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Safflower oil with high oleic is an important source of oil due to its oxidative stability and nutritional value as well as its industrial benefits. Identification of molecular markers associated with oil quality traits may aid in genotype selection in breeding efforts of safflower. To determine the genetic basis of seed oil, oleic acid, and linoleic acid content in safflower seeds, a recombinant inbred population was developed from a cross between Goldasht (low oleic line) and Mex 22–191 (high oleic line) by individual self-pollinated plants in each generation (from F2 to F9). Fatty acid components were detected by gas chromatography and subsequently analyzed by QTL analysis. The high variability in fatty acid composition was demonstrated and the first high-oleic safflower genotype was generated. A new linkage map with 69 AFLP and 45 ISSR polymorphic markers was made, covering 1472,441 cM of the safflower genome. A total of 63 individual QTLs and 238 epistatic QTLs, related to oil quality, were detected in 14 genomic regions. Some major QTLs with phenotypic variation greater than 10% were identified for the measured traits. These results demonstrate that major QTL may play an important role in increase the oleic acid of the genetic material used. Furthermore, the larger number of minor QTLs and epistatic QTLs, both of which have additive effects, indicates that the expression of these traits is also influenced by interactions between multiple genes. Our results provide additional genetic resources that can be used to precisely breed these traits in the future. [ABSTRACT FROM AUTHOR]

• This work represents the first-ever detailed multiple marker studies in Stevia. • QTL identification is the novelty of the work that will promote stevia breeding. • The QTL identified could be used in the mapping of genes responsible for rebaudioside-A. • Functional polymorphic markers will be useful in genetic dissection of traits. Stevia rebaudiana Bertoni, belonging to the family Asteraceae, is one of the most important perennial medicinal plants. It is used as a no-calorie sweetener therefore; mining of sweetening genes using molecular markers is a thrust area. In the present study combinations of markers were screened. Out of 170 RAPD, 26 ISSR, and 89 EST-SSR primers, it was observed that 36 RAPD, 10 ISSR, and 33 EST-SSR were found to be polymorphic. Using MAPMAKER/EXP version 3.0 b, four linkage groups were constructed spanning a distance of 927.3 cM with an average distance between loci of 16.29 cM. On QTL identification, a total of 53 locations were found for both trait 1 (rebaudioside-A) and trait 2 (stevioside). Among 53 QTL locations of trait 1, two major QTL were found for rebaudioside-A namely, in the marker interval of L67–L71 (ISSR HB-11)–(IISRS-3-L) in LG1 and the marker interval of L38–L40 (Sigma-5383–027)- (Sigma-5383-029) in LG3 at LOD of 2.5 and 2.7, respectively. The map constructed and QTL identified will greatly facilitate further genetic studies. It is hoped that this work will have a practical impact on the advancement of stevia breeding and genome analysis. [ABSTRACT FROM AUTHOR]

Soybean is a major crop in the world and an essential source for minerals, including iron (Fe) and zin (Zn). Deficiency of Fe and Zn in soil and soybean plants result in yield loss and poor seed nutritional qualities. Information on genomic regions and candidate genes controlling seed Fe and Zn accumulation in soybean seed is limited. Therefore, The objective of this research was to identify genetic regions, known as quantitative trait loci (QTL), and candidate genes that control the accumulation of Fe and Zn in soybean mature seeds. A ‘Forrest’ by ‘Williams 82’ (F × W82) recombinant inbred line (RIL) population (n = 306) was used and genotyped using a total of 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted across two environments: North Carolina in 2018 (NC) and Illinois in 2020 (IL). Only QTL with LOD scores ≥ 2.5, as identified by the composite interval mapping (CIM) method, are reported here. In total, 6 QTL were identified for seed Fe; specifically, 3 QTL (qFe-01-[NC-2018], qFe-02-[NC-2018], and qFe-03-[NC-2018]) were located on chromosomes 1, 2, and 6, respectively, in the NC environment, and 3 QTL (qFe-01-[IL-2020], qFe-02-[IL-2020], and qFe-03-[IL-2020]) were positioned on chromosomes 1, 2, and 12, respectively, in the IL environment. A total of 6 QTL associated with seed Zn were also identified; 4 QTL (qZn-01-[NC-2018]; qZn-02-[NC-2018]; qZn-03-[NC-2018]; and qZn-04-[NC-2018]), respectively on Chr 2, 3, 7, and 19 in NC; and 2 QTL (qZn-01-[IL-2020] and qZn-02-[IL-2020]), respectively, on Chr 5 and 8 in IL. Several functional genes encode Fe- and Zn-proteins, transcription factors, proteins-zinc finger motifs (involved in DNA binding and transcriptional regulation; crosstalk between the regulatory pathways of Zn and Fe transporters) were identified and located within the QTL interval. To our knowledge, and based on the literature available, the QTL identified here on Chr 2 and Chr 6 are novel and were not previously identified. This current research provides a new knowledge of the genetic basis of seed Fe and Zn and the markers associated with QTL. The QTL identified here will contribute to efficient marker assisted selection for higher Fe and Zn content in soybean seeds. The candidate genes and metal-responsive transcription factors may coordinate the expression of both Zn and Fe transporters in response to changes in metal availability, providing new knowledge on minerals uptake and transport mechanisms, allowing for possible genetic engineering application.

Genotyping‐by‐sequencing (GBS) is a widely used strategy for obtaining large numbers of genetic markers in model and non‐model organisms. In crop plants, GBS‐derived marker datasets are frequently used to perform quantitative trait locus (QTL) mapping. In some plant species, however, high heterozygosity and complex genome structure mean that researchers must use care in handling GBS data to conduct QTL mapping most effectively. Such outbred crops include most of the perennial grass and tree species used for bioenergy. To identify strategies for increasing accuracy and precision of QTL mapping using GBS data in outbred crops, we conducted an empirical study of SNP‐calling and genetic map‐building pipeline parameters in a Miscanthus sinensis population, and a complementary simulation study to estimate the relationship between genome‐wide error rate, read depth, and marker number. The bioenergy grass Miscanthus is an obligate outcrossing species with a recent (diploidized) whole‐genome duplication. For the study of empirical M. sinensis data, we compared two SNP‐calling methods (one non‐reference‐based and one reference‐based), a series of depth filters (12×, 20×, 30×, and 40×) and two map‐construction methods (i.e., marker ordering: linkage‐only and order‐corrected based on a reference genome). We found that correcting the order of markers on a linkage map by using a high‐quality reference genome improved QTL precision (shorter confidence intervals). For typical GBS datasets of between 1000 and 5000 markers to build a genetic map for biparental populations, a depth filter set at 30× to 40× applied to outbred populations provided a genome‐wide genotype‐calling error rate of less than 1%, improved accuracy of QTL point estimates and minimized type I errors for identifying QTL. Based on these results, we recommend using a reference genome to correct the marker order of genetic maps and a robust genotype depth filter to improve QTL mapping for outbred crops. [ABSTRACT FROM AUTHOR]

Sheath blight (ShB) is the most serious disease of rice (Oryza sativa L.), caused by the soil-borne fungus Rhizoctonia solani Kühn (R. solani). It poses a significant threat to global rice productivity, resulting in approximately 50% annual yield loss. Managing ShB is particularly challenging due to the broad host range of the pathogen, its necrotrophic nature, the emergence of new races, and the limited availability of highly resistant germplasm. In this study, we conducted QTL mapping using an F2 population derived from a cross between a partially resistant accession (IRGC81941A) of Oryza nivara and the susceptible rice cultivar Punjab rice 121 (PR121). Our analysis identified 29 QTLs for ShB resistance, collectively explaining a phenotypic variance ranging from 4.70 to 48.05%. Notably, a cluster of four QTLs (qRLH1.1, qRLH1.2, qRLH1.5, and qRLH1.8) on chromosome 1 consistently exhibit a resistant response against R. solani. These QTLs span from 0.096 to 420.1 Kb on the rice reference genome and contain several important genes, including Ser/Thr protein kinase, auxin-responsive protein, protease inhibitor/seed storage/LTP family protein, MLO domain-containing protein, disease-responsive protein, thaumatin-like protein, Avr9/Cf9-eliciting protein, and various transcription factors. Additionally, simple sequence repeats (SSR) markers RM212 and RM246 linked to these QTLs effectively distinguish resistant and susceptible rice cultivars, showing great promise for marker-assisted selection programs. Furthermore, our study identified pre-breeding lines in the advanced backcrossed population that exhibited superior agronomic traits and sheath blight resistance compared to the recurrent parent. These promising lines hold significant potential for enhancing the sheath blight resistance in elite cultivars through targeted improvement efforts. [ABSTRACT FROM AUTHOR]

Soybean is a major crop in the world and an essential source for minerals, including iron (Fe) and zin (Zn). Deficiency of Fe and Zn in soil and soybean plants result in yield loss and poor seed nutritional qualities. Information on genomic regions and candidate genes controlling seed Fe and Zn accumulation in soybean seed is limited. Therefore, The objective of this research was to identify genetic regions, known as quantitative trait loci (QTL), and candidate genes that control the accumulation of Fe and Zn in soybean mature seeds. A 'Forrest' by 'Williams 82' (F × W82) recombinant inbred line (RIL) population (n = 306) was used and genotyped using a total of 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted across two environments: North Carolina in 2018 (NC) and Illinois in 2020 (IL). Only QTL with LOD scores ≥ 2.5, as identified by the composite interval mapping (CIM) method, are reported here. In total, 6 QTL were identified for seed Fe; specifically, 3 QTL (qFe-01-[NC-2018], qFe-02-[NC-2018], and qFe-03-[NC-2018]) were located on chromosomes 1, 2, and 6, respectively, in the NC environment, and 3 QTL (qFe-01-[IL-2020], qFe-02-[IL-2020], and qFe-03-[IL-2020]) were positioned on chromosomes 1, 2, and 12, respectively, in the IL environment. A total of 6 QTL associated with seed Zn were also identified; 4 QTL (qZn-01-[NC-2018]; qZn-02-[NC-2018]; qZn-03-[NC-2018]; and qZn-04-[NC-2018]), respectively on Chr 2, 3, 7, and 19 in NC; and 2 QTL (qZn-01-[IL-2020] and qZn-02-[IL-2020]), respectively, on Chr 5 and 8 in IL. Several functional genes encode Fe- and Zn-proteins, transcription factors, proteins-zinc finger motifs (involved in DNA binding and transcriptional regulation; crosstalk between the regulatory pathways of Zn and Fe transporters) were identified and located within the QTL interval. To our knowledge, and based on the literature available, the QTL identified here on Chr 2 and Chr 6 are novel and were not previously identified. This current research provides a new knowledge of the genetic basis of seed Fe and Zn and the markers associated with QTL. The QTL identified here will contribute to efficient marker assisted selection for higher Fe and Zn content in soybean seeds. The candidate genes and metal-responsive transcription factors may coordinate the expression of both Zn and Fe transporters in response to changes in metal availability, providing new knowledge on minerals uptake and transport mechanisms, allowing for possible genetic engineering application. [ABSTRACT FROM AUTHOR]

Abstract Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.

Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors. [ABSTRACT FROM AUTHOR]

Crop yield is a complex phenomenon, controlled by several genes, each contributing to the overall phenotypic expression; which is affected by environment and genotype x environment interactions. Identifying and mapping quantitative trait loci (QTLs), make tracing these traits during breeding possible and easy. The objective of this study was to identify QTLs associated with chickpea (Cicer arietinum L.) grain yield and its associated traits, under drought stress. The experiment was conducted using 188 F3:5 genotypes from ICCV 05107 x ICCV 94954 crosses. Genotypic data were from 49 polymorphic simple sequence repeat (SSR) markers; while phenotypic data were obtained from a field evaluation designed in a 19 x 10 alpha lattice. The study was replicated thrice on three sites, namely at Koibatek Agricultural Training Centre, Muserech; and at the Kenya Agricultural Research and Livestock Organisation, Marigat. Eight QTLs were mapped on a linkage map spanning a total length of 335.04 cM, with varying phenotypic variation expression (PVE%). These QTLs include, one each for days to maturity, 100-seed weight, and two each for above-ground biomass, harvest index, and grain yield. Five major QTLs having PVE ranging from 10.37 to 32.39%, were identified for days to maturity, 100-seed weight above-ground biomass, harvest index (HI), and grain yield. Four of the eight QTLs were mapped on linkage group 4 (LG4); days to maturity, 2 for above-ground biomass, and grain yield. The QTLs mapped are useful in genomic-assisted breeding for chickpea yield improvement. However, there is a need for marker saturation on LGs and specific genes identified for effective marker-assisted breeding. [ABSTRACT FROM AUTHOR]

Psidium guajava L. is an important fruit crop in the tropical and subtropical regions of the world. The advanced breeding methods are not employed for important commercial traits like peel and pulp color, seed hardiness, fruit size, etc., due to the scarcity of genome-wide molecular markers and high-density linkage maps. In this study, we employed single-nucleotide polymorphism (SNP) markers and identified quantitative trait loci (QTL) regions that are associated with color traits of leaf, peel, and pulp in the guava intervarietal mapping population. The mapping population was developed from the contrasting genotypes of fruit and leaf color. Variations in color among the segregating hybrids were recorded both visually and using a Color reader. A high-density linkage map of guava was constructed using the SNP markers from genotyping by sequencing (GBS) of 150 hybrid individuals of the cross 'Arka Poorna' (green) x 'Purple Local' (purple). The integrated linkage map consisted of 1426 SNPs mapped on 11 linkage groups (LG), spanning a total distance of around 730 cM with an average of 129.6 markers per LG. Through QTL analysis for color traits, a minor QTL region was identified for visually scored leaf color and peel color on LG1, whereas a major QTL was detected for pulp color in LG4. The Hunter color values (L* and, a*) also had major QTLs with overlapping marker intervals for leaf and peel colors, establishing the association of SNP markers to the trait. The QTLs harbored genes and transcription factors involved in lycopene and anthocyanin pigment biosynthesis. This is the first report of a high-density linkage map based on SNP markers in guava and QTL mapping for color characters in leaf, fruit peel and pulp. The genotyping information generated in this study can aid in genetic engineering and marker-assisted breeding in guava. [ABSTRACT FROM AUTHOR]

The reinvention of potato, from a tetraploid clonal crop into a diploid seed-based hybrid crop, requires insight in the mutational load, recombination landscape, and the genetic basis of fertility. Genomics-based breeding and QTL discovery rely on efficient genotyping strategies such as skim sequencing, to gather genotypic information. The application of skim sequencing to full-sib population of non-inbred parents remains challenging. Here, we report on an R implementation of the OutcrossSeq pipeline for diploids. We applied this pipeline to a large diploid skim sequenced potato population. We used the resulting bin-markers for the construction of high-density parent specific linkage maps, highlighting variation in parental recombination rate and structural variations. We subsequently explored transmission ratio distortion and non-independent assortment of alleles, indicative of large-effect deleterious mutations. Finally, we identified QTLs for seedling tuber yield in pots and pollen shed. This study showcases the range of genetic analyses, from marker inference, identification of transmission ratio distortion, and linkage map construction to QTL mapping, resulting in new insights that contribute to breeding diploid potato. [ABSTRACT FROM AUTHOR]

Abstract Genotyping‐by‐sequencing (GBS) is a widely used strategy for obtaining large numbers of genetic markers in model and non‐model organisms. In crop plants, GBS‐derived marker datasets are frequently used to perform quantitative trait locus (QTL) mapping. In some plant species, however, high heterozygosity and complex genome structure mean that researchers must use care in handling GBS data to conduct QTL mapping most effectively. Such outbred crops include most of the perennial grass and tree species used for bioenergy. To identify strategies for increasing accuracy and precision of QTL mapping using GBS data in outbred crops, we conducted an empirical study of SNP‐calling and genetic map‐building pipeline parameters in a Miscanthus sinensis population, and a complementary simulation study to estimate the relationship between genome‐wide error rate, read depth, and marker number. The bioenergy grass Miscanthus is an obligate outcrossing species with a recent (diploidized) whole‐genome duplication. For the study of empirical M. sinensis data, we compared two SNP‐calling methods (one non‐reference‐based and one reference‐based), a series of depth filters (12×, 20×, 30×, and 40×) and two map‐construction methods (i.e., marker ordering: linkage‐only and order‐corrected based on a reference genome). We found that correcting the order of markers on a linkage map by using a high‐quality reference genome improved QTL precision (shorter confidence intervals). For typical GBS datasets of between 1000 and 5000 markers to build a genetic map for biparental populations, a depth filter set at 30× to 40× applied to outbred populations provided a genome‐wide genotype‐calling error rate of less than 1%, improved accuracy of QTL point estimates and minimized type I errors for identifying QTL. Based on these results, we recommend using a reference genome to correct the marker order of genetic maps and a robust genotype depth filter to improve QTL mapping for outbred crops.

A large seed mutant, TG 89 having a 76.7% increment in hundred kernel weight in comparison to its parent TG 26, was isolated from an electron beam-induced mutagenized population. Studies based on environmental scanning electron microscopy of both parent and mutant revealed that the mutant seed cotyledon had significantly bigger cell size than parent. A mapping population with 122 F2 plants derived from the mutant and a distant normal seed genotype (ICGV 15007) was utilized to map the QTL associated with higher HKW. Bulk segregant analysis revealed putative association of three markers with this mutant large seed trait. Further, genotyping of F2 individuals with polymorphic markers detected 14 linkage groups with a map distance of 1053 cM. QTL analysis revealed a significant additive major QTL for the mutant large seed trait on linkage group A05 explaining 12.7% phenotypic variation for the seed size. This QTL was located between flanking markers AhTE333 and AhTE810 having a map interval of 4.7 cM which corresponds to 90.65 to 107.24 Mbp in A05 chromosome, respectively. Within this genomic fragment, an ortholog of the BIG SEEDS 1 gene was found at 102,476,137 bp. Real-time PCR revealed down-regulation of this BIG SEEDS 1 gene in the mutant indicating a loss of function mutation giving rise to a large seed phenotype. This QTL was validated in 11 advanced breeding lines having large seed size from this mutant but with varied genetic backgrounds. This validation showcased a highly promising selection accuracy of 90.9% for the marker-assisted selection. [ABSTRACT FROM AUTHOR]

Background: The Yellowfin tuna (Thunnus albacares) is a large tuna exploited by major fisheries in tropical and subtropical waters of all oceans except the Mediterranean Sea. Genomic studies of population structure, adaptive variation or of the genetic basis of phenotypic traits are needed to inform fisheries management but are currently limited by the lack of a reference genome for this species. Here we report a draft genome assembly and a linkage map for use in genomic studies of T. albacares. Methods and results: Illumina and PacBio SMRT sequencing were used in combination to generate a hybrid assembly that comprises 743,073,847 base pairs contained in 2,661 scaffolds. The assembly has a N50 of 351,587 and complete and partial BUSCO scores of 86.47% and 3.63%, respectively. Double-digest restriction associated DNA (ddRAD) was used to genotype the 2 parents and 164 of their F1 offspring resulting from a controlled breeding cross, retaining 19,469 biallelic single nucleotide polymorphism (SNP) loci. The SNP loci were used to construct a linkage map that features 24 linkage groups that represent the 24 chromosomes of yellowfin tuna. The male and female maps span 1,243.8 cM and 1,222.9 cM, respectively. The map was used to anchor the assembly in 24 super-scaffolds that contain 79% of the yellowfin tuna genome. Gene prediction identified 46,992 putative genes 20,203 of which could be annotated via gene ontology. Conclusions: The draft reference will be valuable to interpret studies of genome wide variation in T. albacares and other Scombroid species. [ABSTRACT FROM AUTHOR]

Shiitake mushroom (Lentinula edodes) is one of the most popular and widely cultivated mushrooms, especially in East Asia. Temperature is the critical factor affecting fruiting body induction. Due to climate change and increasing fuel costs, there is a growing demand for breeding L. edodes strains that fruit at higher temperatures. This study constructed a linkage map using 94 single spore isolates (SSIs) from the hybrid strain MCR 487 derived from crossing the high-temperature type (H-type) strain KRCF 1097 and the low to medium-temperature type (LM-type) strain KRCF 1101. Quantitative trait loci (QTLs) associated with higher-temperature fruiting ability were analyzed using 92 dikaryotic strains obtained from crossing SSIs from MCR 487 with the L-type strain KRCF 1098SS-2. Four QTLs (qTF1-4) controlling temperature for fruiting body induction were identified on the linkage map. Specific DNA marker was developed based on the sequence of the H-type qTF1, enabling the selection of strains capable of fruiting at higher temperatures. This study demonstrates that genome sequencing and QTL analysis can be used to obtain parental strains for breeding L. edodes strains suited for higher temperature cultivation, offering promising prospects for future breeding strategies. [ABSTRACT FROM AUTHOR]

Psidium guajava L. is an important fruit crop in the tropical and subtropical regions of the world. The advanced breeding methods are not employed for important commercial traits like peel and pulp color, seed hardiness, fruit size, etc., due to the scarcity of genome-wide molecular markers and high-density linkage maps. In this study, we employed single-nucleotide polymorphism (SNP) markers and identified quantitative trait loci (QTL) regions that are associated with color traits of leaf, peel, and pulp in the guava intervarietal mapping population. The mapping population was developed from the contrasting genotypes of fruit and leaf color. Variations in color among the segregating hybrids were recorded both visually and using a Color reader. A high-density linkage map of guava was constructed using the SNP markers from genotyping by sequencing (GBS) of 150 hybrid individuals of the cross ‘Arka Poorna’ (green) x ‘Purple Local’ (purple). The integrated linkage map consisted of 1426 SNPs mapped on 11 linkage groups (LG), spanning a total distance of around 730 cM with an average of 129.6 markers per LG. Through QTL analysis for color traits, a minor QTL region was identified for visually scored leaf color and peel color on LG1, whereas a major QTL was detected for pulp color in LG4. The Hunter color values (L* and, a*) also had major QTLs with overlapping marker intervals for leaf and peel colors, establishing the association of SNP markers to the trait. The QTLs harbored genes and transcription factors involved in lycopene and anthocyanin pigment biosynthesis. This is the first report of a high-density linkage map based on SNP markers in guava and QTL mapping for color characters in leaf, fruit peel and pulp. The genotyping information generated in this study can aid in genetic engineering and marker-assisted breeding in guava.

The Florida Scrub-Jay (Aphelocoma coerulescens), a Federally Threatened, cooperatively-breeding bird, is an emerging model system in evolutionary biology and ecology. Extensive individual-based monitoring and genetic sampling for decades has yielded a wealth of data, allowing for the detailed study of social behavior, demography, and population genetics of this natural population. Here, we report a linkage map and a chromosome-level genome assembly and annotation for a female Florida Scrub-Jay made with long-read sequencing technology, chromatin conformation data, and the linkage map. We constructed a linkage map comprising 4,468 SNPs that had 34 linkage groups and a total sex-averaged autosomal genetic map length of 2446.78 cM. The new genome assembly is 1.33 Gb in length, consisting of 33 complete or near-complete autosomes and the sex chromosomes (ZW). This highly contiguous assembly has an NG50 of 68 Mb and a Benchmarking Universal Single-Copy Orthologs (BUSCO) completeness score of 97.1% with respect to the Aves database. The annotated gene set has a BUSCO transcriptome completeness score of 95.5% and 17,964 identified protein-coding genes, 92.5% of which have associated functional annotations. This new, high-quality genome assembly and linkage map of the Florida Scrub-Jay provides valuable tools for future research into the evolutionary dynamics of small, natural populations of conservation concern., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

Miscanthus is a perennial rhizomatous grass belonging to Andropogoneae, which is now used as lignocellulosic biomass crop. Miscanthus has a short history of domestication, so further breeding effort for improved biomass production is needed. Flowering plays a critical role in controlling vegetative growth and physiology of plant, so this study focused on finding quantitative trait locus controlling flowering of Miscanthus for use in marker assisted breeding. A cross-pollinated M. sinensis F 1 population consisted of 278 progenies was produced, transplanted to the field and assessed in their flowering traits. Julian dates of flag leaf extension, first heading, half heading, first flowering, and half flowering were assessed in 2019, the third year after transplanting when Miscanthus was well established. The average Julian dates of the flowering traits were 198±9, 224±14, 233±14, 239±15 and 253±12, respectively, and showed a proximal normal distribution. Genotype by sequencing conducted by Illumina-hiseq using leaf DNA samples revealed the genetic map consisted of 1988 SNPs with average distance of 1.35 cM. Nine QTLs controlling flowering were detected exceeded 60% of LOD score threshold. These QTLs were neighboring 16 SNP markers, 5 of which were contained in known functional genes including RGL2 (gibberellin pathway regulation). Genetic map and marker information acquired through this study, including either annotated or not annotated markers, are expected to help understand and help breeding new Miscanthus in the future with desired flowering. • Five flowering traits were closely related to each other. • A dense linkage map of 1988 SNPs within 2663 cM was constructed by NGS and reference mapping for Miscanthus sinensis. • Nine QTLs and neighboring 16 SNPs were discovered to control flowering traits. • One common SNP associated with 3 QTLs located at linkage group 7 newly discovered was related to all five flowering traits. [ABSTRACT FROM AUTHOR]

• 17 SCAR markers linked to double flower trait of petunia hybrida was developed. • A high-density genetic map was constructed with 4474 SNP markers. • The double flower genes in petunia were first mapped to a genomic region of 1.82 Mb. Double flower (DF) is one of the important ornamental traits of horticultural plants, but the molecular mechanism of its formation is still unclear. In this study, forward genetic approaches were applied to explore the key genes involved in the DF formation in petunia. We developed a total of 17 SCAR markers linked to DF, and initially mapped the DF genes onto 9 scaffolds (totally 13.3 Mb) in the genome of the DF petunia. We also constructed a genetic linkage map of petunia, which contained 4474 SNP markers, with a total genetic distance of 2022.786 cM and an average genetic distance of 0.758 cM. In QTL analysis, a QTL associated with DF trait was detected. The LOD value of the marker was 3.82–12.71, and the contribution rate was 16.0 %–44.0 %. To further shorten the mapping interval of the DF genes, we constructed a near-isogenic line (NIL) population containing 2757 plants, and used these 17 SCAR markers to screen the recombinant plants, and located the DF genes within a 2.2 Mb interval. Combined with Bacterial Artificial Chromosome (BAC) library screening, the mapping interval of the DF genes was ultimately shortened from 2.2 Mb to 1.82 Mb. Presently, research on the DF trait of petunia mainly focused on the ABC(D)E model. The fine mapping results of petunia DF genes obtained in this study provide a new idea for revealing the molecular mechanism of DF formation in petunia. [ABSTRACT FROM AUTHOR]