and 18 for both flooding tolerance scores (FTSs) and survival rates (SRs) using best linear unbiased prediction (BLUP) values over three years. Three QTLs, located on Chrs 1, 5 and 16, were in common for both FTS and SR traits, and individual QTLs explained 7.6% to 11.0% of the phenotypic variation. The results suggested that flooding tolerance is a complex trait, which is controlled by multiple QTLs and significantly affected by QTL × environment interactions. The QTL and marker information could be used to assist soybean breeders to develop flooding tolerant cultivars.

Identification of QTL Associated with Regrowth Vigor Using the Nested Association Mapping Population in Switchgrass.

Switchgrass (Panicum virgatum L.) is a warm-season perennial grass species that is utilized as forage for livestock and biofuel feedstock. The stability of biomass yield and regrowth vigor under changing harvest frequency would help manage potential fluctuations in the feedstock market and would provide a continuous supply of quality forage for livestock. This study was conducted to (i) assess the genetic variation and (ii) identify the quantitative trait loci (QTL) associated with regrowth vigor after multiple cuttings in lowland switchgrass. A nested association mapping (NAM) population comprising 2000 pseudo F2 progenies was genotyped with single nucleotide polymorphism (SNP) markers derived from exome-capture sequencing and was evaluated for regrowth vigor in 2017 and 2018. The results showed significant variation among the NAM families in terms of regrowth vigor (p < 0.05). A total of 10 QTL were detected on 6 chromosomes: 1B, 5A, 5B, 6B, 7B, and 8A, explaining the phenotypic variation by up to 4.7%. The additive genetic effects of an individual QTL ranged from −0.13 to 0.26. No single QTL showed a markedly large effect, suggesting complex genetics underlying regrowth vigor in switchgrass. The homologs of candidate genes that play a variety of roles in developmental processes, including plant hormonal signal transduction, nucleotide biosynthesis, secondary metabolism, senescence, and responses to both biotic and abiotic stresses, were identified in the vicinity of QTL.

Simultaneous dissection of grain carotenoid levels and kernel color in biparental maize populations with yellow-to-orange grain.

Maize enriched in provitamin A carotenoids could be key in combatting vitamin A deficiency in human populations relying on maize as a food staple. Consumer studies indicate that orange maize may be regarded as novel and preferred. This study identifies genes of relevance for grain carotenoid concentrations and kernel color, through simultaneous dissection of these traits in 10 families of the US maize nested association mapping panel that have yellow to orange grain. Quantitative trait loci were identified via joint-linkage analysis, with phenotypic variation explained for individual kernel color quantitative trait loci ranging from 2.4% to 17.5%. These quantitative trait loci were cross-analyzed with significant marker-trait associations in a genome-wide association study that utilized ∼27 million variants. Nine genes were identified: four encoding activities upstream of the core carotenoid pathway, one at the pathway branchpoint, three within the α- or β-pathway branches, and one encoding a carotenoid cleavage dioxygenase. Of these, three exhibited significant pleiotropy between kernel color and one or more carotenoid traits. Kernel color exhibited moderate positive correlations with β-branch and total carotenoids and negligible correlations with α-branch carotenoids. These findings can be leveraged to simultaneously achieve desirable kernel color phenotypes and increase concentrations of provitamin A and other priority carotenoids.

Meta-analysis and validation of genomic loci governing seedling and reproductive stage salinity tolerance in rice.

Identification of concurrent genomic regions contributing tolerance to salinity at the seedling and reproductive stages were done using 45 quantitative trait loci (QTL) mapping studies reporting 915 individual QTLs. The QTL-data were used to perform a meta-analysis to predict, validate and analyze the Meta-QTLs governing component traits contributing to salinity tolerance. We predicted a total of 65 and 49 Meta-QTLs distributed across the genome governing seedling and reproductive stage salinity tolerance, respectively. Salinity stress (EC ~10.0dSm-1 ) was evaluated in a set of 32 genotypes grown hydroponically, from these eight extreme (highly tolerant and highly susceptible) genotypes were selected for validation of significant Meta-QTLs. Another set of eight previously known and reported (highly tolerant and highly susceptible) genotypes were evaluated under saline micro plot conditions (EC ~8.0dSm-1 ) and used for validation of significant Meta-QTLs for reproductive stage salinity tolerance. The microsatellite marker "RM5635" linked to MSQTL4.2 (~295.43 kb) was able to clearly differentiate contrasting genotypes for seedling stage salinity tolerance, whereas at the reproductive stage, none of the markers were able to validate the predicted Meta-QTL for salinity tolerance. Earlier reported, gene expression studies were used for candidate gene analysis of validated MSQTL4.2, which indicated the down regulation of Os04g0423100, a gene encoding Mono-oxygenase-FAD binding domain containing protein. The traits associated with this Meta-QTL were root and shoot sodium and potassium concentration and leaf chlorophyll content. The identified and validated genomic region assumes a great significant role in seedling stage salinity tolerance in rice, and it can be used for marker-assisted backcross breeding programs.

Genetic Mapping of Quantitative Trait Loci for End-Use Quality and Grain Minerals in Hard Red Winter Wheat

To meet the demands of different wheat-based food products, traits related to end-use quality become indispensable components in wheat improvement. Thus, markers associated with these traits are valuable for the timely evaluation of protein content, kernel physical characteristics, and rheological properties. Hereunder, we report the mapping results of quantitative trait loci (QTLs) linked to end-use quality traits. We used a dense genetic map with 5199 SNPs from a 90K array based on a recombinant inbred line (RIL) population derived from ‘CO960293-2’/‘TAM 111’. The population was evaluated for flour protein concentration, kernel characteristics, dough rheological properties, and grain mineral concentrations. An inclusive composite interval mapping model for individual and across-environment QTL analyses revealed 22 consistent QTLs identified in two or more environments. Chromosomes 1A, 1B, and 1D had clustered QTLs associated with rheological parameters. Glu-D1 loci from CO960293-2 and either low-molecular-weight glutenin subunits or gliadin loci on 1A, 1B, and 1D influenced dough mixing properties substantially, with up to 34.2% of the total phenotypic variation explained (PVE). A total of five QTLs associated with grain Cd, Co, and Mo concentrations were identified on 3B, 5A, and 7B, explaining up to 11.6% of PVE. The results provide important genetic resources towards understanding the genetic bases of end-use quality traits. Information about the novel and consistent QTLs provided solid foundations for further characterization and marker designing to assist selections for end-use quality improvements.

Genomic regions associated with salinity tolerance in lowland switchgrass

AbstractSwitchgrass (Panicum virgatum L.) is a promising candidate species for sustainable biofuel feedstock production on marginal land. Soil salinity is one of the significant factors that limits sustainable agricultural production worldwide including a substantial reduction in biomass yield of switchgrass. This study was designed to assess genetic variation in lowland switchgrass and identify genomic regions contributing to an increased level of salinity tolerance using a subset (550 pseudo F2) of a nested association mapping (NAM) population. Salinity tolerance was evaluated based on salt injury score (SIS) using a 1 to 9 scale (1 = the most tolerant, 9 = the most sensitive) and stress tolerance index (STI) calculated from plant height measurements. Substantial variation among NAM families and genotypes within families was observed (p &lt; .05) for SIS. However, no variation was evident among NAM families and genotypes within families for STI. The switchgrass plant accessions EG 1104–1, and EG 1104–2 displayed a high level of salinity tolerance based on SIS which can be used as source material for cultivar development. A total of five quantitative trait loci (QTL) associated with SIS were detected by composite interval mapping on four chromosomes 2B, 6B, 7B, and 9B. The phenotypic variation explained by an individual QTL ranged from 1.4 to 6.5%. The additive genetic effects of individual QTL ranged from −0.07 to 0.63. Homologs of five candidate genes were identified, which were reported to be associated with salinity tolerance.

A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study.

The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix () in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between −0.14 and −0.08 and from −0.62 to −0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne 250) and less (Ne 100) genetically diverse populations, respectively, and the bias ranged between −0.36 and −0.32 and from −0.78 to −0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.

Genetic Architecture of Chile Pepper (Capsicum spp.) QTLome Revealed Using Meta-QTL Analysis

In recent years, quantitative trait loci (QTL) mapping approaches have been widely implemented to identify genomic regions affecting variation for different traits for marker-assisted selection (MAS). Meta-QTL analysis for different traits in chile peppers (Capsicum spp.) remains lacking, and therefore it would be necessary to re-evaluate identified QTL for a more precise MAS for genetic improvement. We report the first known meta-QTL analysis for diverse traits in the chile pepper QTLome. A literature survey using 29 published linkage mapping studies identified 766 individual QTL from five different trait classes. A total of 311 QTL were projected into a consensus map. Meta-analysis identified 30 meta-QTL regions distributed across the 12 chromosomes of Capsicum. MQTL5.1 and MQTL5.2 related to Phytophthora capsici fruit and root rot resistance were delimited to &lt; 1.0 cM confidence intervals in chromosome P5. Candidate gene analysis for the P5 meta-QTL revealed functions related to histone methylation and demethylation, indicating the potential role of epigenetics for P. capsici resistance. Allele-specific markers for the meta-QTL will be developed and validated for MAS of P. capsici resistant lines. Altogether, results from meta-QTL analysis for chile pepper QTLome rendered further insights into the genetic architecture of different traits for this valuable horticultural crop.

Meta-QTL analysis and identification of candidate genes for quality, abiotic and biotic stress in durum wheat

The genetic improvement of durum wheat and enhancement of plant performance often depend on the identification of stable quantitative trait loci (QTL) and closely linked molecular markers. This is essential for better understanding the genetic basis of important agronomic traits and identifying an effective method for improving selection efficiency in breeding programmes. Meta-QTL analysis is a useful approach for dissecting the genetic basis of complex traits, providing broader allelic coverage and higher mapping resolution for the identification of putative molecular markers to be used in marker-assisted selection. In the present study, extensive QTL meta-analysis was conducted on 45 traits of durum wheat, including quality and biotic and abiotic stress-related traits. A total of 368 QTL distributed on all 14 chromosomes of genomes A and B were projected: 171 corresponded to quality-related traits, 127 to abiotic stress and 71 to biotic stress, of which 318 were grouped in 85 meta-QTL (MQTL), 24 remained as single QTL and 26 were not assigned to any MQTL. The number of MQTL per chromosome ranged from 4 in chromosomes 1A and 6A to 9 in chromosome 7B; chromosomes 3A and 7A showed the highest number of individual QTL (4), and chromosome 7B the highest number of undefined QTL (4). The recently published genome sequence of durum wheat was used to search for candidate genes within the MQTL peaks. This work will facilitate cloning and pyramiding of QTL to develop new cultivars with specific quantitative traits and speed up breeding programs.