Recombinant Inbred Lines Research Articles

Genetic Basis and Exploration of Major Expressed QTL qLA2-3 Underlying Leaf Angle in Maize

Leaf angle (LA) is closely related to plant architecture, photosynthesis and density tolerance in maize. In the current study, we used a recombinant inbred line population constructed by two maize-inbred lines to detect quantitative trait loci (QTLs) controlling LA. Based on the average LA in three environments, 13 QTLs were detected, with the logarithm of odds ranging from 2.7 to 7.21, and the phenotypic variation explained by a single QTL ranged from 3.93% to 12.64%. A stable QTL, qLA2-3, on chromosome 2 was detected and was considered to be the major QTL controlling the LA. On the basis of verifying the genetic effect of qLA2-3, a fine map was used to narrow the candidate interval, and finally, the target segment was located at a physical distance of approximately 338.46 kb (B73 RefGen_v4 version), containing 16 genes. Re-sequencing and transcriptome results revealed that five candidate genes may be involved in the regulation of LA. The results enrich the information for molecular marker-assisted selection of maize LA and provide genetic resources for the breeding of dense planting varieties.

Development and application of the GenoBaits WheatSNP16K array to accelerate wheat genetic research and breeding

Single-nucleotide polymorphisms (SNPs) are widely used as molecular markers for constructing genetic linkage maps in wheat. Compared with available SNP-based genotyping platforms, a genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology has become the favored genotyping technology because it is less demanding and more cost effective, flexible, and user-friendly. In this study, a new GenoBaits WheatSNP16K (GBW16K) GBTS array was designed using datasets generated by the wheat 660K SNP array and resequencing platforms in our previous studies. The GBW16K array contains 14 868 target SNP regions that are evenly distributed across the wheat genome, and 37 669 SNPs in these regions can be identified in a diversity panel consisting of 239 wheat accessions from around the world. Principal component and neighbor-joining analyses using the called SNPs are consistent with the pedigree information and geographic distributions or ecological environments of the accessions. For the GBW16K marker panel, the average genetic diversity among the 239 accessions is 0.270, which is sufficient for linkage map construction and preliminary mapping of targeted genes or quantitative trait loci (QTLs). A genetic linkage map, constructed using the GBW16K array-based genotyping of a recombinant inbred line population derived from a cross of the CIMMYT wheat line Yaco“S” and the Chinese landrace Mingxian169, enables the identification of Yr27, Yr30, and QYr.nwafu-2BL.4 for adult-plant resistance to stripe rust from Yaco“S” and of Yr18 from Mingxian169. QYr.nwafu-2BL.4 is different from any previously reported gene/QTL. Three haplotypes and six candidate genes have been identified for QYr.nwafu-2BL.4 on the basis of haplotype analysis, micro-collinearity, gene annotation, RNA sequencing, and SNP data. This array provides a new tool for wheat genetic analysis and breeding studies and for achieving durable control of wheat stripe rust.

Identification of candidate genes and genomic prediction of soybean fatty acid components in two soybean populations.

Soybean, a source of plant-derived lipids, contains an array of fatty acids essential for health. A comprehensive understanding of the fatty acid profiles in soybean is crucial for enhancing soybean cultivars and augmenting their qualitative attributes. Here, 180 F10 generation recombinant inbred lines (RILs), derived from the cross-breeding of the cultivated soybean variety 'Jidou 12' and the wild soybean 'Y9,' were used as primary experimental subjects. Using inclusive composite interval mapping (ICIM), this study undertook a quantitative trait locus (QTL) analysis on five distinct fatty acid components in the RIL population from 2019 to 2021. Concurrently, a genome-wide association study (GWAS) was conducted on 290 samples from a genetically diverse natural population to scrutinize the five fatty acid components during the same timeframe, thereby aiming to identify loci closely associated with fatty acid profiles. In addition, haplotype analysis and the Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed to predict candidate genes. The QTL analysis elucidated 23 stable QTLs intricately associated with the five fatty acid components, exhibiting phenotypic contribution rates ranging from 2.78% to 25.37%. In addition, GWAS of the natural population unveiled 102 significant loci associated with these fatty acid components. The haplotype analysis of the colocalized loci revealed that Glyma.06G221400 on chromosome 6 exhibited a significant correlation with stearic acid content, with Hap1 showing a markedly elevated stearic acid level compared with Hap2 and Hap3. Similarly, Glyma.12G075100 on chromosome 12 was significantly associated with the contents of oleic, linoleic, and linolenic acids, suggesting its involvement in fatty acid biosynthesis. In the natural population, candidate genes associated with the contents of palmitic and linolenic acids were predominantly from the fatty acid metabolic pathway, indicating their potential role as pivotal genes in the critical steps of fatty acid metabolism. Furthermore, genomic selection (GS) for fatty acid components was conducted using ridge regression best linear unbiased prediction based on both random single nucleotide polymorphisms (SNPs) and SNPs significantly associated with fatty acid components identified by GWAS. GS accuracy was contingent upon the SNP set used. Notably, GS efficiency was enhanced when using SNPs derived from QTL mapping analysis and GWAS compared with random SNPs, and reached a plateau when the number of SNP markers exceeded 3,000. This study thus indicates that Glyma.06G221400 and Glyma.12G075100 are genes integral to the synthesis and regulatory mechanisms of fatty acids. It provides insights into the complex biosynthesis and regulation of fatty acids, with significant implications for the directed improvement of soybean oil quality and the selection of superior soybean varieties. The SNP markers delineated in this study can be instrumental in establishing an efficacious pipeline for marker-assisted selection and GS aimed at improving soybean fatty acid components.

Linkage Mapping and Discovery of Candidate Genes for Drought Tolerance in Rice During the Vegetative Growth Period

Drought is a major abiotic stress affecting crop yields. Mapping quantitative trait loci (QTLs) and mining genes for drought tolerance in rice are important for identifying gene functions and targets for molecular breeding. Here, we performed linkage analysis of drought tolerance using a recombinant inbred line population derived from Jileng 1 (drought sensitive) and Milyang 23 (drought tolerant). An ultra-high-density genetic map, previously constructed by our research team using genotype data from whole-genome sequencing, was used in combination with phenotypic data for rice grown under drought stress conditions in the field in 2017–2019. Thirty-nine QTLs related to leaf rolling index and leaf withering degree were identified, and QTLs were found on all chromosomes except chromosomes 6, 10, and 11. qLWD4-1 was detected after 32 days and 46 days of drought stress in 2017 and explained 7.07–8.19% of the phenotypic variation. Two loci, qLRI2-2 and qLWD4-2, were identified after 29, 42, and 57 days of drought stress in 2018. These loci explained 10.59–17.04% and 5.14–5.71% of the phenotypic variation, respectively. There were 281 genes within the QTL interval. Through gene functional annotation and expression analysis, two candidate genes, Os04g0574600 and OsCHR731, were found. Quantitative reverse transcription PCR analysis showed that the expression levels of these genes were significantly higher under drought stress than under normal conditions, indicating positive regulation. Notably, Os04g0574600 was a newly discovered drought tolerance gene. Haplotype analysis showed that the RIL population carried two haplotypes (Hap1 and Hap2) of both genes. Lines carrying Hap2 exhibited significantly or extremely stronger drought tolerance than those carrying Hap1, indicating that Hap2 is an excellent haplotype. Among rice germplasm resources, there were two and three haplotypes of Os04g0574600 and OsCHR731, respectively. A high proportion of local rice resources in Sichuan, Yunnan, Anhui, Guangdong and Fujian provinces had Hap of both genes. In wild rice, 50% of accessions contained Hap1 of Os04g0574600 and 50% carried Hap4; 13.51%, 59.46% and 27.03% of wild rice accessions contained Hap1, Hap2, and Hap3, respectively. Hap2 of Os04g0574600 was found in more indica rice resources than in japonica rice. Therefore, Hap2 has more potential for utilization in future drought tolerance breeding of japonica rice.

Multiomics of a rice population identifies genes and genomic regions that bestow low glycemic index and high protein content

To counter the rising incidence of diabetes and to meet the daily protein needs, we created low glycemic index (GI) rice varieties with protein content (PC) surpassing 14%. In the development of recombinant inbred lines using Samba Mahsuri and IR36 amylose extender (IR36ae) as parental lines, we identified quantitative trait loci and genes associated with low GI, high amylose content (AC), and high PC. By integrating genetic techniques with classification models, this comprehensive approach identified candidate genes on chromosome 2 (qGI2.1/qAC2.1 spanning the region from 18.62 Mb to 19.95 Mb), exerting influence on low GI and high amylose. Notably, the phenotypic variant with high value was associated with the recessive allele of the starch branching enzyme 2b (sbeIIb). The genome-edited sbeIIb line confirmed low GI phenotype in milled rice grains. Further, combinations of alleles created by the highly significant SNPs from the targeted associations and epistatically interacting genes showed ultralow GI phenotypes with high amylose and high protein. Metabolomics analysis of rice with varying AC, PC, and GI revealed that the superior lines of high AC and PC, and low GI were preferentially enriched in glycolytic and amino acid metabolisms, whereas the inferior lines of low AC and PC and high GI were enriched with fatty acid metabolism. The high amylose high protein recombinant inbred line (HAHP_101) was enriched in essential amino acids like lysine. Such lines may be highly relevant for food product development to address diabetes and malnutrition.

Independent genetic factors control floret number and spikelet number in Triticum turgidum ssp.

Wheat grain yield is a complex trait resulting from a trade-off among many distinct components. During wheat evolution, domestication events and then modern breeding have strongly increased the yield potential of wheat plants, by enhancing spike fertility. To address the genetic bases of spike fertility in terms of spikelet number per spike and floret number per spikelet, a population of 110 recombinant inbred lines (RILS) obtained crossing a Triticum turgidum ssp. durum cultivar (Latino) and a T. dicoccum accession (MG5323) was exploited. Being a modern durum and a semi-domesticated genotype, respectively, the two parents differ for spike architecture and fertility, and thus the corresponding RIL population is the ideal genetic material to dissect genetic bases of yield components. The RIL population was phenotyped in four environments. Using a high-density SNP genetic map and taking advantage of several genome sequencing available for Triticeae, a total of 94 QTLs were identified for the eight traits considered; these QTLs were further reduced to 17 groups, based on their genetic and physical co-location. QTLs controlling floret number per spikelet and spikelet number per spike mapped in non-overlapping chromosomal regions, suggesting that independent genetic factors determine these fertility-related traits. The physical intervals of QTL groups were considered for possible co-location with known genes functionally involved in spike fertility traits and with yield-related QTLs previously mapped in tetraploid wheat. The most interesting result concerns a QTL group on chromosome 5B, associated with spikelet number per spike, since it could host genes still uncharacterized for their association to spike fertility. Finally, we identified two different regions where the trade-off between fertility related traits and kernel weight is overcome. Further analyses of these regions could pave the way for a future identification of new genetic loci contributing to fertility traits essential for yield improvement in durum wheat.

Association for Yield and Yield Related Traits in an RIL Population of Green Gram (Vigna radiata (L.) Wilezek)

Background: Green gram (Vigna radiata (L.) Wilczek), a significant annual legume, is a member of the family Fabaceae, subfamily Papilionoideae, and genus Vigna. Despite its importance, large-scale cultivation of green gram faces limitations due to various biotic and abiotic constraints. To address this challenge, our research is focused on the development of green gram genotypes with broader adaptability, enhanced genetic variability, and high-yielding potential. This involves a comprehensive study of the nature and magnitude of associations among yield and related traits to facilitate more effective and sustainable production practices. Methods: The present investigation was carried out at the experimental plots of Institute of Biotechnology (IBT), College farm, College of Agriculture, Hyderabad (TS). The present experiment was conducted to determine correlation and path analysis among morphological traits and their contribution towards yield among the F6 Recombinant inbred lines (RILs) in green gram. Results: Correlation estimate revealed that number of pods cluster-1, the Number of clusters plant-1, the Number of pods plant-1, Seed yield plant-1, plant height (cm), seed weight (g), and pod length (cm) showed a maximum direct contribution to seed yield both phenotypically and genotypically. Path analysis revealed that the parameters demonstrating a significant positive direct impact include days to 50% flowering, plant height (cm), number of branches plant-1 and number of clusters plant-1. The RILs for each character examined showed considerable variability. Conclusion: The green gram improvement program will benefit from a selection strategy that focuses on traits with a significant direct impact and a positive correlation with seed yield.

QTL Mapping and Candidate Gene Analysis for Starch-Related Traits in Tartary Buckwheat (Fagopyrum tataricum (L.) Gaertn).

Starch is the main component that determines the yield and quality of Tartary buckwheat. As a quantitative trait, using quantitative trait locus (QTL) mapping to excavate genes associated with starch-related traits is crucial for understanding the genetic mechanisms involved in starch synthesis and molecular breeding of Tartary buckwheat varieties with high-quality starch. Employing a recombinant inbred line population as research material, this study used QTL mapping to investigate the amylose, amylopectin, and total starch contents across four distinct environments. The results identified a total of 20 QTLs spanning six chromosomes, which explained 4.07% to 14.41% of the phenotypic variation. One major QTL cluster containing three stable QTLs governing both amylose and amylopectin content, qClu-4-1, was identified and located in the physical interval of 39.85-43.34 Mbp on chromosome Ft4. Within this cluster, we predicted 239 candidate genes and analyzed their SNP/InDel mutations, expression patterns, and enriched KEGG pathways. Ultimately, five key candidate genes, namely FtPinG0004897100.01, FtPinG0002636200.01, FtPinG0009329200.01, FtPinG0007371600.01, and FtPinG0005109900.01, were highlighted, which are potentially involved in starch synthesis and regulation, paving the way for further investigative studies. This study, for the first time, utilized QTL mapping to detect major QTLs controlling amylose, amylopectin, and total starch contents in Tartary buckwheat. The QTLs and candidate genes would provide valuable insights into the genetic mechanisms underlying starch synthesis and improving starch-related traits of Tartary buckwheat.

A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans.

Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rate caused by Ethyl Methane Sulfonate (EMS) mutagenesis in the C. elegans genome, indicating that mutation frequency is influenced by proximate sequence context and chromatin status. Leveraging these factors, we developed a machine learning enhanced pipeline to create a comprehensive EMS mutagenesis probability map for the C. elegans genome. This map operates on the principle that causative mutations are enriched in genetic screens targeting specific phenotypes among random mutations. Applying this map to Whole Genome Sequencing (WGS) data of genetic suppressors that rescue a C. elegans ciliary kinesin mutant, we successfully pinpointed causal mutations without generating recombinant inbred lines. This method can be adapted in other species, offering a scalable approach for identifying causal genes and revitalizing the effectiveness of forward genetic screens.

Identification and Mapping of QTLs for Adult Plant Resistance in Wheat Line XK502.

Stripe rust is a serious wheat disease occurring worldwide. At present, the most effective way to control it is to grow resistant cultivars. In this study, a population of 221 recombinant inbred lines (RILs) derived via single-seed descent from a hybrid of a susceptible wheat line, SY95-71, and a resistant line, XK502, was tested in three crop seasons from 2022 to 2024 in five environments. A genetic linkage map was constructed using 12,577 single-nucleotide polymorphisms (SNPs). Based on the phenotypic data of infection severity and the linkage map, five quantitative trait loci (QTL) for adult plant resistance (APR) were detected using the inclusive composite interval mapping (ICIM) method. These five loci are QYrxk502.swust-1BL, QYrxk502.swust-2BL, QYrxk502.swust-3AS, QYrxk502.swust-3BS, and QYrxk502.swust-7BS, explaining 5.67-19.64%, 9.63-36.74%, 9.58-11.30%, 9.76-23.98%, and 8.02-12.41% of the phenotypic variation, respectively. All these QTL originated from the resistant parent XK502. By comparison with the locations of known stripe rust resistance genes, three of the detected QTL, QYrxk502.swust-3AS, QYrxk502.swust-3BS, and QYrxk502.swust-7BS, may harbor new, unidentified genes. From among the tested RILs, 16 lines were selected with good field stripe rust resistance and acceptable agronomic traits for inclusion in breeding programs.

Identification of a Major QTL for Seed Protein Content in Cultivated Peanut (Arachis hypogaea L.) Using QTL-Seq.

Peanut (Arachis hypogaea L.) is a great plant protein source for human diet since it has high protein content in the kernel. Therefore, seed protein content (SPC) is considered a major agronomic and quality trait in peanut breeding. However, few genetic loci underlying SPC have been identified in peanuts, and the underlying regulatory mechanisms remain unknown, limiting the effectiveness of breeding for high-SPC peanut varieties. In this study, a major QTL (qSPCB10.1) controlling peanut SPC was identified within a 2.3 Mb interval in chromosome B10 by QTL-seq using a recombinant inbred line population derived from parental lines with high and low SPCs, respectively. Sequence comparison, transcriptomic analysis, and annotation analysis of the qSPCB10.1 locus were performed. Six differentially expressed genes with sequence variations between two parents were identified as candidate genes underlying qSPCB10.1. Further locus interaction analysis revealed that qSPCB10.1 could not affect the seed oil accumulation unless qOCA08.1XH13 was present, a high seed oil content (SOC) allele for a major QTL underlying SOC. In summary, our study provides a basis for future investigation of the genetic basis of seed protein accumulation and facilitates marker-assisted selection for developing high-SPC peanut genotypes.

Bread wheat cultivar Popo harbors QTLs for seedling and adult plant resistance to leaf rust in South African and Argentine environments

Leaf rust, caused by the fungus Puccinia triticina Eriks (Pt), is a destructive disease affecting wheat (Triticum aestivum L.) production in many countries, and a serious threat to food security. As a result, several breeding programs have included leaf rust resistance as an important trait. The discovery and identification of new resistance genes that could aid in incorporating durable or long-lasting leaf rust resistance into wheat is fundamental in these breeding programs. The present study aimed to identify quantitative trait loci (QTLs) for leaf rust resistance in 127 recombinant inbred lines (RIL) developed from the cross between the resistant wheat cultivar Popo and the susceptible cultivar Kariega. The RIL population and parental lines were phenotyped for leaf rust infection type and severity at seedling and adult plant stage, respectively. The former in the greenhouse (in Argentina) and the latter in multiple field test environments comprising 3 locations in South Africa (in Tygerhoek in the Western Cape Province during the 2014, 2015, 2017 and 2018 cropping seasons; Clarens during 2014, 2016 and 2017 cropping seasons and in Bethlehem in the Free State Province during 2017 cropping season) and in 1 location in Argentina (during the 2017 and 2018 cropping seasons in Marcos Juárez, Córdoba Province). The population was genotyped using genotyping-by-sequencing. A total of 12,080 silicoDArT and 2,669 SNP markers were used for QTL analysis. In total, 25 putative QTLs for resistance to leaf rust at seedling and adult plant stages were identified, including 5 QTLs for seedling and 20 QTLs for adult plant resistance (APR). Interestingly, both Popo and Kariega contributed with alleles for resistance. Significant loci for reducing leaf rust infection at seedling stage were designated QLr.arc-1A, QLr.arc-2B, QLr.arc-5B, QLr.arc-6A and QLr.arc-6D. Three minor QTLs derived from Popo designated as QLr.arc-1B1, QLr.arc-2D and QLr.arc-3D were also detected from the field tests, explaining 5–10%, 10–16% and 5–7% of the phenotypic variance, respectively. The identified QTLs and their closely linked silicoDArT and SNP-based markers can be used for fine mapping and candidate gene discovery in wheat breeding programs targeting durable leaf rust resistance.

QTL Detection of Salt Tolerance at Soybean Seedling Stage Based on Genome-Wide Association Analysis and Linkage Analysis.

The utilization of saline land is a global challenge, and cultivating salt-tolerant soybean varieties is beneficial for improving the efficiency of saline land utilization. Exploring the genetic basis of salt-tolerant soybean varieties and developing salt-tolerant molecular markers can effectively promote the process of soybean salt-tolerant breeding. In the study, the membership function method was used to evaluate seven traits related to salt tolerance and comprehensive salt tolerance at the soybean seedling stage; genome-wide association analysis (GWAS) was performed in a natural population containing 200 soybean materials; and linkage analysis was performed in 112 recombinant inbred lines (RIL) population to detect quantitative trait loci (QTLs) of salt tolerance. In the GWAS, 147 SNPs were mapped, explaining 5.28-17.16% of phenotypic variation. In the linkage analysis, 10 QTLs were identified, which could explain 6.9-16.16% of phenotypic variation. And it was found that there were two co-located regions between the natural population and the RIL population, containing seven candidate genes of salt tolerance in soybean. In addition, one colocalization interval was found to contain qZJS-15-1, rs47665107, and rs4793412, all of which could explain more than 10% of phenotypic variation rates, making it suitable for molecular marker development. The physical positions of rs47665107 and rs47934112 were included in qZJS-15-1. Therefore, a KASP marker was designed and developed using Chr. 15:47907445, which was closely linked to the qZJS-15-1. This marker could accurately and clearly cluster the materials of salt-tolerant genotypes in the heterozygous population tested. The QTLs and KASP markers found in the study provide a theoretical and technical basis for accelerating the salt-tolerant breeding of soybean.

Two dwarfing genes Rht-B1b and Rht-D1b show pleiotropic effects on grain protein content in bread wheat (Triticum aestivum L.).

Five QTL for wheat grain protein content were identified, and the effects of two dwarfing genes Rht-B1b and Rht-D1b on grain protein content were validated in multiple populations. Grain protein content (GPC) plays an important role in wheat quality. Here, a recombinant inbred line (RIL) population derived from a cross between Yangmai 12 (YM12) and Yanzhan 1 (YZ1) was used to identify quantitative trait loci (QTL) for GPC. Two hundred and five RILs and their parents were grown in three years in randomized complete blocks each with two replications, and genotyped using the wheat 55K SNP array. Five QTL were identified for GPC on chromosomes 1A, 1B, 2D, 4B, and 4D. Notably, QGpc.yaas-4B (co-located with Rht-B1) and QGpc.yaas-4D (co-located with Rht-D1) were consistently detected across all experiments and best linear unbiased estimating, accounting for 6.61-8.39% and 6.05-10.21% of the phenotypic variances, respectively. The effects of these two dwarfing alleles Rht-B1b and Rht-D1b on reducing GPC and plant height were validated in two additional RIL populations and one natural population. This study lays a foundation for further investigating the effects of dwarfing genes Rht-B1b and Rht-D1b on wheat GPC.

Direct inference of the distribution of fitness effects of spontaneous mutations from recombinant inbred Caenorhabditis elegans mutation accumulation lines.

The distribution of fitness effects of new mutations plays a central role in evolutionary biology. Estimates of the distribution of fitness effect from experimental mutation accumulation lines are compromised by the complete linkage disequilibrium between mutations in different lines. To reduce the linkage disequilibrium, we constructed 2 sets of recombinant inbred lines from a cross of 2 Caenorhabditis elegans mutation accumulation lines. One set of lines ("RIAILs") was intercrossed for 10 generations prior to 10 generations of selfing; the second set of lines ("RILs") omitted the intercrossing. Residual linkage disequilibrium in the RIAILs is much less than in the RILs, which affects the inferred distribution of fitness effect when the sets of lines are analyzed separately. The best-fit model estimated from all lines (RIAILs + RILs) infers a large fraction of mutations with positive effects (∼40%); models that constrain mutations to have negative effects fit much worse. The conclusion is the same using only the RILs. For the RIAILs, however, models that constrain mutations to have negative effects fit nearly as well as models that allow positive effects. When mutations in high linkage disequilibrium are pooled into haplotypes, the inferred distribution of fitness effect becomes increasingly negative-skewed and leptokurtic. We conclude that the conventional wisdom-most mutations have effects near 0, a handful of mutations have effects that are substantially negative, and mutations with positive effects are very rare-is likely correct, and that unless it can be shown otherwise, estimates of the distribution of fitness effect that infer a substantial fraction of mutations with positive effects are likely confounded by linkage disequilibrium.

Exploration and genetic analyses of canopy leaf pigmentation changes in soybean (Glycine max L.): unveiling a novel phenotype

Key messagePigmentation changes in canopy leaves were first reported, and subsequent genetic analyses identified a major QTL associated with levels of pigmentation changes, suggesting Glyma.06G202300 as a candidate gene.An unexpected reddish-purple pigmentation in upper canopy leaves was discovered during the late reproductive stages in soybean (Glycine max L.) genotypes. Two sensitive genotypes, ‘Uram’ and PI 96983, exhibited anomalous canopy leaf pigmentation changes (CLPC), while ‘Daepung’ did not. The objectives of this study were to: (i) characterize the physiological features of pigmented canopy leaves compared with non-pigmented leaves, (ii) evaluate phenotypic variation in a combined recombinant inbred line (RIL) population (N = 169 RILs) under field conditions, and (iii) genetically identify quantitative trait loci (QTL) for CLPC via joint population linkage analysis. Comparison between pigmented and normal leaves revealed different Fv/Fm of photosystem II, hyperspectral reflectance, and cellular properties, suggesting the pigmentation changes occur in response to an undefined abiotic stress. A highly significant QTL was identified on chromosome 6, explaining ~ 62.8% of phenotypic variance. Based on the QTL result, Glyma.06G202300 encoding flavonoid 3′-hydroxylase (F3′H) was identified as a candidate gene. In both Uram and PI 96983, a 1-bp deletion was confirmed in the third exon of Glyma.06G202300 that results in a premature stop codon in both Uram and PI 96983 and a truncated F3′H protein lacking important domains. Additionally, gene expression analyses uncovered significant differences between pigmented and non-pigmented leaves. This is the first report of a novel symptom and an associated major QTL. These results will provide soybean geneticists and breeders with valuable knowledge regarding physiological changes that may affect soybean production. Further studies are required to elucidate the causal environmental stress and the underlying molecular mechanisms.

Identification of QTLs and candidate genes for water-soluble protein content in soybean seeds

Soybean represents a vital source of premium plant-based proteins for human nutrition. Importantly, the level of water-soluble protein (WSP) is crucial for determining the overall quality and nutritional value of such crops. Enhancing WSP levels in soybean plants is a high-priority goal in crop improvement. This study aimed to elucidate the genetic basis of WSP content in soybean seeds by identifying quantitative trait loci (QTLs) and set the foundation for subsequent gene cloning and functional analysis. Using 180 F10 recombinant inbred lines generated by crossing the high-protein soybean cultivar JiDou 12 with the wild variety Ye 9, our researcher team mapped the QTLs influencing protein levels, integrating Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and gene expression profiling to identify candidate genes. During the 2020 and 2022 growing seasons, a standard bell-shaped distribution of protein content trait data was observed in these soybean lines. Eight QTLs affecting protein content were found across eight chromosomes, with LOD scores ranging from 2.59 to 7.30, explaining 4.15–11.74% of the phenotypic variance. Notably, two QTLs were newly discovered, one with a elite allele at qWSPC-15 from Ye 9. The major QTL, qWSPC-19, on chromosome 19 was stable across conditions and contained genes involved in nitrogen metabolism, amino acid biosynthesis, and signaling. Two genes from this QTL, Glyma.19G185700 and Glyma.19G186000, exhibited distinct expression patterns at maturity, highlighting the influence of these genes on protein content. This research revealed eight QTLs for WSP content in soybean seeds and proposed a gene for the key QTL qWSPC-19, laying groundwork for gene isolation and enhanced soybean breeding through the use of molecular markers. These insights are instrumental for developing protein-rich soybean cultivars.

Optimization of Genotyping‐by‐Sequencing (GBS) for Germplasm Fingerprinting and Trait Mapping in Faba Bean

ABSTRACTFaba bean (Vicia faba L.) is an important pulse crop with a wide range of agroecological adaptations. The development of genomic tools and a comprehensive catalog of extant genetic diversity are crucial for developing improved faba bean cultivars. The lack of a cost‐effective genotyping platform limits the characterization of large germplasm collections, understanding of genetic diversity across populations, and implementing breeder's tools like genomic selection. Genotyping‐by‐sequencing (GBS) offers high‐resolution genotyping for both model and crop plant species, even without a reference genome sequence. The genome fragments targeted by GBS depend substantially on the restriction enzyme (RE) used for the complexity reduction step. Species with complex genomic architecture require optimization of GBS with proper RE to realize the full potential of GBS. Here, we evaluated various REs in the GBS method and identified that the combination of ApeKI/MseI proved to be the most appropriate for faba bean based on the best library quality, a high number of genomic loci spread across chromosomes, and high enrichment loci associated with the gene space. With the new optimized protocol, we constructed a genetic map using a recombinant inbred line (RIL) population and identified a QTL for seed hilum color on Chromosome 1. In addition, we also genotyped a diversity panel and performed a genome‐wide association studies (GWAS) for important agronomic traits, including plant height (PH), flowering time (FT), and number of pods per plant (PPP). We identified six SNP markers significantly associated with these traits and listed potential candidate genes. The optimized faba bean‐specific GBS procedure will facilitate access to the untapped genetic diversity for genetic research and breeding and may facilitate functional genomics.

QTL mapping for pod quality and yield traits in snap bean (Phaseolus vulgaris L.).

Pod quality and yield traits in snap bean (Phaseolus vulgaris L.) influence consumer preferences, crop adoption by farmers, and the ability of the product to be commercially competitive locally and globally. The objective of the study was to identify the quantitative trait loci (QTL) for pod quality and yield traits in a snap × dry bean recombinant inbred line (RIL) population. A total of 184 F6 RILs derived from a cross between Vanilla (snap bean) and MCM5001 (dry bean) were grown in three field sites in Kenya and one greenhouse environment in Davis, CA, USA. They were genotyped at 5,951 single nucleotide polymorphisms (SNPs), and composite interval mapping was conducted to identify QTL for 16 pod quality and yield traits, including pod wall fiber, pod string, pod size, and harvest metrics. A combined total of 44 QTL were identified in field and greenhouse trials. The QTL for pod quality were identified on chromosomes Pv01, Pv02, Pv03, Pv04, Pv06, and Pv07, and for pod yield were identified on Pv08. Co-localization of QTL was observed for pod quality and yield traits. Some identified QTL overlapped with previously mapped QTL for pod quality and yield traits, with several others identified as novel. The identified QTL can be used in future marker-assisted selection in snap bean.

Identifying Genetic Loci Determining Grain Yield Under Drought Stress in Rice (Oryza sativa L.)

ABSTRACTIdentifying the genomic regions (quantitative trait loci (QTL)) significantly linked to grain yield under drought stress could expedite the development of novel rice cultivars suited for rainfed areas through marker‐assisted breeding. This study identified QTL regions linked to plant phenology and production traits by evaluating 122 recombinant inbred lines (RILs) of Danteshwari × Daggaddeshi under different environmental conditions. A consistent QTL region associated with grain yield under water stress (60.4 Mbp) was mapped on chromosome 1 between RM428 and RM24 with an LOD score of 4.0. Another QTL region (9.4 Mbp) linked to plant height under all environmental conditions was mapped on chromosome 1 between RM1‐HvSSR1‐87 with a LOD score of 7.5 and phenotypic variance of 25%. A core set of 402 diverse rice accessions was also evaluated under water stress conditions and subjected to genome‐wide association analysis. Twelve markers linked to grain yield under drought were identified, out of which five were significantly associated with grain yield and days to flowering under drought. The markers linked to grain yield were compared between the bi‐parental population and germplasm accessions to identify the common markers. Three markers (RM1, RM259 and RM201) were found to be consistently linked to drought stress across the seasons in both bi‐parental population and germplasm accessions studied and could be potential candidates for application in marker‐assisted selection for improving grain yield under drought stress in rice.