Quantitative Trait Loci Clusters Research Articles

Unraveling the genetic basis of superior traits in Gossypium barbadense: From phenotype to genotype

G. barbadense is renowned for its high-quality fiber and is highly regarded as a natural material in the textile industry. However, research on cotton has mainly focused on G. hirsutum, and progress in sea island cotton has been relatively slow. There was limited understanding of the genetic loci and transcriptional regulatory mechanisms of important traits, and the genetic basis for the development of superior traits remains unclear. In this study, a recombinant inbred line (RIL) population was constructed using two sea island cotton parents from different cotton regions. The phenotypic characteristics of the two parents and the RIL population, as well as the phenotypic correlations, heritability, and genetic models of the RIL population, were comprehensively analyzed. The RIL population was genotyped using whole-genome resequencing technology, and a high-density intraspecific linkage map was constructed, consisting of 5295 bin markers and a total genetic map distance of 2721.79 centimorgans (cM). Phenotypic data collected from five different environments were used to detect 169 quantitative trait loci (QTLs) using the composite interval mapping method. Among these QTLs, 30 were related to agronomic traits, 61 were related to yield traits, and 78 were related to fiber quality traits. Additionally, 17 QTL clusters were detected, and the additive effects of these clusters explained the correlation between different phenotypic traits. Candidate genes for stable QTLs related to agronomic and yield traits were identified through variant annotation and functional prediction. Two bin markers associated with lint percentage (LP) were validated as potential breeding markers. Furthermore, using RNA-seq data of cotton fiber from the RIL population parents, the dynamic changes in gene expression during different developmental stages of cotton fiber were revealed, and important differentially expressed genes in the secondary cell wall development and metabolism network of fibers were identified. Importantly, through the combined analysis of fiber trait QTLs and transcriptomes, eight candidate genes involved in regulating fiber quality traits were predicted, and the genetic basis of the excellent allele site qFL_D04_1 in the breeding history of Chinese sea island cotton was elucidated. In summary, this study provides new genetic resources and potential breeding markers for cotton variety improvement, valuable theoretical information for understanding the genetic basis of important traits in sea island cotton, and effectively promotes the development of cotton biotechnology breeding.

Flavour improvement in early generations of fresh market tomatoes (Solanum lycopersicum): I. Identification of QTL for sensory attributes, physicochemical measurements and volatile compounds

AbstractHuman sensory analysis is the most appropriate method for assessing the flavour of fresh market tomatoes, but it is very labour and time consuming. Therefore, sensory attributes are often neglected in early generations of breeding programmes and genetic studies, although there is a demand for tomatoes with improved flavour. In this study, the recently developed Breeders' Sensory Test was applied to an F2 mapping population derived from two parents with superior flavour. Sensory attributes, physicochemical measurements, volatiles and fruit weight were assessed in organic low‐input and hydroponic cultivation. A linkage map spanning 1070 cM was developed. In total, 71 quantitative trait loci (QTL) were detected for the means of both cultivation systems, 61 for organic and 46 for hydroponic cultivation. A proportion of 27% of the loci were co‐localized between both cultivation systems. Nine distinct QTL clusters for flavour‐related traits were identified, including a large cluster on chromosome 6 comprising five sensory and nine volatile QTL. The sensory QTL on chromosomes 2, 5, 6, 10 and 11, partly within clusters, are recommended for marker‐assisted selection.

Construction of Genetic Map and QTL Mapping for Seed Size and Quality Traits in Soybean (Glycine max L.).

Soybean (Glycine max L.) is the main source of vegetable protein and edible oil for humans, with an average content of about 40% crude protein and 20% crude fat. Soybean yield and quality traits are mostly quantitative traits controlled by multiple genes. The quantitative trait loci (QTL) mapping for yield and quality traits, as well as for the identification of mining-related candidate genes, is of great significance for the molecular breeding and understanding the genetic mechanism. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiangchun 2 and Yushuxian 2 were selected as the mapping population to construct a molecular genetic linkage map. A genetic map containing 445 SSR markers with an average distance of 5.3 cM and a total length of 2375.6 cM was obtained. Based on constructed genetic map, 11 traits including hundred-seed weight (HSW), seed length (SL), seed width (SW), seed length-to-width ratio (SLW), oil content (OIL), protein content (PRO), oleic acid (OA), linoleic acid (LA), linolenic acid (LNA), palmitic acid (PA), stearic acid (SA) of yield and quality were detected by the multiple- d size traits and 113 QTLs related to quality were detected by the multiple QTL model (MQM) mapping method across generations F2, F2:3, F2:4, and F2:5. A total of 71 QTLs related to seed size traits and 113 QTLs related to quality traits were obtained in four generations. With those QTLs, 19 clusters for seed size traits and 20 QTL clusters for quality traits were summarized. Two promising clusters, one related to seed size traits and the other to quality traits, have been identified. The cluster associated with seed size traits spans from position 27876712 to 29009783 on Chromosome 16, while the cluster linked to quality traits spans from position 12575403 to 13875138 on Chromosome 6. Within these intervals, a reference genome of William82 was used for gene searching. A total of 36 candidate genes that may be involved in the regulation of soybean seed size and quality were screened by gene functional annotation and GO enrichment analysis. The results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean.

Genetic dissection of major QTL for grain number per spike on chromosomes 5A and 6A in bread wheat (Triticum aestivum L.).

Grain number per spike (GNS) is a crucial component of grain yield and plays a significant role in improving wheat yield. To identify quantitative trait loci (QTL) associated with GNS, a recombinant inbred line (RIL) population derived from the cross of Zhongkemai 13F10 and Chuanmai 42 was employed to conduct QTL mapping across eight environments. Based on the bulked segregant exome sequencing (BSE-Seq), genomic regions associated with GNS were detected on chromosomes 5A and 6A. According to the constructed genetic maps, two major QTL QGns.cib-5A (LOD = 4.35-8.16, PVE = 8.46-14.43%) and QGns.cib-6A (LOD = 3.82-30.80, PVE = 5.44-12.38%) were detected in five and four environments, respectively. QGns.cib-6A is a QTL cluster for other seven yield-related traits. QGns.cib-5A and QGns.cib-6A were further validated using linked Kompetitive Allele Specific PCR (KASP) markers in different genetic backgrounds. QGns.cib-5A exhibited pleiotropic effects on productive tiller number (PTN), spike length (SL), fertile spikelet number per spike (FSN), and ratio of grain length to grain width (GL/GW) but did not significantly affect thousand grain weight (TGW). Haplotype analysis revealed that QGns.cib-5A and QGns.cib-6A were the targets of artificial selection during wheat improvement. Candidate genes for QGns.cib-5A and QGns.cib-6A were predicted by analyzing gene annotation, spatiotemporal expression patterns, and orthologous and sequence differences. These findings will be valuable for fine mapping and map-based cloning of genes underlying QGns.cib-5A and QGns.cib-6A.

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.

CmWAT6.1, mined by high-density genetic map-based QTL mapping, enhances waterlogging tolerance in chrysanthemum

High-density genetic maps are a valuable tool for quantitative trait locus (QTL) mapping and gene discovery for important traits in plants. However, such work in chrysanthemum remains largely unexplored, primarily owing to its large genome and complex genetic background. In this study, a high-density map containing 11,941 SNP bins spanning a total of 2967.76 cM in 27 linkage groups (LGs) with an average intermarker distance of 0.26 cM was developed with a genotyping by sequencing (GBS) approach. A total of 34 QTL clusters for waterlogging tolerance (WAT) were detected via multiple-QTL mapping (MQM) algorithms. A total of 186 candidate genes within these QTL clusters were mined in combination with transcriptome data. One key candidate gene within the QTL cluster cWAT6.1, which belongs to group VII ethylene response factors (CmWAT6.1), was proven to be a positive regulator of WAT in chrysanthemum. Furthermore, transcriptomic assays of transgenic and wild-type chrysanthemums under normal conditions as well as further experiments provided evidence that CmWAT6.1 regulated WAT by promoting cell wall formation and lignin biosynthesis and coordinating oligosaccharide metabolic processes. This study represents important progress in gene mining through forward genetics, provides valuable genetic resources for further WAT improvement and provides new insight into the molecular mechanism of WAT in chrysanthemum.

QTL Verification and Candidate Gene Screening of Fiber Quality and Lint Percentage in the Secondary Segregating Population of Gossypium hirsutum.

Fiber quality traits, especially fiber strength, length, and micronaire (FS, FL, and FM), have been recognized as critical fiber attributes in the textile industry, while the lint percentage (LP) was an important indicator to evaluate the cotton lint yield. So far, the genetic mechanism behind the formation of these traits is still unclear. Quantitative trait loci (QTL) identification and candidate gene validation provide an effective methodology to uncover the genetic and molecular basis of FL, FS, FM, and LP. A previous study identified three important QTL/QTL cluster loci, harboring at least one of the above traits on chromosomes A01, A07, and D12 via a recombinant inbred line (RIL) population derived from a cross of Lumianyan28 (L28) × Xinluzao24 (X24). A secondary segregating population (F2) was developed from a cross between L28 and an RIL, RIL40 (L28 × RIL40). Based on the population, genetic linkage maps of the previous QTL cluster intervals on A01 (6.70-10.15 Mb), A07 (85.48-93.43 Mb), and D12 (0.40-1.43 Mb) were constructed, which span 12.25, 15.90, and 5.56 cM, with 2, 14, and 4 simple sequence repeat (SSR) and insertion/deletion (Indel) markers, respectively. QTLs of FL, FS, FM, and LP on these three intervals were verified by composite interval mapping (CIM) using WinQTL Cartographer 2.5 software via phenotyping of F2 and its derived F2:3 populations. The results validated the previous primary QTL identification of FL, FS, FM, and LP. Analysis of the RNA-seq data of the developing fibers of L28 and RIL40 at 10, 20, and 30 days post anthesis (DPA) identified seven differentially expressed genes (DEGs) as potential candidate genes. qRT-PCR verified that five of them were consistent with the RNA-seq result. These genes may be involved in regulating fiber development, leading to the formation of FL, FS, FM, and LP. This study provides an experimental foundation for further exploration of these functional genes to dissect the genetic mechanism of cotton fiber development.

QTL mapping of nitrogen use efficiency traits at the seedling and maturity stages under different nitrogen conditions in barley (Hordeum vulgare L.)

AbstractNitrogen (N) is an essential element for plant growth and development. The identification and utilization of N use efficiency (NUE) loci are essential for breeding high NUE cultivars. In this study, 15 NUE traits were measured in a recombinant inbred line population containing 121 lines derived from the cross between a cultivated barley (Baudin) and a wild barley (CN4027). The hydroponic culture was conducted with normal N and low N treatments in one‐time frame, and field trials were conducted with N sufficiency and N deficiency treatments in two growing seasons. Twenty‐two quantitative trait loci (QTLs) and four clusters were detected. Of them, the five stable QTLs Qgna.sau‐3H for grain N concentration, Qtna.sau‐3H for total N accumulation per plant, Qnhi.sau‐3H for N harvest index, Qnutegy.sau‐3H for N utilization efficiency for grain yield and Qanutedm.sau‐3H.1 for N utilization efficiency for aboveground dry matter were co‐located on chromosome 3H flanked by the markers bpb6282426 and bpb4786261. These two novel QTL clusters simultaneously controlled NUE traits at the seedling and maturity stages. Some genes related to NUE traits in intervals of the major QTLs were predicted. The significant relationships between NUE traits and agronomic and physiological traits were detected and discussed. In conclusion, this study uncovers the most promising genomic regions for the marker‐assisted selection of NUE traits to improve NUE in barley.

Identification of Advantaged Genes for Lodging Resistance-Related Traits in the Temperate geng Group (Oryza sativa L.) Using a Genome-Wide Association Study

With the endless growth of the population and development of the economy and living standards, a high yield with good grain quality has become the prime objective of breeding. However, lodging is a major factor leading to a significant decline in rice (Oryza sativa L.) production and quality. We conducted genome-wide association analysis to detect quantitative trait loci (QTL)-related lodging resistance, using 395 temperate geng accessions. A total of 50 QTL affecting the six traits were detected, using 1,438,279 high-quality single nucleotide polymorphism markers. Five important QTL clusters were detected affecting the lodging resistance-related traits. The ten candidate genes were detected by performing gene differential expression analysis and haplotype analysis. Among them, LOC_Os07g48570 (OsDof-23) for qLA7.2, qSA7.2 and qPR7.3, LOC_Os08g29110 (wp2) for qLA8 and qSA8, and LOC_Os11g36440 (MHZ5) for qPR11 and qBR11.1 were considered the most likely candidate genes based on functional annotations. The results may facilitate the breeding of rice varieties resistant to lodging, to overcome the adverse effects of climate change.

Identification of a Seed Vigor–Related QTL Cluster Associated with Weed Competitive Ability in Direct–Seeded Rice (Oryza Sativa L.)

Direct seeding of rice (Oryza sativa L.) is a low-labor and sustainable cultivation method that is used worldwide. Seed vigor and early vigor are important traits associated with seedling stand density (SSD) and weed competitive ability (WCA), which are key factors in direct-seeded rice (DSR) cultivation systems. Here, we developed a set of chromosome segment substitution lines with Xiushui134 as receptor parent and Yangdao6 as donor parent and used these lines as a mapping population to identify quantitative trait loci (QTLs) for seed vigor, which we evaluated based on germinability–related indicators (germination percentage (GP), germination energy (GE), and germination index (GI)) and seedling vigor–related indicators (root number (RN), root length (RL), and shoot length (SL) at 14 days after imbibition) under controlled conditions in an incubator. Ten QTLs were detected across four chromosomes, of which a cluster of QTLs (qGP11, qGE11, qGI11, and qRL11) co-localized on Chr. 11 with high LOD values (12.03, 8.13, 7.14, and 8.75, respectively). Fine mapping narrowed down the QTL cluster to a 0.7-Mb interval between RM26797 and RM6680. Further analysis showed that the QTL cluster has a significant effect (p < 0.01) on early vigor under hydroponic culture (root length, total dry weight) and direct seeding conditions (tiller number, aboveground dry weight). Thus, our combined analysis revealed that the QTL cluster influenced both seed vigor and early vigor. Identifying favorable alleles at this QTL cluster could facilitate the improvement of SSD and WCA, thereby addressing both major factors in DSR cultivation systems.

Integrating genome-wide association study with transcriptomic data to predict candidate genes influencing Brassica napus root and biomass-related traits under low phosphorus conditions

BackgroundRapeseed (Brassica napus L.) is an essential source of edible oil and livestock feed, as well as a promising source of biofuel. Breeding crops with an ideal root system architecture (RSA) for high phosphorus use efficiency (PUE) is an effective way to reduce the use of phosphate fertilizers. However, the genetic mechanisms that underpin PUE in rapeseed remain elusive. To address this, we conducted a genome-wide association study (GWAS) in 327 rapeseed accessions to elucidate the genetic variability of 13 root and biomass traits under low phosphorus (LP; 0.01 mM P +). Furthermore, RNA-sequencing was performed in root among high/low phosphorus efficient groups (HP1/LP1) and high/low phosphorus stress tolerance groups (HP2/LP2) at two-time points under control and P-stress conditions.ResultsSignificant variations were observed in all measured traits, with heritabilities ranging from 0.47 to 0.72, and significant correlations were found between most of the traits. There were 39 significant trait–SNP associations and 31 suggestive associations, which integrated into 11 valid quantitative trait loci (QTL) clusters, explaining 4.24–24.43% of the phenotypic variance observed. In total, RNA-seq identified 692, 1076, 648, and 934 differentially expressed genes (DEGs) specific to HP1/LP1 and HP2/LP2 under P-stress and control conditions, respectively, while 761 and 860 DEGs common for HP1/LP1 and HP2/LP2 under both conditions. An integrated approach of GWAS, weighted co-expression network, and differential expression analysis identified 12 genes associated with root growth and development under LP stress. In this study, six genes (BnaA04g23490D, BnaA09g08440D, BnaA09g04320D, BnaA09g04350D, BnaA09g04930D, BnaA09g09290D) that showed differential expression were identified as promising candidate genes for the target traits.Conclusion11 QTL clusters and 12 candidate genes associated with root and development under LP stress were identified in this study. Our study's phenotypic and genetic information may be exploited for genetic improvement of root traits to increase PUE in rapeseed.

QTL mapping for kernel-related traits in a durum wheat x T. dicoccum segregating population.

Durum wheat breeding relies on grain yield improvement to meet its upcoming demand while coping with climate change. Kernel size and shape are the determinants of thousand kernel weight (TKW), which is a key component of grain yield, and the understanding of the genetic control behind these traits supports the progress in yield potential. The present study aimed to dissect the genetic network responsible for kernel size components (length, width, perimeter, and area) and kernel shape traits (width-to-length ratio and formcoefficient) as well as their relationships with kernel weight, plant height, and heading date in durum wheat. Quantitative Trait Locus (QTL) mapping was performed on a segregating population of 110 recombinant inbred lines, derived from a cross between the domesticated emmer wheat accession MG5323 and the durum wheat cv. Latino, evaluated in four different environments. A total of 24 QTLs stable across environments were found and further grouped in nine clusters on chromosomes 2A, 2B, 3A, 3B, 4B, 6B, and 7A. Among them, a QTL cluster on chromosome 4B was associated with kernel size traits and TKW, where the parental MG5323 contributed the favorable alleles, highlighting its potential to improve durum wheat germplasm. The physical positions of the clusters, defined by the projection on the T. durum reference genome, overlapped with already known genes (i.e., BIG GRAIN PROTEIN 1 on chromosome 4B). These results might provide genome-based guidance for the efficient exploitation of emmer wheat diversity in wheat breeding, possibly through yield-related molecular markers.

Characterization and fine mapping analysis of a major stable QTL qKnps-4A for kernel number per spike in wheat.

A major stable QTL for kernel number per spike was narrowed down to a 2.19-Mb region containing two potential candidate genes, and its effects on yield-related traits were characterized. Kernel number per spike (KNPS) in wheat is a key yield component. Dissection and characterization of major stable quantitative trait loci (QTLs) for KNPS would be of considerable value for the genetic improvement of yield potential using molecular breeding technology. We had previously reported a major stable QTL controlling KNPS, qKnps-4A. In the current study, primary fine-mapping analysis, based on the primary mapping population, located qKnps-4A to an interval of approximately 6.8-Mb from 649.0 to 655.8 Mb on chromosome 4A refering to 'Kenong 9204' genome. Further fine-mapping analysis based on a secondary mapping population narrowed qKnps-4A to an approximately 2.19-Mb interval from 653.72 to 655.91 Mb. Transcriptome sequencing, gene function annotation analysis and homologous gene related reports showed that TraesKN4A01HG38570 and TraesKN4A01HG38590 were most likely to be candidate genes of qKnps-4A. Phenotypic analysis based on paired near-isogenic lines in the target region showed that qKnps-4A increased KNPS mainly by increasing the number of central florets per spike. We also evaluated the effects of qKnps-4A on other yield-related traits. Moreover, we dissected the QTL cluster of qKnps-4A and qTkw-4A and proved that the phenotypic effects were probably due to close linkage of two or more genes rather than pleiotropic effects of a single gene. This study provides molecular marker resource for wheatmolecular breeding designed to improve yield potential, and lay the foundation for gene functional analysis of qKnps-4A.

Mapping QTL for Yield and Its Component Traits Using Wheat (Triticum aestivum L.) RIL Mapping Population from TAM 113 × Gallagher

Understanding genetic architectures of yield and yield-related traits of wheat (Triticum aestivum L.) grown under dryland or irrigated conditions is pivotal for developing modern high-yielding germplasm and cultivars. The objectives of this study were to detect quantitative trait loci (QTL) linked to yield and yield components using a mapping population derived from ‘TAM 113’/‘Gallagher’, including 191 recombinant inbred lines (RILs). The population was grown in McGregor, College Station, and Bushland, Texas, for three consecutive years from 2019 to 2021. A high-density genetic map covering all 21 chromosomes was constructed using a set of 8,075 single nucleotide polymorphisms (SNPs) obtained using genotyping-by-sequencing (GBS). A total of 147 QTLs for 16 yield-related traits were identified, which included 16 QTLs consistently detected in multiple experiments and 8 QTLs that showed pleiotropic effects. Of them, five pleiotropic QTLs overlapped with the consistent QTL. They increased grain yield (YLD) up to 37.64 g m−2, thousand kernel weight (TKW) up to 1.33 g, harvest (HI) up to 0.97%, kernel length up to 0.08 mm, and kernel width up to 0.04 mm with Gallagher alleles and increased YLD up to 22.21 g m−2, kernels spike−1 up to 1.77, TKW up to 1.14 g, and HI up to 3.72% with TAM 113 alleles. One major and consistent QTL on chromosome 2D at 34.4 Mbp overlapped with the major photoperiod gene Ppd-D1 and was affected by multiple traits, including kernel diameter (DIAM), TKW, kernel hardness index (KHI), heading date (HD), and plant height (PH). Another QTL cluster region on 7D between 52 and 66 Mbp, encompassing one consistent and three pleiotropic QTLs. One of the pleiotropic QTLs at 52 Mbp increased YLD up to 24.16 g m−2, HI up to 1%, and DIAM up to 0.03 mm. This study dissected genetic loci associated with yield and yield-related traits, providing valuable information on wheat improvement using marker-assisted selection (MAS).

Construction of a genetic map and QTL mapping of seed size traits in soybean.

Soybean seed size and seed shape traits are closely related to plant yield and appearance quality. In this study, 186 individual plants of the F2 generation derived from crosses between Changjiang Chun 2 and JiYu 166 were selected as the mapping population to construct a molecular genetic linkage map, and the phenotypic data of hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean under three generations of F2 single plants and F2:3 and F2:4 lines were combined to detect the QTL (quantitative trait loci) for the corresponding traits by ICIM mapping. A soybean genetic map containing 455 markers with an average distance of 6.15cM and a total length of 2799.2cM was obtained. Forty-nine QTLs related to the hundred-grain weight, seed length, seed width, and seed length-to-width ratio of soybean were obtained under three environmental conditions. A total of 10 QTLs were detected in more than two environments with a phenotypic variation of over 10%. Twelve QTL clusters were identified on chromosomes 1, 2, 5, 6, 8, 13, 18, and 19, with the majority of the overlapping intervals for hundred-grain weight and seed width. These results will lay the theoretical and technical foundation for molecularly assisted breeding in soybean seed weight and seed shape. Eighteen candidate genes that may be involved in the regulation of soybean seed size were screened by gene functional annotation and GO enrichment analysis.

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 mapping identified three hotspot genomic regions and candidate genes controlling heat tolerance-related traits in groundnut.

Groundnut productivity and quality have been impeded by rising temperatures in semi-arid environments. Hence, understanding the effects and molecular mechanisms of heat stress tolerance will aid in tackling yield losses. In this context, a recombinant inbred line (RIL) population was developed and phenotyped for eight seasons at three locations for agronomic, phenological, and physiological traits under heat stress. A genetic map was constructed using genotyping-by-sequencing with 478 single-nucleotide polymorphism (SNP) loci spanning a map distance of 1,961.39 cM. Quantitative trait locus (QTL) analysis using phenotypic and genotypic data identified 45 major main-effect QTLs for 21 traits. Intriguingly, three QTL clusters (Cluster-1-Ah03, Cluster-2-Ah12, and Cluster-3-Ah20) harbor more than half of the major QTLs (30/45, 66.6%) for various heat tolerant traits, explaining 10.4%-38.6%, 10.6%-44.6%, and 10.1%-49.5% of phenotypic variance, respectively. Furthermore, important candidate genes encoding DHHC-type zinc finger family protein (arahy.J0Y6Y5), peptide transporter 1 (arahy.8ZMT0C), pentatricopeptide repeat-containing protein (arahy.4A4JE9), Ulp1 protease family (arahy.X568GS), Kelch repeat F-box protein (arahy.I7X4PC), FRIGIDA-like protein (arahy.0C3V8Z), and post-illumination chlorophyll fluorescence increase (arahy.92ZGJC) were the underlying three QTL clusters. The putative functions of these genes suggested their involvement in seed development, regulating plant architecture, yield, genesis and growth of plants, flowering time regulation, and photosynthesis. Our results could provide a platform for further fine mapping, gene discovery, and developing markers for genomics-assisted breeding to develop heat-tolerant groundnut varieties.

Identification of quantitative trait loci and candidate genes controlling the tocopherol synthesis pathway in soybean (Glycine max)

AbstractA recombinant inbred line (RIL) population was used to identify quantitative trait loci (QTLs) and their candidate genes controlling the tocopherol (Toc) synthesis pathway. The RIL population was cultivated in field conditions in 3 years. A genetic map constructed using 1624 DNA markers was used for QTL analysis. We identified 22 QTLs for seed tocopherol contents and their ratios, of which two QTL clusters on chromosomes (Chr) 9 and 14 exerted consistent large effects on tocopherol composition across the 3 years. The QTL cluster localized on Chr 9 might correspond to γ‐TMT3, which controls the conversion of γ‐Toc into α‐Toc. The QTL cluster localized on Chr 14 was novel, which might regulate the conversion of MPBQ (a precursor of δ‐Toc) into DMPBQ (the precursor of γ‐Toc). The effect of the QTL cluster on Chr 14 was validated in a pair of near isogenic lines, and its candidate gene was mined. The identified QTLs and their candidate genes might be used in breeding programmes to improve α‐Toc content in soybean seeds.

Genetic analysis and QTL mapping of domestication-related traits in chili pepper (Capsicum annuum L.).

Chili pepper (Capsicum annuum L.) is one of the oldest and most phenotypically diverse pre-Columbian crops of the Americas. Despite the abundance of genetic resources, the use of wild germplasm and landraces in chili pepper breeding is limited. A better understanding of the evolutionary history in chili peppers, particularly in the context of traits of agronomic interest, can contribute to future improvement and conservation of genetic resources. In this study, an F2:3 mapping population derived from a cross between a C. annuum wild accession (Chiltepin) and a cultivated variety (Puya) was used to identify genomic regions associated with 19 domestication and agronomic traits. A genetic map was constructed consisting of 1023 single nucleotide polymorphism (SNP) markers clustered into 12 linkage groups and spanning a total of 1,263.87cM. A reciprocal translocation that differentiates the domesticated genome from its wild ancestor and other related species was identified between chromosomes 1 and 8. Quantitative trait locus (QTL) analysis detected 20 marker-trait associations for 13 phenotypes, from which 14 corresponded to previously identified loci, and six were novel genomic regions related to previously unexplored domestication-syndrome traits, including form of unripe fruit, seedlessness, deciduous fruit, and growth habit. Our results revealed that the genetic architecture of Capsicum domestication is similar to other domesticated species with few loci with large effects, the presence of QTLs clusters in different genomic regions, and the predominance of domesticated recessive alleles. Our analysis indicates the domestication process in chili pepper has also had an effect on traits not directly related to the domestication syndrome. The information obtained in this study provides a more complete understanding of the genetic basis of Capsicum domestication that can potentially guide strategies for the exploitation of wild alleles.

Identification and characterization of stripe rust, leaf rust, leaf spot, and common bunt resistance in spring wheat

AbstractHundreds of quantitative trait loci (QTLs) have been reported in diverse types of hexaploid wheat (Triticum aestivum L.) populations, but direct comparisons of QTLs in different studies and populations are still challenging due to the lack of physical positions for most QTLs. Here, we used the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map of all markers to map QTLs associated with leaf spot (Ls), leaf rust (Lr), stripe rust (Yr), and common bunt (Cbt) resistance in two recombinant inbred line populations. QTL mapping was conducted using the IWGSC physical map of 3158 and 5732 markers and disease severity data of Peace/Carberry and Attila/CDC Go populations evaluated in three to eight environments. We uncovered a total of 82 QTLs associated with Yr (36), Ls (18), Lr (15), and Cbt (13) resistance in the individual and overall means of all combined environments. Among them, 29 were associated with all combined environments, which accounted for 0.5%–20.9% individually and 12.4%–41.2% of the total disease severity per trait. Three (QLr.dms‐2D.2, QLs.dms‐5B, and QYr.dms‐5B.2) of the 29 QTLs were common in both populations. Fourteen out of the 29 QTLs were stable as they were identified both in the overall means and most of the individual environments. Ten chromosome arms harbored a cluster of QTLs associated with resistance to two to four diseases. This methodology would serve as one of the resources to compare QTLs identified in different populations and studies based on the improved physical information of all markers instead of population‐specific and consensus linkage maps.