Informative Single Nucleotide Polymorphisms Research Articles

A genome-wide association study on growth traits of Korean commercial pig breeds using Bayesian methods.

This study aims to identify the significant regions and candidate genes of growth-related traits (adjusted backfat thickness [ABF], average daily gain [ADG], and days to 90 kg [DAYS90]) in Korean commercial GGP pig (Duroc, Landrace, and Yorkshire) populations. A genome-wide association study (GWAS) was performed using single-nucleotide polymorphism (SNP) markers for imputation to Illumina PorcineSNP60. The BayesB method was applied to calculate thresholds for the significance of SNP markers. The identified windows were considered significant if they explained ≥1% genetic variance. A total of 28 window regions were related to genetic growth effects. Bayesian GWAS revealed 28 significant genetic regions including 52 informative SNPs associated with growth traits (ABF, ADG, DAYS90) in Duroc, Landrace, and Yorkshire pigs, with genetic variance ranging from 1.00% to 5.46%. Additionally, 14 candidate genes with previous functional validation were identified for these traits. The identified SNPs within these regions hold potential value for future markerassisted or genomic selection in pig breeding programs. Consequently, they contribute to an improved understanding of genetic architecture and our ability to genetically enhance pigs. SNPs within the identified regions could prove valuable for future marker-assisted or genomic selection in pig breeding programs.

The use of SNP markers for cattle breed identification.

A potential application of single nucleotide polymorphisms (SNPs) in animal husbandry and production is identification of the animal breed. In this study, using chosen marker selection methods and genotypic data obtained with the use of Illumina Bovine SNP50 BeadChip for individuals belonging to ten cattle breeds, the reduced panels containing the most informative SNP markers were developed. The suitability of selected SNP panels for the effective and reliable assignment of the studied individuals to the breed of origin was checked by three allocation algorithms implemented in GeneClass 2. The studied breeds set included both Polish-native breeds under the genetic resources conservation programs and highly productive breeds with a global range. For all of the tested marker selection methods ("delta" and two FST-based variants), two separate methodological approaches of marker assortment were used and three marker panels were created with 96, 192, and 288 SNPs respectively, to determine the minimum number of markers required for effective differentiation of the studied breeds. Moreover, the usefulness of the most effective panels of markers to assess the population structure and genetic diversity of the analyzed breeds was examined. The conducted analyses showed the possibility of using SNP subsets from medium-density genotypic microarrays to distinguish breeds of cattle kept in Poland and to analyze their genetic structure.

Unlocking the genetic diversity and population structure of the newly introduced two-row spring European HerItage Barley collecTion (ExHIBiT).

In the last century, breeding programs have traditionally favoured yield-related traits, grown under high-input conditions, resulting in a loss of genetic diversity and an increased susceptibility to stresses in crops. Thus, exploiting understudied genetic resources, that potentially harbour tolerance genes, is vital for sustainable agriculture. Northern European barley germplasm has been relatively understudied despite its key role within the malting industry. The European Heritage Barley collection (ExHIBiT) was assembled to explore the genetic diversity in European barley focusing on Northern European accessions and further address environmental pressures. ExHIBiT consists of 363 spring-barley accessions, focusing on two-row type. The collection consists of landraces (~14%), old cultivars (~18%), elite cultivars (~67%) and accessions with unknown breeding history (~1%), with 70% of the collection from Northern Europe. The population structure of the ExHIBiT collection was subdivided into three main clusters primarily based on the accession's year of release using 26,585 informative SNPs based on 50k iSelect single nucleotide polymorphism (SNP) array data. Power analysis established a representative core collection of 230 genotypically and phenotypically diverse accessions. The effectiveness of this core collection for conducting statistical and association analysis was explored by undertaking genome-wide association studies (GWAS) using 24,876 SNPs for nine phenotypic traits, four of which were associated with SNPs. Genomic regions overlapping with previously characterised flowering genes (HvZTLb) were identified, demonstrating the utility of the ExHIBiT core collection for locating genetic regions that determine important traits. Overall, the ExHIBiT core collection represents the high level of untapped diversity within Northern European barley, providing a powerful resource for researchers and breeders to address future climate scenarios.

A high-density genetic linkage map and QTL identification for growth traits in dusky kob (Argyrosomus japonicus)

High-density genetic linkage maps provide a foundational genomic resource for quantitative trait locus (QTL) mapping, candidate gene localisation, and comparative genomic analysis. Data from such studies, in turn, can be incorporated into marker or genomics assisted selective breeding strategies for increased efficiency of aquaculture production. This study aimed to construct the first high-density genetic linkage map for dusky kob (Argyrosomus japonicus) a commercially important marine finfish in Asia, Australia, and South Africa. Using 2b-restriction site-associated DNA (2b-RAD) sequencing for genome-wide single nucleotide polymorphism (SNP) genotyping, in three full-sib F1 families (Family 1, n = 69 offspring; Family 2, n = 73 offspring; and Family 3, n = 70 offspring), led to the discovery and genotyping of 22,789 SNPs. Among these, 3992 quality filtered and informative SNPs were mapped to 24 linkage groups (LGs), aligning with the species' proposed haploid chromosome number. The total integrated genetic map spanned a length of 2550 cM with an average marker spacing of 0.68 cM, achieving a genome coverage of 98.61%. Male and female specific maps highlighted differential map lengths and recombination rates between the sexes, with the female map slightly longer and a lower recombination frequency than for the male. QTL analysis for growth-related traits, weight (W), standard length (sL), and Fulton's condition factor (K), identified a total of 25 QTLs of which five QTLs were found to be consistently shared across the traits. Collectively, these shared QTLs explained a substantial proportion of the phenotypic variance, accounting for between 9.30 and 19.30% of the observed variation, suggesting potential major genes for growth. These QTL regions led to the discovery of 65 potential candidate genes, including 11 genes that were specifically located within the shared QTLs. These genes were linked to essential biological processes, including metabolism, immunity, stress response, development, and vascular function. The establishment of this high-density genetic linkage map represents an important genomic resource for comprehending the genetic determinants of economically important traits in A. japonicus, with implications for advancing and optimising ongoing marker-assisted selection (MAS) initiatives in the species.

Systematic analyses of AISNPs screening and classification algorithms based on genome-wide data for forensic biogeographic ancestry inference

Identifying the biogeographic ancestral origin of biological sample left at a crime scene can provide important evidence for judicial case, as well as clue for narrowing down suspect. Ancestry informative single nucleotide polymorphism (AISNP) has become one of the most important genetic markers in recent years for screening ancestry information loci and analyzing the population genetic background and structure due to their high number and wide distributions in the human genome. In this study, based on data from 26 populations in the 1000 Genomes Project Phase 3, a Random Forest classification model was constructed with one-vs-rest classification strategy for embedded feature selection in order to obtain a panel with a small number of efficient AISNPs. The research aim was to clarify differentiations of population genetic structures among continents and subregions of East Asia. ADMIXTURE results showed that based on the 58 AISNPs selected by the machine learning algorithm, the 26 populations involved in the study could be categorized into six intercontinental ancestry components: North East Asia, South East Asia, Africa, Europe, South Asia, and America. The 24 continental-specific AISNPs and 34 East Asian-specific AISNPs were finally obtained, and used to construct the ancestry prediction model using XGBoost algorithm, resulting in the Matthews correlation coefficients of 0.94 and 0.89, and accuracies of 0.94 and 0.92, respectively. The machine learning models that we constructed using population-specific AISNPs were able to accurately predict the ancestral origins of continental and intra-East Asian populations. To summarize, screening a set of high-perform AISNPs to infer biogeographical ancestral information using embedded feature selection has potential application in creating a layered inference system that accurately differentiates from intercontinental populations to local subpopulations.

Estimation of Additive and Dominance Effects in an Acacia crassicarpa Multi-Environment Progeny Trial Using Genomic Pedigree Reconstruction

Abstract Acacia crassicarpa is an important tree species in Southeast Asia, where hundreds of thousands of hectares of planted forests are supported by advancements in silviculture and genetic improvement. Although possible, controlled pollination is impractical for advancing breeding populations, requiring an unreasonable effort to produce more than a few crosses per year. For this reason, breeding populations often are bred by open pollination. This study used large-scale pedigree reconstruction in multi-environment trials to assess full-sib families to model the genetics of the quantitative traits survival, straightness, height, diameter at breast height, tree volume, mean annual increment (MAI), and basic density. The traits were predominantly controlled by additive effects, with heritabilities between 0.09 for survival and 0.45 for basic density. The genetic correlation across sites was high for all traits, showing the low impact of genotype-by-environment interaction. The trait-trait correlation showed that straightness was independent of any other traits, survival was only correlated with MAI, and growth traits were highly correlated among themselves. Basic density was positively correlated with growth traits and MAI. Study Implications: Parentage analysis using an informative single nucleotide polymorphism panel was used to reconstruct pedigree and allow a full-sib family model to estimate additive and dominance effects and genetic correlations across sites and among important traits in an open-pollinated population. The genetic control of all traits assessed in this study was mainly additive. In this scenario, the recommended breeding strategy is forward selection of outstanding progeny for advanced generation breeding and backward selection of outstanding parents to produce seed for deployment via family forestry. Full-sib families can be identified by pedigree reconstruction at a seedling stage, followed by tissue culture multiplication, rooted cutting propagation, and plantation establishment.

Genotyping by sequencing for the construction of oil palm (Elaeis guineensis Jacq.) genetic linkage map and mapping of yield related quantitative trait loci.

Oil palm (Elaeis guineensis Jacq.) is one of the major oil-producing crops. Improving the quality and increasing the production yield of oil palm have been the primary focuses of both conventional and modern breeding approaches. However, the conventional breeding approach for oil palm is very challenging due to its longevity, which results in a long breeding cycle. Thus, the establishment of marker assisted selection (MAS) for oil palm breeding programs would speed up the breeding pipeline by generating new oil palm varieties that possess high commercial traits. With the decreasing cost of sequencing, Genotyping-by-sequencing (GBS) is currently feasible to many researchers and it provides a platform to accelerate the discovery of single nucleotide polymorphism (SNP) as well as insertion and deletion (InDel) markers for the construction of a genetic linkage map. A genetic linkage map facilitates the identification of significant DNA regions associated with the trait of interest via quantitative trait loci (QTL) analysis. A mapping population of 112 F1 individuals from a cross of Deli dura and Serdang pisifera was used in this study. GBS libraries were constructed using the double digestion method with HindIII and TaqI enzymes. Reduced representation libraries (RRL) of 112 F1 progeny and their parents were sequenced and the reads were mapped against the E. guineensis reference genome. To construct the oil palm genetic linkage map, informative SNP and InDel markers were used to discover significant DNA regions associated with the traits of interest. The nine traits of interest in this study were fresh fruit bunch (FFB) yield, oil yield (OY), oil to bunch ratio (O/B), oil to dry mesocarp ratio (O/DM) ratio, oil to wet mesocarp ratio (O/WM), mesocarp to fruit ratio (M/F), kernel to fruit ratio (K/F), shell to fruit ratio (S/F), and fruit to bunch ratio (F/B). A total of 2.5 million SNP and 153,547 InDel markers were identified. However, only a subset of 5,278 markers comprising of 4,838 SNPs and 440 InDels were informative for the construction of a genetic linkage map. Sixteen linkage groups were produced, spanning 2,737.6 cM for the maternal map and 4,571.6 cM for the paternal map, with average marker densities of one marker per 2.9 cM and one per 2.0 cM respectively, were produced. A QTL analysis was performed on nine traits; however, only QTL regions linked to M/F, K/F and S/F were declared to be significant. Of those QTLs were detected: two for M/F, four for K/F and one for S/F. These QTLs explained 18.1-25.6% of the phenotypic variance and were located near putative genes, such as casein kinase II and the zinc finger CCCH domain, which are involved in seed germination and growth. The identified QTL regions for M/F, K/F and S/F from this study could be applied in an oil palm breeding program and used to screen palms with desired traits via marker assisted selection (MAS).

An Improved Clinical and Genetics-Based Prediction Model for Diabetic Foot Ulcer Healing.

Objective: The goal of this investigation was to use comprehensive prediction modeling tools and available genetic information to try to improve upon the performance of simple clinical models in predicting whether a diabetic foot ulcer (DFU) will heal. Approach: We utilized a cohort study (n = 206) that included clinical factors, measurements of circulating endothelial precursor cells (CEPCs), and fine sequencing of the NOS1AP gene. We derived and selected relevant predictive features from this patient-level information using statistical and machine learning techniques. We then developed prognostic models using machine learning approaches and assessed predictive performance. The presentation is consistent with TRIPOD requirements. Results: Models using baseline clinical and CEPC data had an area under the receiver operating characteristic curve (AUC) of 0.73 (0.66-0.80). Models using only single nucleotide polymorphisms (SNPs) of the NOS1AP gene had an AUC of 0.67 (95% confidence interval, CI: [0.59-0.75]). However, models incorporating baseline and SNP information resulted in improved AUC (0.80, 95% CI [0.73-0.87]). Innovation: We provide a rigorous analysis demonstrating the predictive potential of genetic information in DFU healing. In this process, we present a framework for using advanced statistical and bioinformatics techniques for creating superior prognostic models and identify potentially predictive SNPs for future research. Conclusion: We have developed a new benchmark for which future predictive models can be compared against. Such models will enable wound care experts to more accurately predict whether a patient will heal and aid clinical trialists in designing studies to evaluate therapies for subjects likely or unlikely to heal.

Species-specific SNP arrays for non-invasive genetic monitoring of a vulnerable bat

Genetic tagging from scats is one of the minimally invasive sampling (MIS) monitoring approaches commonly used to guide management decisions and evaluate conservation efforts. Microsatellite markers have traditionally been used but are prone to genotyping errors. Here, we present a novel method for individual identification in the Threatened ghost bat Macroderma gigas using custom-designed Single Nucleotide Polymorphism (SNP) arrays on the MassARRAY system. We identified 611 informative SNPs from DArTseq data from which three SNP panels (44–50 SNPs per panel) were designed. We applied SNP genotyping and molecular sexing to 209 M. gigas scats collected from seven caves in the Pilbara, Western Australia, employing a two-step genotyping protocol and identifying unique genotypes using a custom-made R package, ScatMatch. Following data cleaning, the average amplification rate was 0.90 ± 0.01 and SNP genotyping errors were low (allelic dropout 0.003 ± 0.000) allowing clustering of scats based on one or fewer allelic mismatches. We identified 19 unique bats (9 confirmed/likely males and 10 confirmed/likely females) from a maternity and multiple transitory roosts, with two male bats detected using roosts, 9 km and 47 m apart. The accuracy of our SNP panels enabled a high level of confidence in the identification of individual bats. Targeted SNP genotyping is a valuable tool for monitoring and tracking of non-model species through a minimally invasive sampling approach.

Genetic diversity and breed-informative SNPs identification in domestic pig populations using coding SNPs.

Background: The use of breed-informative genetic markers, specifically coding Single Nucleotide Polymorphisms (SNPs), is crucial for breed traceability, authentication of meat and dairy products, and the preservation and improvement of pig breeds. By identifying breed informative markers, we aimed to gain insights into the genetic mechanisms that influence production traits, enabling informed decisions in animal management and promoting sustainable pig production to meet the growing demand for animal products. Methods: Our dataset consists of 300 coding SNPs genotyped from three Italian commercial pig populations: Landrace, Yorkshire, and Duroc. Firstly, we analyzed the genetic diversity among the populations. Then, we applied a discriminant analysis of principal components to identify the most informative SNPs for discriminating between these populations. Lastly, we conducted a functional enrichment analysis to identify the most enriched pathways related to the genetic variation observed in the pig populations. Results: The alpha diversity indexes revealed a high genetic diversity within the three breeds. The higher proportion of observed heterozygosity than expected revealed an excess of heterozygotes in the populations that was supported by negative values of the fixation index (FIS) and deviations from the Hardy-Weinberg equilibrium. The Euclidean distance, the pairwise FST, and the pairwise Nei's GST genetic distances revealed that Yorkshire and Landrace breeds are genetically the closest, with distance values of 2.242, 0.029, and 0.033, respectively. Conversely, Landrace and Duroc breeds showed the highest genetic divergence, with distance values of 2.815, 0.048, and 0.052, respectively. We identified 28 significant SNPs that are related to phenotypic traits and these SNPs were able to differentiate between the pig breeds with high accuracy. The Functional Enrichment Analysis of the informative SNPs highlighted biological functions related to DNA packaging, chromatin integrity, and the preparation of DNA into higher-order structures. Conclusion: Our study sheds light on the genetic underpinnings of phenotypic variation among three Italian pig breeds, offering potential insights into the mechanisms driving breed differentiation. By prioritizing breed-specific coding SNPs, our approach enables a more focused analysis of specific genomic regions relevant to the research question compared to analyzing the entire genome.

Validation of next-generation sequencing-based chimerism testing for accurate detection and monitoring of engraftment in hematopoietic stem cell transplantation

Allogenic hematopoietic stem cell transplantation (allo-HSCT) is a life-saving treatment for various hematological disorders. The success of allo-HSCT depends on the engraftment of donor cells and the elimination of recipient cells monitored through chimerism testing. We aimed to validate a next-generation sequencing (NGS)-based chimerism assay for engraftment monitoring and to emphasize the importance of including the most prevalent cell subsets in proficiency testing (PT) programs. We evaluated the analytical performance of NGS-based chimerism testing (AlloSeq-HCT and CareDx) with a panel of targeted 202 informative single-nucleotide polymorphisms (SNPs) (i.e., linearity and precision, analytical sensitivity and specificity, system accuracy, and reproducibility). We further compared the performance of our NGS panel with conventional short tandem repeat (STR) analysis in unfractionated whole blood and cell-subset-enriched CD3 and CD66. Our NGS-based chimerism monitoring assay has an impressive detection limit (0.3% host DNA) for minor alleles and analytical specificity (99.9%). Pearson’s correlation between NGS- and STR-based chimerism monitoring showed a linear relationship with a slope of 0.8 and r = 0.973. The concordance of allo-HSCT patients using unfractionated whole blood, CD3, and CD66 was 0.95, 0.96, and 0.54, respectively. Utilization of CD3+ cell subsets for mixed chimerism detection yielded an average of 7.3 ± 7-fold higher donor percentage detection compared to their corresponding unfractionated whole blood samples. The accuracy of the NGS assay achieved a concordance of 98.6% on blinded external quality control STR samples. The reproducibility series showed near 100% concordance with respect to inter-assay, inter-tech, inter-instrument, cell flow kits, and AlloSeq-HCT software versions. Our study provided robust validation of NGS-based chimerism testing for accurate detection and monitoring of engraftment in allo-HSCT patients. By incorporating the cell subsets (CD3 and CD66), the sensitivity and accuracy of engraftment monitoring are significantly improved, making them an essential component of any PT program. Furthermore, the implementation of NGS-based chimerism testing shows potential to streamline high-volume transplant services and improve clinical outcomes by enabling early relapse detection and guiding timely interventions.

Genetic diversity and population structure of maize inbred lines using phenotypic traits and single nucleotide polymorphism (SNP) markers

Understanding germplasm’s genetic diversity is essential for developing new and improved cultivars with stable yields under diverse environments. The objective of this study was to determine the genetic diversity and population structure of 128 maize inbred lines sourced from the International Institute of Tropical Agriculture (IITA), the International Maize and Wheat Improvement Centre (CIMMYT), and the University of KwaZulu-Natal (UKZN) using 11,450 informative single nucleotide polymorphism (SNP) markers. The inbred lines revealed highly significant (p < 0.001) levels of variability for the key phenotypic traits. The SNP markers had a mean gene diversity (GD) and polymorphic information content (PIC) of 0.40 and 0.31, respectively, indicating the existence of substantial genetic variation across the germplasm panel. The model-based population structure analysis identified three subpopulations (K = 3) among the inbred lines. This corroborated the phylogenetic analysis using phenotypic traits and molecular markers which classified the inbred lines into three groups. The findings of this study identified considerable genetic diversity for the selection of inbred lines with favourable alleles for multiple traits and could be useful to initiate marker-assisted selection (MAS) to identify significant loci associated with agronomic performance and multiple-stress tolerance.

Exploring factors impacting haplotype-based noninvasive prenatal diagnosis for single-gene recessive disorders.

Haplotype-based noninvasive prenatal diagnosis (NIPD) is applicable for various recessive single-gene disorders in proband families. However, a comprehensive exploration of critical factors influencing the assay performance, such as fetal fraction, informative single nucleotide polymorphism (SNP) count, and recombination events, has yet to be performed. It is critical to identify key factors affecting NIPD performance, including its accuracy and success rate, and their impact on clinical diagnostics to guide clinical practice. We conducted a prospective study, recruiting 219 proband families with singleton pregnancies at risk for eight recessive single-gene disorders (Duchenne muscular dystrophy, spinal muscular atrophy, phenylketonuria, methylmalonic acidemia, hemophilia A, hemophilia B, non-syndromic hearing loss, and congenital adrenal hyperplasia) at 7-14 weeks of gestation. Haplotype-based NIPD was performed by evaluating the relative haplotype dosage (RHDO) in maternal circulation, and the results were validated via invasive prenatal diagnosis or newborn follow-ups. Among the 219 families, the median gestational age at first blood draw was 8+5 weeks. Initial testing succeeded for 190 families and failed for 29 due to low fetal fraction (16), insufficient informative SNPs (9), and homologous recombination near pathogenic variation (4). Among low fetal fraction families, successful testing was achieved for 11 cases after a redraw, while 5 remained inconclusive. Test failures linked to insufficient informative SNPs correlated with linkage disequilibrium near the genes, with F8 and MMUT exhibiting the highest associated failure rates (14.3% and 25%, respectively). Homologous recombination was relatively frequent around the DMD and SMN1 genes (8.8% and 4.8%, respectively) but led to detection failure in only 44.4% (4/9) of such cases. All NIPD results from the 201 successful families were consistent with invasive diagnostic findings or newborn follow-up. Fetal fraction, informative SNPs count, and homologous recombination are pivotal to NIPD performance. Redrawing blood effectively improves the success rate for low fetal fraction samples. However, informative SNPs count and homologous recombination rates vary significantly across genes, necessitating careful consideration in clinical practice. We have designed an in silico method based on linkage disequilibrium data to predict the number of informative SNPs. This can identify genomic regions where there might be an insufficient number of SNPs, thereby guiding panel design. With these factors properly accounted for, NIPD is highly accurate and reliable.

Deciphering the evolution of the temporal and geographic distribution of French Mycobacterium bovis genotypes using a high throughput SNP-targeted amplicon sequencing method.

Mycobacterium bovis, which belongs to the Mycobacterium tuberculosis complex, is a highly clonal pathogen. However, several lineages of M. bovis have been described worldwide and nine different clusters were identified in France. Targeted amplicon sequencing using next-generation sequencing technology of eighty-eight phylogenetically informative single nucleotide polymorphisms (SNPs) were used to infer the phylogenetic relationship of 630 strains of the National Reference Laboratory isolated between 1979 and 2018 from various animal species. This study allowed classifying 618 different genotypic profiles (combination of a spoligotype and 8 loci-MIRU-VNTR profiles) into the nine previously identified clusters. A global analysis of the entire collection of the National Reference Laboratory has made it possible to represent the evolution of clonal complexes and clusters in time and space for better assessing epidemiological changes of bovine tuberculosis in France.

Local and Bayesian Survival FDR Estimations to Identify Reliable Associations in Whole Genome of Bread Wheat.

Estimating the FDR significance threshold in genome-wide association studies remains a major challenge in distinguishing true positive hypotheses from false positive and negative errors. Several comparative methods for multiple testing comparison have been developed to determine the significance threshold; however, these methods may be overly conservative and lead to an increase in false negative results. The local FDR approach is suitable for testing many associations simultaneously based on the empirical Bayes perspective. In the local FDR, the maximum likelihood estimator is sensitive to bias when the GWAS model contains two or more explanatory variables as genetic parameters simultaneously. The main criticism of local FDR is that it focuses only locally on the effects of single nucleotide polymorphism (SNP) in tails of distribution, whereas the signal associations are distributed across the whole genome. The advantage of the Bayesian perspective is that knowledge of prior distribution comes from other genetic parameters included in the GWAS model, such as linkage disequilibrium (LD) analysis, minor allele frequency (MAF) and call rate of significant associations. We also proposed Bayesian survival FDR to solve the multi-collinearity and large-scale problems, respectively, in grain yield (GY) vector in bread wheat with large-scale SNP information. The objective of this study was to obtain a short list of SNPs that are reliably associated with GY under low and high levels of nitrogen (N) in the population. The five top significant SNPs were compared with different Bayesian models. Based on the time to events in the Bayesian survival analysis, the differentiation between minor and major alleles within the association panel can be identified.

Genetic Diversity and Population Structure in Ethiopian Mustard (Brassica carinata A. Braun) as Revealed by Single Nucleotide Polymorphism Markers.

Ethiopian mustard (Brassica carinata A. Braun) is currently one of the potential oilseeds dedicated to the production for biofuel and other bio-industrial applications. The crop is assumed to be native to Ethiopia where a number of diversified B. carinata germplasms are found and conserved ex situ. However, there is very limited information on the genetic diversity and population structure of the species. This study aimed to investigate the genetic diversity and population structure of B. carinata genotypes of different origins using high-throughput single nucleotide polymorphism (SNP) markers. We used Brassica 90K Illumina InfiniumTM SNP array for genotyping 90 B. carinata genotypes, and a total of 11,499 informative SNP markers were used for investigating the population structure and genetic diversity. The structure analysis, principal coordinate analysis (PcoA) and neighbor-joining tree analysis clustered the 90 B. carinata genotypes into two distinct subpopulations (Pop1 and Pop2). The majority of accessions (65%) were clustered in Pop1, mainly obtained from Oromia and South West Ethiopian People (SWEP) regions. Pop2 constituted dominantly of breeding lines and varieties, implying target selection contributed to the formation of distinct populations. Analysis of molecular variance (AMOVA) revealed a higher genetic variation (93%) within populations than between populations (7%), with low genetic differentiation (PhiPT = 0.07) and poor correlation between genetic and geographical distance (R = 0.02). This implies the presence of gene flow (Nm > 1) and weak geographical structure of accessions. Genetic diversity indices showed the presence of moderate genetic diversity in B. carinata populations with an average genetic diversity value (HE = 0.31) and polymorphism information content (PIC = 0.26). The findings of this study provide important and relevant information for future breeding and conservation efforts of B. carinata.

Monitoring Drought Tolerance in Oil Palm: Choline Monooxygenase as a Novel Molecular Marker.

Water scarcity negatively impacts oil palm production, necessitating the development of drought-tolerant varieties. This study aimed to develop molecular markers for oil palm breeding programs focused on drought tolerance. Genes associated with drought tolerance were selected, and single nucleotide polymorphism (SNP)-based markers were developed. Genomic DNA was successfully extracted from 17 oil palm varieties, and 20 primers out of 44 were effectively amplified. Screening with single-strand conformation polymorphism (SSCP) revealed an informative SNP marker from the choline monooxygenase (CMO) gene, exhibiting CC, CT, and TT genotypes. Notably, the oil palm variety La Mé showed the CT genotype, while Surat Thani 2 (Deli × La Mé) exhibited the CT and CC genotypes in a 1:1 ratio. Gene expression analysis confirmed the association of the CMO gene with drought tolerance in commercial oil palm varieties. The full-length CMO gene was 1308 bp long and shared sequence similarities with other plant species. However, amino acid sequence variations were observed compared with existing databases. These findings highlight the potential utility of the CMO marker for drought tolerance selection, specifically within the La Mé parent of oil palm Surat Thani 2 varieties, and strongly confirm the La Mé S5 population and Surat Thani 2 as drought-tolerant varieties.

Evaluating the Classification Accuracy of Expression Quantitative Trait Loci Calculated Polygenic Risk Scores in Alzheimer's Disease.

Polygenic risk scores (PRS) hold promise for the early identification of those at risk from neurodegenerative disorders such as Alzheimer's Disease (AD), allowing for intervention to occur prior to neuronal damage. The current selection of informative single nucleotide polymorphisms (SNPs) to generate the risk scores is based on the modelling of large genome-wide association data using significance thresholds. However, the biological relevance of these SNPs is largely unknown. This study, in contrast, aims to identify SNPs with biological relevance to AD and then assess them for their ability to accurately classify cases and controls. Samples selected from the Brains for Dementia Research (BDR) were used to produce gene expression data to identify potential expression quantitative trait loci (eQTLs) relevant to AD. These SNPs were then incorporated into a PRS model to classify AD and controls in the full BDR cohort. Models derived from these eQTLs demonstrate modest classification potential with an accuracy between 61% and 67%. Although the model accuracy is not as high as some values in the literature based on significance thresholds from genome-wide association studies, these models may reflect a more biologically relevant model, which may provide novel targets for therapeutic intervention.

Whole-genome resequencing reveals genetic diversity and signatures of selection in mono-female grass carp (Ctenopharyngodon idella)

Grass carp (Ctenopharyngodon idella) is an important aquaculture species in China even in the world, and the production of mono-female population can effectively increase its yield as its females grow faster than males. Assessing the genetic basis of mono-female population is of great importance for breeding and culture, and the detection of signature of selection may shed light on genes and mutations associated with important phenotypic traits. In this study, whole-genome resequencing technology was employed to detect the genetic variation in wild populations of grass carp from the Yangtze River and the Pearl River, as well as in a mono-female population based on combining gynogenesis with sex-reversal breeding method. A total of 9,510,648 informative single nucleotide polymorphisms (SNPs) were identified across the three populations. The mean observed heterozygosity (Ho) and mean expected heterozygosity (He) of SNPs were calculated to be 0.2139 and 0.2894, 0.2313 and 0.2888, and 0.2523 and 0.2418 in the Yangtze River, Pearl River and mono-female population, respectively. The genetic distance between the mono-female population and the wild populations indicated that there was a moderate genetic differentiation between the three populations. Furthermore, analysis of run of homozygosity (ROH) revealed that the mono-female population exhibited a lower level of genomic inbreeding (inbreed coefficient = 0.075) compared to higher levels in the Yangtze River (inbreeding coefficient = 0.2410) and Pearl River populations (inbreed coefficient = 0.1894). Short ROHs were more frequently observed in the mono-female population, while long ROHs were more common in the Yangtze River and the Pearl River populations, providing insight into the population history of grass carp. Using the Yangtze River and Pearl River populations as references, 646 and 594 genes with hypothesized selection signature were identified by selective sweeps in the mono-female population, respectively. These genes were involved in 25 KEGG pathways associated with some important traits, such as growth (esm1 and zbed1), reproduction (ccnb1 and ptpn13), development (grin1a, mcc and brd2) and immune response (npr2, psmb9a, psmb8, tap2 and h2a). These results will be helpful for the molecular marker-assisted breeding and development conservation strategies of grass carp germplasm resources.

Systematic selection of ancestry informative SNPs for differentiating Han, Japanese, Dai, and Kinh populations.

Biogeographical origin inferences of different populations can provide valuable clues in the forensic investigation by narrowing down the detection scope. However, much research mainly focuses on forensic ancestral origin analyses of major continental populations, which may provide limited information in forensic practice. To improve the ancestral resolution of East Asian populations, we systematically selected ancestry informative single-nucleotide polymorphisms (AISNPs) for differentiating Han, Dai, Japanese, and Kinh populations. In addition, we evaluated the performance of the selected AISNPs to differentiate these populations via multiple methods. Totally 116 AISNPs were selected from the genome-wide data to infer the population origins of these four populations. Results of principle component analysis and population genetic structure of these populations indicated that the selected 116 AISNPs could achieve ancestral resolution of most individuals. Furthermore, the machine learning model built by 116 AISNPs unveiled that most individuals from these four populations could be assigned to correct population origins. To sum up, the selected 116 SNPs could be available for ancestral origin predictions of Han, Dai, Japanese, and Kinh populations, which could provide valuable information for forensic research and genome-wide association study in East Asian populations to some extent.