Single Nucleotide Polymorphism Data Research Articles

Efficient HLA imputation from sequential SNPs data by transformer.

Human leukocyte antigen (HLA) genes are associated with a variety of diseases, yet the direct typing of HLA alleles is both time-consuming and costly. Consequently, various imputation methods leveraging sequential single nucleotide polymorphisms (SNPs) data have been proposed, employing either statistical or deep learning models, such as the convolutional neural network (CNN)-based model, DEEP*HLA. However, these methods exhibit limited imputation efficiency for infrequent alleles and necessitate a large size of reference dataset. In this context, we have developed a Transformer-based model to HLA allele imputation, named "HLA Reliable IMpuatioN by Transformer (HLARIMNT)" designed to exploit the sequential nature of SNPs data. We evaluated HLARIMNT's performance using two distinct reference panels; Pan-Asian reference panel (n = 530) and Type 1 Diabetes genetics Consortium (T1DGC) reference panel (n = 5225), alongside a combined panel (n = 1060). HLARIMNT demonstrated superior accuracy to DEEP*HLA across several indices, particularly for infrequent alleles. Furthermore, we explored the impact of varying training data sizes on imputation accuracy, finding that HLARIMNT consistently outperformed across all data size. These findings suggest that Transformer-based models can efficiently impute not only HLA types but potentially other gene types from sequential SNPs data.

Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning

PurposeWe aimed to develop a machine learning-based prediction model for severe radiation pneumonitis (RP) by integrating relevant clinicopathological and genetic factors, considering the associations of clinical, dosimetric parameters, and single nucleotide polymorphisms (SNPs) of genes in the TGF-β1 pathway with RP. MethodsWe prospectively enrolled 59 primary lung cancer patients undergoing radiotherapy and analyzed pretreatment blood samples, clinicopathological/dosimetric variables, and 11 functional SNPs in TGFβ pathway genes. Using the Synthetic Minority Over-sampling Technique (SMOTE) and nested cross-validation, we developed a machine learning-based prediction model for severe RP (grade ≥ 2). Feature selection was conducted using four methods (filtered-based, wrapper-based, embedded, and logistic regression), and performance was evaluated using three machine learning models. ResultsSevere RP occurred in 20.3 % of patients with a median follow-up of 39.7 months. In our final model, age (>66 years), smoking history, PTV volume (>300 cc), and AG/GG genotype in BMP2 rs1979855 were identified as the most significant predictors. Additionally, incorporating genomic variables for prediction alongside clinicopathological variables significantly improved the AUC compared to using clinicopathological variables alone (0.822 vs. 0.741, p = 0.029). The same feature set was selected using both the wrapper-based method and logistic model, demonstrating the best performance across all machine learning models (AUC: XGBoost 0.815, RF 0.805, SVM 0.712, respectively). ConclusionWe successfully developed a machine learning-based prediction model for RP, demonstrating age, smoking history, PTV volume, and BMP2 rs1979855 genotype as significant predictors. Notably, incorporating SNP data significantly enhanced predictive performance compared to clinicopathological factors alone.

ONeSAMP 3.0: Estimation of Effective Population Size via SNP Data from One Population.

The genetic effective size (Ne) is arguably one of the most important characteristics of a population as it impacts the rate of loss of genetic diversity. Methods that estimate Ne are important in population and conservation genetic studies as they quantify the risk of a population being inbred or lacking genetic diversity. Yet there are very few methods that can estimate the Ne from data from a single population and without extensive information about the genetics of the population, such as a linkage map, or a reference genome of the species of interest. We present ONeSAMP 3.0, an algorithm for estimating Ne from single nucleotide polymorphism (SNP) data collected from a single population sample using Approximate Bayesian Computation and local linear regression. We demonstrate the utility of this approach using simulated Wright-Fisher populations, and empirical data from five endangered Channel Island fox (Urocyon littoralis) populations to evaluate the performance of ONeSAMP 3.0 compared to a commonly used Ne estimator. Our results show that ONeSAMP 3.0 is is broadly applicable to natural populations and is flexible enough that future versions could easily include summary statistics appropriate for a suite of biological and sampling conditions. ONeSAMP 3.0 is publicly available under the GNU license at https://github.com/AaronHong1024/ONeSAMP_3.

Drivers of genomic diversity and phenotypic development in early phases of domestication in Hermetia illucens.

The black soldier fly (BSF), Hermetia illucens, has the ability to efficiently bioremediate organic waste into usable bio-compounds. Understanding the impact of domestication and mass rearing on fitness and production traits is therefore important for sustainable production. This study aimed to assess patterns of genomic diversity and its association to phenotypic development across early generations of mass rearing under two selection strategies: selection for greater larval mass (SEL lines) and no direct artificial selection (NS lines). Genome-wide single nucleotide polymorphism (SNP) data were generated using 2bRAD sequencing, while phenotypic traits relating to production and population fitness were measured. Declining patterns of genomic diversity were observed across three generations of captive breeding, with the lowest diversity recorded for the F3 generation of both selection lines, most likely due to founder effects. The SEL cohort displayed statistically significantly greater larval weight com the NS lines with pronounced genetic and phenotypic directional changes across generations. Furthermore, lower genetic and phenotypic diversity, particularly for fitness traits, were evident for SEL lines, illustrating the trade-off between selecting for mass and the resulting decline in population fitness. SNP-based heritability was significant for growth, but was low or non-significant for fitness traits. Genotype-phenotype correlations were observed for traits, but individual locus effect sizes where small and very few of these loci demonstrated a signature for selection. Pronounced genetic drift, due to small effective population sizes, is likely overshadowing the impacts of selection on genomic diversity and consequently phenotypic development. The results hold particular relevance for genetic management and selective breeding for BSF in future.

O-271 Two component resolution of mosaic samples in PGT-A: primary template amplification (PTA) and independent analysis using copy-number variation and allelic balance

Abstract Study question How does accurate, unbiased whole genome amplification and tiered, independent, two component analysis impact PGT-A results? Summary answer When compared to previous methods a PGT-A workflow utilizing PTA followed by a CNV and allelic balance-based analysis generated more conclusive results. What is known already Accurate PGT-A results require unbiased error-free whole genome amplification from the trophectoderm biopsy and smart bioinformatics utilities to fully analyze and consider the large complex datasets. PTA generates fewer errors, greater coverage, and reduced allele drop-out offering the best starting point to interpret the genome of each embryo. Current PGT-A platforms leverage either normalized CNV data OR allelic balance at single nucleotide polymorphisms (SNPs), to detect alterations in the embryonic genome. A pipeline leveraging improved amplification technology and consolidated CNV and SNP data, particularly for putative mosaic and segmental mosaic calls, could reduce uncertainty and provide more accurate PGT-A results. Study design, size, duration A retrospective analysis of PGT-A results was undertaken with samples originating from multiple clinics and processed in a single laboratory, from January through December, 2023. Approximately 500 samples were processed simultaneously through two independent PGT-A pipelines. The primary objective was to compare the performance of CNV-only PGT-A with a test leveraging both CNV and SNP data. A secondary objective was to observe the resolution of mosaic embryos. Participants/materials, setting, methods De-identified samples were amplified with PTA and sequenced with both shotgun (coverage-based CNV) and capture (SNP-based)-approaches. Method specific analysis was applied to the resulting data. An initial bioinformatics approach analyzed CNV data only for variations in copy-number along the genome, proposing stretches of aneuploidy or mosaicism. A second, tiered analysis extended this data and incorporated SNP-based allelic balance to evaluate candidate regions and confirm the presence of aneuploidy and/or quantify mosaic levels. Main results and the role of chance Genome-wide read coverage and SNP data confirmed that PTA accurately and completely amplified the genetic material in each sample with broad distribution of informative data and a low allele drop-out rate. High concordance (95%) was observed for euploid and full aneuploid (whole chromosome and segmental) embryos classified with CNV-alone and CNV/SNV combined analysis. SNP data was processed, and B-allele ratios were used to confirm aneuploidies with particular utility in resolving the location of segmental changes. The classification of mosaic samples was altered in the pipeline leveraging both CNV and SNP data. Of 54 mosaic samples tested, 75% remained mosaic after independent consideration of SNP data, ∼15% and 6% of high-level mosaics were resolved to full aneuploid and euploid, respectively, and ∼25% of low-level mosaics reverted to a euploid classification. The deep SNP data produced during targeted sequencing confirmed the altered allele ratio predicted by CNV data and re-confirmed the presence and proportion of aneuploid cells. Mosaic levels were highly correlated in 90% of re-confirmed mosaic samples. This suggests that the classification of these samples as mosaic is precise and accurate and suggests a strong utility for a two component PGT-A pipeline leveraging CNV and SNV data generated from PTA embryos. Limitations, reasons for caution This was a retrospective study with limited sample numbers. Wider implications of the findings The study highlights the strong advantages of a hybrid PGT-A pipeline, leveraging PTA and two component analysis of coverage and allelic balance, particularly to resolve mosaic embryos more accurately. Overall, this could reduce uncertainty and deliver more confident PGT-A results for embryo transfer decision making. Trial registration number not applicable

On the road to losing connectivity: Faecal samples provide genome-wide insights into anthropogenic impacts on two large herbivore species in central India.

Humans have impacted most of the planet, and the ensuing fragmentation results in small, isolated habitat patches posing a risk of genetic diversity loss, inbreeding, and genetic load. Understanding how natural and anthropogenic landscape features affect gene flow among habitat patches is critical for maintaining connectivity. Genome-wide data are required to comprehend the impacts of recent fragmentation, which can be challenging when only non-invasive samples are available. Here, we build upon advancements in conservation genomics to address connectivity of two large herbivores, gaur (Bos gaurus) and sambar (Rusa unicolor) in central India. Given their habitat associations, we expected these species to respond similarly to habitat fragmentation. We used faecal-DNA and methylation-based host-DNA enrichment with modified ddRAD protocol to generate genome-wide single-nucleotide polymorphism (SNP) data for 124 gaur and 99 sambar individuals. Our findings reveal that gaur populations in central India are fragmented, displaying high genetic differentiation, with drift significantly affecting small populations like Umred Karhandla Wildlife Sanctuary. Although sambar shows low genetic structure, another small population, Bor Tiger Reserve is genetically differentiated. Our results suggest that although land cover change and roads restrict animal movement, the extent of this impact varies across the two species. We show that different species respond differently to landscape features, even with similar habitat associations. We highlight small and isolated populations requiring urgent conservation intervention. Such multi-species approaches enhance our understanding of cross-species connectivity patterns. We suggest shifting from single-species to multi-species holistic conservation approach in rapidly developing landscapes to better manage co-occurring endangered species.

SNP Array for Small-Shrimp (Genus Acetes) Origin Determination Using Machine Learning.

Accurate origin determination of seafood is crucial for consumer trust and safety. This study was performed to develop a machine learning-based single-nucleotide polymorphism (SNP) analysis technique to determine the origin of Acetes species in salted small-shrimp products. Mitochondrial DNA (COI and 16S rRNA) analysis revealed genetic variations among species and origins. Eight candidate SNPs were identified, six of which were developed into markers for genotyping analysis. Using the developed markers, an SNP array was created and SNP data from salted small-shrimp samples were obtained. Machine learning analysis using a supervised learning algorithm achieved 100% accuracy in classifying the origin of Acetes based on SNP data. This method offers a reliable method for regulatory bodies to combat food fraud and ensure product integrity. The approach can be further improved by expanding the data set to encompass a wider range of species and origins. This study highlights the potential of SNP analysis and machine learning for ensuring seafood authenticity and promoting sustainable practices.

Imputation of single nucleotide polymorphism genotypes in ungenotyped sport horses from the genotypes of their progeny

Many sport horse studbooks worldwide use microsatellite markers for parentage verification. However, many have expressed a desire to introduce genomic selection using genome-wide dense single nucleotide polymorphism (SNP) genotypes to complement their current breeding programs. Hence, it does not make sense to genotype the same animal for both microsatellite markers and SNPs markers. Transitioning to SNP-based parentage verification is an obvious solution but one barrier to this transition is the lack of SNP data on parents from which to verify parentage against. Therefore, the objective of this study was to assess the ability to impute the SNP genotype of a stallion from the genotypes of its progeny, with or without the consideration of the genotype of the progeny’s dam. Genotype information from 55 935 SNPs was available on 13 327 horses. A total of 98 stallions had genotype data on 10 progeny and the genotypes of these stallions were used as a test population. Genome-wide genotype imputation was undertaken combining a family- and population-based imputation approach. Several different scenarios were assessed to quantify the ability to accurately impute the genotype of a stallion based on genotype data of incrementally more half-sibling progeny. Using genomic information from four progeny the average genotype concordance of the imputed sire genotype compared to the actual sire’s genotype was 0.932. The average genotype concordance rate increased to 0.960 when the genotypes of 10 progeny were included in the imputation process. The inclusion of the genotypes of the dams of the progeny improved the concordance rate from 0.932 to 0.977 when based on the genotype of just four progeny and their dams and from 0.960 to 0.996 when based on the genotype of 10 progeny and their dams. These results suggest it is possible to impute the genotype of a non-genotyped horse from the genotypes of its progeny and that the inclusion of the genotypes of the dams of the progeny improves this imputation accuracy further.

A QTL on Maize Chromosome 5 is Associated with Root-Knot Nematode Resistance.

This study provides the first report of a quantitative trait locus (QTL) in maize (Zea mays) for resistance to the southern root-knot nematode (SRKN) (Meloidogyne incognita). The SRKN can feed on the roots of maize in the U.S. Southern Coastal Plain region and can cause yield losses of 30% or greater in heavily infested fields. Increases in SRKN density in the soil may reduce the yield for subsequently planted susceptible crops. The use of maize hybrids with resistance to SRKN could prevent an increase in SRKN density, yet no genetic regions have been identified that confer host resistance. In this study, a B73 (susceptible) x Ky21 (resistant) S5 recombinant inbred line (RIL) population was phenotyped for total number of eggs (TE) and root weight. This population has been previously genotyped using single nucleotide polymorphisms (SNPs). By utilizing the SNP data with the phenotype data, a single QTL was identified on chromosome 5 that explained 15% of the phenotypic variation (PV) for the number of eggs and 11% of the PV for the number of eggs per g of root (EGR). Plants that were homozygous for the Ky21 allele for the most associated marker PZA03172.3 had fewer eggs and fewer EGR than the plants that were homozygous or heterozygous for the B73 allele. Thus, the first QTL for SRKN resistance in maize has been identified and could be incorporated into maize hybrids.

Blood metabolomic and postpartum depression: a mendelian randomization study

BackgroundPostpartum depression is a complex mental health condition that often occurs after childbirth and is characterized by persistent sadness, anxiety, and fatigue. Recent research suggests a metabolic component to the disorder. This study aims to investigate the causal relationship between blood metabolites and postpartum depression using mendelian randomization (MR).MethodsThis study used a bi-directional MR framework to investigate the causal relationship between 1,400 metabolic biomarkers and postpartum depression. We used two specific genome-wide association studies datasets: one with single nucleotide polymorphisms data from mothers diagnosed with postpartum depression and another with blood metabolite data, both of which focused on people of European ancestry. Genetic variants were chosen as instrumental variables from both datasets using strict criteria to improve the robustness of the MR analysis. The combination of these datasets enabled a thorough examination of genetic influences on metabolic profiles associated with postpartum depression. Statistical analyses were conducted using techniques such as inverse variance weighting, weighted median, and model-based estimation, which enabled rigorous causal inference from the observed associations. postpartum depression was defined using endpoint definitions approved by the FinnGen study’s clinical expert groups, which included leading experts in their respective medical fields.ResultsThe MR analysis identified seven metabolites that could be linked to postpartum depression. Out of these, one metabolite was found to be protective, while six were associated with an increased risk of developing the condition. The results were consistent across multiple MR methods, indicating a significant correlation.ConclusionsThis study emphasizes the potential of metabolomics for understanding postpartum depression. The discovery of specific metabolites associated with the condition sheds new insights on its pathophysiology and opens up possibilities for future research into targeted treatment strategies.

Neutral processes and taxonomic scale drive beta species-genetic diversity correlations in a submesophotic tropical reef fish.

If similar evolutionary forces maintain intra- and interspecific diversity, patterns of diversity at both levels of biological organization can be expected to covary across space. Although this prediction of a positive species-genetic diversity correlation (SGDC) has been tested for several taxa in natural landscapes, no study has yet evaluated the influence of the community delineation on these SGDCs. In this study, we focused on tropical fishes of the Indo-Pacific Ocean, using range-wide single nucleotide polymorphism data for a deep-sea fish (Etelis coruscans) and species presence data of 4878 Teleostei species. We investigated whether a diversity continuum occurred, for different community delineations (subfamily, family, order and class) and spatial extents, and which processes explained these diversity patterns. We found no association between genetic diversity and species richness (α-SGDC), regardless of the community and spatial extent. In contrast, we evidenced a positive relationship between genetic and species dissimilarities (β-SGDC) when the community was defined at the subfamily or family level of the species of interest, and when the Western Indian Ocean was excluded. This relationship was related to the imprint of dispersal processes across levels of biological organization in Lutjanidae. However, this positive β-SGDC was lost when considering higher taxonomic communities and at the scale of the entire Indo-Pacific, suggesting different responses of populations and communities to evolutionary processes at these scales. This study provides evidence that the taxonomic scale at which communities are defined and the spatial extent are pivotal to better understand the processes shaping diversity across levels of biological organization.

Effect of serum uric acid on the risk of aortic aneurysm and dissection: A mendelian randomization analysis

Aortic aneurysm and dissection (AAD) are severe vascular diseases with high mortality rates. However, the causal relationship between serum uric acid levels and the occurrence of AAD remains a subject of controversy. To address this issue, we conducted a two-sample Mendelian randomization (MR) analysis to investigate whether there is a causal association between these factors. We obtained single-nucleotide polymorphisms (SNPs) data related to serum uric acid levels from the FinnGen study and data on AAD from the UK Biobank. Various two-sample MR methods, including inverse variance weighted (IVW) analysis, MR-Egger regression analysis, weighted median analysis, and contamination mixture method, were employed to assess the causal relationship between serum uric acid and the risk of AAD. Sensitivity analysis was conducted to evaluate the stability and reliability of the results. The findings revealed a positive association between serum uric acid levels and the risk of aortic aneurysm (AA) (odds ratio [OR] = 1.200, 95 % confidence interval [CI]: 1.020–1.400, P = 0.0239). However, no significant correlation was observed between serum uric acid levels and the occurrence of aortic dissection (AD) (OR = 0.893, 95 % CI = 0.602–1.326, P = 0.576). Our study, which employed MR analysis, identified a positive association between serum uric acid levels and the risk of AA. However, we did not observe a significant correlation with AD.

HaploMAGIC: accurate phasing and detection of recombination in multiparental populations despite genotyping errors.

Recombination is a key mechanism in breeding for promoting genetic variability. Multiparental populations (MPPs) constitute an excellent platform for precise genotype phasing, identification of genome-wide crossovers (COs), estimation of recombination frequencies, and construction of recombination maps. Here, we introduce haploMAGIC, a pipeline to detect COs in MPPs with single-nucleotide polymorphism (SNP) data by exploiting the pedigree relationships for accurate genotype phasing and inference of grandparental haplotypes. haploMAGIC applies filtering to prevent false-positive COs due to genotyping errors (GEs), a common problem in high-throughput SNP analysis of complex plant genomes. Hence, it discards haploblocks not reaching a specified minimum number of informative alleles. A performance analysis using populations simulated with AlphaSimR revealed that haploMAGIC improves upon existing methods of CO detection in terms of recall and precision, most notably when GE rates are high. Furthermore, we constructed recombination maps using haploMAGIC with high-resolution genotype data from 2 large multiparental populations of winter rapeseed (Brassica napus). The results demonstrate the applicability of the pipeline in real-world scenarios and showed good correlations in recombination frequency compared with alternative software. Therefore, we propose haploMAGIC as an accurate tool at CO detection with MPPs that shows robustness against GEs.

Computing linkage disequilibrium aware genome embeddings using autoencoders.

The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using, e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction. We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data. Data are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders.

Accuracy of genomic prediction using multiple Atlantic salmon populations

BackgroundThe accuracy of genomic prediction is partly determined by the size of the reference population. In Atlantic salmon breeding programs, four parallel populations often exist, thus offering the opportunity to increase the size of the reference set by combining these populations. By allowing a reduction in the number of records per population, multi-population prediction can potentially reduce cost and welfare issues related to the recording of traits, particularly for diseases. In this study, we evaluated the accuracy of multi- and across-population prediction of breeding values for resistance to amoebic gill disease (AGD) using all single nucleotide polymorphisms (SNPs) on a 55K chip or a selected subset of SNPs based on the signs of allele substitution effect estimates across populations, using both linear and nonlinear genomic prediction (GP) models in Atlantic salmon populations. In addition, we investigated genetic distance, genetic correlation estimated based on genomic relationships, and persistency of linkage disequilibrium (LD) phase across these populations.ResultsThe genetic distance between populations ranged from 0.03 to 0.07, while the genetic correlation ranged from 0.19 to 0.99. Nonetheless, compared to within-population prediction, there was limited or no impact of combining populations for multi-population prediction across the various models used or when using the selected subset of SNPs. The estimates of across-population prediction accuracy were low and to some extent proportional to the genetic correlation estimates. The persistency of LD phase between adjacent markers across populations using all SNP data ranged from 0.51 to 0.65, indicating that LD is poorly conserved across the studied populations.ConclusionsOur results show that a high genetic correlation and a high genetic relationship between populations do not guarantee a higher prediction accuracy from multi-population genomic prediction in Atlantic salmon.

Causal association of circulating metabolites with diabetic retinopathy: a bidirectional Mendelian randomization analysis.

The retina is a highly metabolically active tissue, and there is a lack of clarity about the relationship between metabolites and diabetic retinopathy (DR). This study used two-sample bidirectional Mendelian randomization (MR) analyses to identify causal relationships between metabolites and DR. Genetic variants were selected from the open-access Genome-Wide Association Studies (GWAS) summary database as proxies for the 1400 most recently published metabolites. MR analysis was performed to examine associations between these metabolite traits and DR. Single nucleotide polymorphism (SNP) data that were significantly associated with exposure were screened through association analysis. Validated instrumental variables (IVs) were obtained by removing SNPs with linkage disequilibrium (LD) and F-statistic values below 10. MR analyses were performed using the inverse variance weighted (IVW) method as the primary approach. The robustness of the results was verified by sensitivity analyses, including assessments of heterogeneity, horizontal pleiotropy, and the leave-one-out method. In the IVW approach and in the primary analysis of several sensitivity analyses, genetically determined glycolithocholate sulfate levels, androstenediol (3 beta, 17 beta) monosulfate (1) levels, 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4) levels, 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) levels, 1-oleoyl-2-linoleoyl-GPE (18:1/18:2) levels, X-26109 levels, N6-methyllysine levels, (N6,N6-dimethyllysine levels), and (N2-acetyl,N6,N6-dimethyllysine levels) were negatively associated with the risk of DR. 5-hydroxymethyl-2-furoylcarnitine levels and the glutamate-to-alanine ratio were positively associated with the risk of DR. No reverse causal association was found between DR and metabolites. This MR study suggests that nine metabolites may have a protective effect in DR, while two metabolites may be associated with an increased risk of DR. However, further research is needed to confirm these findings. Supplementation with beneficial metabolites may reduce DR risk and could potentially be a novel therapeutic approach to DR treatment.

Genetic diversity and population structure of four Anatolian sheep revealed by genome-wide ddRADseq data

This is the first wide-scale study to reveal next-generation sequencing-based and comprehensive study to reveal genetic diversity, population structure, and phylogenetic relationships of some of Türkiye's indigenous fat-tailed sheep breeds. A total of 80 animals belonging to Akkaraman (AKR), Güney Karaman (GKR), Morkaraman (MKR), and Karakaş (KRK) sheep breeds were genotyped with genome-wide of 296.097 single nucleotide polymorphisms (SNPs) data recovered from double digest restriction site-associated DNA sequencing (ddRADseq) libraries processed by Illumina HiSeq X Ten instrument. Minor allele frequency (MAF) ranged from 0.311 (MKR) to 0.316 (AKR and GKR). Observed heterozygosity (HO) values were higher than expected values (HE) across all populations with a mean of 0.307. Negative inbreeding coefficient (FIS) values were observed in four sheep populations varying between −0.034 (AKR) to −0.060 (MKR). Cross-entropy criterion-based ADMIXTURE analysis demonstrated that four Anatolian sheep breeds were represented by two ancestral populations in which GKR and AKR were distinct, while KRK and MKR were mixed populations. Genetic distance-based tree, discriminant analysis of principal components (DAPC), and ADMIXTURE analyses confirmed that GKR, which is believed to be derived from the AKR breed, has become genetically different from other sheep populations. Moreover, no migration from and into the GKR breed was detected, while a migration edge was drawn from the KRK and MKR clade into the AKR breed by the TreeMix algorithm. This study confirmed that high-density SNP data obtained from advanced next-generation sequencing (NGS) platforms are efficient in genetically distinguishing animal breeds, even varieties. It is recommended that further NGS-based studies could be carried out to better characterize different indigenous livestock populations raised in Türkiye.

Genetic Diversity, Linkage Disequilibrium, and Population Structure in a Common Bean Reference Collection

An in-depth understanding of the extent and pattern of genetic diversity and population structure in crop populations is of paramount importance for any crop improvement program to efficiently promote the translation of genetic diversity into genetic gain. A reference collection of 150 common bean genotypes selected from the International Center for Tropical Agriculture’s global core collection was evaluated using single-nucleotide polymorphism (SNP) markers to quantify the amount of genetic diversity, linkage disequilibrium, and population structure. The cultivars and landraces of the collection were diverse and originated from 14 countries, and wild accessions were used as controls for each gene pool. The collection was genotyped using an SNP array, generating a total of 5398 locus calls distributed across the entire bean genome. The SNP data quality was checked, and two datasets were generated. The first dataset (Dataset_1) comprised a set of 5108 SNPs and 150 genotypes after filtering for 10% missing alleles and an MAF < 0.05. The second dataset (Dataset_2) comprised a set of 2300 SNPs that remained after removing any null-allele SNPs and LD pruning for a criterion of r2 < 0.2. Dataset_1 was used for a principal coordinate analysis (PCoA), phylogenetic relationship determination, an analysis of molecular variance (AMOVA), and a discriminant analysis of principal components. Dataset_2 was used for a population structure analysis using STRUCTURE software and is proposed for a genome-wide association study (GWAS). The population structure analysis split the reference collection into two subpopulations according to an Andean or Mesoamerican gene pool. The Mesoamerican populations displayed higher genetic differentiation and tended to split into more groups that were somewhat aligned with common bean races. Andean beans were characterized by a larger average LD but lower LD percentage, a small average genetic distance between members of the population, and a higher major allele frequency, which suggested narrower genetic diversity compared to the Mesoamerican gene pool. In conclusion, the results indicated the presence of high genetic diversity, which is useful for a GWAS. However, the presence of significant linkage disequilibrium requires that genetic distance be considered as a co-factor for any further genetic studies. Overall, the molecular variation observed in the genotypes shows that this reference collection is valuable as a genebank-derived diversity panel which is useful for marker trait association studies.

Runs of homozygosity analysis for selection signatures in the Yellow Korean native chicken.

Yellow Korean native chicken (KNC-Y) is one of the five pure Korean indigenous chicken breeds that were restored through a government project in 1992. KNC-Y is recognized for its superior egg production performance compared to other KNC lines. In this study, we performed runs of homozygosity (ROH) analysis to discover selection signatures associated with egg production traits in the KNC-Y population. A total of 675 DNA samples from KNC-Y were genotyped to generate single nucleotide polymorphism (SNP) data using custom 60K Affymetrix SNP chips. ROH analysis was performed using PLINK software, with predefined parameters set for the analysis. The threshold of ROH island was defined as the top 1% frequency of SNPs withing the ROH among the population. In the KNC-Y population, a total of 29,958 runs of homozygosity (ROH) fragments were identified. The average total length of ROH was 120.84 Mb, with each ROH fragment having an average length of 2.71 Mb. The calculated ROH-based inbreeding coefficient (FROH) was 0.13. Furthermore, we revealed the presence of ROH islands on chromosomes 1, 2, 4, 5, 7, 8, and 11. Within the identified regions, a total of 113 genes were annotated, and among them were genes related to economic traits, including PRMT3, ANO5, HDAC4, LSS, PLA2G4A, and PTGS2. Most of the overlapping quantitative trait locus (QTL) regions with ROH islands were found to be associated with production traits. This study conducted a comprehensive analysis of ROH in the KNC-Y population. Notably, among the findings, the PTGS2 gene is believed to play a crucial role in influencing the laying performance of KNC-Y.

Ability of Genomic Prediction to Bi-Parent-Derived Breeding Population Using Public Data for Soybean Oil and Protein Content.

Genomic selection (GS) is a marker-based selection method used to improve the genetic gain of quantitative traits in plant breeding. A large number of breeding datasets are available in the soybean database, and the application of these public datasets in GS will improve breeding efficiency and reduce time and cost. However, the most important problem to be solved is how to improve the ability of across-population prediction. The objectives of this study were to perform genomic prediction (GP) and estimate the prediction ability (PA) for seed oil and protein contents in soybean using available public datasets to predict breeding populations in current, ongoing breeding programs. In this study, six public datasets of USDA GRIN soybean germplasm accessions with available phenotypic data of seed oil and protein contents from different experimental populations and their genotypic data of single-nucleotide polymorphisms (SNPs) were used to perform GP and to predict a bi-parent-derived breeding population in our experiment. The average PA was 0.55 and 0.50 for seed oil and protein contents within the bi-parents population according to the within-population prediction; and 0.45 for oil and 0.39 for protein content when the six USDA populations were combined and employed as training sets to predict the bi-parent-derived population. The results showed that four USDA-cultivated populations can be used as a training set individually or combined to predict oil and protein contents in GS when using 800 or more USDA germplasm accessions as a training set. The smaller the genetic distance between training population and testing population, the higher the PA. The PA increased as the population size increased. In across-population prediction, no significant difference was observed in PA for oil and protein content among different models. The PA increased as the SNP number increased until a marker set consisted of 10,000 SNPs. This study provides reasonable suggestions and methods for breeders to utilize public datasets for GS. It will aid breeders in developing GS-assisted breeding strategies to develop elite soybean cultivars with high oil and protein contents.