Mapping Population Research Articles

Identification of quantitative trait loci for salinity tolerance in rice (Oryza sativa L.) through Sahel 328/NERICA-L-9 mapping population at seedling stage

Identification of quantitative trait loci for salinity tolerance in rice (Oryza sativa L.) through Sahel 328/NERICA-L-9 mapping population at seedling stage

A call to action for inventorying and monitoring of cliff ecosystems to support conservation

Cliffs harbor unique and specialized biodiversity that warrants attention and conservation. At the same time, cliffs are under increased threat from anthropogenic disturbance and climate change. Since cliffs are highly heterogeneous, spatially isolated, and often inaccessible compared to nearby habitats, land managers require up-to-date and site-specific information to protect them. Cliffs are often overlooked due to the technical and logistical challenges posed by surveying these environments, but field inventorying and monitoring can fill this gap. We present three case studies of cliff monitoring in action: mapping populations of an endemic rare plant in the Southern Appalachian Mountains (US), photo-sampling of cliff specialist plants in Spain, and surveying peregrine falcons in Western North Carolina (US). These case studies highlight the application of various monitoring techniques, the possibilities for collaboration among stakeholders, and some ways that data from monitoring can inform cliff conservation and stewardship. To facilitate the development of easy-to-implement monitoring, we outline three approaches and associated best practices for monitoring cliff plants. Methods range from simple photo point monitoring to more in-depth species inventories and could be implemented by community scientists alongside a broader audience interested in providing up-to-date data on cliff environments. We call for action, urging the expansion and advancement of cliff biodiversity monitoring.

Genetics and plasticity of white leaf mark variegation in white clover (Trifolium repens L.).

Leaf variegation is common in plants and confers diverse adaptive functions. However, its genetic underpinnings remain largely unresolved; this is particularly true for variegation that arises through modified leaf tissue structure that affects light reflection. White clover is naturally polymorphic for structure-based white leaf mark variegation. It therefore provides a useful system to examine the genetic basis of this phenotype, and to assess potential costs to photosynthetic efficiency resulting from modified leaf structures. This study sought to map the loci controlling the white leaf mark in white clover and evaluate the relationship between white leaf mark, leaf thickness, and photosynthetic efficiency. We generated a high-density genetic linkage map from an F3 mapping population, employing reference genome-based SNP markers. White leaf mark was quantified through detailed phenotypic evaluations alongside leaf thickness to test how tissue thickness may affect the variegation phenotype. Quantitative trait locus (QTL) mapping was performed to characterize their genetic bases. Photosynthetic efficiency measurements were used to test for physiological trade-offs between variegation and photosynthetic output. The V locus, a major gene responsible for the white leaf mark polymorphism, was mapped to the distal end of chromosome 5, and several modifier loci were also mapped that contribute additively to variegation intensity. The presence and intensity of white leaf mark was associated with greater leaf thickness; however, increased variegation did not detectably affect photosynthetic efficiency. We have successfully mapped the major locus governing the white leaf mark in white clover, along with several modifier loci, revealing a complex basis for this structure-based variegation. The apparent absence of compromised photosynthesis in variegated leaves challenges the notion that variegation creates fitness trade-offs between photosynthetic efficiency and other adaptive functions. This finding suggests that other factors may maintain the white leaf mark polymorphism in white clover.

Yield Trait Association Studies through Principal Component Analysis in Bi-Parental Population Derived from indica/tropical japonica Inter Sub-Specific Cross in Rice (Oryza sativa L.)

In the present investigation, a mapping population consisting of 189 lines derived from an inter sub-specific cross between an indica cultivar, ‘Samba Mahsuri’ and a tropical japonica accession, ‘Cuba 65’ was investigated to assess the trait contribution to variability employing principal component analysis (PCA). The study aimed at determination of inter-relationships among various yield attributing traits vis-à-vis individuals of the mapping population. Out of eleven principal components (PC), first four PCs exhibited Eigen value more than one accounting to a total of 68.73% cumulative variance among the characters. The first PC accounted for the highest variability (PC1 31.6%) to the total variability, followed by 14.51% (PC2), 12.69% (PC3) and 9.91% (PC4). The highest positive Eigen value observed for the tiller number (0.33) and panicle number (0.32) in PC1 indicated their pronounced effect in the overall variation in the population. The analysis revealed the traits that contribute maximum for the variation. PCA also revealed negligible effect of days to fifty percent flowering, grain size and spikelet fertility to the variability in the mapping population. Although all the studied traits except grain size showed significant positive association with grain yield; negative correlation was observed between some of the component traits. Panicle weight, thousand grain weight and grain number showed negative association with tiller number and panicle number. The latter two traits contributed to the maximum variability in the population. The trait associations identified in the present investigation will help in determining the selection criteria to be adopted in the development of high yielding cultivars involving indica/tropical japonica populations.

Comprehensive identification of genomic and environmental determinants of phenotypic plasticity in maize.

Maize phenotypes are plastic, determined by the complex interplay of genetics and environmental variables. Uncovering the genes responsible and understanding how their effects change across a large geographic region are challenging. In this study, we conducted systematic analysis to identify environmental indices that strongly influence 19 traits (including flowering time, plant architecture, and yield component traits) measured in the maize nested association mapping (NAM) population grown in 11 environments. Identified environmental indices based on day length, temperature, moisture, and combinations of these are biologically meaningful. Next, we leveraged a total of more than 20 million SNP and SV markers derived from recent de novo sequencing of the NAM founders for trait prediction and dissection. When combined with identified environmental indices, genomic prediction enables accurate performance predictions. Genome-wide association studies (GWASs) detected genetic loci associated with the plastic response to the identified environmental indices for all examined traits. By systematically uncovering the major environmental and genomic factors underlying phenotypic plasticity in a wide variety of traits and depositing our results as a track on the MaizeGDB genome browser, we provide a community resource as well as a comprehensive analytical framework to facilitate continuing complex trait dissection and prediction in maize and other crops. Our findings also provide a conceptual framework for the genetic architecture of phenotypic plasticity by accommodating two alternative models, regulatory gene model and allelic sensitivity model, as special cases of a continuum.

The pseudo-type response regulator gene Clsc regulates rind stripe coloration in watermelon1

The color and pattern of watermelon's rind are crucial external traits that directly affect consumer preferences. Watermelons with stripes having a stronger color than the background rind are ideal for studying plant stripes, while there is still limited knowledge about the genetic mechanisms underlying stripe coloration due to the lack of germplasm resources. In this study, we focused on a watermelon germplasm with colorless stripes, and genetic analysis revealed that the trait is controlled by a single recessive gene. The gene Clsc (Citrullus lanatus stripe coloration), which is responsible for the colorless stripe, was confined into a 147.6 kb region on chromosome 9 by linkage analysis in a large F2 mapping population. Further analysis revealed that the Cla97C09G175170 gene, encoding the APRR2 transcription factor, plays a crucial role in determining watermelon's colorless stripe phenotype and was deduced to be related to chlorophyll and chloroplast development. Based on the physiology experiment results, it can be inferred that Cla97C09G175170 may significantly influence chloroplast development and chlorophyll synthesis in watermelon. The results of this study will be helpful for better understanding the molecular mechanism of stripe coloration in watermelon and for the development of marker-assisted selection (MAS) for new watermelon cultivars.

Genetic Variability Studies for Yield and its Attributing Traits in Rice F4 Mapping Population (Oryza sativa L.)

An experiment was conducted at ICAR-IIRR during the wet season of 2023 in a mapping population derived from the cross TI-128 X BPT-5204, along with five checks in an augmented block design with eight blocks. This study revealed that, significant variability was present among the population for all the traits examined. High genotypic coefficient of variation was observed for number of tillers per plant (29.68), number of productive tillers per plant (30.56) and total number of grains per panicle (29.66). High phenotypic coefficient of variation and genotypic coefficient of variation was observed for the trait grain yield per plant. High heritability coupled with high genetic advance as percent of mean was recorded for number of tillers per plant, number of productive tillers per plant, total number of grains per panicle and grain yield per plant showing that these traits are regulated by additive gene effects, making them suitable for selection criteria in rice breeding programs. The traits viz., number of tillers per plant (0.42***), number of productive tillers per plant (0.41***), filled grains per panicle (0.20**), total number of grains per panicle (0.18**) and test weight (0.18**) exhibited significantly positive association with grain yield per plant.

Densification of Genetic Map and Stable Quantitative Trait Locus Analysis for Amino Acid Content of Seed in Soybean (Glycine max L.).

Soybean, a primary vegetable protein source, boasts favorable amino acid profiles; however, its composition still falls short of meeting human nutritional demands. The soybean amino acid content is a quantitative trait controlled by multiple genes. In this study, an F2 population of 186 individual plants derived from the cross between ChangJiangChun2 and JiYu166 served as the mapping population. Based on the previously published genetic map of our lab, we increased the density of the genetic map and constructed a new genetic map containing 518 SSR (simple sequence repeats) markers and 64 InDel (insertion-deletion) markers, with an average distance of 5.27 cm and a total length of 2881.2 cm. The content of eight essential amino acids was evaluated in the F2:5, F2:6, and BLUP (best linear unbiased prediction). A total of 52 QTLs (quantitative trait loci) were identified, and 13 QTL clusters were identified, among which loci02.1 and loci11.1 emerged as stable QTL clusters, exploring candidate genes within these regions. Through GO enrichment and gene annotation, 16 candidate genes associated with soybean essential amino acid content were predicted. This study would lay the foundation for elucidating the regulatory mechanisms of essential amino acid content and contribute to germplasm innovation in soybeans.

Modeling the impact of resource allocation decisions on genomic prediction using maize multi‐environment data

AbstractIn a hybrid maize (Zea mays L.) breeding program that utilizes genomic selection, resource allocation used in phenotypic data acquisition must be balanced between population size, number of environments, and the number of testers used for generating hybrids. Plant breeders evaluate newly developed inbred lines using multi‐environment trials to account for genotype‐by‐environment interaction effects. The replication of hybrids across environments in these trials impacts the training data accuracy for developing genomic prediction models. This study examined the impact of resource allocation scenarios on genomic prediction accuracy using a multi‐environment trial dataset generated using inbred lines crossed to multiple testers. A total of 369 Stiff Stalk double haploid lines from a synthetic mapping population were testcrossed to three non‐Stiff Stalk inbred lines as testers, PHZ51, PHK76, and PHP02, and evaluated across 34 environments by the Genomes to Fields Initiative in 2020 and 2021. Resource allocation scenarios significantly impacted site‐specific genomic prediction accuracy for unobserved hybrids in unobserved environments. A training set with three to five environments that had the highest quality data produced similar prediction accuracy as data from 10 random environments for both observed and unobserved hybrids, indicating that strong prediction models can be built with a limited set of environments for both grain yield and plant height. We found that resource‐efficient prediction models that use data from one tester and three to five environments can effectively conduct selection of untested hybrids and in untested environments. Public research programs are often limited in testing capacity, and this study provides support for genomic selection in resource‐limited breeding programs.

Mapping quantitative trait loci for seminal root angle in a selected durum wheat population.

Seminal root angle (SRA) is an important root architectural trait associated with drought adaptation in cereal crops. To date, all attempts to dissect the genetic architecture of SRA in durum wheat (Triticum durum Desf.) have used large association panels or structured mapping populations. Identifying changes in allele frequency generated by selection provides an alternative genetic mapping approach that can increase the power and precision of QTL detection. This study aimed to map quantitative trait loci (QTL) for SRA by genotyping durum lines created through divergent selection using a combination of marker-assisted selection (MAS) for the major SRA QTL (qSRA-6A) and phenotypic selection for SRA over multiple generations. The created 11 lines (BC1F2:5) were genotyped with genome-wide single-nucleotide polymorphism (SNP) markers to map QTL by identifying markers that displayed segregation distortion significantly different from the Mendelian expectation. QTL regions were further assessed in an independent validation population to confirm their associations with SRA. The experiment revealed 14 genomic regions under selection, 12 of which have not previously been reported for SRA. Five regions, including qSRA-6A, were confirmed in the validation population. The genomic regions identified in this study indicate that the genetic control of SRA is more complex than previously anticipated. Our study demonstrates that selection mapping is a powerful approach to complement genome-wide association studies for QTL detection. Moreover, the verification of qSRA-6A in an elite genetic background highlights the potential for MAS, although it is necessary to combine additional QTL to develop new cultivars with extreme SRA phenotypes.

Genetic mapping of stripe rust resistance in a geographically diverse barley collection and selected biparental populations.

Barley stripe or yellow rust (BYR) caused by Puccinia striiformis f. sp. hordei (Psh) is a significant constraint to barley production. The disease is best controlled by genetic resistance, which is considered the most economical and sustainable component of integrated disease management. In this study, we assessed the diversity of resistance to Psh in a panel of international barley genotypes (n = 266) under multiple disease environments (Ecuador, India, and Mexico) using genome-wide association studies (GWASs). Four quantitative trait loci (QTLs) (three on chromosome 1H and one on 7H) associated with resistance to Psh were identified. The QTLs were validated by mapping resistance to Psh in five biparental populations, which detected key genomic regions on chromosomes 1H (populations Pompadour/Zhoungdamei, Pompadour/Zug161, and CI9214/Baudin), 3H (Ricardo/Gus), and 7H (Fumai8/Baronesse). The QTL RpshQ.GWA.1H.1 detected by GWAS and RpshQ.Bau.1H detected using biparental mapping populations co-located were the most consistent and stable across environments and are likely the same resistance region. RpshQ.Bau.1H was saturated using population CI9214/Baudin by enriching the target region, which placed the resistance locus between 7.9 and 8.1 Mbp (flanked by markers sun_B1H_03, 0.7 cM proximal to Rpsh_1H and sun_B1H_KASP_02, 3.2 cM distal on 1HS) in the Morex reference genome v.2. A Kompetitive Allele Specific PCR (KASP) marker sun_B1H_KASP_01 that co-segregated for RpshQ.Bau.1H was developed. The marker was validated on 50 Australian barley cultivars, showing well-defined allelic discrimination and presence in six genotypes (Baudin, Fathom, Flagship, Grout, Sakurastar, and Shepherd). This marker can be used for reliable marker-assisted selection and pyramiding of resistance to Psh and in diversifying the genetic base of resistance to stripe rust.

Haplotype-based association mapping of genomic regions associated with Zymoseptoria tritici resistance using 217 diverse wheat genotypes

BackgroundSeptoria tritici blotch (STB) is considered to be one of the most destructive foliar wheat diseases and is caused by Zymoseptoria tritici. The yield losses are severe and in Northwestern Europe can reach up to 50%. The efficacy of fungicides is diminishing due to changes in the genetic structure of the pathogen. Therefore, resistance breeding is the most effective strategy of disease management. Recently, genome-wide association studies (GWAS) have become more popular due to their robustness in dissecting complex traits, including STB resistance in wheat. This was made possible by the use of large mapping populations and new sequencing technologies. High-resolution mapping benefits from historical recombination and greater allele numbers in GWAS.ResultsIn our study, 217 wheat genotypes of diverse origin were phenotyped against five Z. tritici isolates (IPO323, IPO88004, IPO92004, IPO86036 and St1-03) and genotyped on the DArTseq platform. In polytunnel tests two disease parameters were evaluated: the percentage of leaf area covered by necrotic lesions (NEC) and the percentage of leaf area covered by lesions bearing pycnidia (PYC). The disease escape parameters heading date (Hd) and plant height (Ht) were also measured. Pearson’s correlation showed a positive effect between disease parameters, providing additional information. The Structure analysis indicated four subpopulations which included from 28 (subpopulation 2) to 79 genotypes (subpopulation 3). All of the subpopulations showed a relatively high degree of admixture, which ranged from 60% of genotypes with less than 80% of proportions of the genome attributed to assigned subpopulation for group 2 to 85% for group 4. Haplotype-based GWAS analysis allowed us to identify 27 haploblocks (HBs) significantly associated with analysed traits with a p-value above the genome-wide significance threshold (5%, which was –log10(p) > 3.64) and spread across the wheat genome. The explained phenotypic variation of identified significant HBs ranged from 0.2% to 21.5%. The results of the analysis showed that four haplotypes (HTs) associated with disease parameters cause a reduction in the level of leaf coverage by necrosis and pycnidia, namely: Chr3A_HB98_HT2, Chr5B_HB47_HT1, Chr7B_HB36_HT1 and Chr5D_HB10_HT3.ConclusionsGWAS analysis enabled us to identify four significant chromosomal regions associated with a reduction in STB disease parameters. The list of valuable HBs and wheat varieties possessing them provides promising material for further molecular analysis of resistance loci and development of breeding programmes.

MAPtools: command-line tools for mapping-by-sequencing and QTL-Seq analysis and visualization

BackgroundClassical mutagenesis is a powerful tool that has allowed researchers to elucidate the molecular and genetic basis of a plethora of processes in many model species. The integration of these methods with modern massively parallel sequencing techniques, initially in model species but currently also in many crop species, is accelerating the identification of genes underlying a wide range of traits of agronomic interest.ResultsWe have developed MAPtools, an open-source Python3 application designed specifically for the analysis of genomic data from bulked segregant analysis experiments, including mapping-by-sequencing (MBS) and quantitative trait locus sequencing (QTL-seq) experiments. We have extensively tested MAPtools using datasets published in recent literature.ConclusionsMAPtools gives users the flexibility to customize their bioinformatics pipeline with various commands for calculating allele count-based statistics, generating plots to pinpoint candidate regions, and annotating the effects of SNP and indel mutations. While extensively tested with plants, the program is versatile and applicable to any species for which a mapping population can be generated and a sequenced genome is available.Availability and implementationMAPtools is available under GPL v3.0 license and documented as a Python3 package at https://github.com/hcandela/MAPtools.

Mapping of Aegilops speltoides derived leaf rust and stripe rust resistance genes using 35K SNP array

Wheat is an essential food commodity cultivated throughout the world. However, this crop faces continuous threats from fungal pathogens, leaf rust (LR) and stripe rust (YR). To continue feeding the growing population, these major destructors of wheat must be effectively countered by enhancing the genetic diversity of cultivated germplasm. In this study, an introgression line with hexaploid background (ILsp3603) carrying resistance against Pt pathotypes 77−5 (121R63-1), 77−9 (121R60-1) and Pst pathotypes 46S119 (46E159), 110S119 (110E159), 238S119 (238E159) was developed from donor wheat wild progenitor, Aegilops speltoides acc pau 3603. To understand the genetic basis of resistance and map these genes (named Lrsp3603 and Yrsp3603), inheritance studies were carried out in F6 and F7 mapping population, developed by crossing ILsp3603 with LR and YR susceptible cultivar WL711, which revealed a monogenic (single gene) inheritance pattern for each of these traits. Bulk segregant analysis combined with 35 K Axiom SNP array genotyping mapped both genes as separate entities on the short arm of chromosome 6B. A genetic linkage map, comprising five markers, 1 SNP, 1 PLUG and three gene based SSRs, covered a genetic distance of 12.65 cM. Lrsp3603 was flanked by markers Tag-SSR14 (located proximally at 2.42 cM) and SNP AX-94542331 (at 3.28 cM) while Yrsp3603 was mapped at one end closest to AX-94542331 at 6.62 cM distance. Functional annotation of Lrsp3603 target region (∼ 1 Mbp) revealed 10 gene IDs associated with disease resistance mechanisms including three encoding typical R gene domains.

Genome-wide association studies using chromosome-scale genomes of male and female lines redefines two sex-linked loci in linkage disequilibrium in Ficus carica L.

Despite its significance in breeding and agricultural practices, the genetic mechanisms that determine sex in the fig (Ficus carica) are not clearly understood, highlighting the need for comprehensive genomic and genetic research to clarify these complex processes. With the aim to accurately identifying the genetic loci involved in sex-related traits of the fig, two chromosome-scale genome sequences for ‘Horaishi’ (a female line) and ‘Caprifig 6085′ (a male line) were determined, and a genome-wide association study (GWAS) was subsequently conducted using single nucleotide polymorphisms (SNPs) obtained from whole genome sequencing across genetically diverse lines and a biparental mapping population derived from a cross between the two lines. As a result, two major GWAS signals were detected. Of these, only the SNPs on FcRAN1 were found to perfectly associate with the sex phenotype, which was suggested as a sex determinant in our previous study. On the other hand, the SNP pattern of FcAG did not perfectly match the sex phenotype, but FcAG was located near and showed another strong GWAS signal, and in contrast, the polymorphisms in the deletion of FcAG did not contradict the sex phenotype or the SNP pattern of FcRAN1. These results suggest that the candidate genes for the sex-linked traits in figs are narrowed down to both FcRAN1 and FcAG, indicating that they are not limited to one or the other. Furthermore, they suggest some potential scenarios, including the possibility that the sex-linked traits are controlled either complementarily or collaboratively by these two genes, which are in linkage disequilibrium. In addition, we confirmed that a non-recombining sex-linked region exists between these two gene loci. The genome information and the finding for sex-related traits would be helpful to understand sex determination mechanisms in Ficus plants as well as the fig breeding programs.

Genomic prediction in Brassica napus: evaluating the benefit of imputed whole-genome sequencing data.

Advances in sequencing technology allow whole plant genomes to be sequenced with high quality. Combining genotypic and phenotypic data in genomic prediction helps breeders to select crossing partners in partially phenotyped populations. In plant breeding programs, the cost of sequencing entire breeding populations still exceeds available genotyping budgets. Hence, the method for genotyping is still mainly single nucleotide polymorphism (SNP) arrays; however, arrays are unable to assess the entire genome- and population-wide diversity. A compromise involves genotyping the entire population using an SNP array and a subset of the population with whole-genome sequencing. Both datasets can then be used to impute markers from whole-genome sequencing onto the entire population. Here, we evaluate whether imputation of whole-genome sequencing data enhances genomic predictions, using data from a nested association mapping population of rapeseed (Brassica napus). Employing two cross-validation schemes that mimic scenarios for the prediction of close and distant relatives, we show that imputed marker data do not significantly improve prediction accuracy, likely due to redundancy in relationship estimates and imputation errors. In simulation studies, only small improvements were observed, further corroborating the findings. We conclude that SNP arrays are already equipped with the information that is added by imputation through relationship and linkage disequilibrium.

Unraveling the genetic basis of quantitative resistance to diseases in tomato: a meta-QTL analysis and mining of transcript profiles.

Whole-genome QTL mining and meta-analysis in tomato for resistance to bacterial and fungal diseases identified 73 meta-QTL regions with significantly refined/reduced confidence intervals. Tomato production is affected by a range of biotic stressors, causing yield losses and quality reductions. While sources of genetic resistance to many tomato diseases have been identified and characterized, stability of the resistance genes or quantitative trait loci (QTLs) across the resources has not been determined. Here, we examined 491 QTLs previously reported for resistance to tomato diseases in 40 independent studies and 54 unique mapping populations. We identified 29 meta-QTLs (MQTLs) for resistance to bacterial pathogens and 44 MQTLs for resistance to fungal pathogens, and were able to reduce the average confidence interval (CI) of the QTLs by 4.1-fold and 6.7-fold, respectively, compared to the average CI of the original QTLs. The corresponding physical length of the CIs of MQTLs ranged from 56kb to 6.37Mb, with a median of 921 kb, of which 27% had a CI lower than 500kb and 53% had a CI lower than 1Mb. Comparison of defense responses between tomato and Arabidopsis highlighted 73 orthologous genes in the MQTL regions, which were putatively determined to be involved in defense against bacterial and fungal diseases. Intriguingly, multiple genes were identified in some MQTL regions that are implicated in plant defense responses, including PR-P2, NDR1, PDF1.2, Pip1, SNI1, PTI5, NSL1, DND1, CAD1, SlACO, DAD1, SlPAL, Ph-3, EDS5/SID1, CHI-B/PR-3, Ph-5, ETR1, WRKY29, and WRKY25. Further, we identified a number of candidate resistance genes in the MQTL regions that can be useful for both marker/gene-assisted breeding as well as cloning and genetic transformation.

Deciphering the genetic architecture of β-glucan content in oats (Avena sativa L.) through QTL mapping

Oat is a cereal grain grown for human and animal nutrition. It is known for its high dietary fibre content, phytochemicals, and nutritional value. The primary component of oat fibre is β-glucan, a group of polysaccharides comprising mixed-linkage (1,3) (1,4)-D-glucose units. The β-glucan concentrations of oat cultivars usually range from 3 to 6% and are considered a polygenic trait controlled by multiple genes with additive effects. The primary goal of this study was to identify quantitative trait loci (QTLs) for β-glucan content. Two contrasting parents for β-glucan content, OL10 (5.8%) and OL11 (0.8%), were selected from 91 germplasm lines, and an F2:4 mapping population was generated for QTL mapping. Five genotypes with the highest levels of β-glucan content were observed. The QTLs affecting β-glucan content were identified using QTL cartographer, employing single marker analysis (SMA) and composite interval mapping (CIM). Two putative QTL regions were identified, one on linkage group (LG) 1 and the other on LG 4, with LOD (logarithm of the odds) scores of 3.7 and 4.3, respectively, explaining 31 and 33% of the phenotypic variation. The information generated is important in identifying the genomic regions responsible for β-glucan content in oats.

Unraveling popping quality through insights on kernel physical, agro-morphological, and quality traits of diverse popcorn (Zea mays var. everta) inbreds from indigenous and exotic germplasm

Popcorn is a specialty corn with worldwide popularity as a snack. Despite having great market demand, genetic improvement in popping quality is limited, which is caused by the limited germplasm utilization and narrow genetic base. An assortment of diverse germplasm, their effective characterization, and integration into popcorn breeding pipeline is the foundation for an efficient breeding program. Here, kernel characteristics, popping quality traits, and agro-morphological traits were evaluated across three locations on a diverse panel of 48 popcorn inbreds derived from diverse landraces and populations of exotic and indigenous origin. The variations due to genotypes, locations, and genotype × location interaction were highly significant. The popping quality traits recorded wide variation with a high coefficient of genotypic determination. The kernel dimensions, kernel density, test weight, and grain yield were negatively correlated with popping quality traits. Genotypes with rice-type kernels exhibited better popping quality than pearl-type kernels. Analysis of genotype × location (G×L) interaction identified two target locations for the key popping quality trait, popping expansion volume. PMI-PC-175, PMI-PC-187, PMI-PC-188, and PMI-PC-189 were identified as superior genotypes over checks for desirable popping quality, agronomic performance, and high grain yield. The contrasting inbreds for popping quality and flake shape (mushroom vs. butterfly) can be utilized for developing mapping populations to enhance our understanding of molecular aspects of popping quality traits. Further, the promising inbreds can be utilized in the genetic improvement of popcorn and crossed to develop superior popcorn hybrids. The results suggest a potential opportunity to establish an efficient popcorn breeding program.

Marker Assisted Characterization of TGMS Gene in Rice Cultigen Ts 29 (Oryza Sativa L.) to Develop Two Line Hybrids

The application of the thermsensitive genic male sterility (TGMS) system has great potential for revolutionizing hybrid rice production through simple and effective seed production technology. For the successful utilization of this novel male sterility system, knowledge of the fertility behaviour and genetic control of a TGMS line is essential. In this study, an investigation was carried out to characterize the TGMS gene present in the mapping population of TS 29 x CO(R)49. Through Bulk Segregant Analysis RM 3476 alone produced a polymorphic relationship between the parents and corresponding bulks and was used for surveying the individual plants of F3/F4 population. The marker RM 3476 produced contrasting fragments between sterile and fertile plants. The result was associated with the phenotypic observations on pollen fertility under the field conditions, confirming co-segregation of the marker with the gene of interest. Hence, the tight linkage of marker with TGMS gene was identified by RM 3476 which was already identified as a marker located near tms 6 gene in chromosome 5. From this study four TGMS lines were also identified with desirable floral traits for high out crossing and medium slender grain type. Bangladesh J. Bot. 53(2): 345-351, 2024 (June)