Genomic Strategies Research Articles

Genomic strategies to facilitate breeding for increased β-Glucan content in oat (Avena sativa L.)

BackgroundHexaploid oat (Avena sativa L.) is a commercially important cereal crop due to its soluble dietary fiber β-glucan, a hemicellulose known to prevent cardio-vascular diseases. To maximize health benefits associated with the consumption of oat-based food products, breeding efforts have aimed at increasing the β-glucan content in oat groats. However, progress has been limited. To accelerate oat breeding efforts, we leveraged existing breeding datasets (1,230 breeding lines from South Dakota State University oat breeding program grown in multiple environments between 2015 and 2022) to conduct a genome-wide association study (GWAS) to increase our understanding of the genetic control of beta-glucan content in oats and to compare strategies to implement genomic selection (GS) to increase genetic gain for β-glucan content in oat.ResultsLarge variation for β-glucan content was observed with values ranging between 3.02 and 7.24%. An independent GWAS was performed for each breeding panel in each environment and identified 22 loci distributed over fourteen oat chromosomes significantly associated with β-glucan content. Comparison based on physical position showed that 12 out of 22 loci coincided with previously identified β-glucan QTLs, and three loci are in the vicinity of cellulose synthesis genes, Cellulose synthase-like (Csl). To perform a GWAS analysis across all breeding datasets, the β-glucan content of each breeding line was predicted for each of the 26 environments. The overall GWAS identified 73 loci, of which 15 coincided with loci identified for individual environments and 37 coincided with previously reported β-glucan QTLs not identified when performing the GWAS in single years. In addition, 21 novel loci were identified that were not reported in the previous studies. The proposed approach increased our ability to detect significantly associated markers. The comparison of multiple GS scenarios indicated that using a specific set of markers as a fixed effect in GS models did not increase the prediction accuracy. However, the use of multi-environment data in the training population resulted in an increase in prediction accuracy (0.61–0.72) as compared to single-year (0.28–0.48) data. The use of USDA-SoyWheOatBar-3 K genotyping array data resulted in a similar level of prediction accuracy as did genotyping-by-sequencing data.ConclusionThis study identified and confirmed the location of multiple loci associated with β-glucan content. The proposed genomic strategies significantly increase both our ability to detect significant markers in GWAS and the accuracy of genomic predictions. The findings of this study can be useful to accelerate the genetic improvement of β-glucan content and other traits.

Non-conventional yeasts: promising cell factories for organic acid bioproduction.

Microbial production of organic acids has been hindered by the poor acid tolerance of microorganisms and the high costs of waste salt reprocessing. The robustness of non-conventional microorganisms in an acidic environment makes it possible to produce organic acids at low pH and greatly simplifies downstream processing. In this review we discuss the environmental adaptability features of non-conventional yeasts, as well as the latest developments in genomic engineering strategies that have facilitated metabolic engineering of these strains. We also use selected examples of three-carbon (C3), C4, and C6 organic acids to illustrate the ongoing efforts and challenges of using non-conventional yeasts for organic acid production. This review provides theoretical guidance for the construction of highly robust organic acid producers.

The highly allo-autopolyploid modern sugarcane genome and very recent allopolyploidization in Saccharum.

Modern sugarcane, a highly allo-autopolyploid organism, has a very complex genome. In the present study, the karyotype and genome architecture of modern sugarcane were investigated, resulting in a genome assembly of 97 chromosomes (8.84 Gb). The allopolyploid genome was divided into subgenomes from Saccharum officinarum (Soh) and S. spontaneum (Ssh), with Soh dominance in the Saccharum hybrid (S. hybrid). Genome shock affected transcriptome dynamics during allopolyploidization. Analysis of an inbreeding population with 192 individuals revealed the underlying genetic basis of transgressive segregation. Population genomics of 310 Saccharum accessions clarified the breeding history of modern sugarcane. Using the haplotype-resolved S. hybrid genome as a reference, genome-wide association studies identified a potential candidate gene for sugar content from S. spontaneum. These findings illuminate the complex genome evolution of allopolyploids, offering opportunities for genomic enhancements and innovative breeding strategies for sugarcane.

Recent advances in synthetic biology toolkits and metabolic engineering of Ralstonia eutropha H16 for production of value-added chemicals.

Ralstonia eutropha H16, a facultative chemolithoautotrophic Gram-negative bacterium, demonstrates remarkable metabolic flexibility by utilizing either diverse organic substrates or CO2 as the sole carbon source, with H2 serving as the electron donor under aerobic conditions. The capacity of carbon and energy metabolism of R. eutropha H16 enabled development of synthetic biology technologies and strategies to engineer its metabolism for biosynthesis of value-added chemicals. This review firstly outlines the development of synthetic biology tools tailored for R. eutropha H16, including construction of expression vectors, regulatory elements, and transformation techniques. The availability of comprehensive omics data (i.e., transcriptomic, proteomic, and metabolomic) combined with the fully annotated genome sequence provides a robust genetic framework for advanced metabolic engineering. These advancements facilitate efficient reprogramming metabolic network of R. eutropha. The potential of R. eutropha as a versatile microbial platform for industrial biotechnology is further underscored by its ability to utilize a wide range of carbon sources for the production of value-added chemicals through both autotrophic and heterotrophic pathways. The integration of state-of-the-art genetic and genomic engineering tools and strategies with high cell-density fermentation processes enables engineered R. eutropha as promising microbial cell factories for optimizing carbon fluxes and expanding the portfolio of bio-based products.

Genome-wide association studies and functional annotation of pre-weaning calf mortality and reproductive traits in Nellore cattle from experimental selection lines

BackgroundReproductive efficiency is crucial for the long-term economic sustainability of beef cattle production. Pregnancy loss and stillbirth are complex reproductive traits that do not yet have their genomic background fully understood, especially in zebu breeds (Bos taurus indicus). Hence, this study aimed to perform a genome-wide association study (GWAS) and functional annotation for conception success (CS), pregnancy loss (PL), stillbirth (SB), and pre-weaning calf mortality (PWM) in Nellore cattle. In this study, 3,728 cows with 17,094 reproductive records and 11,785 calves were evaluated. A total of 3,351 genotyped animals and 383,739 SNP markers were considered for GWAS analyses. SNP effects were estimated using the weighted single-step GWAS (WssGWAS), which considered two iterations. The top ten genomic windows with the highest contribution to the additive genetic variance of the traits were selected for gene annotation. Candidate genes were then analyzed for Gene Ontology terms (GO) and metabolic pathways.ResultsThe top ten genomic windows that explained the largest proportion of the direct additive genetic variance () for CS, PL, SB, and PWM accounted for 17.03% (overlapping with 79 genes), 16.76% (57 genes), 11.71% (73 genes), and 12.03% (65 genes) of the total , respectively. For CS, significant GO terms included Somitogenesis (GO:0001756), Somite Development (GO:0061053), and Chromosome Segregation (GO:0007059). Considering PL, the processes annotated were the Regulation of Hormone Secretion (GO:0046883), and Hormone Transport (GO:0009914), along with the Glucagon Signaling Pathway (bta04922). Embryonic Development (GO:0045995), and Cerebellum Development (GO:0021549) were the main biological processes found in the gene enrichment analysis for SB. For PWM, the Regulation of Glucose metabolic processes (GO:0010906), Zinc Ion Homeostasis (GO:0055069), Lactation (GO:0007595), and Regulation of Insulin Secretion (GO:0050796) were the most significant GO terms observed.ConclusionsThese findings provide valuable information on genomic regions, candidate genes, biological processes, and metabolic pathways that may significantly influence the expression of complex reproductive traits in Nellore cattle, offering potential contributions to breeding strategies and future genomic selection strategies.

Promising candidate drug target genes for repurposing in cervical cancer: A bioinformatics-based approach.

Cervical cancer is the fourth most common cancer among women globally, and studies have shown that genetic variants play a significant role in its development. A variety of germline and somatic mutations are associated with cervical cancer. However, genomic data derived from these mutations have not been extensively utilized for the development of repurposed drugs for cervical cancer. The objective of this study was to identify novel potential drugs that could be repurposed for cervical cancer treatment through a bioinformatics approach. A comprehensive genomic and bioinformatics database integration strategy was employed to identify potential drug target genes for cervical cancer. Using the GWAS and PheWAS databases, a total of 232 genes associated with cervical cancer were identified. These pharmacological target genes were further refined by applying a biological threshold of six functional annotations. The drug target genes were then cross-referenced with cancer treatment candidates using the DrugBank database. Among the identified genes, LTA, TNFRSF1A, PRKCZ, PDE4B, and PARP were highlighted as promising targets for repurposed drugs. Notably, these five target genes overlapped with 12 drugs that could potentially be repurposed for cervical cancer treatment. Among these, talazoparib, a potent PARP inhibitor, emerged as a particularly promising candidate. Interestingly, talazoparib is currently being investigated for safety and tolerability in other cancers but has not yet been studied in the context of cervical cancer. Further clinical trials are necessary to validate this finding and explore its potential as a repurposed drug for cervical cancer.

Genomic prediction of genotyped and non-genotyped Jeju black cattle using single- and multi-trait methods for carcass traits

Jeju black cattle (JBC) are recognised as one of Korea’s native breeds that are well-suited to the local environmental conditions and known for their excellent meat quality. This study investigated the variance components and genomic prediction accuracy for carcase traits such as carcase weight (CWT), eye muscle area (EMA), backfat thickness (BF) and marbling score (MS) in JBC using various statistical models. We employed single-step genomic best linear unbiased prediction (ssGBLUP) and single-step Bayesian regression (SSBR) models, which utilised imputed genotypes for non-genotyped individuals. Phenotypic data were collected from 2045 JBC, and 3759 JBC individuals were genotyped using the Affymetrix 160K SNP Axiom array. Estimated heritability ranged from 0.24 to 0.40 for both single-trait and multi-trait models. Multi-trait models consistently demonstrated higher genomic prediction accuracy compared to single-trait models, underscoring the value of integrating multiple traits. For instance, CWT accuracy improved from 0.24 in single-trait PBLUP (st-PBLUP) to 0.47 in multi-trait ssGBLUP. Similarly, SSBR models showed significant improvements, with single-trait SSBR Bayes B achieving 0.46 and its multi-trait version reaching 0.49. EMA accuracy improved from 0.21 in st-PBLUP to 0.39 in the multi-trait SSBR Bayes C model. BF and MS also exhibited consistent accuracy improvements with multi-trait Bayesian regression models. These results demonstrate the superior accuracy of ssGBLUP and SSBR models compared to traditional BLUP, particularly for traits with higher heritability values. This findings offer valuable insights for enhancing genomic selection strategies and improving carcase traits in JBC breeding programs, contributing to the breed’s economic sustainability and meat production efficiency.

Dominance of recombinant DWV genomes with changing viral landscapes as revealed in national US honey bee and varroa mite survey

Honey bees are essential pollinators for global agriculture. The viromes of US commercial apiaries and their ectoparasitic mites are poorly characterized at a strain level and there is a need to integrate genomics into pathogen surveillance. We sequenced RNA viromes from 383 adult bees and 173 mites pooled samples from 11 major US beekeeping hubs in 2021, assembling 45 complete and 1702 partial genomes. Protein sequence similarity networks and recombinant genome identification revealed a new viral landscape. Sinaivirus (n = 312), Iflavirus sacbroodi (n = 280), and Iflavirus aladeformis (DWV, n = 135) genomes were common. Recombinant DWV genomes with high nucleotide identity were widespread, and DWV type A master variants were rare, with an indication that RT-PCR surveillance may over-detect type A due to the prevalence of recombinant DWV genomes. Future work should use genomic strategies to avoid misidentification of common honey bee virus genomes and their impact on colony health.

Soybean Oil and Protein: Biosynthesis, Regulation and Strategies for Genetic Improvement.

Soybean (Glycine max [L.] Merr.) is one of the world's most important sources of oil and vegetable protein. Much of the energy required for germination and early growth of soybean seeds is stored in fatty acids, mainly as triacylglycerols (TAGs), and the main seed storage proteins are β-conglycinin (7S) and glycinin (11S). Recent research advances have deepened our understanding of the biosynthetic pathways and transcriptional regulatory networks that control fatty acid and protein synthesis in organelles such as the plastid, ribosome and endoplasmic reticulum. Here, we review the composition and biosynthetic pathways of soybean oils and proteins, summarizing the key enzymes and transcription factors that have recently been shown to regulate oil and protein synthesis/metabolism. We then discuss the newest genomic strategies for manipulating these genes to increase the food value of soybeans, highlighting important priorities for future research and genetic improvement of this staple crop.

Biobanking with genetics shapes precision medicine and global health.

Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype-phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all.

Bioinformatic Multi-Strategy Profiling of Congenital Heart Defects for Molecular Mechanism Recognition.

Congenital heart defects (CHDs) rank among the most common birth defects, presenting diverse phenotypes. Genetic and environmental factors are critical in molding the process of cardiogenesis. However, these factors' interactions are not fully comprehended. Hence, this study aimed to identify and characterize differentially expressed genes involved in CHD development through bioinformatics pipelines. We analyzed experimental datasets available in genomic databases, using transcriptome, gene enrichment, and systems biology strategies. Network analysis based on genetic and phenotypic ontologies revealed that EP300, CALM3, and EGFR genes facilitate rapid information flow, while NOTCH1, TNNI3, and SMAD4 genes are significant mediators within the network. Differential gene expression (DGE) analysis identified 2513 genes across three study types, (1) Tetralogy of Fallot (ToF); (2) Hypoplastic Left Heart Syndrome (HLHS); and (3) Trisomy 21/CHD, with LYVE1, PLA2G2A, and SDR42E1 genes found in three of the six studies. Interaction networks between genes from ontology searches and the DGE analysis were evaluated, revealing interactions in ToF and HLHS groups, but none in Trisomy 21/CHD. Through enrichment analysis, we identified immune response and energy generation as some of the relevant ontologies. This integrative approach revealed genes not previously associated with CHD, along with their interactions and underlying biological processes.

Scoping the requirements for a genomic competency framework to inform the integration of genomics into pharmacy undergraduate curricula

Abstract Introduction The application of genomics is due to have a significant impact on the role of pharmacy within future healthcare systems. Since 2021, the updated GPhC education standards have included a requirement to apply the principles of genomics,1 but a lack of consistency across UK pharmacy programmes has led to inequity of genomics literacy for pharmacy students.1 National UK genomic workforce strategies include a pharmacy undergraduate indicative syllabus focussing on upskilling the NHS workforce in readiness for the wider implementation of genomics.2,3 There is a lack of UK research exploring the undergraduate genomic education requirements of pharmacy professionals. Aim Scope the requirements of a genomic competency framework for undergraduate pharmacy curricula. Methods Ethics approval was obtained from Swansea University ethics committee in July 2023. Fifteen multi-professional stakeholders from across the UK, perceived as genomic experts and/or pharmacy educationalists from each devolved nation, were selected using purposive sampling and all were invited to participate within a virtual semi-structured interview (SSI) and a consecutive focus group. SSIs and the focus group were facilitated and recorded using Microsoft® Teams and pre-designed open question topic frameworks were used. All data was anonymously transcribed verbatim and analysed using thematic analysis. Results Twelve participants consented and SSIs were held between September/October 2023. Five participants attended the consecutive focus group in December 2023. Five SSI themes were identified: Competency development - where the need for pharmacists to have comprehensive knowledge of the aspects and factors affecting the use of genomic testing within clinical practice (e.g. research and consent) was identified, whilst others described the need for pharmacists to focus on pharmacogenomics only; Curriculum approach – where there were variations in the depth of general genomic training and appropriate topics needed by pharmacists and the requirement for genomics to be scattered throughout undergraduate programmes; Future practice roles - the current postgraduate pharmacogenomic module could be used to establish the future undergraduate requirements and the need for pharmacy curricula to focus on the application of genomics to medicines rather than as a diagnostic tool; Genomic topics - participants acknowledged the complex nature of determining the genomic undergraduate curricula requirements without routine mainstream use of genomics within clinical practice but suggested pharmacy genomic competencies needing further refinement; Level of knowledge - participants highlighted the need for overall genomic pharmacy knowledge but a deeper level of pharmacogenomic knowledge and recommended that the competency framework should be relevant and achievable across healthcare sectors. Discussion and conclusion Genomic competencies should be established for pharmacy professionals as appropriate to their job roles. Once genomics is widely implemented, clarity around genomic competence requirements will develop, following workforce upskilling. The varied backgrounds of study participants impacted their opinions which were potentially influenced by their knowledge of the pharmacist role across sectors and their lack of familiarity with current pharmacy training. Study limitations are small participant numbers and low data generalisability. A genomic competency framework is needed to standardise pharmacy training and study findings will be used to develop a UK consensus for UK genomic undergraduate pharmacy training.

A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding.

Deep learning methods have been applied when working to enhance the prediction accuracy of traditional statistical methods in the field of plant breeding. Although deep learning seems to be a promising approach for genomic prediction, it has proven to have some limitations, since its conventional methods fail to leverage all available information. Multimodal deep learning methods aim to improve the predictive power of their unimodal counterparts by introducing several modalities (sources) of input information. In this review, we introduce some theoretical basic concepts of multimodal deep learning and provide a list of the most widely used neural network architectures in deep learning, as well as the available strategies to fuse data from different modalities. We mention some of the available computational resources for the practical implementation of multimodal deep learning problems. We finally performed a review of applications of multimodal deep learning to genomic selection in plant breeding and other related fields. We present a meta-picture of the practical performance of multimodal deep learning methods to highlight how these tools can help address complex problems in the field of plant breeding. We discussed some relevant considerations that researchers should keep in mind when applying multimodal deep learning methods. Multimodal deep learning holds significant potential for various fields, including genomic selection. While multimodal deep learning displays enhanced prediction capabilities over unimodal deep learning and other machine learning methods, it demands more computational resources. Multimodal deep learning effectively captures intermodal interactions, especially when integrating data from different sources. To apply multimodal deep learning in genomic selection, suitable architectures and fusion strategies must be chosen. It is relevant to keep in mind that multimodal deep learning, like unimodal deep learning, is a powerful tool but should be carefully applied. Given its predictive edge over traditional methods, multimodal deep learning is valuable in addressing challenges in plant breeding and food security amid a growing global population.

The genetic basis of resistance to Enterocytozoon hepatopenaei (EHP) and White feces syndrome (WFS) in the whiteleg shrimp (Litopenaeus vannamei)

The microsporidian Enterocytozoon hepatopenaei (EHP) is a yeast-like intracellular fungus, that in the last decade has significantly contributed to major economic losses in the whiteleg shrimp (Litopenaeus vannamei) industry worldwide. In commercial ponds, EHP affected shrimp frequently also shows symptoms of White Feces Syndrome (WFS), characterized by floating white fecal strings, reduced feed consumption, and growth retardation. To elucidate the genetic basis of resistance to EHP + WFS, a challenge test was conducted using a pedigreed shrimp population from the Benchmark Genetics breeding program in Colombia. Out of the total genotyped animals, 1436 individuals from 47 families successfully passed quality control, underwent parentage analysis, had phenotypic records and were available for further analysis. Moderate heritabilities were estimated for binary survival to EHP + WFS using pedigree based (0.31 ± 0.04) and genomic analysis (0.41 ± 0.06). Furthermore, a low genetic correlation between body weight and EHP + WFS challenge test survival traits was observed, implying that genetic improvement of resistance to EHP + WFS can be achieved without compromising growth performance. The genome-wide association analysis showed little evidence for any major QTL, indicating that multiple genes with small effects collectively contribute to EHP + WFS resistance. Furthermore, cross validation analysis showed 0.54 ± 0.01 accuracy of prediction for genomic analysis and 0.49 ± 0.001 for the pedigree analysis, indicating the relevance of a genomic selection strategy for achieving genetic improvement for EHP + WFS resistance in L. vannamei.

Genomic and metabolomic insights into the selection and differentiation of bioactive compounds in citrus

Bioactive compounds play an increasingly prominent role in breeding functional and nutritive fruit crops such as citrus. However, the genomic and metabolic bases for the selection and differentiation underlying bioactive compound variations in citrus remain poorly understood. In this study, we constructed a species-level variation atlas of genomes and metabolomes using 299 citrus accessions. A total of 19 829 significant SNPs were targeted to 653 annotated metabolites, among which multiple significant signals were identified for secondary metabolites, especially flavonoids. Significant differential accumulation of bioactive compounds in the phenylpropane pathway, mainly flavonoids and coumarins, was unveiled across ancestral citrus species during differentiation, which is likely associated with the divergent haplotype distribution and/or expression profiles of relevant genes, including p-coumaroyl coenzyme A 2′-hydroxylases, flavone synthases, cytochrome P450 enzymes, prenyltransferases, and UDP-glycosyltransferases. Moreover, we systematically evaluated the beneficial bioactivities such as the antioxidant and anticancer capacities of 219 citrus varieties, and identified robust associations between distinct bioactivities and specific metabolites. Collectively, these findings provide citrus breeding options for enrichment of beneficial flavonoids and avoidance of potential risk of coumarins. Our study will accelerate the application of genomic and metabolic engineering strategies in developing modern healthy citrus cultivars.

Research Progress on Genomic Regions and Candidate Genes Related to Milk Composition Traits of Dairy Goats Based on Functional Genomics: A Narrative Review.

Goat milk has gained global attention for its unique nutritional properties and potential health benefits. Advancements in functional genomic technologies have significantly progressed genetic research on milk composition traits in dairy goats. This review summarizes various research methodologies applied in this field. Genome-wide association studies (GWAS) have identified genomic regions associated with major milk components, with the diacylglycerol acyltransferase 1 (DGAT1) gene and casein gene cluster consistently linked to milk composition traits. Transcriptomics has revealed gene expression patterns in mammary tissue across lactation stages, while the role of non-coding RNAs (such as miRNAs and circRNAs) in regulating milk composition has been confirmed. Proteomic and metabolomic studies have not only helped us gain a more comprehensive understanding of goat milk composition characteristics but have also provided crucial support for the functional validation of genes related to milk components. The integration of multi-omics data has emerged as an effective strategy for elucidating complex regulatory networks from a systems biology perspective. Despite progress, challenges remain, including refining reference genomes, collecting large-scale phenotypic data, and conducting functional validations. Future research should focus on improving reference genomes, expanding study populations, investigating functional milk components, exploring epigenetic regulation and non-coding RNAs, and studying microbiome-host genome interactions. These efforts will inform more precise genomic and marker-assisted selection strategies, advancing genetic improvements in milk composition traits in dairy goats.

A Next-Generation Combinatorial Genomic Strategy Scans Genomic Loci Governing Heat Stress Tolerance in Chickpea.

In the wake of rising earth temperature, chickpea crop production is haunted by the productivity crisis. Chickpea, a cool season legume manifests tolerance in several agro-physiological level, which is complex quantitative in nature, and regulated by multiple genes and genetic networks. Understanding the molecular genetic basis of this tolerance and identifying key regulators can leverage chickpea breeding against heat stress. This study employed a genomics-assisted breeding strategy utilizing multi-locus GWAS to identify 10 key genomic regions linked to traits contributing to heat stress tolerance in chickpea. These loci subsequently delineated few key candidates and hub regulatory genes, such as RAD23b, CIPK25, AAE19, CK1 and WRKY40, through integrated genomics, transcriptomics and interactive analyses. The differential transcript accumulation of these identified candidates in contrasting chickpea accessions suggests their potential role in heat stress tolerance. Differential ROS accumulation along with their scavengers' transcript abundance aligning with the expression of identified candidates in the contrasting chickpea accessions persuade their regulatory significance. Additionally, their functional significance is ascertained by heterologous expression and subsequent heat stress screening. The high confidence genomic loci and the superior genes and natural alleles delineated here has great potential for swift genomic interventions to enhance heat resilience and yield stability in chickpea.

Sesame, an Underutilized Oil Seed Crop: Breeding Achievements and Future Challenges.

Sesame seeds and their edible oil are highly nutritious and rich in mono- and polyunsaturated fatty acids. Bioactive compounds such as sterols, tocopherols, and sesamol provide significant medicinal benefits. The high oil content (50%) and favorable mono- and polyunsaturated fatty acid balance, as well as resilience to water stress, make sesame a promising candidate crop for global agricultural expansion. However, sesame production faces challenges such as low yields, poor response to agricultural inputs, and losses due to capsule dehiscence. To enhance yield, traits like determinate growth, dwarfism, a high harvest index, non-shattering capsules, disease resistance, and photoperiod sensitivity are needed. These traits can be achieved through variation or induced mutation breeding. Crossbreeding methods often result in unwanted genetic changes. The gene editing CRISPR/Cas9 technology has the potential to suppress detrimental alleles and improve the fatty acid profile by inhibiting polyunsaturated fatty acid biosynthesis. Even though sesame is an orphan crop, it has entered the genomic era, with available sequences assisting molecular breeding efforts. This progress aids in associating single-nucleotide polymorphisms (SNPs) and simple sequence repeats (SSR) with key economic traits, as well as identifying genes related to adaptability, oil production, fatty acid synthesis, and photosynthesis. Additionally, transcriptomic research can reveal genes involved in abiotic stress responses and adaptation to diverse climates. The mapping of quantitative trait loci (QTL) can identify loci linked to key traits such as capsule size, seed count per capsule, and capsule number per plant. This article reviews recent advances in sesame breeding, discusses ongoing challenges, and explores potential strategies for future improvement. Hence, integrating advanced genomic tools and breeding strategies provides promising ways to enhance sesame production to meet global demands.

452 Data-driven strategic sustainability initiatives of beef and dairy genetics consortiums: A comprehensive landscape analysis for USA, Brazil, and Europe

Abstract The past two decades have witnessed beef and dairy genetic consortiums implementing data-driven germplasm propagation programs in global cattle systems. These programs optimize for multi-dimensional economic, environmental, and animal welfare related sustainability goals and are driven by consumer demands and shifts in supply chains. To summarize these programs and gain insights into the sustainability journey of industry, we did a comprehensive landscape analysis of the strategic initiatives of key global genetics companies, their germplasm offerings, and data platforms offered in three major cattle producing geographies, namely the USA, Brazil, and Europe. Within these locales, they followed selection index-based genomic and reproductive management strategies for both beef, dairy, and beef-on-dairy production, tailored to relevant market and climatic conditions (Figure 1). While individual traits in selection indices ranged between 40 to 90 traits, all traits could be broadly grouped into three: namely production, longevity, health and reproduction. In the USA, companies like ABS Global, Select Sires, Genex Cooperative, STGenetics, Zoetis, Vytelle offer genomic selection programs encompassing germplasm products, and genetic and production data tracking platforms across the beef, dairy, and beef-on-dairy value chains. These companies have implemented reproductive management programs such as in-vitro fertilized embryo transfer, sexed semen, along with genomic selection with a focus on future replacements based on multi-trait sustainability indices. These initiatives are customized for each of the cow-calf, backgrounding, and feedlot sector of beef production. In dairy, these companies have diversified data-driven production programs customizable to conventional and organic production systems. In Europe, companies like CRV, Viking Genetics, and Alta Genetics prioritize traits related to resource efficiency, animal welfare and environmental impact. Semen companies also offer data management tools tailored to European market requirements, alongside services for on-farm semen production and storage to reduce transportation-related emissions. In Brazil, companies such as ABS Pecplan, CRV Brazil, and Semex Brazil, developed herd management solutions focusing on traits like heat tolerance, tropical environmental adaptability, and disease resistance. The landscape analysis revealed a dynamic industry, evolving to meet sustainability goals and market demands. These enterprises collaborate with stakeholders across the value chain, including producers, policymakers, and industry associations, to scale data-driven production systems that balance profitability and environmental footprint of cattle production. As such, they have positioned themselves to capitalize on the growing interest in sustainable livestock production practices worldwide by aligning scientific and technical strategies with evolving regulatory requirements and consumer demands. The global cattle industry is positioned to venture forward by leveraging niche carbon markets and new marketing channels propelled by emerging principles of environmental, social, and corporate governance. Strategic germplasm and data-tracking platform initiatives by these groups thus become an integral part of the livestock sustainability solution palette and will contribute its share to a climate-neutral Earth by 2050.

Genomic selection strategies for the German Merino sheep breeding programme - A simulation study.

Genomic selection is widely implemented in livestock breeding programmes across species. Its potential is also evident for sheep breeding; however, it has several limitations, particularly because of the high genetic diversity across and within sheep breeds. In Germany, the predominant sheep breed is the Merino sheep. Until now, there has been no use of genomic selection in the German Merino sheep breeding programme. In this simulation study, different genomic selection strategies were compared with a reference scenario with a breeding value estimation based on pedigree BLUP. A simplified version of the German Merino sheep breeding programme, including a health and a production trait in the breeding goal, was simulated via the R package Modular Breeding Program Simulator (MoBPS). Real genotype data were used to create a population specific simulation. The reference scenario was compared with several alternative scenarios in which selection was based on single-step GBLUP (ssGBLUP) breeding value estimation with varying genotyping strategies. In addition to scenarios in which all male and all male plus all female lambs were genotyped, scenarios with a preselection of lambs, that is only a certain proportion (top 25%, top 50%) genotyped, were simulated. The results revealed that genetic gain increased with increasing numbers of available genotypes. However, marginal gains decreased with increasing numbers of genotypes. Compared with the reference scenario, genotyping the top 25% of male lambs increased the genetic gain for the breeding ram population by 13% for both traits whereas genotyping the top 50% of male lambs or all male lambs led to increases of 18% (17%) or 26% (21%) for the health (production) trait, respectively. The potential of genotyping females in addition to male lambs was less evident on the male side with no significant differences between the scenarios with different proportions of genotyped females. The results have shown that genomic selection can be a valuable tool to increase genetic gain in the German Merino sheep population and that the genotyping of a certain proportion of animals might lead to substantial improvement over pedigree-based breeding value estimation. Nevertheless, further studies, especially economic evaluations, are needed before practical implementation.