Common Quantitative Trait Loci Research Articles

No Common Candidate Genes for Resistance to Fusarium graminearum, F. proliferatum, F. sporotrichioides, and F. subglutanins in Soybean (Glycine max L.) Accessions from Maturity Groups 0 and I: Findings from Genome-Wide Association Mapping.

Seedling diseases and root rot, caused by species of Fusarium, can limit soybean (Glycine max L.) production in the United States. Currently, there are few commercially available cultivars resistant to Fusarium. This study was conducted to assess the resistance of soybean maturity group (MG) accessions from 0 and I to Fusarium proliferatum, F. sporotrichioides, and F. subglutinans, as well as to identify common quantitative trait loci (QTL) for resistance to these pathogens, in addition to F. graminearum, using a genome-wide association study (GWAS). A total of 155, 91, and 48 accessions from the USDA soybean germplasm collection from maturity groups 0 and I were screened with a single isolate each of F. proliferatum, F. sporotrichioides, and F. subglutinans, respectively, using the inoculum layer inoculation method in the greenhouse. The disease severity was assessed 21 days post-inoculation and analyzed using non-parametric statistics to determine the relative treatment effects (RTE). Eleven and seven accessions showed significantly lower RTEs when inoculated with F. proliferatum and F. subglutinans, respectively, compared to the susceptible cultivar 'Williams 82'. One accession was significantly less susceptible to both F. proliferatum and F. subglutinans. The GWAS conducted with 41,985 single-nucleotide markers identified one QTL associated with resistance to both F. proliferatum and F. sporotrichioides, as well as another QTL for resistance to both F. subglutinans and F. graminearum. However, no common QTLs were identified for the four pathogens. The USDA accessions and QTLs identified in this study can be utilized to selectively breed resistance to multiple species of Fusarium.

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations.

Quantifying the temporal or longitudinal growth dynamics of crops in diverse environmental conditions is crucial for understanding plant development, requiring further modeling techniques. In this study, we analyzed the growth patterns of two different maize (Zea mays L.) populations using high-throughput phenotyping with a maize population consisting of 515 recombinant inbred lines (RILs) grown in Texas and a hybrid population containing 1090 hybrids grown in Missouri. Two models, Gaussian peak and functional principal component analysis (FPCA), were employed to study the Normalized Green-Red Difference Index (NGRDI) scores. The Gaussian peak model showed strong correlations (c. 0.94 for RILs and c. 0.97 for hybrids) between modeled and non-modeled temporal trajectories. Functional principal component analysis differentiated NGRDI trajectories in RILs under different conditions, capturing substantial variability (75%, 20%, and 5% for RILs; 88% and 12% for hybrids). By comparing these models with conventional BLUP values, common quantitative trait loci (QTLs) were identified, containing candidate genes of brd1, pin11, zcn8 and rap2. The harmony between these loci's additive effects and growing degree days, as well as the differentiation of RIL haplotypes across growth stages, underscores the significant interplay of these loci in driving plant development. These findings contribute to advancing understanding of plant-environment interactions and have implications for crop improvement strategies.

Dissecting the complex genetic basis of pre- and post-harvest traits in Vitis vinifera L. using genome-wide association studies.

Addressing the pressing challenges in agriculture necessitates swift advancements in breeding programs, particularly for perennial crops like grapevines. Moving beyond the traditional biparental quantitative trait loci (QTL) mapping, we conducted a genome-wide association study (GWAS) encompassing 588 Vitis vinifera L. cultivars from a Chilean breeding program, spanning three seasons and testing 13 key yield-related traits. A strong candidate gene, Vitvi11g000454, located on chromosome 11 and related to plant response to biotic and abiotic stresses through jasmonic acid signaling, was associated with berry width and holds potential for enhancing berry size in grape breeding. We also mapped novel QTL associated with post-harvest traits across chromosomes 2, 4, 9, 11, 15, 18, and 19, broadening our grasp on the genetic intricacies dictating fruit post-harvest behavior, including decay, shriveling, and weight loss. Leveraging gene ontology annotations, we drew parallels between traits and scrutinized candidate genes, laying a robust groundwork for future trait-feature identification endeavors in plant breeding. We also highlighted the importance of carefully considering the choice of the response variable in GWAS analyses, as the use of best linear unbiased estimators (BLUEs) corrections in our study may have led to the suppression of some common QTL in grapevine traits. Our results underscore the imperative of pioneering non-destructive evaluation techniques for long-term conservation traits, offering grape breeders and cultivators insights to improve post-harvest table grape quality and minimize waste.

Development of Gossypium hirsutum-Gossypium raimondii introgression lines and its usages in QTL mapping of agricultural traits

Gossypium raimondii (2n=2x=26, D5), an untapped wild species, is the putative progenitor of the D-subgenome of G. hirsutum (2n=4x=52, AD1), an extensively cultivated species. Here, we developed a G. hirsutum (recipient)-G. raimondii (donor) introgression population to exploit favorable QTLs/genes and mapped potential quantitative trait loci (QTLs) from wild cotton species. The introgression population consists of 256 lines with an introgression rate of 52.33% of the genome G. raimondii. The range of introgression segment length was 0.03-19.12 Mb, with an average of 1.22 Mb. The coverage of total introgression fragments was 386.98 Mb from G. raimondii. Further genome-wide association analysis (Q+K+MLM) and QTL mapping (RSTEP-LRT) identified 59 common QTLs, including 14 stable QTLs and 6 common QTL (co-QTL) clusters, and one hotspot of MIC. The common QTLs for seed index showed all positive additive effects, while the common QTLs for boll weight were all negative additive effects, indicating that the linkage between seed index and boll weight could be broken. QTLs for lint percentage showed positive effect and could be beneficial for improving cotton yield. Most QTLs for fiber quality had negative additive effects, implying these QTLs were domesticated/improved in G. hirsutum. A few fiber quality QTLs showed positive additive effects, which could be used to improve cotton fiber quality. These introgression lines developed would be useful for molecular marker-assisted breeding and facilitate us to map QTLs precisely for mining desirable genes from the wild species G. raimondii to improve cultivated cotton in future via a design-breeding approach.

Genome-wide association studies identify loci controlling specialized seed metabolites in Arabidopsis.

Plants synthesize specialized metabolites to facilitate environmental and ecological interactions. During evolution, plants diversified in their potential to synthesize these metabolites. Quantitative differences in metabolite levels of natural Arabidopsis (Arabidopsis thaliana) accessions can be employed to unravel the genetic basis for metabolic traits using genome-wide association studies (GWAS). Here, we performed metabolic GWAS on seeds of a panel of 315 A. thaliana natural accessions, including the reference genotypes C24 and Col-0, for polar and semi-polar seed metabolites using untargeted ultra-performance liquid chromatography-mass spectrometry. As a complementary approach, we performed quantitative trait locus (QTL) mapping of near-isogenic introgression lines between C24 and Col-0 for specific seed specialized metabolites. Besides common QTL between seeds and leaves, GWAS revealed seed-specific QTL for specialized metabolites, indicating differences in the genetic architecture of seeds and leaves. In seeds, aliphatic methylsulfinylalkyl and methylthioalkyl glucosinolates associated with the ALKENYL HYDROXYALKYL PRODUCING loci (GS-ALK and GS-OHP) on chromosome 4 containing alkenyl hydroxyalkyl producing 2 (AOP2) and 3 (AOP3) or with the GS-ELONG locus on chromosome 5 containing methylthioalkyl malate synthase (MAM1) and MAM3. We detected two unknown sulfur-containing compounds that were also mapped to these loci. In GWAS, some of the annotated flavonoids (kaempferol 3-O-rhamnoside-7-O-rhamnoside, quercetin 3-O-rhamnoside-7-O-rhamnoside) were mapped to transparent testa 7 (AT5G07990), encoding a cytochrome P450 75B1 monooxygenase. Three additional mass signals corresponding to quercetin-containing flavonols were mapped to UGT78D2 (AT5G17050). The association of the loci and associating metabolic features were functionally verified in knockdown mutant lines. By performing GWAS and QTL mapping, we were able to leverage variation of natural populations and parental lines to study seed specialized metabolism. The GWAS data set generated here is a high-quality resource that can be investigated in further studies.

High-Density Genetic Map and QTL Analysis in Cunninghamia lanceolate: Insights into Growth and Wood-Color Traits

Cunninghamia lanceolata is one of the most important tree species in China due to its significance both in economy and ecology. The aims of the present study were to construct a high-density genetic map and identify a quantitative trait locus (QTL) for C. lanceolata. In this study, an F1 population comprising 81 individuals was developed. Using specific length amplified fragment sequencing (SLAF-seq) technology, a total of 254,899 loci were found to be polymorphic. After linkage analysis, 2574 markers were used to construct genetic linkage maps. Specifically, 1632 markers were allocated to 11 linkage groups (LGs) for the female map, 1038 for the male map, and 2574 for the integrated map. The integrated map consisted of 4596 single-nucleotide polymorphisms (SNPs) loci, resulting in an average of 1.79 SNP loci per SLAF marker. The marker coverage was 1665.76 cM for the female map, 1436.39 cM for the male map, and 1748.40 cM for the integrated map. The average interval between two adjacent mapped markers was 1.03 cM, 1.40 cM, and 0.68 cM for the female map, male map, and integrated map, respectively. Using the integrated map, we performed interval mapping (logarithm of odds, LOD > 2.0) to detect traits of interest. We identified a total of 2, 1, 2, 5, 1, 2, 1, and 3 QTLs for diameter at breast height, heartwood diameter, heartwood proportion, heartwood a*, heartwood b*, heartwood L*, sapwood a*, and sapwood L*, respectively. The number of markers associated with each QTL ranged from 1 to 14, and each marker explained phenotypic variances ranging from 12.70% to 23.60%. Furthermore, a common QTL was identified for diameter at breast height and heartwood color a*, while another common QTL was observed for heartwood color L* and heartwood color a*. These findings suggest possible pleiotropic effects of the same genes on these traits. In conclusion, we successfully constructed high-density genetic maps for C. lanceolata using the SLAF-seq method with an F1 population. Notably, these linkage maps represent the most comprehensive and densest ones available to date for C. lanceolata and will facilitate future chromosome assignments for C. lanceolata whole-genome sequencing. These identified QTLs will serve as a valuable resource for conducting fine-scale QTL mapping and implementing marker-assisted selection in C. lanceolata, particularly for growth and wood-color traits.

KASP mapping of QTLs for yield components using a RIL population in Basmati rice (Oryza sativa L.)

Super Basmati is an elite variety with extra-long grains and superior quality but its yield is severely affected by water stress. Recombinant inbred lines (RILs) developed from the cross of Super Basmati and IR55419-04 (a coarse grained variety with high yield potential) were used to identify and map yield-related quantitative trait loci (QTLs) under normal field conditions. Genotypes for 244 KASP markers were obtained from 188 F9 RILs and a linkage map constructed of 1369.4 cM, with average marker interval of 10.5 cM. Eleven agronomic traits were phenotyped in RILs and parents. Composite interval mapping and inclusive composite interval mapping model identified 21 common QTLs related to nine agronomic traits; of the QTLs identified, twelve mapped at novel positions. LOD scores ranged from 2.51 for number of filled grains (qFG1.1) to 16.8 for plant height (qPH1.1). Four major effect QTLs could explain 20% of variation related for plant height, hundred-grain weight and grain width. Alleles from IR55419-04 improved grain filling, grain weight and grain width, while alleles from Super Basmati contributed to longer grains, panicles and flag leaves. Putative candidate genes were identified for 18 yield-related QTLs. These results validate the use of KASP genotyping for QTL mapping of yield-related traits in a bi-parental segregating population. SNPs in the QTLs identified in this study can be used in marker assisted selection for yield components to improve Basmati rice.

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.

Genetic Dissection of Extreme Seed-Flooding Tolerance in a Wild Soybean PI342618B by Linkage Mapping and Candidate Gene Analysis.

Seed-flooding stress is one of major abiotic constraints that adversely affects soybean production worldwide. Identifying tolerant germplasms and revealing the genetic basis of seed-flooding tolerance are imperative goals for soybean breeding. In the present study, high-density linkage maps of two inter-specific recombinant inbred line (RIL) populations, named NJIRNP and NJIR4P, were utilized to identify major quantitative trait loci (QTLs) for seed-flooding tolerance using three parameters viz., germination rate (GR), normal seedling rate (NSR), and electrical conductivity (EC). A total of 25 and 18 QTLs were detected by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 12 common QTLs were identified through both methods. All favorable alleles for the tolerance are notably from the wild soybean parent. Moreover, four digenic epistatic QTL pairs were identified, and three of them showed no main effects. In addition, the pigmented soybean genotypes exhibited high seed-flooding tolerance compared with yellow seed coat genotypes in both populations. Moreover, out of five identified QTLs, one major region containing multiple QTLs associated with all three traits was identified on Chromosome 8, and most of the QTLs within this hotspot were major loci (R2 > 10) and detectable in both populations and multiple environments. Based on the gene expression and functional annotation information, 10 candidate genes from QTL "hotspot 8-2" were screened for further analysis. Furthermore, the results of qRT-PCR and sequence analysis revealed that only one gene, GmDREB2 (Glyma.08G137600), was significantly induced under flooding stress and displayed a TTC tribasic insertion mutation of the nucleotide sequence in the tolerant wild parent (PI342618B). GmDREB2 encodes an ERF transcription factor, and the subcellular localization analysis using green fluorescent protein (GFP) revealed that GmDREB2 protein was localized in the nucleus and plasma membrane. Furthermore, overexpression of GmDREB2 significantly promoted the growth of soybean hairy roots, which might indicate its critical role in seed-flooding stress. Thus, GmDREB2 was considered as the most possible candidate gene for seed-flooding tolerance.

Molecular mapping of QTL for type I Fusarium head blight resistance in two recombinant inbred lines wheat populations

AbstractFusarium head blight (FHB) is a major concern for wheat production and food safety, globally. Resistance to infection (type I resistance) is important for breeding varieties with good FHB resistance. To better understand the genetic architecture underlying type I FHB resistance, quantitative trait loci (QTL) mapping was conducted using two recombinant inbred line populations derived from the crosses Yangmai 4 (YM4)/Yanzhan 1 (YZ1) and Yangmai 5 (YM5)/YZ1. A total of five resistance QTL, including QFhb.yaas‐2D, QFhb.yaas‐4D, QFhb.yaas‐5B, QFhb.yaas‐6B, and QFhb.yaas‐3A, were detected from these two populations. Except for QFhb.yaas‐2D and QFhb.yaas‐5B, the resistance effects of all the other QTLs are derived from YM4 or YM5. QFhb.yaas‐4D is a common QTL detected both in YM4/YZ1 and YM5/YZ1 populations. QFhb.yaas‐2D, QFhb.yaas‐4D, and QFhb.yaas‐5B were colocalized with known plant height (PH) genes Rht8, Rht‐D1, and flowering date (FD) gene Vrn‐B1. However, QFhb.yaas‐6B and QFhb.yaas‐3A were not associated with PH and FD. Kompetitive Allele‐Specific PCR markers for QFhb.yaas‐6B and QFhb.yaas‐3A had been developed and validated in an additional panel of 211 wheat cultivars/lines. The result showed that these two resistance alleles decreased 46.65% of the percentage of diseased spikelets. These results provide valuable information for the fine mapping of QFhb.yaas‐6B and QFhb.yaas‐3A in the future.

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize.

Maize productivity is significantly impacted by drought; therefore, improvement of drought tolerance is a critical goal in maize breeding. To achieve this, a better understanding of the genetic basis of drought tolerance is necessary. Our study aimed to identify genomic regions associated with drought tolerance-related traits by phenotyping a mapping population of recombinant inbred lines (RILs) for two seasons under well-watered (WW) and water-deficit (WD) conditions. We also used single nucleotide polymorphism (SNP) genotyping through genotyping-by-sequencing to map these regions and attempted to identify candidate genes responsible for the observed phenotypic variation. Phenotyping of the RILs population revealed significant variability in most of the traits, with normal frequency distributions, indicating their polygenic nature. We generated a linkage map using 1,241 polymorphic SNPs distributed over 10 chromosomes (chrs), covering a total genetic distance of 5,471.55cM. We identified 27 quantitative trait loci (QTLs) associated with various morphophysiological and yield-related traits, with 13 QTLs identified under WW conditions and 12 under WD conditions. We found one common major QTL (qCW2-1) for cob weight and a minor QTL (qCH1-1) for cob height that were consistently identified under both water regimes. We also detected one major and one minor QTL for the Normalized Difference Vegetation Index (NDVI) trait under WD conditions on chr 2, bin 2.10. Furthermore, we identified one major QTL (qCH1-2) and one minor QTL (qCH1-1) on chr 1 that were located at different genomic positions to those identified in earlier studies. We found co-localized QTLs for stomatal conductance and grain yield on chr 6 (qgs6-2 and qGY6-1), while co-localized QTLs for stomatal conductance and transpiration rate were identified on chr 7 (qgs7-1 and qTR7-1). We also attempted to identify the candidate genes responsible for the observed phenotypic variation; our analysis revealed that the major candidate genes associated with QTLs detected under water deficit conditions were related to growth and development, senescence, abscisic acid (ABA) signaling, signal transduction, and transporter activity in stress tolerance. The QTL regions identified in this study may be useful in designing markers that can be utilized in marker-assisted selection breeding. In addition, the putative candidate genes can be isolated and functionally characterized so that their role in imparting drought tolerance can be more fully understood.

Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines.

Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

An atypical NLR gene confers bacterial wilt susceptibility in Arabidopsis

Quantitative disease resistance (QDR) remains the most prevalent form of plant resistance in crop fields and wild habitats. Genome-wide association studies (GWAS) have proved to be successful in deciphering the quantitative genetic basis of complex traits such as QDR. To unravel the genetics of QDR to the devastating worldwide bacterial pathogen Ralstonia solanacearum, we performed a GWAS by challenging a highly polymorphic local mapping population of Arabidopsis thaliana with four R. solanacearum type III effector (T3E) mutants, identified as key pathogenicity determinants after a first screen on an A. thaliana core collection of 25 accessions. Although most quantitative trait loci (QTLs) were highly specific to the identity of the T3E mutant (ripAC, ripAG, ripAQ, and ripU), we finely mapped a common QTL located on a cluster of nucleotide-binding domain and leucine-rich repeat (NLR) genes that exhibited structural variation. We functionally validated one of these NLRs as a susceptibility factor in response to R. solanacearum, named it Bacterial Wilt Susceptibility 1 (BWS1), and cloned two alleles that conferred contrasting levels of QDR. Further characterization indicated that expression of BWS1 leads to suppression of immunity triggered by different R. solanacearum effectors. In addition, we showed a direct interaction between BWS1 and RipAC T3E, and BWS1 and SUPPRESSOR OF G2 ALLELE OF skp1 (SGT1b), the latter interaction being suppressed by RipAC. Together, our results highlight a putative role for BWS1 as a quantitative susceptibility factor directly targeted by the T3E RipAC, mediating negative regulation of the SGT1-dependent immune response.

Identification of Novel QTLs Associated with Frost Tolerance in Winter Wheat (Triticum aestivum L.).

Low temperature (cold) and freezing stress is a major problem during winter wheat growth. Low temperature tolerance (LT) is an important agronomic trait in winter wheat and determines the plants' ability to cope with below-freezing temperatures; thus, the development of cold-tolerant cultivars has become a major goal of breeding in various regions of the world. In this study, we sought to identify quantitative trait loci (QTL) using molecular markers related to freezing tolerance in winter. Thirty-four polymorphic markers among 425 SSR markers were obtained for the population, including 180 inbred lines of F12 generation wheat, derived from crosses (Norstar × Zagros) after testing with parents. LT50 is used as an effective selection criterion for identifying frost-tolerance genotypes. The progeny of individual F12 plants were used to evaluate LT50. Several QTLs related to wheat yield, including heading time period, 1000-seed weight, and number of surviving plants after overwintering, were identified. Single-marker analysis illustrated that four SSR markers with a total of 25% phenotypic variance determination were linked to LT50. Related QTLs were located on chromosomes 4A, 2B, and 3B. Common QTLs identified in two cropping seasons based on agronomical traits were two QTLs for heading time period, one QTL for 1000-seed weight, and six QTLs for number of surviving plants after overwintering. The four markers identified linked to LT50 significantly affected both LT50 and yield-related traits simultaneously. This is the first report to identify a major-effect QTL related to frost tolerance on chromosome 4A by the marker XGWM160. It is possible that some QTLs are closely related to pleiotropic effects that control two or more traits simultaneously, and this feature can be used as a factor to select frost-resistant lines in plant breeding programs.

The Genome Regions Associated with Abiotic and Biotic Stress Tolerance, as Well as Other Important Breeding Traits in Triticale.

This review article presents the greatest challenges in modern triticale breeding. Genetic maps that were developed and described thus far, together with the quantitative trait loci and candidate genes linked to important traits are also described. The most important part of this review is dedicated to a winter triticale mapping population based on doubled haploid lines obtained from a cross of the cultivars 'Hewo' and 'Magnat'. Many research studies on this population have focused on the analysis of quantitative trait loci regions associated with abiotic (drought and freezing) and biotic (pink snow mold and powdery mildew) stress tolerance as well as related to other important breeding traits such as stem length, plant height, spike length, number of the productive spikelets per spike, number of grains per spike, and thousand kernel weight. In addition, candidate genes located among these regions are described in detail. A comparison analysis of all of these results revealed the location of common quantitative trait loci regions on the rye chromosomes 4R, 5R, and 6R, with a particular emphasis on chromosome 5R. Described here are the candidate genes identified in the above genome regions that may potentially play an important role in the analysis of trait expression. Nevertheless, these results should guide further research using molecular methods of gene identification and it is worth extending the research to other mapping populations. The article is also a review of research led by other authors on the triticale tolerance to the most current stress factors appearing in the breeding.

White Lupin Drought Tolerance: Genetic Variation, Trait Genetic Architecture, and Genome-Enabled Prediction.

White lupin is a high-protein crop requiring drought tolerance improvement. This study focused on a genetically-broad population of 138 lines to investigate the phenotypic variation and genotype × environment interaction (GEI) for grain yield and other traits across drought-prone and moisture-favourable managed environments, the trait genetic architecture and relevant genomic regions by a GWAS using 9828 mapped SNP markers, and the predictive ability of genomic selection (GS) models. Water treatments across two late cropping months implied max. available soil water content of 60-80% for favourable conditions and from wilting point to 15% for severe drought. Line yield responses across environments featured a genetic correlation of 0.84. Relatively better line yield under drought was associated with an increased harvest index. Two significant QTLs emerged for yield in each condition that differed across conditions. Line yield under stress displayed an inverse linear relationship with the onset of flowering, confirmed genomically by a common major QTL. An adjusted grain yield computed as deviation from phenology-predicted yield acted as an indicator of intrinsic drought tolerance. On the whole, the yield in both conditions and the adjusted yield were polygenic, heritable, and exploitable by GS with a high predictive ability (0.62-0.78). Our results can support selection for climatically different drought-prone regions.

QTL analysis identified two major all-internodes solidness loci from a completely solid-stemmed spring wheat line.

The culms of solid-stemmed wheat cultivars are filled with "pith" - a parenchymatous tissue largely composed of soft, spongy, and compact parenchyma cells. Breeding solid-stemmed cultivars is the most effective way to decrease the detrimental impact of wheat stem sawfly (WSS), Cephus cinctus Norton (Hymenoptera: Cephidae) on wheat production. Althougha major solid stem gene has been previously identified from durum wheat, it produces an intermediate level of stem solidness in common wheat which is insufficient to provide the required level of WSS resistance. The maximum resistance is achieved when stems are totally filled with pith. Thus, to identify a secondary source of solidness in common wheat, we developed three mapping populations from wheat cvs. Sadash, 'AAC Innova' and 'AAC Cameron', each crossed separatelywith P2711, a completely solid-stemmed hexaploid wheat breeding line. All populations were genotyped using either wheat 15K or 90K Infinium iSelect SNP Assay and high-density linkage maps were generated from individual populations along with consensus maps for chromosomes 3B and 3D from all populations. 'Sadash/P2711' and 'AAC Innova/P2711' populations were subjected to extensive phenotyping in ≥3 environments followed by quantitative trait loci (QTL) analyses using population-specific and consensus linkage maps. We identified two major solid stem QTLs in the distal regions of chromosome arms 3BL and 3DL in both populations in addition to several population-specific or common minor QTLs. Internode-specific QTL analyses detected both major QTLs of chromosomes 3B and 3D across internodes, from top to bottom of the stalk, but minor QTLs were largely detected in upper or middle internodes. Our results suggest that both major QTLs are sufficient to develop highly solid-stemmed cvs; however, the minor loci, which additively enhance the pith expression, can be coupled with major genes to achieve a complete solid stem phenotype in common wheat. Comparative and haplotype analyses showed that the 3B locus is homoeologous to 3D, the former being mapped to a 1.1 Mb genomic region. Major QTLs identified in this study can be incorporated in modern wheat cultivars to achieve maximum WSS resistance from high pith expression.

Genetic background- and environment-independent QTL and candidate gene identification of appearance quality in three MAGIC populations of rice.

Many QTL have been identified for grain appearance quality by linkage analysis (LA) in bi-parental mapping populations and by genome-wide association study (GWAS) in natural populations in rice. However, few of the well characterized genes/QTL have been successfully applied in molecular rice breeding due to genetic background (GB) and environment effects on QTL expression and deficiency of favorable alleles. In this study, GWAS and LA were performed to identify QTL for five grain appearance quality-related traits using three multi-parent advanced generation inter-cross (MAGIC) populations. A total of 22 QTL on chromosomes 1-3, 5-8 were identified by GWAS for five traits in DC1, DC2 and 8way, and four combined populations DC12 (DC1+DC2), DC18 (DC1+8way), DC28 (DC2+8way) and DC128 (DC1+DC2+8way). And a total of 42 QTL were identified on all 12 chromosomes except 10 by LA in the three single populations. Among 20 QTL identified by GWAS in DC1, DC2 and 8way, 10, four and three QTL were commonly detected in DC18, DC28, and DC128, respectively. Similarly, among 42 QTL detected by LA in the three populations, four, one and two QTL were commonly detected in DC18, DC28, and DC128, respectively. There was no QTL mapped together in DC12 by both two mapping methods, indicating that GB could greatly affect the mapping results, and it was easier to map the common QTL among populations with similar GB. The 8way population was more powerful for QTL mapping than the DC1, DC2 and various combined populations. Compared with GWAS, LA can not only identify large-effect QTL, but also identify minor-effect ones. Among 11 QTL simultaneously detected by the two methods in different GBs and environments, eight QTL corresponded to known genes, including AqGL3b and AqGLWR3a for GL and GLWR, AqGW5a, AqGLWR5, AqDEC5 and AqPGWC5 for GW, GLWR, DEC and PGWC, and AqDEC6b and AqPGWC6b for DEC and PGWC, respectively. AqGL7, AqGL3c/AqGLWR3b, AqDEC6a/AqPGWC6a, and AqPGWC7 were newly identified and their candidate genes were analyzed and inferred. It was discussed to further improve grain appearance quality through designed QTL pyramiding strategy based on the stable QTL identified in the MAGIC populations.

Identification of quantitative trait loci of agronomic traits in bread wheat using a Pamyati Azieva×Paragon mapping population harvested in three regions of Kazakhstan.

Although genome-wide association studies (GWAS) are an increasingly informative tool in the mining of new quantitative trait loci (QTLs), a classical biparental mapping approach is still a powerful, widely used method to search the unique genetic factors associated with important agronomic traits in bread wheat. In this study, a newly constructed mapping population of Pamyati Azieva (Russian Federation)×Paragon (UK), consisting of 94 recombinant inbred lines (RILs), was tested in three different regions of Kazakhstan with the purpose of QTL identification for key agronomic traits. The RILs were tested in 11 environments of two northern breeding stations (Petropavlovsk, North Kazakhstan region, and Shortandy, Aqmola region) and one southeastern station (Almalybak, Almaty region). The following eight agronomic traits were studied: heading days, seed maturation days, plant height, spike length, number of productive spikes, number of kernels per spike, thousand kernel weight, and yield per square meter. The 94 RILs of the PAxP cross were genotyped using Illumina's iSelect 20K single nucleotide polymorphism (SNP) array and resulted in the identification of 4595 polymorphic SNP markers. The application of the QTL Cartographer statistical package allowed the identification of 53 stable QTLs for the studied traits. A survey of published studies related to common wheat QTL identification suggested that 28 of those 53 QTLs were presumably novel genetic factors. The SNP markers for the identified QTLs of the analyzed agronomic traits of common wheat can be efficiently applied in ongoing breeding activities in the wheat breeding community using a marker-assisted selection approach.

Genome-Wide Association Study Reveals Novel QTLs and Candidate Genes for Grain Number in Rice.

Grain number per panicle (GNPP), determined mainly by panicle branching, is vital for rice yield. The dissection of the genetic basis underlying GNPP could help to improve rice yield. However, genetic resources, including quantitative trait loci (QTL) or genes for breeders to enhance rice GNPP, are still limited. Here, we conducted the genome-wide association study (GWAS) on the GNPP, primary branch number (PBN), and secondary branch number (SBN) of 468 rice accessions. We detected a total of 18 QTLs, including six for GNPP, six for PBN, and six for SBN, in the whole panel and the indica and japonica subpanels of 468 accessions. More importantly, qPSG1 was a common QTL for GNPP, PBN, and SBN and was demonstrated by chromosome segment substitution lines (CSSLs). Considering gene annotation, expression, and haplotype analysis, seven novel and strong GNPP-related candidate genes were mined from qPSG1. Our results provide clues to elucidate the molecular regulatory network of GNPP. The identified QTLs and candidate genes will contribute to the improvement of GNPP and rice yield via molecular marker-assisted selection (MAS) breeding and genetic engineering techniques.