Genome-wide Markers Research Articles

Abstract 2071: Identification Of Genetic Loci Influencing Lethal Atherothrombosis In Mice

Introduction: Atherothrombosis leading to myocardial infarction and stroke is a major cause of mortality. Ruptured atherosclerotic plaques release prothrombotic molecules such as tissue factor, which initiates thrombosis. Both genetic and environmental factors contribute to atherothrombosis. Combined complete deficiency of the Scavenger Receptor Class B type 1 (Scarb1) and Apolipoprotein E (ApoE) genes (dKO) on a mixed C57BL/6J (B6) and 129S1/SvImJ (129S1) background leads to an atherothrombotic phenotype and early death before 8 weeks of age. However, the dKO phenotype is dramatically different if the Scarb1 and ApoE deficient mice are backcrossed extensively to the B6 mouse strain, with no dKOs surviving to the weaning age. Hypothesis/Goals: We hypothesized that 129S1 genetic variants reduce atherothrombosis severity in dKO mice and used mouse genetics to identify these loci. Methods: To identify potential genetic factors, we produced and analyzed Scarb1 and ApoE dKO mice from 6 backcross/intercross generations with progressively more B6 genomic contribution. Weights were taken for 38 dKO mice and littermates from 3 weeks old until death of the dKO mice at 8 weeks of age. dKO mice from F1-F3 generations were divided into 3 distinct phenotypes based on their weights relative to their littermates. dKO mouse DNA was subjected to genome wide marker genotyping using the GigaMUGA platform. Quantitative trait loci analysis (QTL) was performed to identify loci responsible for the phenotypic weight differences. Results: dKO weights fell into 3 categories: indistinguishable from littermates, 14%-39% less than littermates, and greater than 39% less than littermates. QTL analyses identified significant interactions of Chr 12 with Chrs 1, 2, 3, and 14. Niemann Pick type C2 (Npc2), an intracellular cholesterol transporter, is within the interaction region on Chr 12 and is a prime candidate dKO modifier. Conclusion: We used weight data as a representative of overall health to categorize dKO phenotypes relative to non dKO littermates. We identified 6 significant genetic loci, with Npc2 as a candidate modifier gene for the Chromosome 12 locus. Identification of atherothrombosis modifier genes will provide novel therapeutic targets for this complex disease.

PySmooth: a Python tool for the removal and correction of genotyping errors

SummaryIn genetic mapping studies involving many individuals, genome-wide markers such as single nucleotide polymorphisms (SNPs) can be detected using different methods. However, it comes with some errors. Some SNPs associated with diseases can be in regions encoding long noncoding RNAs (lncRNAs). Therefore, identifying the errors in genotype file and correcting them is crucial for accurate genetic mapping studies. We develop a Python tool called PySmooth, that offers an easy-to-use command line interface for the removal and correction of genotyping errors. PySmooth uses the approach of a previous tool called SMOOTH with some modifications. It inputs a genotype file, detects errors and corrects them. PySmooth provides additional features such as imputing missing data, better user-friendly usage, generates summary and visualization files, has flexible parameters, and handles more genotype codes.Availability and implementationPySmooth is available at https://github.com/lncRNAAddict/PySmooth.

Overview of Marker-assisted Selection in Animal Breeding

Marker-assisted selection (MAS) in animal breeding has undergone transformative advancements, reshaping genetic improvement and reproductive traits in livestock and expanding into new domains. From its inception in the late 20th century to current developments, MAS has evolved dynamically through the integration of genomic technologies, sophisticated statistical models, and innovative gene editing techniques. These advancements have significantly enhanced the precision and impact of MAS, revolutionizing genetic improvement and reproductive performance. By synergizing MAS with cutting-edge technologies, the collective aim is to optimize reproductive performance and genetic potential, fostering a sustainable and productive future for livestock breeding and beyond. The genetic markers, typically DNA sequences or genes linked to specific traits, revolutionized animal breeding by enabling the identification of animals carrying favorable genetic variants without the need for resource-intensive phenotypic evaluations. Marker-assisted selection in reproduction also laid the foundation for genomic selection, an advanced approach utilizing genome-wide markers to estimate breeding values and predict genetic potential. This comprehensive review encapsulates the journey of MAS, emphasizing its historical significance, current advancements, and promising future trajectories, showcasing its pivotal role in shaping the landscape of animal breeding and reproductive science.

Population genomics provide insight into ancestral relationships and diversity of the feral horses of Theodore Roosevelt National Park.

Theodore Roosevelt National Park (TRNP) manages a herd of feral horses (Equus caballus) which was present on the landscape prior to the establishment of the park. The population presents a unique scenario in that it has experienced fairly intensive and well-documented management since the park's establishment, including herd size reductions, intentional introduction of diversity, and subsequent attempts to remove introduced lineages. This provides an interesting case study on the genetic effects of diverse evolutionary forces on an isolated feral population. To explore the effects of these forces and clarify the relationship of this feral herd with other horses, we used genome-wide markers to examine the population structure of a combined dataset containing common established breeds. Using the Illumina Equine 70k BeadChip, we sampled SNPs across the genome for 118 TRNP horses and evaluated the inbreeding coefficient f and runs of homozygosity (RoH). To identify breed relationships, we compared 23 representative TRNP samples with 792 horses from 35 different breeds using genomic population structure analyses. Mean f of TRNP horses was 0.180, while the mean f for all other breeds in the dataset was 0.116 (SD 0.079). RoH analysis indicates that the TRNP population has experienced recent inbreeding in a timeframe consistent with their management. With Bayesian clustering, PCA, and maximum likelihood phylogeny, TRNP horses show genetic differentiation from other breeds, likely due to isolation, historical population bottlenecks, and genetic drift. However, maximum likelihood phylogeny places them with moderate confidence (76.8%) among draft breeds, which is consistent with the known history of breeds used on early North Dakota ranches and stallions subsequently introduced to the park herd. These findings will help resolve speculation about the origins of the herd and inform management decisions for the TRNP herd.

An integrated molecular risk score early in life for subsequent childhood asthma risk.

Numerous children present with early wheeze symptoms, yet solely a subgroup develops childhood asthma. Early identification of children at risk is key for clinical monitoring, timely patient-tailored treatment, and preventing chronic, severe sequelae. For early prediction of childhood asthma, we aimed to define an integrated risk score combining established risk factors with genome-wide molecular markers at birth, complemented by subsequent clinical symptoms/diagnoses (wheezing, atopic dermatitis, food allergy). Three longitudinal birth cohorts (PAULINA/PAULCHEN, n = 190 + 93 = 283, PASTURE, n = 1133) were used to predict childhood asthma (age 5-11) including epidemiological characteristics and molecular markers: genotype, DNA methylation and mRNA expression (RNASeq/NanoString). Apparent (ap) and optimism-corrected (oc) performance (AUC/R2) was assessed leveraging evidence from independent studies (Naïve-Bayes approach) combined with high-dimensional logistic regression models (LASSO). Asthma prediction with epidemiological characteristics at birth (maternal asthma, sex, farm environment) yielded an ocAUC = 0.65. Inclusion of molecular markers as predictors resulted in an improvement in apparent prediction performance, however, for optimism-corrected performance only a moderate increase was observed (upto ocAUC = 0.68). The greatest discriminate power was reached by adding the first symptoms/diagnosis (up to ocAUC = 0.76; increase of 0.08, p = .002). Longitudinal analysis of selected mRNA expression in PASTURE (cord blood, 1, 4.5, 6 years) showed that expression at age six had the strongest association with asthma and correlation of genes getting larger over time (r = .59, p < .001, 4.5-6 years). Applying epidemiological predictors alone showed moderate predictive abilities. Molecular markers from birth modestly improved prediction. Allergic symptoms/diagnoses enhanced the power of prediction, which is important for clinical practice and for the design of future studies with molecular markers.

Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.

To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architecture and heritability, marker density, linkage disequilibrium, statistical models, and training set. The selection of a minimal and optimal marker set with high prediction accuracy can lower genotyping costs, computational time, and multicollinearity. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. The evaluation was performed on three populations: recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adopted for marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation of marker effects, randomly selected markers, and genome-wide association study. Reduction of the number of genotypes was performed by selecting a core set from the initial population based on marker data, yet maintaining the original population's genetic diversity. Prediction ability using all markers and genotypes was different among examined populations. The subsets obtained by the model-based strategy can be considered the most suitable for marker selection for all populations. The selective phenotyping based on makers in all cases had higher values of prediction ability compared to minimal values of prediction ability of multiple cycles of random selection, with the highest values of prediction obtained using AN approach and 75% population size. The obtained results indicate that selective genotyping and phenotyping hold great potential and can be integrated as tools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs for genomic selection.

Genome-wide data support recognition of an additional species of Neotropical river otter (Mammalia, Mustelidae, Lutrinae)

Abstract Cryptic biodiversity continues to be revealed worldwide, even in apparently well-known groups such as carnivorans. The Neotropical Otter (Lontra longicaudis) presents shape variation in its nose pad, a character that has been used to differentiate species in this group. Based on this, 3 subspecies are recognized: L. l. annectens (Mexico, Central America, and South America west of the Andes), L. l. enudris (Amazon and Orinoco basins), and L. l. longicaudis (Paraná basin and remaining distribution). Previous studies partially supported their distinctness based on mitochondrial DNA markers, morphometrics, and ecological niche modeling. We analyzed genome-wide nuclear markers (ultraconserved elements) of 29 L. longicaudis individuals across the species’ range to assess its population structure. Phylogenomic analysis recovered L. longicaudis as paraphyletic with robust support, with 1 clade comprising samples from Mexico and Colombia (trans-Andean populations) and another encompassing the remaining samples (cis-Andean populations), which grouped with 2 other South American species, L. felina and L. provocax. Principal component and admixture analyses strongly differentiated the 2 main L. longicaudis groups, and distinguished the Amazonian individuals from the remaining cis-Andean samples. Our results support the recognition of trans-Andean populations of L. longicaudis as a distinct otter species, which should be recognized as Lontra annectens.

Assessing genetic diversity and geographical differentiation in a global collection of wild soybean (Glycine soja Sieb. et Zucc.) and assigning a mini-core collection.

Wild soybean (Glycine soja), the ancestor of the cultivated soybean (G. max), is a crucial resource for capturing the genetic diversity of soybean species. In this study, we used a set of 78 genome-wide microsatellite markers to analyse the genetic diversity and geographic differentiation patterns in a global collection of 2,050 G. soja accessions and a mini-core collection of G. max stored in two public seed banks. We observed a notable reduction in the genetic diversity of G. max compared with G. soja and identified a close phylogenetic relationship between G. max and a G. soja subpopulation located in central China. Furthermore, we revealed substantial genetic divergence between northern and southern subpopulations, accompanied by diminished genetic diversity in the northern subpopulations. Two clusters were discovered among the accessions from north-eastern China-one genetically close to those from South Korea and Southern Japan, and another close to those from Amur Oblast, Russia. Finally, 192 accessions were assigned to a mini-core collection of G. soja, retaining 73.8% of the alleles detected in the entire collection. This mini-core collection is accessible to those who need it, facilitating efficient evaluation and utilization of G. soja genetic resources in soybean breeding initiatives.

Development of Genome-Wide Intron Length Polymorphism (ILP) Markers in Tea Plant (Camellia sinensis) and Related Applications for Genetics Research.

The market value of tea is largely dependent on the tea species and cultivar. Therefore, it is important to develop efficient molecular markers covering the entire tea genome that can be used for the identification of tea varieties, marker-assisted breeding, and mapping important quantitative trait loci for beneficial traits. In this study, genome-wide molecular markers based on intron length polymorphism (ILP) were developed for tea trees. A total of 479, 1393, and 1342 tea ILP markers were identified using the PCR method in silico from the 'Shuchazao' scaffold genome, the chromosome-level genome of 'Longjing 43', and the ancient tea DASZ chromosome-level genome, respectively. A total of 230 tea ILP markers were used to amplify six tea tree species. Among these, 213 pairs of primers successfully characterize products in all six species, with 112 primer pairs exhibiting polymorphism. The polymorphism rate of primer pairs increased with the improvement in reference genome assembly quality level. The cross-species transferability analysis of 35 primer pairs of tea ILP markers showed an average amplification rate of 85.17% through 11 species in 6 families, with high transferability in Camellia reticulata and tobacco. We also used 40 pairs of tea ILP primers to evaluate the genetic diversity and population structure of C. tetracocca with 176 plants from Puan County, Guizhou Province, China. These genome-wide markers will be a valuable resource for genetic diversity analysis, marker-assisted breeding, and variety identification in tea, providing important information for the tea industry.

Genomewide marker estimates of the level of dominance in maize heterosis

AbstractHeterosis is the superiority of an F1 hybrid over its parents, and overdominance and dominance are the two main hypotheses for the genetic basis of heterosis. Our objectives were to determine if genomewide marker effects accurately reflect the level of dominance at quantitative trait loci (QTL) and assess the distribution of per‐marker estimates of the level of dominance in maize (Zea mays L.). We simulated different levels of dominance at QTL that were in coupling, repulsion, and random linkage phases. Genomewide marker effects from ridge regression‐best linear unbiased prediction led to accurate estimates of the level of dominance when all linkages were in coupling phase, but repulsion and random linkage phases led to an upward bias. We then estimated levels of dominance from genomewide marker effects in a reciprocal testcross experiment with 1642 hybrids genotyped with 4805 single nucleotide polymorphism markers, and in a legacy Design III experiment with 351 backcross progenies (subjected to three generations of random mating to dissipate linkages) genotyped with 160 simple sequence repeats. In both experiments, the level of dominance was higher for yield than for moisture. For each trait, some loci showed little or no dominance, others showed partial to complete dominance, and others showed overdominance. A high level of dominance was typically due to a small difference between homozygote values. Overall, the results were consistent with previous QTL mapping results indicating that heterosis in maize is due to varying levels of dominance rather than a prevalent level of dominance.

Genetic Improvement of Drought Tolerance in a Mega-Rice Variety Improved White Ponni through Marker-Assisted Backcross Breeding

In the ever-changing climatic conditions, it has become important to enhance rice productivity to ensure global food security. Drought is one of the major limiting factors in rice pro- duction. Drought during the reproductive stage results in maximum or complete yield loss. Efforts have been taken to develop drought-tolerant rice lines by introgressing three major drought-effect QTLs, viz. qDTY1.1, qDTY2.1, and qDTY3.1, from Apo into a susceptible popular rice variety, Improved White Ponni (IWP). Backcross inbred lines of IWP × Apo were developed through the marker-assisted backcross breeding approach. Foreground analysis using linked markers resulted in the identification of 17 progenies carrying two or more QTLs, and the recurrent parent genome recoveries of these lines were &gt;95.6% using 72 genome-wide SSR markers distributed throughout all chromosomes. Upon phenotypic evaluation of 17 IWP BILs, the water limited condition resulted in the identification of improved lines by recording the yield and the yield-related parameters. The promising performance of IWP BILs in terms of spikelet fertility (63.3%) and grain yield per plant (&gt;10 g) under drought stress indicated the positive effects of introgressed qDTYs, while IWP recorded complete yield loss (94.2%). Out of the cultivars considered, the best-performing lines which truly exhibited drought tolerance, with more increased yield than the recurrent parent under water-limited conditions, and the effects of these QTLs and their interactions were examined in this research work.

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel.

Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)-an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. Several major genes have been identified for these traits, 2 of which have particularly been leveraged in marker-assisted selection. This project assesses the predictive ability of several genomic prediction strategies for maize grain carotenoid traits within and between 4 environments in Mexico. Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (β-carotene, β-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, predictive abilities were higher when using genome-wide markers rather than only the markers proximal to 2 or 13 genes. These findings suggest that genomic prediction models using genome-wide markers (and assuming equal variance of marker effects) are worthwhile for these traits even though key genes have already been identified, especially if breeding for additional grain carotenoid traits alongside β-carotene. Predictive ability was maintained for all traits except lutein in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of genomic prediction methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

An Optimal Model to Improve Genomic Prediction for Protein Content and Test Weight in a Diverse Spring Wheat Panel

In recent years, genomic selection has been widely used in plant breeding to increase genetic gain. Selections are based on breeding values of each genotype estimated using genome-wide markers. The present study developed genomic prediction models for grain protein content (GPC) and test weight (TW) in a diverse panel of 170 spring wheat lines phenotyped in five environments. Five prediction models (GBLUP, RRBLUP, EGBLUP, RF, RKHS) were investigated. The population was genotyped for genome-wide markers with the Infinium iSelect 90 K SNP assay. Environmental variation was adjusted by calculating BLUPs across environments using the complete random effect GxE model. Both GPC and TW showed high heritability of 0.867 and 0.854, respectively. When using the five-fold cross-validation scheme in the five statistical models, we found that the EGBLUP model had the highest mean prediction accuracy (0.743) for GPC, while the RRBLUP model showed the highest mean prediction accuracy (0.650) for TW. Testing various proportions of the training population indicated that a minimum of 100 genotypes were required to train the model for optimum accuracy. Testing the prediction across environments showed that BLUPs outperformed 80% of the tested environments, even though at least one of the environments had higher prediction accuracies for each trait. Thus, the optimized GS model for GPC and TW has the potential to predict trait values accurately. Implementing GS would aid breeding through accurate early generation selection of superior lines, leading to higher genetic gain per breeding cycle.

Using genome-wide data to ascertain taxonomic status and assess population genetic structure for Houston toads (Bufo [= Anaxyrus] houstonensis)

The Houston toad (Bufo [= Anaxyrus] houstonensis) is an endangered amphibian with a small geographic range. Land-use changes have primarily driven decline in B. houstonensis with population supplementation predominant among efforts to reduce its current extinction risk. However, there has been historic uncertainty regarding the evolutionary and conservation significance of B. houstonensis. To this end, we used 1170 genome-wide nuclear DNA markers to examine phylogenetic relationships between our focal taxon, representatives of the Nearctic B. americanus group, and B. nebulifer, a sympatric Middle American species. Phylogenetic analyses indicate B. houstonensis is a taxon that is distinct from B. americanus. We corroborated such genetic distinctiveness with an admixture analysis that provided support for recent reproductive isolation between B. americanus and B. houstonensis. However, ABBA-BABA tests for ancient admixture indicated historic gene flow between Nearctic species while no signal of historic gene flow was detected between Nearctic and Middle-American species. We used an admixture analysis to recognize four Management Units (MU) based on observed genetic differentiation within B. houstonensis and recommend captive propagation, population supplementation, and habitat restoration efforts specific to each MU. Our results re-affirm the evolutionary novelty of an endangered relict.

A Genome-Wide Association Study of Biomass Yield and Feed Quality in Buffel Grass (Cenchrus ciliaris L.)

The development of modern genomic tools has helped accelerate genetic gains in the breeding program of food crops. More recently, genomic resources have been developed for tropical forages, providing key resources for developing new climate-resilient high-yielding forage varieties. In this study, we present a genome-wide association study for biomass yield and feed quality traits in buffel grass (Cenchrus ciliaris L. aka Pennisetum ciliare L.). Genome-wide markers, generated using the DArTSeq platform and mapped onto the Setaria italica reference genome, were used for the genome-wide association study. The results revealed several markers associated with biomass yield and feed quality traits. A total of 78 marker–trait associations were identified with R2 values ranging from 0.138 to 0.236. The marker–trait associations were distributed across different chromosomes. Of these associations, the most marker–trait associations (23) were observed on Chr9, followed by Chr5 with 12. The fewest number of marker–trait associations were observed on Chr4 with 2. In terms of traits, 17 markers were associated with biomass yield, 24 with crude protein, 26 with TDN, 14 with ADF, 10 with NDF and 6 with DMI. A total of 20 of the identified markers were associated with at least two traits. The identified marker–trait associations provide a useful genomic resource for the future improvement and breeding of buffel grass.

DdRAD-seq generated genomic SNP dataset of Central and Southeast European Turkey oak (Quercus cerris L.) populations

Turkey oak (Quercus cerris L.) is one of the most ecologically and economically significant deciduous tree species in the Central and Southeast European regions. The species has long been known to exhibit high levels of genetic and phenotypic variation. Recent climate response predictions for Turkey oak suggest a significant distribution extension in Europe under climate change. Since Turkey oak has relative drought-tolerant behaviour, it is regarded as a potential alternative for other forest tree species during forestry climate adaptation efforts, not only in its native regions but also in Western Europe. For this reason, the survey of existing genetic variability, genetic resources, and adaptability of this species has great significance. Next-generation sequencing approaches, such as ddRAD-seq (Double digest restriction-site associated DNA sequencing), allow the attainment of high-resolution genome-wide single nucleotide polymorphisms (SNPs). This study provides the first highly variable genome-wide SNP data for Turkey oak generated by ddRAD-seq. The dataset comprises 17 607 de novo and 26 059 reference mapped SNPs for 88 individuals from eight populations, two from Bulgaria, one from Kosovo, and five from Hungary. Reference mapping was carried out by using cork oak’s (Quercus suber L.) reference genome. The obtained high-resolution genome-wide markers are suitable for investigating selection and local adaptation and inferring genetic diversity, differentiation, and population structure. The dataset is accessible at: https://doi.org/10.5281/zenodo.8091252

Review of sheep breeding and genetic research in Türkiye

The livestock industry in Türkiye is vital to the country’s agricultural sector and economy. In particular, sheep products are an important source of income and livelihood for many Turkish smallholder farmers in semi-arid and highland areas. Türkiye is one of the largest sheep producers in the world and its sheep production system is heavily dependent on indigenous breeds. Given the importance of the sheep industry in Türkiye, a systematic literature review on sheep breeding and genetic improvement in the country is needed for the development and optimization of sheep breeding programs using modern approaches, such as genomic selection. Therefore, we conducted a comprehensive literature review on the current characteristics of sheep populations and farms based on the most up-to-date census data and breeding and genetic studies obtained from scientific articles. The number of sheep has increased in recent years, mainly due to the state’s policy of supporting livestock farming and the increase in consumer demand for sheep dairy products with high nutritional and health benefits. Most of the genetic studies on indigenous Turkish sheep have been limited to specific traits and breeds. The use of genomics was found to be incipient, with genomic analysis applied to only two major breeds for heritability or genome-wide association studies. The scope of heritability and genome-wide association studies should be expanded to include traits and breeds that have received little or no attention. It is also worth revisiting genetic diversity studies using genome-wide single nucleotide polymorphism markers. Although there was no report of genomic selection in Turkish sheep to date, genomics could contribute to overcoming the difficulties of implementing traditional pedigree-based breeding programs that require accurate pedigree recording. As indigenous sheep breeds are better adapted to the local environmental conditions, the proper use of breeding strategies will contribute to increased income, food security, and reduced environmental footprint in a sustainable manner.

Genome-wide development of intra- and inter-specific transferable SSR markers and construction of a dynamic web resource for yam molecular breeding: Y2MD.

Microsatellite markers are widely used in population genetics and breeding. Despite the economic significance of yams in developing countries, there is a paucity of microsatellite markers, and as of now, no comprehensive microsatellite marker database exists. In this study, we conducted genome-wide microsatellite marker development across four yam species, identified cross-species transferable markers, and designed an easy-to-use web portal for the yam researchers. The screening of Dioscorea alata, Dioscorea rotundata, Dioscorea dumetorum, and Dioscorea zingiberensis genomes resulted in 318,713, 322,501, 307,040, and 253,856 microsatellites, respectively. Mono-, di-, and tri-nucleotides were the most important types of repeats in the different species, and a total of 864,128 primer pairs were designed. Furthermore, we identified 1170 cross-species transferable microsatellite markers. Among them, 17 out of 18 randomly selected were experimentally validated with good discriminatory power, regardless of the species and ploidy levels. Ultimately, we created and deployed a dynamic Yam Microsatellite Markers Database (Y2MD) available at https://y2md.ucad.sn/. Y2MD is embedded with various useful tools such as JBrowse, Blast, insilicoPCR, and SSR Finder to facilitate the exploitation of microsatellite markers in yams. This study represents the first comprehensive microsatellite marker mining across several yam species and will contribute to advancing yam genetic research and marker-assisted breeding. The released user-friendly database constitutes a valuable platform for yam researchers.

ADVANCEMENTS IN GENOMIC TECHNOLOGIES AND THEIR IMPACT ON CROP IMPROVEMENT AND BREEDING METHODS

Recent advances in genome sequencing of essential crop plants allow genotype and phenotype data integration in crop development. Advanced statistical methods identified quantitative trait genes. Genomic prediction has been used successfully in breeding animals and is now also used in breeding plants. Biometric statistics, genome-wide markers, and phenotyping enhance gene discovery. This makes biotechnology agricultural plant solutions possible. Improved fertilizer efficiency requires precise plant phenotyping in multiple habitats and seasons, which was previously expensive. DNA sequencing, genetic engineering, and PCR-based marker-assisted selection have made it cheaper. New methods like next-generation sequencing can target climate-responsive crop development. It examines Oryza sativa L. flower opening and closing molecularly and hybrid breeding success in diverse plant types. It discusses CRISPR/Cas9 for crop trait improvement and metabolic studies for Triticum aestivum L. quality group determination. Genetic analysis should use species-specific techniques, according to the study. A Zea mays L. callus induction and growth study examined how media and growth conditions affect callus development. Another drought-stressed Triticum aestivum L. cultivar gene expression study suggests employing RNA editing to respond to environmental stress. PCR-based markers have helped scientists find important genes in landraces that have changed to survive harsh farming conditions, giving them targets for crop growth.

Genome-wide SNP markers provided insights into the reproductive strategy and genetic diversity of the green tide causative species Ulva prolifera in China

Genome-wide SNP markers provided insights into the reproductive strategy and genetic diversity of the green tide causative species Ulva prolifera in China