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This research aims to estimate heritability and repeatability based on the data on milk production traits (MY – milk yield; FY – milk fat yield; FC – milk fat content; PY – milk protein yield and PC – milk protein content) as well as pedigree and genomic information. A total of 6,041 animals were included in the research, while 2,565 of them had data for milk production traits. In order to form a genomic relationship matrix, 58K SNP data were used for a total of 1,491 cows. Several software tools were used in the preparation and analysis of data, which were provided by the Central Breeding Organization, Department of Animal Science, Faculty of Agriculture, University of Novi Sad. PreGSF90, in combination with RENUMF90, was used for quality control of genomic information. Genetic analysis was performed in WOMBAT software by the REML using standard repeatability univariate analysis (BLUPpe) and repeatability models for genomic prediction (GBLUPpe and ssGBLUPpe). In all three analyses, the highest heritability (0.410, 0.378 and 0.389, respectively) and repeatability (0.449, 0.429 and 0.440, respectively) were calculated for FC. Heritability estimates for all other traits were lower. Heritability ranged from 0.158 to 0.185 for MY, from 0.166 to 0.178 for FY, from 0.141 to 0.154 for PY and from 0.135 to 0.221 for PC. Heritability estimates indicate that it is possible to achieve genetic improvement but it is necessary to introduce the best model for prediction of breeding values of cow.

Balancing selection is the process of selection that preserves various alleles within a population. Studying the areas undergoing balancing selection is essential, because it preserves genetic diversity in a population. Finding genes that exhibit signs of balancing selection during the domestication of cattle is the goal of this study. To identify regions where polymorphism has persisted in the cattle population for millions of years, we examined the genome of cattle. In this study, we used bovine SNP 50 k data to conduct a detailed genome-wide assessment of selection signatures for balancing selection. We have included the genotyped data from 427 animals, including five taurines, two crossbreds, and eight Indian cattle breeds. For this study, we employed Tajima's D approach to identify signature regions undergoing balancing selection. Using the NCBI database, PANTHER 17.0, and CattleQTL database, the annotation was carried out after finding the relevant areas under balancing selection. The number of genomic regions undergoing balancing selection in Ayrshire, Brown-Swiss, Frieswal, Gir, Guernsey, Hariana, Holstein Friesian, Jersey, Kankrej, Nelore, Ongole, Red Sindhi, Sahiwal, Tharparkar, and Vrindavani was 11, 13, 13, 19, 18, 11, 17, 14, 14, 12, 10, 12, 13, 13, and 11, respectively. We have observed multiple immune system-related genes going through balancing selection, including KIT, NFATC2, GBP4, LRRC32, SYT7, RAG1, RAG2, LOC513659, and ZBTB17. In our study, we found that the majority of the immune-related genes and a few genes associated with growth, reproduction, production, and adaptation are undergoing balancing selection. [ABSTRACT FROM AUTHOR]

Most of what is known about extra‐pair paternity in hole‐nesting birds derives from studies using artificial nesting sites, such as nestboxes. However, it has rarely been investigated whether inference drawn from breeding events taking place in nestboxes matches what would be observed under natural conditions, that is, in natural cavities. We here report on a variation in promiscuity in blue tits and great tits nesting in natural cavities and nestboxes in an urban forest in Warsaw, Poland. Specifically, we tested whether local breeding density, local breeding synchrony, and extra‐pair paternity (inferred from SNP data generated with a high‐throughput genotyping by sequencing method) differed between birds nesting in natural cavities and nestboxes. In both blue tits and great tits, the frequency of extra‐pair paternity was similar between the two cavity types. In blue tits, we observed shorter nearest neighbor distance, higher neighbor density, and higher synchronous neighbor density (i.e., density of fertile females) in nestboxes relative to natural cavities. No such pattern was found in great tits. Moreover, we detected a positive relationship between the proportion of extra‐pair offspring in the nest and neighbor density around the nest in blue tits. Our results revealed that the provisioning of nestboxes did not change rates of extra‐pair paternity, suggesting that conclusions drawn from nestbox studies might adequately represent the natural variation in extra‐pair matings in some species or sites. However, the observed differences in spatiotemporal components of breeding dynamics highlight the fact that these parameters should be carefully considered when comparing mating behavior across studies and/or sites. [ABSTRACT FROM AUTHOR]

The systematics of the Old World Spiranthes sinensis (Pers.) Ames species complex (Orchidaceae) has been complicated by its wide distribution and morphological variations. Within the species complex, S. australis Lindl. has been generally accepted as the only Spiranthes Rich. species distributed on the Japanese mainland. The present study provides morphological, phylogenetic, and ecological evidence for the recognition of S. hachijoensis Suetsugu as a new species of the S. sinensis species complex on the Japanese mainland. Spiranthes hachijoensis is morphologically similar to S. hongkongensis S.Y. Hu & Barretto and S. nivea T.P. Lin & W.M. Lin, sharing a degenerated rostellum, pollinia without a viscidium, and distinctly trilobed stigma. However, the taxon can be morphologically distinguished from S. hongkongensis by its glabrous rachis, ovaries, and sepals, and from S. nivea by its papillate labellum disc, larger papillate basal labellum callosities, and glabrous rachis, ovaries, and sepals. The autogamy and flowering phenology (i.e., earlier flowering) of S. hachijoensis are most likely responsible for premating isolation from the sympatric S. australis. A MIG-seq-based high-throughput molecular analysis indicated that the genetic difference between S. hachijoensis and its putative sister species S. sinensis is comparable to, or even greater than, the genetic difference between pairs of other species within the S. sinensis species complex. Our multifaceted approach strongly supports the recognition of S. hachijoensis as a morphologically, phenologically, phylogenetically, and ecologically distinct species. [ABSTRACT FROM AUTHOR]

Due to their reduced morphology, non-photosynthetic plants have been one of the most challenging groups to delimit to species level. The mycoheterotrophic genus Monotropastrum, with the monotypic species M. humile, has been a particularly taxonomically challenging group, owing to its highly reduced vegetative and root morphology. Using integrative species delimitation, we have focused on Japanese Monotropastrum, with a special focus on an unknown taxon with rosy pink petals and sepals. We investigated its flowering phenology, morphology, molecular identity, and associated fungi. Detailed morphological investigation has indicated that it can be distinguished from M. humile by its rosy pink tepals and sepals that are generally more numerous, elliptic, and constantly appressed to the petals throughout its flowering period, and by its obscure root balls that are unified with the surrounding soil, with root tips that hardly protrude. Based on genome-wide single-nucleotide polymorphisms, molecular data has provided clear genetic differentiation between this unknown taxon and M. humile. Monotropastrum humile and this taxon are associated with different Russula lineages, even when they are sympatric. Based on this multifaceted evidence, we describe this unknown taxon as the new species M. kirishimense. Assortative mating resulting from phenological differences has likely contributed to the persistent sympatry between these two species, with distinct mycorrhizal specificity. [ABSTRACT FROM AUTHOR]

The study of population genetic structure and historical dynamics of species with disjunct distribution can reveal the mechanisms through with they were formed. Pinus densiflora is an essential tree species with ecological and economic value, and its natural distribution shows a disjunct pattern. Using transcriptome-level SNP data from 220 samples representing 32 naturally-distributed populations in East Asia, we investigated Pinus densiflora genetic diversity and structure, divergence time, and ancestral distribution. We identified five subpopulations which diverged approximately 2.02–1.49 million years ago, and found relatively low genetic differentiation among the three large subpopulations (SL, JH, and JK). Northeast China is the most likely origin, and its current distribution is the result of dispersal and vicariance events. It migrated southwest through the Liaodong Peninsula to the Shandong Peninsula and southeast through the Korean Peninsula to Japan. These results provide a basis for the conservation and management of P. densiflora in the future and the evolutionary study of species with similar life histories. [ABSTRACT FROM AUTHOR]

Lineage‐based species definitions applying coalescent approaches to species delimitation have become increasingly popular. Yet, the application of these methods and the recognition of lineage‐only definitions have recently been questioned. Species delimitation criteria that explicitly consider both lineages and evidence for ecological role shifts provide an opportunity to incorporate ecologically meaningful data from multiple sources in studies of species boundaries. Here, such criteria were applied to a problematic group of mycoheterotrophic orchids, the Corallorhiza striata complex, analysing genomic, morphological, phenological, reproductive‐mode, niche, and fungal host data. A recently developed method for generating genomic polymorphism data–ISSRseq–demonstrates evidence for four distinct lineages, including a previously unidentified lineage in the Coast Ranges and Cascades of California and Oregon, USA. There is divergence in morphology, phenology, reproductive mode, and fungal associates among the four lineages. Integrative analyses, conducted in population assignment and redundancy analysis frameworks, provide evidence of distinct genomic lineages and a similar pattern of divergence in the extended data, albeit with weaker signal. However, none of the extended data sets fully satisfy the condition of a significant role shift, which requires evidence of fixed differences. The four lineages identified in the current study are recognized at the level of variety, short of comprising different species. This study represents the most comprehensive application of lineage + role to date and illustrates the advantages of such an approach. [ABSTRACT FROM AUTHOR]

The study of population genetic structure and historical dynamics of species with disjunct distribution can reveal the mechanisms through with they were formed. Pinus densiflora is an essential tree species with ecological and economic value, and its natural distribution shows a disjunct pattern. Using transcriptome-level SNP data from 220 samples representing 32 naturally-distributed populations in East Asia, we investigated Pinus densiflora genetic diversity and structure, divergence time, and ancestral distribution. We identified five subpopulations which diverged approximately 2.02–1.49 million years ago, and found relatively low genetic differentiation among the three large subpopulations (SL, JH, and JK). Northeast China is the most likely origin, and its current distribution is the result of dispersal and vicariance events. It migrated southwest through the Liaodong Peninsula to the Shandong Peninsula and southeast through the Korean Peninsula to Japan. These results provide a basis for the conservation and management of P. densiflora in the future and the evolutionary study of species with similar life histories.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Summary: Chromosomal abnormalities are a common cause of infertility in horses. However, they are difficult to detect using automated methods. Here, we propose a simple methodology based on single nucleotide polymorphism (SNP)‐array data that allows us to detect the main chromosomal abnormalities in horses in a single procedure. As proof of concept, we were able to detect chromosomal abnormalities in 33 out of 268 individuals, including monosomies, chimerisms, and male and female sex‐reversions, by analyzing the raw signal intensity produced by an SNP array‐based genotyping platform. We also demonstrated that the procedure is not affected by the SNP density of the array employed or by the inbreeding level of the individuals. Finally, the methodology proposed in this study could be performed in an open bioinformatic environment, thus permitting its integration as a flexible screening tool in diagnostic laboratories and genomic breeding programs. [ABSTRACT FROM AUTHOR]

Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as removal of noise, imputation, for better understanding underlying patterns, or even exchanging data under privacy constraints. Yet, it is still unclear how well these approaches work with limited sample size. We investigate such settings specifically for binary data, e.g. as relevant when considering SNP measurements, and evaluate three frequently employed generative modeling approaches, variational autoencoders (VAEs), deep Boltzmann machines (DBMs) and generative adversarial networks (GANs). This includes conditional approaches, such as when considering gene expression conditional on SNPs. Recovery of pair-wise odds ratios (ORs) is considered as a primary performance criterion. For simulated as well as real SNP data, we observe that DBMs generally can recover structure for up to 300 variables, with a tendency of over-estimating ORs when not carefully tuned. VAEs generally get the direction and relative strength of pairwise relations right, yet with considerable under-estimation of ORs. GANs provide stable results only with larger sample sizes and strong pair-wise relations in the data. Taken together, DBMs and VAEs (in contrast to GANs) appear to be well suited for binary omics data, even at rather small sample sizes. This opens the way for many potential applications where synthetic observations from omics data might be useful. [ABSTRACT FROM AUTHOR]

Previous research has demonstrated that predictive performance can be improved whenever an undirected graph can be built over the set of predictors for a continuous response and the neighborhood structure is exploited. These methods are extended to a binary outcome in the new doubly sparse logistic regression incorporating graphical structure among predictors (DSLRIG) model. DSLRIG uses a decomposed representation of the regression parameters and encourages sparsity both within and among the groups that contribute to estimation. Compared to conventional regularized methods, DSLRIG offers improved predictive performance and identification of the true non-zero regression coefficients, while retaining their relative ranking. [ABSTRACT FROM AUTHOR]

SNP data sets can be used to infer a wealth of information about natural populations, including information about their structure, genetic diversity, and the presence of loci under selection. However, SNP data analysis can be a time‐consuming and challenging process, not in the least because at present many different software packages are needed to execute and depict the wide variety of mainstream population‐genetic analyses. Here, we present SambaR, an integrative and user‐friendly R package which automates and simplifies quality control and population‐genetic analyses of biallelic SNP data sets. SambaR allows users to perform mainstream population‐genetic analyses and to generate a wide variety of ready to publish graphs with a minimum number of commands (less than 10). These wrapper commands call functions of existing packages (including adegenet, ape, LEA, poppr, pcadapt and StAMPP) as well as new tools uniquely implemented in SambaR. We tested SambaR on online available SNP data sets and found that SambaR can process data sets of over 100,000 SNPs and hundreds of individuals within hours, given sufficient computing power. Newly developed tools implemented in SambaR facilitate optimization of filter settings, objective interpretation of ordination analyses, enhance comparability of diversity estimates from reduced representation library SNP data sets, and generate reduced SNP panels and structure‐like plots with Bayesian population assignment probabilities. SambaR facilitates rapid population genetic analyses on biallelic SNP data sets by removing three major time sinks: file handling, software learning, and data plotting. In addition, SambaR provides a convenient platform for SNP data storage and management, as well as several new utilities, including guidance in setting appropriate data filters. The SambaR source script, manual and example data set are distributed through GitHub: https://github.com/mennodejong1986/SambaR. [ABSTRACT FROM AUTHOR]

Domestication of Atlantic salmon started approximately 40 years ago, using artificial selection through genetic improvement programs. Selection is likely to have imposed distinctive signatures on the salmon genome, which are often characterized by high genetic differentiation across population and/or reduction in genetic diversity in regions associated to traits under selection. The identification of such selection signatures may give insights into the candidate genomic regions of biological and commercial interest. Here, we used three complementary statistics to detect selection signatures, two haplotype-based (iHS and XP-EHH), and one FST-based method (BayeScan) among four populations of Atlantic salmon with a common genetic origin. Several regions were identified for these techniques that harbored genes, such as kind1 and chp2, which have been associated with growth-related traits or the kcnb2 gene related to immune system in Atlantic salmon, making them particularly relevant in the context of aquaculture. Our results provide candidate genes to inform the evolutionary and biological mechanisms controlling complex selected traits in Atlantic salmon.

Domestication of Atlantic salmon started approximately 40 years ago, using artificial selection through genetic improvement programs. Selection is likely to have imposed distinctive signatures on the salmon genome, which are often characterized by high genetic differentiation across population and/or reduction in genetic diversity in regions associated to traits under selection. The identification of such selection signatures may give insights into the candidate genomic regions of biological and commercial interest. Here, we used three complementary statistics to detect selection signatures, two haplotype-based (iHS and XP-EHH), and one FST-based method (BayeScan) among four populations of Atlantic salmon with a common genetic origin. Several regions were identified for these techniques that harbored genes, such as kind1 and chp2 , which have been associated with growth-related traits or the kcnb2 gene related to immune system in Atlantic salmon, making them particularly relevant in the context of aquaculture. Our results provide candidate genes to inform the evolutionary and biological mechanisms controlling complex selected traits in Atlantic salmon. [ABSTRACT FROM AUTHOR]

Species delimitation is a major quest in biology and is essential for adequate management of the organismal diversity. A challenging example comprises the fish species of red snappers in the Western Atlantic. Red snappers have been traditionally recognized as two separate species based on morphology: Lutjanus campechanus (northern red snapper) and L. purpureus (southern red snapper). Recent genetic studies using mitochondrial markers, however, failed to delineate these nominal species, leading to the current lumping of the northern and southern populations into a single species (L. campechanus). This decision carries broad implications for conservation and management as red snappers have been commercially over-exploited across the Western Atlantic and are currently listed as vulnerable. To address this conflict, we examine genome-wide data collected throughout the range of the two species. Population genomics, phylogenetic and coalescent analyses favour the existence of two independent evolutionary lineages, a result that confirms the morphology-based delimitation scenario in agreement with conventional taxonomy. Despite finding evidence of introgression in geographically neighbouring populations in northern South America, our genomic analyses strongly support isolation and differentiation of these species, suggesting that the northern and southern red snappers should be treated as distinct taxonomic entities. [ABSTRACT FROM AUTHOR]

Genetic material sequenced from ancient samples is revolutionizing our understanding of the recent evolutionary past. However, ancient DNA is often degraded, resulting in low coverage, error-prone sequencing. Several solutions exist to this problem, ranging from simple approach, such as selecting a read at random for each site, to more complicated approaches involving genotype likelihoods. In this work, we present a novel method for assessing the relationship of an ancient sample with a modern population, while accounting for sequencing error and postmortem damage by analyzing raw reads from multiple ancient individuals simultaneously. We show that, when analyzing SNP data, it is better to sequence more ancient samples to low coverage: two samples sequenced to 0.5× coverage provide better resolution than a single sample sequenced to 2× coverage. We also examined the power to detect whether an ancient sample is directly ancestral to a modern population, finding that, with even a few high coverage individuals, even ancient samples that are very slightly diverged from the modern population can be detected with ease. When we applied our approach to European samples, we found that no ancient samples represent direct ancestors of modern Europeans. We also found that, as shown previously, the most ancient Europeans appear to have had the smallest effective population sizes, indicating a role for agriculture in modern population growth. [ABSTRACT FROM AUTHOR]

The LIPGENE-SU.VI.MAX study, like many others, recorded high-dimensional continuous phenotypic data and categorical genotypic data. LIPGENE-SU.VI.MAX focuses on the need to account for both phenotypic and genetic factors when studying the metabolic syndrome (MetS), a complex disorder that can lead to higher risk of type 2 diabetes and cardiovascular disease. Interest lies in clustering the LIPGENE-SU.VI.MAX participants into homogeneous groups or sub-phenotypes, by jointly considering their phenotypic and genotypic data, and in determining which variables are discriminatory. A novel latent variable model that elegantly accommodates high dimensional, mixed data is developed to cluster LIPGENE-SU.VI.MAX participants using a Bayesian finite mixture model. A computationally efficient variable selection algorithm is incorporated, estimation is via a Gibbs sampling algorithm and an approximate BIC-MCMC criterion is developed to select the optimal model. Two clusters or sub-phenotypes ('healthy' and 'at risk') are uncovered. A small subset of variables is deemed discriminatory, which notably includes phenotypic and genotypic variables, highlighting the need to jointly consider both factors. Further, 7 years after the LIPGENE-SU.VI.MAX data were collected, participants underwent further analysis to diagnose presence or absence of the MetS. The two uncovered sub-phenotypes strongly correspond to the 7-year follow-up disease classification, highlighting the role of phenotypic and genotypic factors in the MetS and emphasising the potential utility of the clustering approach in early screening. Additionally, the ability of the proposed approach to define the uncertainty in sub-phenotype membership at the participant level is synonymous with the concepts of precision medicine and nutrition. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Genotype imputation is a common technique in genetic research. Genetic similarity between target population and reference dataset is crucial for high-quality results. Although several reference panels are available, it is often not clear which is the most optimal for a particular target dataset to be imputed. Maximizing genetic similarity between study sample and intended reference panels may be the straight forward method for selecting the genetically best-matched reference. However, the impact of genetic similarity on imputation accuracy has not yet been studied in detail. Results: We performed a simulation study in 20 ethnic groups obtained from POPRES. High-quality SNPs were masked and re-imputed with MaCH, MaCH-minimac and IMPUTE2 using four different HapMap reference panels (CEU, CHB-JPT, MEX and YRI). Imputation accuracy was assessed by different statistics. Genetic similarity between ethnic groups and reference populations were measured by F -statistics (FST) originally proposed by Wright and G -statistics (GST) introduced by Nei and others. To assess the predictive power of these measures regarding imputation accuracy, we analysed relations between them and corresponding imputation accuracy scores. We found that population genetic distances between homogeneous reference and target populations were strongly linearly correlated with resulting imputation accuracies irrespective of considered distance measure, imputation accuracy measure, missingness and imputation software used. Possible exception was African population. Conclusion: Usage of GST or FST-related measures for predicting the optimal reference panel for imputation frameworks relying on a specific reference is highly recommended. A cut-off of GST < 0.01 is recommended to achieve good imputation results for high-frequency variants and small data sets. The linear relationship is less pronounced for low-frequency variants for which we also observed a dependence of imputation accuracy on the number of polymorphic sites in the reference. We also show that the software specific measures MaCH-Rsq and IMPUTE-info must be interpreted with caution if the genetic distance of target and reference population is high. [ABSTRACT FROM AUTHOR]

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer DZ declared a shared affiliation with the author YM to the handling editor at the time of review.