Single Nucleotide Polymorphism Datasets Research Articles

Genotyping Error Detection and Customised Filtration for SNP Datasets.

A major challenge in analysing single-nucleotide polymorphism (SNP) genotype datasets is detecting and filtering errors that bias analyses and misinterpret ecological and evolutionary processes. Here, we present a comprehensive method to estimate and minimise genotyping error rates (deviations from the 'true' genotype) in any SNP datasets using triplicates (three repeats of the same sample) in a four-step filtration pipeline. The approach involves: (1) SNP filtering by missing data; (2) SNP filtering by error rates; (3) sample filtering by missing data and (4) detection of recaptured individuals by using estimated SNP error rates. The modular pipeline is provided in an R script that allows customised adjustments. We demonstrate the applicability of the method using non-invasive sampling from the Asiatic wild ass (Equus hemionus) population in Israel. We genotyped 756 samples using 625 SNPs, of which 255 were triplicates of 85 samples. The average SNP error rate, calculated based on the number of mismatching genotypes across triplicates before filtration, was 0.0034 and was reduced to 0.00174 following filtration. Evaluating genetic distance (GD) and relatedness (r) between triplicates before and after filtration (expected to be at the minimum and maximum respectively) showed a significant reduction in the average GD, from 58.1 to 25.3 (p = 0.0002) and a significant increase in relatedness, from r = 0.98 to r = 0.991 (p = 0.00587). We demonstrate how error rate estimation enhances recapture detection and improves genotype quality.

CannSeek? Yes we Can! Anopen-source single nucleotide polymorphism database and analysis portal for Cannabis sativa.

The CannSeek portal is available at https://icgrc.info/cannseek, https://icgrc.info/genotype_viewer.

A newly developed 20 K SNP array reveals QTLs for disease resistance to Cryptocaryon irritans in tiger pufferfish (Takifugu rubripes)

This study describes the development and application of a 20 K single nucleotide polymorphism (SNP) array in fugu (Takifugu rubripes). The main goal of this newly developed SNP array is to enhance selective breeding practice via the incorporation of genomic information. Fugu is one of the economic aquaculture species and mainly threatened by parasite disease. The performance of the array was evaluated in a farmed pufferfish population challenged with Cryptocaryon irritans and benchmarked against whole-genome sequencing of a subset of the animals. The array showed very high call rates, consistency between technical replicates, and concordance with genome-resequencing data. Heritability estimates using pedigree and SNP datasets demonstrated a significant genetic component of resistance to C. irritans, with heritability estimates ranging from 0.53 to 0.67, and prediction accuracies were significantly improved with the use of genomic data. Genome-wide association study identified two putative quantitative trait loci (QTLs) on chromosomes 3 and 17. Our findings suggest resistance to C. irritans can be rapidly increased in fugu populations by selective breeding, further expedited via the application of genetic markers. Therefore, they represent a valuable resource for selective breeding programmes aimed at enhancing disease resistance in fugu aquaculture.

Genetic diversity of the Turkish accessions of two progenitor species, Triticum baeoticum Boiss. and Triticum urartu Thum. ex Gandil., using DArTSeq markers

AbstractThe aim of this study was to reveal the intra and interspecies differences between Triticum baeoticum and Triticum urartu using Diversity Arrays Technology sequencing (DArTseq) on 94 accessions representing Turkish populations. Seeds were gathered from the US Department of Agriculture, and from the Turkish Seed Gene Bank. Isolated and purified DNA samples were sent to Diversity Arrays Technologies for DArTseq. After the necessary quality filtering, a total of 16,898 and 100,103 loci were obtained respectively from the single nucleotide polymorphism (SNP) and SilicoDArT datasets. ADMIXTURE software was used to reveal the intra and interspecies population structures. Analysis of molecular variance was carried out to reveal the variance between the populations of the T. urartu and T. baeoticum species. Principal coordinate analysis was conducted to visualize the main sources of variation between the populations on a 2-dimensional plane. To reveal the evolutionary relationship, SNP dataset was used to reconstruct the phylogenetic dendrograms by using the maximum likelihood statistical method and the unweighted pair group method with arithmetic mean clustering algorithm. As a result of this study, the accessions of T. urartu and T. baeoticum species formed separate clusters and revealed as two different species. In line with the results obtained, it is obvious that the identification of some accessions should be re-evaluated. The results demonstrated that DArTseq, is a fast, low-cost, and high-accuracy method that can be used in species and population discrimination and an effective tool for Gene Bank management.

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).

Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson’s disease

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson’s disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.

Genomic inbreeding coefficients using imputation genotypes: Assessing the effect of ancestral genotyping in Holstein-Friesian dairy cows

The objective of this study was to assess the effect of using or not the genotypes of the parents of a cow for imputing single nucleotide polymorphisms (SNP), on the estimation of genomic inbreeding coefficients of cows. Imputation (i.e., genotyped plus imputed) genotypes from 68,127 Italian Holstein dairy cows registered in the Italian National Association of Holstein, Brown and Jersey Breeders (ANAFIBJ) were analyzed. Cows were genotyped with the HD Illumina Infinium BovineHD BeadChip and GeneSeek Genomic Profiler HD-150K, and the MD GeneSeek Genomic Profiler 3, GeneSeek Genomic Profiler 4, GeneSeek MD and the Labogena MD. To assess differences among estimators genomic inbreeding coefficients were estimated with 4 PLINK v1.9 estimators (F, Fhat1, 2, 3), 2 genomic relationship matrix (grm) based estimators (Fgrm and Fgrm2; with the latter including also pedigree information) and one estimator of runs of homozygosity (ROH; FROH). Assuming that the correct genomic inbreeding coefficients should be those estimated from genotyped SNP, a comparison of the genomic inbreeding coefficients estimated either with the genotyped SNP or the SNP after imputation was made. Information on the presence or absence of genotypic information from sire, dam and maternal grandsire during the imputation was investigated. Genomic inbreeding coefficients estimated with genotyped SNP or SNP after imputation were consistent for F, Fhat3, Fgrm2 and FROH, when at least one of the parents was genotyped. Biased (mainly higher) genomic inbreeding coefficients of imputation SNP were observed in cows that were genotyped with MD SNP panels whose SNP were poorly represented in the selected imputation SNP data set and also did not have their parents genotyped compared with what expected based on actual genotype data. For cows genotyped with MD the estimators Fhat1, Fhat2 and Fgrm provided higher genomic inbreeding coefficients of imputation SNP even with both parents and the maternal grandsire genotyped. Overall, FROH was the most robust estimator, followed by F and Fhat3. Our findings suggest that SNP selection, parental genotyping and estimator should be considered for designing imputation strategies in dairy cattle for estimating genomic inbreeding with imputation SNP. For computing genomic inbreeding coefficients, it is recommendable to have at least one parent genotyped and use an ROH based estimator.

Influence of molecular marker type on estimating effective population size and other genetic parameters in a critically endangered parrot

AbstractGenetics is a fast‐moving field, and for conservation practitioners or ecologists, it can be bewildering. The choice of marker used in studies is fundamental; in the literature, preference has recently shifted from microsatellites to single nucleotide polymorphism (SNP) loci. Understanding how marker type affects estimates of population genetic parameters is important in the context of conservation, especially because the accuracy of estimates has a bearing on the actions taken to protect threatened species. We compare parameter estimates between seven microsatellites, 3761 SNP loci, and a random subset of 100 SNPs for the exact same 324 individual swift parrots, Lathamus discolor, and also use 457 additional samples from subsequent years to compare SNP estimates. Both marker types estimated a lower HO than HE. We show that microsatellites and SNPs mainly indicate a lack of spatial genetic structure, except when a priori collection locations were used on the SNP data in a discriminant analysis of principal components (DAPC). The 100‐SNP subset gave comparable results to when the full dataset was used. Estimates of effective population size (Ne) were comparable between markers when the same individuals were considered, but SNPs had narrower confidence intervals. This is reassuring because conservation assessments that rely on population genetic estimates based on a few microsatellites are unlikely to be nullified by the general shift toward SNPs in the literature. However, estimates between markers and datasets varied considerably when only adult samples were considered; hence, including samples of all age groups is recommended to be used when available. The estimated Ne was higher for the full SNP dataset (2010–2019) than the smaller comparison data (2010–2015), which might be a better reflection of the species status. The lower precision of microsatellites may not necessarily be a barrier for most conservation applications; however, SNPs will improve confidence limits, which may be useful for practitioners.

The sweet tabaiba or there and back again: phylogeographical history of the Macaronesian Euphorbia balsamifera.

Biogeographical relationships between the Canary Islands and north-west Africa are often explained by oceanic dispersal and geographical proximity. Sister-group relationships between Canarian and eastern African/Arabian taxa, the 'Rand Flora' pattern, are rare among plants and have been attributed to the extinction of north-western African populations. Euphorbia balsamifera is the only representative species of this pattern that is distributed in the Canary Islands and north-west Africa; it is also one of few species present in all seven islands. Previous studies placed African populations of E. balsamifera as sister to the Canarian populations, but this relationship was based on herbarium samples with highly degraded DNA. Here, we test the extinction hypothesis by sampling new continental populations; we also expand the Canarian sampling to examine the dynamics of island colonization and diversification. Using target enrichment with genome skimming, we reconstructed phylogenetic relationships within E. balsamifera and between this species and its disjunct relatives. A single nucleotide polymorphism dataset obtained from the target sequences was used to infer population genetic diversity patterns. We used convolutional neural networks to discriminate among alternative Canary Islands colonization scenarios. The results confirmed the Rand Flora sister-group relationship between western E. balsamifera and Euphorbia adenensis in the Eritreo-Arabian region and recovered an eastern-western geographical structure among E. balsamifera Canarian populations. Convolutional neural networks supported a scenario of east-to-west island colonization, followed by population extinctions in Lanzarote and Fuerteventura and recolonization from Tenerife and Gran Canaria; a signal of admixture between the eastern island and north-west African populations was recovered. Our findings support the Surfing Syngameon Hypothesis for the colonization of the Canary Islands by E. balsamifera, but also a recent back-colonization to the continent. Populations of E. balsamifera from northwest Africa are not the remnants of an ancestral continental stock, but originated from migration events from Lanzarote and Fuerteventura. This is further evidence that oceanic archipelagos are not a sink for biodiversity, but may be a source of new genetic variability.

Ecological opportunity leads to higher diversity and probability of trophic specialization in Arctic charr

Abstract Understanding the extrinsic factors that drive the emergence of biological diversity is critical to its protection and management. However, even superficially similar local environments can vary considerably, and so large-scale datasets are needed to understand the key drivers across a representative portion of a species distribution. Arctic charr (Salvelinus alpinus) is a widespread salmonid fish that represents an excellent study system for these processes because the species shows substantial diversification to a range of freshwater environments and multiple instances of trophic specialization in the form of sympatric ecotype populations. However, the processes behind this variation remain largely unknown due to a lack of broad-scale studies. To address this, we investigated the drivers of diversity via a national-scale study of 64 Arctic charr populations in lakes across Scotland using a genome-wide dataset of single nucleotide polymorphisms (N = 24 878) and phenotypic data of head depth relative to body size. We found that the extent of genetic and phenotypic diversity was predicted by ecosystem size (a proxy of lake size, depth, and complexity). We suggest this is because larger, deeper lakes generally provided more ecological opportunity and diversification potential. Additionally, using environmental data from all 187 lakes containing Arctic charr in Scotland, we found that ecosystem size strongly predicted the potential for trophic specialization and the presence of sympatric divergent ecotypes. Our results show the importance of ecological opportunity in underlying adaptive radiations.

Drought-induced growth phenotypes are associated with genetic variation across a white pine hybrid zone

Understanding relationships among warming climate, increased drought severity, and the genetic architecture of hybrid drought resilience is necessary for forest conservation and management. We calculated three drought-related tree-ring-growth indices (dendrophenotypes) using tree-ring data from hybrid Pinus strobiformis–Pinus flexilis study trees at nine sites across Colorado, New Mexico, Arizona, and Texas. Along with hybrid index (percentage of P. strobiformis ancestry inherited by a single tree) and climate variables, we used dendrophenotypes to (Q1) examine relationships among climate and hybrid index, (Q2) examine relationships between dendrophenotypes and hybrid index, and (Q3) conduct a genotype–phenotype analysis. We observed a significant correlation between hybrid index and dendrophenotypes resulting from high-temperature drought in addition to a significant correlation between those dendrophenotypes and our dataset of single-nucleotide polymorphisms. We conclude that P. strobiformis–P. flexilis trees exhibiting higher hybrid indices are more resilient to high-temperature drought events and encourage future research that identifies a genetic linkage between relevant loci and their conferred physiological benefits.

A new wild emmer wheat panel allows to map new loci associated with resistance to stem rust at seedling stage.

Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is a major wheat disease worldwide. A collection of 283 wild emmer wheat [Triticum turgidum L. subsp. dicoccoides (Körn. ex Asch. & Graebn.) Thell] accessions, representative of the entire Fertile Crescent region where wild emmer naturally occurs, was assembled, genotyped, and characterized for population structure, genetic diversity, and rate of linkage disequilibrium (LD) decay. Then, the collection was employed for mapping Pgt resistance genes, as a proof of concept of the effectiveness of genome-wide association studies in wild emmer. The collection was evaluated in controlled conditions for reaction to six common Pgt pathotypes (TPMKC, TTTTF, JRCQC, TRTTF, TTKSK/Ug99, and TKTTF). Most resistant accessions originated from the Southern Levant wild emmer lineage, with some showing a resistance reaction toward three to six tested races. Association analysis was conducted considering a 12K polymorphic single-nucleotide polymorphisms dataset, kinship relatedness between accessions, and population structure. Eleven significant marker-trait associations (MTA) were identified across the genome, which explained from 17% to up to 49% of phenotypic variance with an average 1.5 additive effect (based on the 1-9 scoring scale). The identified loci were either effective against single or multiple races. Some MTAs colocalized with known Pgt resistance genes, while others represent novel resistance loci useful for durum and bread wheat prebreeding. Candidate genes with an annotated function related to plant response to pathogens were identified at the regions linked to the resistance and defined according to the estimated small LD (about 126kb), as typical of wild species.

Dataset of single nucleotide polymorphisms of immune-associated genes in patients with SARS-CoV-2 infection.

The SARS-CoV-2 pandemic has affected nations globally leading to illness, death, and economic downturn. Why disease severity, ranging from no symptoms to the requirement for extracorporeal membrane oxygenation, varies between patients is still incompletely understood. Consequently, we aimed at understanding the impact of genetic factors on disease severity in infection with SARS-CoV-2. Here, we provide data on demographics, ABO blood group, human leukocyte antigen (HLA) type, as well as next-generation sequencing data of genes in the natural killer cell receptor family, the renin-angiotensin-aldosterone and kallikrein-kinin systems and others in 159 patients with SARS-CoV-2 infection, stratified into seven categories of disease severity. We provide single-nucleotide polymorphism (SNP) data on the patients and a protein structural analysis as a case study on a SNP in the SIGLEC7 gene, which was significantly associated with the clinical score. Our data represent a resource for correlation analyses involving genetic factors and disease severity and may help predict outcomes in infections with future SARS-CoV-2 variants and aid vaccine adaptation.

Combined reference-free and multi-reference based GWAS uncover cryptic variation underlying rapid adaptation in a fungal plant pathogen.

Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., k-mer) approach using raw whole-genome sequencing data in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici. We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the k-mer approach explained a higher proportion of phenotypic variation than a reference genome-based approach and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.

Genome-wide association studies using multi-models and multi-SNP datasets provide new insights into pasmo resistance in flax.

Flax (Linum usitatissimum L.) is an economically important crop due to its oil and fiber. However, it is prone to various diseases, including pasmo caused by the fungus Septoria linicola. In this study, we conducted field evaluations of 445 flax accessions over a five-year period (2012-2016) to assess their resistance to pasmo A total of 246,035 single nucleotide polymorphisms (SNPs) were used for genetic analysis. Four statistical models, including the single-locus model GEMMA and the multi-locus models FarmCPU, mrMLM, and 3VmrMLM, were assessed to identify quantitative trait nucleotides (QTNs) associated with pasmo resistance. We identified 372 significant QTNs or 132 tag QTNs associated with pasmo resistance from five pasmo resistance datasets (PAS2012-PAS2016 and the 5-year average, namely PASmean) and three genotypic datasets (the all SNPs/ALL, the gene-based SNPs/GB and the RGA-based SNPs/RGAB). The tag QTNs had R2 values of 0.66-16.98% from the ALL SNP dataset, 0.68-20.54%from the GB SNP dataset, and 0.52-22.42% from the RGAB SNP dataset. Of these tag QTNs, 93 were novel. Additionally, 37 resistance gene analogs (RGAs)co-localizing with 39 tag QTNs were considered as potential candidates for controlling pasmo resistance in flax and 50 QTN-by-environment interactions(QEIs) were identified to account for genes by environmental interactions. Nine RGAs were predicted as candidate genes for ten QEIs. Our results suggest that pasmo resistance in flax is polygenic and potentially influenced by environmental factors. The identified QTNs provide potential targets for improving pasmo resistance in flax breeding programs. This study sheds light on the genetic basis of pasmo resistance and highlights the importance of considering both genetic and environmental factors in breeding programs for flax.

The genome-wide meiotic recombination landscape in ciliates and its implications for crossover regulation and genome evolution

Meiotic recombination is essential for sexual reproduction and its regulation has been extensively studied in many taxa. However, genome-wide recombination landscape has not been reported in ciliates and it remains unknown how it is affected by the unique features of ciliates: the synaptonemal complex (SC)-independent meiosis and the nuclear dimorphism. Here, we show the recombination landscape in the model ciliate Tetrahymena thermophila by analyzing single-nucleotide polymorphism datasets from 38 hybrid progeny. We detect 1021 crossover (CO) events (35.8 per meiosis), corresponding to an overall CO rate of 9.9 cM/Mb. However, gene conversion by non-crossover is rare (1.03 per meiosis) and not biased towards G or C alleles. Consistent with the reported roles of SC in CO interference, we find no obvious sign of CO interference. CO tends to occur within germ-soma common genomic regions and many of the 44 identified CO hotspots localize at the centromeric or subtelomeric regions. Gene ontology analyses show that CO hotspots are strongly associated with genes responding to environmental changes. We discuss these results with respect to how nuclear dimorphism has potentially driven the formation of the observed recombination landscape to facilitate environmental adaptation and the sharing of machinery among meiotic and somatic recombination.

Lifting of the 1,000 wheat exome project SNPs from Triticum aestivum cv. Chinese Spring assembly RefSeq v1.0 to RefSeq v2.1

ObjectiveThe 1,000 wheat exome project captured the single nucleotide variants in the coding regions of a diverse set of 890 wheat accessions to analyse the contribution of introgression to adaptation of wheat. However, this highly useful single nucleotide polymorphism (SNP) dataset is based on RefSeq v1.0 of the International Wheat Genome Sequencing Consortium (IWGSC) assembly of the bread wheat genome of Chinese Spring. This reference sequence has recently been updated using optical maps and long-read sequencing to produce the improved RefSeq v2.1. Our objective was to develop a reliable high-density SNP dataset positioned onto RefSeq v2.1 because it is the current standard reference sequence used by wheat researchers.ResultsThe 3,039,822 SNPs originally positioned on RefSeq v1.0 were projected to v2.1 using Liftoff with four different flanking regions, and 2,946,536 SNPs were consistently lifted to the same location irrespective of the flanking region lengths. Of these, 2,799,166 were located on the ‘+’ ve strand. The distribution of the SNPs across the 21 chromosomes on RefSeq v2.1 was similar to that of RefSeq v1.0. Among the SNPs that were based on unanchored scaffolds in RefSeq v1.0, 11,938 were projected to one of the 21 pseudomolecules in the upgraded assembly. This SNP dataset constitutes a much-needed standardized resource for the wheat research community.

Genetic diversity and signature of divergence in the genome of grapevine clones of Southern Italy varieties.

Sexual reproduction has contributed to a significant degree of variability in cultivated grapevine populations. However, the additional influence of spontaneous somatic mutations has played a pivotal role in shaping the diverse landscape of grapevine agrobiodiversity. These naturally occurring selections, termed 'clones,' represent a vast reservoir of potentially valuable traits and alleles that hold promise for enhancing grape quality and bolstering plant resilience against environmental and biotic challenges. Despite their potential, many of these clones remain largely untapped.In light of this context, this study aims to delve into the population structure, genetic diversity, and distinctive genetic loci within a collection of 138 clones derived from six Campanian and Apulian grapevine varieties, known for their desirable attributes in viticulture and winemaking. Employing two reduced representation sequencing methods, we extracted Single-Nucleotide Polymorphism (SNP) markers. Population structure analysis and fixation index (FST) calculations were conducted both between populations and at individual loci. Notably, varieties originating from the same geographical region exhibited pronounced genetic similarity.The resulting SNP dataset facilitated the identification of approximately two hundred loci featuring divergent markers (FST ≥ 0.80) within annotated exons. Several of these loci exhibited associations with essential traits like phenotypic adaptability and environmental responsiveness, offering compelling opportunities for grapevine breeding initiatives. By shedding light on the genetic variability inherent in these treasured traditional grapevines, our study contributes to the broader understanding of their potential. Importantly, it underscores the urgency of preserving and characterizing these valuable genetic resources to safeguard their intra-varietal diversity and foster future advancements in grapevine cultivation.

A review of machine learning models applied to genomic prediction in animal breeding.

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there's no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.

Speciation patterns of related species under the hybrid zone: A case study of three sclerophyllous oaks in the east Himalaya-Hengduan Mountains.

Speciation is often accompanied by frequent gene exchanges, which have been gradually recognized as a common phenomenon in nature. Although gene flow may influence different reproductive isolations, the specific mechanism of the process still requires more experimental evidence, especially in hybrid populations that have no significant differentiation and isolation. To address this challenge, this study aims to elucidate the underlying mechanisms of sympatry and parapatry in related species. Three sclerophyllous oaks (Quercus spinosa, Quercus aquifolioides and Quercus rehderiana), which are mainly distributed in the sympatry/parapatry of the East Himalaya-Hengduan Mountains and adjacent regions, were used to explore the population dynamics and evolutionary history. Based on 12,420 genome-wide single nucleotide polymorphism datasets, gene flow detection showed that the three species did not present any obvious genetic barriers. Evolutionary analysis indicated that the three species diverged during the Tertiary Period, and no migration events occurred in the early stages of species divergence. Combined with the data of 19 ecological factors, geological movements and climatic turbulence caused the rapid radiated differentiation of the three species during the Neocene, and similar selective pressures resulted in the same evolutionary pattern based on demographic history analysis. In addition, the predicted niche occupancy profiles and Generalized Dissimilarity Modelling revealed that the three species occupied distinct niches and had significant differences in ecological adaptation, which may explain the specific morphological characteristics of the different species. Therefore, we believe that the populations of the three related species underwent adaptive evolution in different habitats during the early stages of divergence. This study provides new experimental evidence of the formation patterns of parallel speciation.