Single Nucleotide Polymorphism Markers Research Articles

Construction of a high-density genetic map and QTL localization of body weight and wool production related traits in Alpine Merino sheep based on WGR

BackgroundThe Alpine Merino is a new breed of fine-wool sheep adapted to the cold and arid climate of the plateau in the world. It has been popularized in Northwest China due to its superior adaptability as well as excellent production performance. Those traits related to body weight, wool yield, and wool fiber characteristics, which are economically essential traits in Alpine Merino sheep, are controlled by QTL (Quantitative Trait Loci). Therefore, the identification of QTL and genetic markers for these key economic traits is a critical step in establishing a MAS (Marker-Assisted Selection) breeding program.ResultsIn this study, we constructed the high-density genetic linkage map of Alpine Merino sheep by sequencing 110 F1 generation individuals using WGR (Whole Genome Resequencing) technology. 14,942 SNPs (Single Nucleotide Polymorphism) were identified and genotyped. The map spanned 2,697.86 cM, with an average genetic marker interval of 1.44 cM. A total of 1,871 high-quality SNP markers were distributed across 27 linkage groups, with an average of 69 markers per LG (Linkage Group). Among them, the smallest genetic distance is 19.62 cM for LG2, while the largest is 237.19 cM for LG19. The average genetic distance between markers in LGs ranged from 0.24 cM (LG2) to 3.57 cM (LG17). The marker density in the LGs ranged from LG14 (39 markers) to LG1 (150 markers).ConclusionsThe first genetic map of Alpine Merino sheep we constructed included 14,942 SNPs, while 46 QTLs associated with body weight, wool yield and wool fiber traits were identified, laying the foundation for genetic studies and molecular marker-assisted breeding. Notably, there were QTL intervals for overlapping traits on LG4 and LG8, providing potential opportunities for multi-trait co-breeding and further theoretical support for selection and breeding of ultra-fine and meaty Alpine Merino sheep.

Genetic diversity and population structure of Piper nigrum (black pepper) accessions based on next-generation SNP markers.

Despite the economic importance of Piper nigrum (black pepper), a highly valued crop worldwide, development and utilization of genomic resources have remained limited, with diversity assessments often relying on only a few samples or DNA markers. Here we employed restriction-site associated DNA sequencing to analyze 175 P. nigrum accessions from eight main black pepper growing regions in Sri Lanka. The sequencing effort resulted in 1,976 million raw reads, averaging 11.3 million reads per accession, revealing 150,356 high-quality single nucleotide polymorphisms (SNPs) distributed across 26 chromosomes. Population structure analysis revealed two subpopulations (K = 2): a dominant group consisting of 152 accessions sourced from both home gardens and large-scale cultivations, and a smaller group comprising 23 accessions exclusively from native collections in home gardens. This clustering was further supported by principal component analysis, with the first two principal components explaining 35.2 and 12.1% of the total variation. Genetic diversity analysis indicated substantial gene flow (Nm = 342.21) and a low fixation index (FST = 0.00073) between the two subpopulations, with no clear genetic differentiation among accessions from different agro-climatic regions. These findings demonstrate that most current black pepper genotypes grown in Sri Lanka share a common genetic background, emphasizing the necessity to broaden the genetic base to enhance resilience to biotic and abiotic stresses. This study represents the first attempt at analyzing black pepper genetic diversity using high-resolution SNP markers, laying the foundation for future genome-wide association studies for SNP-based gene discovery and breeding.

Genetic Diversity and Population Structure of a Large USDA Sesame Collection.

Sesame, Sesamum indicum L., is one of the oldest domesticated crops used for its oil and protein in many parts of the world. To build genomic resources for sesame that could be used to improve sesame productivity and responses to stresses, a USDA sesame germplasm collection of 501 accessions originating from 36 countries was used in this study. The panel was genotyped using genotyping-by-sequencing (GBS) technology to explore its genetic diversity and population structure and the relatedness among its accessions. A total of 24,735 high-quality single-nucleotide polymorphism (SNP) markers were identified over the 13 chromosomes. The marker density was 1900 SNP per chromosome, with an average polymorphism information content (PIC) value of 0.267. The marker polymorphisms and heterozygosity estimators indicated the usefulness of the identified SNPs to be used in future genetic studies and breeding activities. The population structure, principal components analysis (PCA), and unrooted neighbor-joining phylogenetic tree analyses classified two distinct subpopulations, indicating a wide genetic diversity within the USDA sesame collection. Analysis of molecular variance (AMOVA) revealed that 29.5% of the variation in this population was due to subpopulations, while 57.5% of the variation was due to variation among the accessions within the subpopulations. These results showed the degree of differentiation between the two subpopulations as well as within each subpopulation. The high fixation index (FST) between the distinguished subpopulations indicates a wide genetic diversity and high genetic differentiation among and within the identified subpopulations. The linkage disequilibrium (LD) pattern averaged 161 Kbp for the whole sesame genome, while the LD decay ranged from 168 Kbp at chromosome LG09 to 123 Kbp in chromosome LG05. These findings could explain the complications of linkage drag among the traits during selections. The selected accessions and genotyped SNPs provide tools to enhance genetic gain in sesame breeding programs through molecular approaches.

Determination of genomic regions associated with early storage root formation and bulking in cassava.

Early cassava storage root formation and bulking is a medium of escape that farmers and processors tend to adopt in cases of abiotic and biotic stresses like drought, flood, and destruction by domestic animals. In this study, 220 cassava genotypes from the International Institute of Tropical Agriculture (IITA), National Root Crops Research Institute (NRCRI), International Center for Tropical Agriculture (CIAT), local farmers (from farmer's field), and NextGen project were evaluated in three locations (Umudike, Benue, and Ikenne). The trials were laid out using a split plot in a randomized incomplete block design (alpha lattice) with two replications in 2 years. The storage roots for each plant genotype were sampled or harvested at 3, 6, 9, and 12 month after planting (MAP). All data collected were analyzed using the R-statistical package. The result showed moderate to high heritability among the traits, and there were significant differences (p< 0.05) among the performances of the genotypes. The genome-wide association mapping using the BLINK model detected 45 single-nucleotide polymorphism (SNP) markers significantly associated with the four early storage root bulking and formation traits on Chromosomes 1, 2, 3, 4, 5, 6, 8, 9, 10, 13, 14, 17, and 18.A total of 199 putative candidate genes were found to be directly linked to early storage root bulking and formation. The functions of these candidate genes were further characterized to regulate i) phytohormone biosynthesis, ii) cellular growth and development, and iii) biosynthesis of secondary metabolites for accumulation of starch and defense. Genome-wide association study (GWAS) also revealed the presence of four pleiotropic SNPs, which control starch content, dry matter content, dry yield, and bulking and formation index. The information on the GWAS could be used to develop improved cassava cultivars by breeders. Five genotypes (W940006, NR090146, TMS982123, TMS13F1060P0014, and NR010161) were selected as the best early storage root bulking and formation genotypes across the plant age. These selected cultivars should be used as sources of early storage root bulking and formation in future breeding programs.

Genome-wide association analysis of four yield-related traits using a maize (Zea mays L.) F1 population.

Increasing the yield of maize F1 hybrid is one of the most important target for breeders. However, as a result of the genetic complexity and extremely low heritability, it is very difficult to directly dissect the genetic basis and molecular mechanisms of yield, and reports on genetic analysis of F1 hybrid yield are rare. Taking F1 hybrid as the research object and dividing the yield into different affect factors, this approach may be the best strategy for clarifying the genetic mechanism of yield. Therefore, in this study, a maize F1 population consisting of 300 hybrids with 17,652 single nucleotide polymorphisms (SNPs) markers was used for genome-wide association study (GWAS) to filtrate candidate genes associated with the four yield-related traits, i.e., kernel row number (KRN), kernel number per row (KNPR), ear tip-barrenness (ETB), and hundred kernel weight (HKW). Combined with the results of previous studies and functional annotation information of candidate genes, a total of six candidate genes were identified as being associated with the four traits, which were involved in plant growth and development, protein synthesis response, phytohormone biosynthesis and signal transduction. Our results improve the understanding of the genetic basis of the four yield-related traits and may be provide a new strategy for the genetic basis of maize yield.

Genome-wide association study comparison analysis based on Hanwoo full-sib family.

The improvement of carcass traits is essential for the Hanwoo industry because of the Hanwoo grade determination system, and genome-wide association study (GWAS) analysis is an instrumental tool for identifying the genetic factors that impact these traits. While GWAS analysis utilizing family data offers advantages in minimizing genetic bias, research on family-based GWAS in Hanwoo is currently lacking. This study classified Group A using both parental and offspring genetic information, and Group B based solely on offspring genetic information, to compare GWAS analysis results of Hanwoo carcass traits. A total of 16 significant single nucleotide polymorphism (SNP) markers were identified in Group A, comprising 7 for carcass weight (CWT), 3 for back fat thickness (BFT), and 6 for marbling score (MS). In Group B, 7 significant SNP markers were identified, including 3 for CWT, 1 for eye muscle area, 1 for BFT, and 2 for MS. Functional annotation analysis revealed only one common function related to carcass traits between the groups, while protein-protein interaction analysis indicated more gene interactions in Group A. The reliability of estimated values for common SNP markers identified between the groups was higher in Group A. GWAS analysis utilizing parental genetic information holds greater potential for application, owing to its higher reliability of estimated values and the ability to explore numerous candidate genes.

Establishment and Application of a Novel Genetic Detection Panel for SNPs in Mongolian Gerbils.

The Mongolian gerbil is a distinctive experimental animal in China, as its genetic qualities possess significant value in the field of medical biology research. Here, we aimed to establish an economical and efficient panel for genetic quality detection in Mongolian gerbils using single-nucleotide polymorphism (SNP) markers. To search for SNPs, we conducted whole-genome sequencing (WGS) in 40 Mongolian gerbils from outbred populations. Reliable screening criteria were established to preliminarily select SNPs with a wide genome distribution and high levels of polymorphism. Subsequently, a multiple-target regional capture detection system based on second-generation sequencing was developed for SNP genotyping. Based on the results of WGS, 219 SNPs were preliminarily selected, and they were established and optimized in a multiple-amplification system that included 206 SNP loci by genotyping three outbred populations. PopGen.32 analysis revealed that the average effective allele number, Shannon index, observed heterozygosity, expected heterozygosity, average heterozygosity, polymorphism information content, and other population genetic parameters of the Capital Medical University (CMU) gerbils were the highest, followed by those of Zhejiang gerbils and Dalian gerbils. Through scientific screening and optimization, we successfully established a novel, robust, and cost-effective genetic detection system for Mongolian gerbils by utilizing SNP markers for the first time.

Assessing Genetic Variability For NUE in Maize Lines from Agricultural Institute Osijek

Maize (Zea mays) is one of the most important crops in the world, with nitrogen (N) fertilization being the most expensive input in its production. Based on a favorable variability, production costs can be reduced by the means of selection for nitrogen use efficiency (NUE). This study aims to analyze the variability in the collection of maize lines in terms of nitrogen use efficiency (NUE) and to identify the loci associated with the yield and grain moisture. The Agricultural Institute Osijek diversity panel comprises of 1,346 maize inbred lines genes with 56,000 SNP (Single Nucleotide Polymorphism) markers. The experiment was carried out with 109 selected inbreds, based on their performance in the long–term trials. The selected inbreds were testcrossed to a common tester from the Iodent heterotic group. The experiments in three nitrogen environments were carried out in 2023: N0 (no nitrogen fertilization), N69 (low nitrogen supply), and Nfull (sufficient nitrogen supply). Association mapping revealed varying associations between the SNPs and the yield/moisture under different nitrogen treatments, highlighting the influence of nitrogen stress on genetic mechanisms governing these traits. The research contributes valuable insights into maize adaptability to nitrogen levels, aiding in the development of strategies for stable and high–yield maize production.

Enhancing Crop Resilience to Abiotic Stress through Molecular Markers: A Review

Plants are subject to several abiotic stresses, such as extreme heat or cold, drought, and high salinity, which can greatly reduce the yields of agricultural crops. These conditions interfere with nutrient uptake and metabolic functions, causing growth to slow down or stop altogether. Genetically, these stresses challenge plants by impeding their ability to express their genetic potential optimally. A variety of molecular genomic markers are employed to study and improve crops under stress. For example, Random Amplified Polymorphic DNA (RAPD) markers help in identifying and assessing hybrids that are tolerant to drought and salinity, while Simple Sequence Repeat (SSR) markers are crucial for evaluating stress resilience. These markers also play a key role in the Quantitative Trait Loci (QTL) mapping of genes that respond to stress. Genes like dehydrins for drought and salinity are essential in the regulation of responses to these stressors. Furthermore, the use of Single Nucleotide Polymorphisms (SNP) markers for single genes assists in the genetic mapping and sequencing of stress-related traits in inbred lines. DNA markers are central to marker-assisted breeding, which enhances tolerance to abiotic stresses through advanced techniques and improvements in markers.

Conservation genetics of the endangered California Freshwater Shrimp (Syncaris pacifica): watershed and stream networks define gene pool boundaries

Understanding genetic structure and diversity among remnant populations of rare species can inform conservation and recovery actions. We used a population genetic framework to spatially delineate gene pools and estimate gene flow and effective population sizes for the endangered California Freshwater Shrimp Syncaris pacifica. Tissues of 101 individuals were collected from 11 sites in 5 watersheds, using non-lethal tissue sampling. Single Nucleotide Polymorphism markers were developed de novo using ddRAD-seq methods, resulting in 433 unlinked loci scored with high confidence and low missing data. We found evidence for strong genetic structure across the species range. Two hierarchical levels of significant differentiation were observed: (i) five clusters (regional gene pools, FST = 0.38–0.75) isolated by low gene flow were associated with watershed limits and (ii) modest local structure among tributaries within a watershed that are not connected through direct downstream flow (local gene pools, FST = 0.06–0.10). Sampling sites connected with direct upstream-to-downstream water flow were not differentiated. Our analyses suggest that regional watersheds are isolated from one another, with very limited (possibly no) gene flow over recent generations. This isolation is paired with small effective population sizes across regional gene pools (Ne = 62.4–147.1). Genetic diversity was variable across sites and watersheds (He = 0.09–0.22). Those with the highest diversity may have been refugia and are now potential sources of genetic diversity for other populations. These findings highlight which portions of the species range may be most vulnerable to future habitat fragmentation and provide management consideration for maintaining local effective population sizes and genetic connectivity.

Fine mapping of QYrsv.swust-1BL for resistance to stripe rust in durum wheat Svevo.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a serious disease that affects wheat worldwide. There is a great need to develop cultivars with combinations of all-stage resistance (ASR) and adult-plant resistance (APR) genes for sustainable control of the disease. QYrsv.swust-1BL in the Italian durum wheat (Triticum turgidum ssp. durum) cultivar Svevo is effective against Pst races in China and Israel, and the gene has been previously mapped to the long arm of chromosome 1B. The gene is flanked by SNP (single nucleotide polymorphism) markers IWB5732 and IWB4839 (0.75 cM). In the present study, we used high-density 660K SNP array genotyping and the phenotypes of 137 recombinant inbred lines (RILs) to fine map the QYrsv.swust-1BL locus within a 1.066 Mb region in durum wheat Svevo (RefSeq Rel. 1.0) on chromosome arm 1BL. The identified 1.066 Mb region overlaps with a previously described map of Yr29/QYr.ucw-1BL, a stripe rust APR gene. Twenty-five candidate genes for QYrsv.swut-1BL were identified through comparing polymorphic genes within the 1.066 Mb region in the resistant cultivar. SNP markers were selected and converted to Kompetitive allele-specific polymerase chain reaction (KASP) markers. Five KASP markers based on SNP were validated in a F2 and F2:3 breeding population, providing further compelling evidence for the significant effects of QYrsv.swut-1BL. These markers should be useful in marker-assisted selection for incorporating Yr29/QYrsv.swust-1BL into new durum and common wheat cultivars for resistance to stripe rust.

Multi-GWAS reveals significant genomic regions for Mungbean yellow mosaic India virus resistance in urdbean (Vigna mungo (L.) across multiple environments.

Unraveling genetic markers for MYMIV resistance in urdbean, with 8 high-confidence marker-trait associations identified across diverse environments, provides crucial insights for combating MYMIV disease, informing future breeding strategies. Globally, yellow mosaic disease (YMD) causes significant yield losses, reaching up to 100% in favorable environments within major urdbean cultivating regions. The introgression of genomic regions conferring resistance into urdbean cultivars is crucial for combating YMD, including resistance against mungbean yellow mosaic India virus (MYMIV). To uncover the genetic basis of MYMIV resistance, we conducted a genome-wide association study (GWAS) using three multi-locus models in 100 diverse urdbean genotypes cultivated across six individual and two combined environments. Leveraging 4538 high-quality single nucleotide polymorphism (SNP) markers, we identified 28 unique significant marker-trait associations (MTAs) for MYMIV resistance, with 8 MTAs considered of high confidence due to detection across multiple GWAS models and/or environments. Notably, 4 out of 28 MTAs were found in proximity to previously reported genomic regions associated with MYMIV resistance in urdbean and mungbean, strengthening our findings and indicating consistent genomic regions for MYMIV resistance. Among the eight highly significant MTAs, one localized on chromosome 6 adjacent to previously identified quantitative trait loci for MYMIV resistance, while the remaining seven were novel. These MTAs contain several genes implicated in disease resistance, including four common ones consistently found across all eight MTAs: receptor-like serine-threonine kinases, E3 ubiquitin-protein ligase, pentatricopeptide repeat, and ankyrin repeats. Previous studies have linked these genes to defense against viral infections across different crops, suggesting their potential for further basic research involving cloning and utilization in breeding programs. This study represents the first GWAS investigation aimed at identifying resistance against MYMIV in urdbean germplasm.

Tag SNP selection for prediction of adaptation traits in Braford and Hereford cattle using Bayesian methods

AbstractThis study utilized Bayesian inference in a genome‐wide association study (GWAS) to identify genetic markers associated with traits relevant to the adaptation of Hereford and Braford cattle breeds. We focused on eye pigmentation (EP), weaning hair coat (WHC), yearling hair coat (YHC), and breeding standard (BS). Our dataset comprised 126,290 animals in the pedigree. Out of these, 233 sires were genotyped using high‐density (HD) chips, and 3750 animals with medium‐density (50 K) single‐nucleotide polymorphism (SNP) chips. Employing the Bayes B method with a prior probability of π = 0.99, we identified and tagged single nucleotide polymorphisms (Tag SNPs), ranging from 18 to 117 SNPs depending on the trait. These Tag SNPs facilitated the construction of reduced SNP panels. We then evaluated the predictive accuracy of these panels in comparison to traditional medium‐density SNP chips. The accuracy of genomic predictions using these reduced panels varied significantly depending on the clustering method, ranging from 0.13 to 0.65. Additionally, we conducted functional enrichment analysis that found genes associated with the most informative SNP markers in the current study, thereby providing biological insights into the genomic basis of these traits.

Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle

BackgroundVarious blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results.ResultsResults indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (−log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability.ConclusionOur results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.

SnpAIMeR: R package for evaluating ancestry informative marker contributions in non-model population diagnostics.

Single nucleotide polymorphism (SNP) markers are increasingly popular for population genomics and inferring ancestry for individuals of unknown origin. Because large SNP datasets are impractical for rapid and routine analysis, diagnostics rely on panels of highly informative markers. Strategies exist for selecting these markers, however, resources for efficiently evaluating their performance are limited for non-model systems. snpAIMeR is a user-friendly R package that evaluates the efficacy of genomic markers for the cluster assignment of unknown individuals. It is intended to help minimize panel size and genotyping effort by determining the informativeness of candidate diagnostic markers. Provided genotype data from individuals of known origin, it uses leave-one-out cross-validation to determine population assignment rates for individual markers and marker combinations. snpAIMeR is available on CRAN (https://CRAN.R-project.org/package=snpAIMeR).

High-density linkage to physical mapping in a unique Tall × Dwarf coconut (Cocos nucifera L.) outbred F2 uncovers a major QTL for flowering time colocalized with the FLOWERING LOCUS T (FT).

The coconut tree crop (Cocos nucifera L.) provides vital resources for millions of people worldwide. Coconut germplasm is largely classified into 'Tall' (Typica) and 'Dwarf' (Nana) types. While Tall coconuts are outcrossing, stress tolerant, and late flowering, Dwarf coconuts are inbred and flower early with a high rate of bunch emission. Precocity determines the earlier production of a plantation and facilitates management and harvest. A unique outbred F2 population was used, generated by intercrossing F1 hybrids between Brazilian Green Dwarf from Jiqui (BGDJ) and West African Tall (WAT) cultivars. Single-nucleotide polymorphism (SNP) markers fixed for alternative alleles in the two varieties, segregating in an F2 configuration, were used to build a high-density linkage map with ~3,000 SNPs, anchored to the existing chromosome-level genome assemblies, and a quantitative trait locus (QTL) mapping analysis was carried out. The linkage map established the chromosome numbering correspondence between the two reference genome versions and the relationship between recombination rate, physical distance, and gene density in the coconut genomes. Leveraging the strong segregation for precocity inherited from the Dwarf cultivar in the F2, a major effect QTL with incomplete dominance was mapped for flowering time. FLOWERING LOCUS T (FT) gene homologs of coconut previously described as putatively involved in flowering time by alternative splice variant analysis were colocalized within a ~200-kb window of the major effect QTL [logarithm of the odds (LOD) = 11.86]. Our work provides strong phenotype-based evidence for the role of the FT locus as the putative underlying functional variant for the flowering time difference between Dwarf and Tall coconuts. Major effect QTLs were also detected for developmental traits of the palm, plausibly suggesting pleiotropism of the FT locus for other precocity traits. Haplotypes of the two SNPs flanking the flowering time QTL inherited from the Dwarf parent BGDJ caused a reduction in the time to flower of approximately 400 days. These SNPs could be used for high-throughput marker-assisted selection of early-flowering and higher-productivity recombinant lines, providing innovative genetic material to the coconut industry.

Early Detection of Candidate Genes for Body Weight in Indonesian Cattle Breeds with Genome-Wide Association Study (GWAS)

Abstract Genome-wide association study (GWAS) was used to detect candidate genes affecting economic traits in livestock. GWAS can detect single nucleotide polymorphisms (SNPs) in all chromosome regions. This study aimed to determine the genetic markers for body weight by GWAS in native cattle breeds of Indonesia. The Illumina Bovine 50K BeadChip was used to determine the candidate genes in three mixed-sex Indonesian cattle breeds of Bali (16 animals), Madura (16 animals), and Ongole grade (13 animals). All animals were raised at the Pasuruan Regency, East Java, Indonesia breeding station. The GWAS was performed in pooled sample of animals (45 animals) with the general linear model (GLM) method using SNP markers with minimum allele frequency (MAF) values more than 0.05 by TASSEL 5.0. software. Therefore, the body weight of cattle at 1 to 3 years of age was collected for each animal for computing Manhattan plot graphics. This research found that SUGT1, SF3A3, and DSCAM genes were detected as potential genetic markers for body weight in cattle breeds of Indonesia. The SUGT1 and DSCAM genes were monomorphic in Bali cattle (Bos javanicus). In addition, both genes were significantly associated (P&lt;0.05) with the body weight of Ongole-grade cattle (Bos indicus) at three years of age. However, the SF3A3 gene was significantly (P&lt;0.05) associated with body weight of Madura cattle (Bos indicus) at 2 and 3 years of age. In conclusion, the GWAS of pool animals reveals three candidate genes significantly associated with body weight in many cattle breeds of Indonesia. Further study to detect SNPs in candidate genes with sequencing method is essential to apply these findings practically.

Analysis of population structure and genetic diversity of Camellia tachangensis in Guizhou based on SNP markers

BackgroundCamellia tachangensis F. C. Zhang is a five-compartment species in the ovary of tea group plants, which represents the original germline of early differentiation of some tea group plants.Methods and resultsIn this study, we analyzed single-nucleotide polymorphisms (SNPs) at the genome level, constructed a phylogenetic tree, analyzed the genetic diversity, and further investigated the population structure of 100 C. tachangensis accessions using the genotyping-by-sequencing (GBS) method. A total of 91,959 high-quality SNPs were obtained. Population structure analysis showed that the 100 C. tachangensis accessions clustered into three groups: YQ-1 (Village Group), YQ-2 (Forest Group) and YQ-3 (Transition Group), which was further consistent with the results of phylogenetic analysis and principal component analyses (PCA). In addition, a comparative analysis of the genetic diversity among the three populations (Forest, Village, and Transition Groups) detected the highest genetic diversity in the Transition Group and the highest differentiation between Forest and Village Groups.ConclusionsC. tachangensis plants growing in the forest had different genetic backgrounds from those growing in villages. This study provides a basis for the effective protection and utilization of C. tachangensis populations and lays a foundation for future C. tachangensis breeding.

Association mapping of septoria tritici blotch resistance in bread wheat in Bale and Arsi highlands, Ethiopia

Septoria tritici blotch (STB) caused by the fungal pathogen Zymoseptoria tritici, anamorph Septoria tritici Rob. ex Desm., is an important wheat pathogen worldwide, reported to be major wheat production threating factor, posing considerable yield loss every year. Developing resistant cultivars is an efficient, economical, environmentally friendly and simple approach for managing STB. This study was carried out to evaluate spring bread wheat lines for their reaction to STB disease under field conditions; to associate phenotypic and genotypic data for identification of STB disease resistance; and to identify genomic region(s) associated with resistance to STB in spring bread wheat lines. Two hundred forty (240) spring bread wheat lines were evaluated under field conditions in non-replicated trials, using an augmented design. The trials were conducted at three locations (Kulumsa Agricultural Research Center, Madda Walabu University Research Site and Sinana Agricultural Research Center) in 2017 main cropping season (July to December). Out of these 240 wheat lines, 123 of them were genotyped with 10263 single nucleotide polymorphism (SNPs) markers and population structure and association mapping analysis was done. The wheat lines showed significant variations in percentage disease severity and area under the disease progress curve at all the three locations they were evaluated. The wheat lines were classified as resistant, moderately resistant, moderately susceptible and susceptible based on the percentage disease severity scored. Five wheat lines were found to be resistant to STB in all the three locations and are recommended for direct release by the national program and parentage purposes in wheat breeding programs. The 123 wheat lines were clustered into 3 subpopulations in which the first cluster contained 99 wheat lines; the second 17 and the last one 7. Among the polymorphic 8127 SNPs markers, 26 markers on chromosomes 7B, 1D, 3A, 2B, 6B and 3D were found to be significantly (P < 0.001) associated with STB resistance so that they can be utilized for marker assisted selection and gene pyramiding in resistance breeding programs.

A strategy integrating GC–MS, UPLC profiling, and DNA metabarcoding for characterization and discrimination of the medicinal and culinary pieces from four Curcuma species

Curcumae pieces prepared in ready-to-use forms, consumed extensively for their significant medicinal and culinary benefits, present a challenge in discrimination due to their heterogeneous origins, analogous morphologies, and varied components. This study explored the metabolites of nine Curcumae pieces (NCPs) by Gas Chromatography-Mass Spectrometry (GC–MS) and Ultra-Performance Liquid Chromatography/Ultra High-Performance Liquid Chromatography–Quadrupole Time of Flight–Mass Spectrometry (UPLC/UHPLC–QTOF–MS) technologies, augmented by multivariate statistical analysis (Principal Component Analysis, PCA; Orthogonal Partial Least Squares Discriminant Analysis, OPLS–DA) and machine learning algorithms (Random Forest, RF). Twenty-six key differential markers in volatile and non-volatile metabolites each were found subsequently. Additionally, DNA metabarcoding was employed to develop specific Single Nucleotide Polymorphism (SNP) markers for the precise identification of Curcuma species in single and mixed samples, and for CRa even contained in Chinese patent prescriptions. The integration of these three methodologies facilitates a more reliable authentication of NCPs, enhancing the overall strategy for their global discrimination. This research contributes to the advancement of quality control and the development of functional foods or products derived from Curcumae pieces, tailored to meet specific health and dietary requirements.