Polymorphism Data Research Articles

A comprehensive framework for detecting copy number variants from single nucleotide polymorphism data: 'rCNV', a versatile r package for paralogue and CNV detection.

Recent studies have highlighted the significant role of copy number variants (CNVs) in phenotypic diversity, environmental adaptation and species divergence across eukaryotes. The presence of CNVs also has the potential to introduce genotyping biases, which can pose challenges to accurate population and quantitative genetic analyses. However, detecting CNVs in genomes, particularly in non-model organisms, presents a formidable challenge. To address this issue, we have developed a statistical framework and an accompanying r software package that leverage allelic-read depth from single nucleotide polymorphism (SNP) data for accurate CNV detection. Our framework capitalises on two key principles. First, it exploits the distribution of allelic-read depth ratios in heterozygotes for individual SNPs by comparing it against an expected distribution based on binomial sampling. Second, it identifies SNPs exhibiting an apparent excess of heterozygotes under Hardy-Weinberg equilibrium. By employing multiple statistical tests, our method not only enhances sensitivity to sampling effects but also effectively addresses reference biases, resulting in optimised SNP classification. Our framework is compatible with various NGS technologies (e.g. RADseq, Exome-capture). This versatility enables CNV calling from genomes of diverse complexities. To streamline the analysis process, we have implemented our framework in the user-friendly r package 'rCNV', which automates the entire workflow seamlessly. We trained our models using simulated data and validated their performance on four datasets derived from different sequencing technologies, including RADseq (Chinook salmon-Oncorhynchus tshawytscha), Rapture (American lobster-Homarus americanus), Exome-capture (Norway spruce-Picea abies) and WGS (Malaria mosquito-Anopheles gambiae).

A critically endangered estuarine limpet’s only two populations are genomically and morphologically distinct

Abstract Seagrass habitats are declining worldwide, placing several seagrass‐associated animals at risk of extinction. The Critically Endangered limpet Siphonaria compressa is one of the rarest molluscs in Africa, and has been reported from only two disjunctive lagoons in South Africa. Being a highly specialized grazer that lives exclusively on the narrow blades of Cape eelgrass, Zostera capensis, which is itself listed as Endangered in the South African Red List and has decreased in abundance, conservation initiatives are urgently needed to ensure the long‐term survival of S. compressa. Molecular data (sequence data from the mitochondrial genome and single nucleotide polymorphism data of the nuclear genome) and morphological data were analysed to determine whether the two populations are conspecific, with implications for whether translocation between localities could be a viable management strategy to restore either population in the event of a collapse, or to maintain the genetic diversity of each population. Strong evidence emerged for the distinctness of the two populations, including a lack of shared mtDNA haplotypes that indicate an absence of contemporary gene flow, a divergence time that dates to the late Pleistocene, and a number of morphological characters that distinguish their shells. These findings indicate that the two populations are distinct cryptic subspecies. As the two populations occur in different temperature‐defined marine biogeographical regions, they are probably adapted to different thermal environments. Translocations are not recommended, as this management strategy has considerable potential to result in outbreeding depression and exacerbate the extinction risk. Instead, each population should be managed separately, and several alternative conservation measures are discussed, including the protection and restoration of seagrass beds.

GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity.

We introduce an explainable deep neural architecture that combines brain structure with genetic influence to improve disease severity prediction in Alzheimer's disease. Our framework consists of an encoder, a decoder, and a rank-consistent ordinal regression module. The encoder projects neural imaging and genetics data into a low-dimensional latent space regularized by the decoder. The ordinal regression module guides the feature embedding process to find discriminative patterns representative of disease severity. We also add a learnable dropout layer that learns feature importance and extracts explainable biomarkers from the data. We evaluate our model using structural MRI (sMRI) and Single Nucleotide Polymorphism (SNP) data provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In 2-class severity classification comparison, our model has a median F-score of 0.86 (baseline median F-score range: 0.57-0.81). In 3-class classification comparison, our model's median F-score is 0.50 (baseline range: 0.17 - 0.41). In 4-class classification comparison, our model's median F-score is 0.40 (baseline range: 0.14 - 0.39). We demonstrate that our model provides improved disease diagnosis alongside sparse and clinically relevant biomarkers.Clinical relevance-This study provides a deep-learning model that can predict Alzheimer's disease severity levels while identifying consistent and clinically relevant biomarkers.

PoolHelper: An R package to help in designing Pool‐Seq studies

Abstract Next‐generation sequencing of pooled samples (Pool‐seq) is an important tool in population genomics and molecular ecology. In Pool‐seq, the relative number of reads with an allele reflects the allele frequencies in the sample. However, unequal individual contributions to the pool and sequencing errors can lead to inaccurate allele frequency estimates, influencing downstream analysis. When designing Pool‐seq studies, researchers need to decide the pool size (number of individuals) and average depth of coverage (sequencing effort). An efficient sampling design should maximise the accuracy of allele frequency estimates while minimising the sequencing effort. We describe a novel tool to simulate single nucleotide polymorphism (SNP) data using coalescent theory and account for sources of uncertainty in Pool‐seq. We introduce an R package, poolHelper, enabling users to simulate Pool‐seq data under different combinations of average depth of coverage and pool size, accounting for unequal individual contributions and sequencing errors, modelled by adjustable parameters. The mean absolute error is computed by comparing the sample allele frequencies obtained based on individual genotypes with the frequency estimates obtained with Pool‐seq. poolHelper enables users to simulate multiple combinations of pooling errors, average depth of coverage, pool sizes and number of pools to assess how they influence the error of sample allele frequencies and expected heterozygosity. Using simulations under a single population model, we illustrate that increasing the depth of coverage does not necessarily lead to more accurate estimates, reinforcing that finding the best Pool‐seq study design is not straightforward. Moreover, we show that simulations can be used to identify different combinations of parameters with similarly low mean absolute errors. This can help users to define an effective sampling design by using those combinations of parameters that minimise the sequencing effort. The poolHelper package provides tools for performing simulations with different combinations of parameters (e.g. pool size, depth of coverage, unequal individual contribution) before sampling and generating data, allowing users to define sampling schemes based on simulations. This allows researchers to focus on the best sampling scheme to answer their research questions. poolHelper is comprehensively documented with examples to guide effective use.

Genetic structure of the population of wild-growing vines of the Utrish Nature Reserve.

Grapes are one of the most common agricultural crops in the world. Currently, the analysis of genotypes directly at the DNA level is considered to be the most accurate method for studying the plant gene pool. The study of wild vines and ancient varieties in various regions of viticulture is an important direction of research in this field. The purpose of this work was to study the population of wild grapes growing on the territory of the Utrish Nature Reserve on the Black Sea coast of Krasnodar Region. The territory of the reserve is of interest as it is a site of ancient settlements, and the environmental conditions are suitable for the growth of wild grapes. During the survey of the territory, 24 samples of wild grapes were found, which were described according to the main morphological characteristics and analyzed by the molecular genetic method. The found vines were genotyped using 15 DNA markers, including nine commonly used for DNA fingerprinting (VVS2, VVMD5, VVMD7, VVMD25, VVMD27, VVMD28, VVMD32, VrZAG62, VrZAG79) and VVIb23, which allows determining hermaphrodite and dioecious vines. Statistical processing of microsatellite loci polymorphism data was carried out using the GenAlEx 6.5 program. The genetic relationships of the studied vines were evaluated using the PAST 2.17c program. The samples were found to be morphologically and genetically polymorphic. The number of alleles identified in the sample varied from 5 to 18 and averaged 8 alleles per locus. Statistical processing of DNA analysis data made it possible to identify two genetically different populations among the wild discovered vines. An assessment of genetic similarity of the found vines with some local varieties of geographically close viticulture regions, rootstocks and representatives of Vitis sylvestris from other territories was made. One of the populations found in the Utrish Nature Reserve is close to a number of V. sylvestris genotypes, the DNA profiles of which are presented in the Vitis International Variety Catalogue.

A population genetic-level approach to endemic species of Oxalis section Palmatifoliae (Oxalidaceae) in Patagonia

Oxalis section Palmatifoliae (wood sorrel) includes five species that are well differentiated morphologically. A previous population study in one of the species (O. laciniata) suggested a complex phylogeographic history and highlighted the need to explore the genetic variability of all the species in the section. Thus, we performed a population level study of the five species to (1) determine the population structure within and among species, (2) model their current and paleoclimatic distributions, and (3) assess the historical processes that influenced their current genetic and morphological variation. We generated inter-simple sequence repeat (ISSR) polymorphism data and DNA sequences for the plastid region trnH-psbA and used those data to obtain a haplotype network, and used georeferenced coordinates and Maxent to obtain present and past species distribution models. Our major results included: 1) ISSR data showed a significant level of genetic differentiation among the five species and a degree of admixture within species; 2) plastid region trnH-psbA shows that one haplotype is the most frequent and widespread among all the species; 3) modelling of species present distributions showed different climatic variables for each species, whereas modelling of past distribution scenarios suggests broader distributions of all species.

Broad-scale variation in human genetic diversity levels is predicted by purifying selection on coding and non-coding elements.

Analyses of genetic variation in many taxa have established that neutral genetic diversity is shaped by natural selection at linked sites. Whether the mode of selection is primarily the fixation of strongly beneficial alleles (selective sweeps) or purifying selection on deleterious mutations (background selection) remains unknown, however. We address this question in humans by fitting a model of the joint effects of selective sweeps and background selection to autosomal polymorphism data from the 1000 Genomes Project. After controlling for variation in mutation rates along the genome, a model of background selection alone explains ~60% of the variance in diversity levels at the megabase scale. Adding the effects of selective sweeps driven by adaptive substitutions to the model does not improve the fit, and when both modes of selection are considered jointly, selective sweeps are estimated to have had little or no effect on linked neutral diversity. The regions under purifying selection are best predicted by phylogenetic conservation, with ~80% of the deleterious mutations affecting neutral diversity occurring in non-exonic regions. Thus, background selection is the dominant mode of linked selection in humans, with marked effects on diversity levels throughout autosomes.

Genome-wide presence/absence variation discovery and its application in Peach (Prunus persica)

Gene presence/absence variation (PAV) is an important contributor to the studies of genetic diversity, gene identification, and molecular marker development in plants. In the present study, 100 peach (Prunus persica) accessions were used for genome resequencing to identify PAVs. Alignmentwith a reference genome yielded a total of 2.52 Mb non-reference sequences and 923 novel genes were identified. The dispensable PAVs were enriched in resistance, perhaps reflecting their roles in plant adaptation to various environments. Furthermore, selection sweeps associated with peach domestication and improvement were identified based on PAV data. Only 4.3% and 13.4% of domestication and improvement sweeps, respectively, were identified simultaneously using single nucleotide polymorphism (SNP) data, suggesting flexible identification between the different methods. To further verify the applicability of PAV identification, a genome-wide association study was conducted using 21 agronomic traits. Some of the identified loci were consistent with those reported in previous studies, while some were mapped for the first time; the latter included petiole length, petiole gland shape, and petiole gland number. Through tissue-specific expression analysis and gene transformation experiments, a novel gene, evm.model.Contig322_A94.1, was identified and found to be involved in chilling requirements. We speculated that this novel gene might regulate the trait by participating in the ABA signaling pathway. The PAVs identified in P. persica provide valuable resources for mapping the entire gene set and identifying optional markers for molecular selection in future studies.

Repeatability of crossover rate in wild sticklebacks

AbstractCrossovers in meiosis have an important role in sexual reproduction in ensuring the segregation of chromosomes and shuffling of genetic material. Despite their profound importance, little is known about heritability and repeatability of the crossover rate in the wild. We studied crossover rate variation in outbred nine- (Pungitius pungitius) and three-spined (Gasterosteus aculeatus) stickleback populations to estimate its heritability (h2) and repeatability (R). Based on crossover rate interpreted from parent–F1-offspring linkage maps created from single nucleotide polymorphism data, the repeatability of crossover rate was moderate at best, but higher in females (R = 0.24 and R = 0.33) than in males (R = 0.017 and R = 0.11) in both species. Owing to the low degree and variance of parental relatedness and low sample sizes, heritabilities could not be estimated with confidence. However, given that R sets the upper limit to h2, the heritability of crossover rate is indicated to be low in males. A review and comparison with the previously reported repeatability and heritability estimates revealed that the repeatabilities in stickleback females were relatively high, whereas those in males were very low. Collectively, our results and review of earlier estimates suggest low evolvability of the crossover rate.

Sex chromosome heteromorphism and the Fast-X effect in poeciliids.

Fast-X evolution has been observed in a range of heteromorphic sex chromosomes. However, it remains unclear how early in the process of sex chromosome differentiation the Fast-X effect becomes detectible. Recently, we uncovered an extreme variation in sex chromosome heteromorphism across poeciliid fish species. The common guppy, Poecilia reticulata, Endler's guppy, P. wingei, swamp guppy, P. picta and para guppy, P. parae, appear to share the same XY system and exhibit a remarkable range of heteromorphism. Species outside this group lack this sex chromosome system. We combined analyses of sequence divergence and polymorphism data across poeciliids to investigate X chromosome evolution as a function of hemizygosity and reveal the causes for Fast-X effects. Consistent with the extent of Y degeneration in each species, we detect higher rates of divergence on the X relative to autosomes, a signal of Fast-X evolution, in P. picta and P. parae, species with high levels of X hemizygosity in males. In P. reticulata, which exhibits largely homomorphic sex chromosomes and little evidence of hemizygosity, we observe no change in the rate of evolution of X-linked relative to autosomal genes. In P. wingei, the species with intermediate sex chromosome differentiation, we see an increase in the rate of nonsynonymous substitutions on the older stratum of divergence only. We also use our comparative approach to test for the time of origin of the sex chromosomes in this clade. Taken together, our study reveals an important role of hemizygosity in Fast-X evolution.

Species limits and diversification of the Dendropsophus rubicundulus subgroup (Anura, Hylidae) in Neotropical savannas

Understanding the processes that generate and maintain biodiversity at and below the species level is a central goal of evolutionary biology. Here we explore the spatial and temporal drivers of diversification of the treefrog subgroup Dendropsophus rubicundulus, a subgroup of the D. microcephalus species group, over periods of pronounced geological and climatic changes in the Neotropical savannas that they inhabit. This subgroup currently comprises 11 recognized species distributed across the Brazilian and Bolivian savannas, but the taxonomy has been in a state of flux, necessitating reexamination. Using newly generated single nucleotide polymorphism (SNP) data from restriction-site associated DNA sequencing (RADseq) and mitochondrial 16S sequence data for ∼150 specimens, we inferred phylogenetic relationships, tested species limits using a model-based approach, and estimated divergence times to gain insights into the geographic and climatic events that affected the diversification of this subgroup. Our results recognized at least nine species: D. anataliasiasi, D. araguaya, D. cerradensis, D. elianeae, D. jimi, D. rubicundulus, D. tritaeniatus, D. rozenmani, and D. sanborni. Although we did not collect SNP data for the latter two species, they are likely distinct based on mitochondrial data. In addition, we found genetic structure within the widespread species D. rubicundulus, which comprises three allopatric lineages connected by gene flow upon secondary contact. We also found evidence of population structure and perhaps undescribed diversity in D. elianeae, which warrants further study. The D. rubicundulus subgroup is estimated to have originated in the Late Miocene (∼5.45 million years ago), with diversification continuing through the Pliocene and Early Pleistocene, followed by the most recent divergence of D. rubicundulus lineages in the Middle Pleistocene. The epeirogenic uplift followed by erosion and denudation of the central Brazilian plateau throughout the Pliocene and Pleistocene, in combination with the increasing frequency and amplitude of climatic fluctuations during the Pleistocene, was important for generating and structuring diversity at or below the species level in the D. rubicundulus subgroup.

The Association of pre-miR27a Gene Polymorphism and Clinicopathological Data in Thai Breast Cancer Patients.

MiR27a plays an important role in carcinogenesis, cell proliferation, apoptosis, invasion, migration and angiogenesis. Several studies have identified an important role of pre-miR27a (rs895819) A>G polymorphism in several types of cancer. This research aims to investigate the association of pre-miR27a (rs895819) A>G and breast cancer susceptibility, clinicopathological data and survival. Blood DNA samples of 143 Thai breast cancer patients and 100 healthy Thai women were studied for pre-miR27a (rs895819) A>G polymorphism using polymerase chain reaction-restriction fragment-length polymorphism (PCR-RFLP). The results revealed that the frequency of pre-miR27a (rs895819) A>G genotypes was not statistically significant different between breast cancer patient and normal control subjects. The rs895819 A>G genotype was significantly associated with clinicopathological parameter of grade III differentiation (P = 0.006), progesterone receptor (P = 0.011) and triple negative (P = 0.031) in breast cancer patients, but not with breast cancer susceptibility. The pre-miR27a (rs895819) A>G genotype was significantly associated with poorly differentiated, progesterone receptor and triple-negative in breast cancer patients. Therefore, pre-miR27a (rs895819) A>G may be used as a biomarker for poor prognosis.

Whole genome resequencing reveal patterns of genetic variation within Colletotrichum acutatum species complex from rubber trees in China

The Colletotrichum acutatum species complex possesses a diverse number of important traits, such as a wide host range and host preference, different modes of reproduction, and different strategies of host infection. Research using comparative genomics has attempted to find correlations between these traits. Here, we used multi-locus techniques and gene genealogical concordance analysis to investigate the phylogenetic relationships and taxonomic status of the Colletotrichum acutatum species complex using field isolates obtained from rubber trees. The results revealed that the dominant species was C. australisinense, followed by C. bannaense, while strain YNJH17109 was identified as C. laticiphilum. The taxonomic status of strains YNLC510 and YNLC511 was undetermined. Using whole-genome single nucleotide polymorphism data to analyze population structure, 18 strains of C. australisinense were subsequently divided into four populations, one of which was derived from an admixture of two populations. In addition, the strains LD1687, GD1628, and YNLC516, did not belong to any populations, and were considered to be admixtures of two or more populations. A split decomposition network analysis also provided evidence for genetic recombination within Colletotrichum acutatum species complex from rubber trees in China. Overall, a weak phylogeographic sub-structure was observed. Analysis also revealed significant differences in morphological characters and levels of virulence between populations.

A genome-wide association study of freezing tolerance in red clover (Trifolium pratense L.) germplasm of European origin.

Improvement of persistency is an important breeding goal in red clover (Trifolium pratense L.). In areas with cold winters, lack of persistency is often due to poor winter survival, of which low freezing tolerance (FT) is an important component. We conducted a genome wide association study (GWAS) to identify loci associated with freezing tolerance in a collection of 393 red clover accessions, mostly of European origin, and performed analyses of linkage disequilibrium and inbreeding. Accessions were genotyped as pools of individuals using genotyping-by-sequencing (pool-GBS), generating both single nucleotide polymorphism (SNP) and haplotype allele frequency data at accession level. Linkage disequilibrium was determined as a squared partial correlation between the allele frequencies of pairs of SNPs and found to decay at extremely short distances (< 1 kb). The level of inbreeding, inferred from the diagonal elements of a genomic relationship matrix, varied considerably between different groups of accessions, with the strongest inbreeding found among ecotypes from Iberia and Great Britain, and the least found among landraces. Considerable variation in FT was found, with LT50-values (temperature at which 50% of the plants are killed) ranging from -6.0°C to -11.5°C. SNP and haplotype-based GWAS identified eight and six loci significantly associated with FT (of which only one was shared), explaining 30% and 26% of the phenotypic variation, respectively. Ten of the loci were found within or at a short distance (<0.5 kb) from genes possibly involved in mechanisms affecting FT. These include a caffeoyl shikimate esterase, an inositol transporter, and other genes involved in signaling, transport, lignin synthesis and amino acid or carbohydrate metabolism. This study paves the way for a better understanding of the genetic control of FT and for the development of molecular tools for the improvement of this trait in red clover through genomics assisted breeding.

Genome-wide single-nucleotide polymorphism data and mitochondrial hypervariable region 1 nucleotide sequence reveal the origin of the Akhal-Teke horse.

The study investigated the origin of the Akhal-Teke horse using genome-wide single-nucleotide polymorphism (SNP) data and mitochondrial hypervariable region 1 (HVR-1) nucleotide sequences. Genome-wide SNP data from 22 breeds (481 horses) and mitochondrial HVR-1 sequences from 24 breeds (544 sequences) worldwide to examine the origin of the Akhal- Teke horse. The data were analyzed using principal component analysis, linkage disequilibrium analysis, neighbor-joining dendrograms, and ancestry inference to determine the population relationships, ancestral source, genetic structure, and relationships with other varieties. A close genetic relationship between the Akhal-Teke horse and horses from the Middle East was found. Analysis of mitochondrial HVR-1 sequences showed that there were no shared haplotypes between the Akhal-Teke and Tarpan horses, and the mitochondrial data indicated that the Akhal-Teke horse has not historically expanded its group. Ancestral inference suggested that Arabian and Caspian horses were the likely ancestors of the Akhal- Teke horse. The Akhal-Teke horse originated in the Middle East.

Genomewide selection for fruit quality traits in apple: breeding insights gained from prediction and postdiction.

Many fruit quality traits in apple (Malus domestica Borkh.) are controlled by multiple small-effect quantitative trait loci (QTLs). Genomewide selection (genomic selection) might be an effective breeding approach for highly quantitative traits in woody perennial crops with long generation times like apple. The goal of this study was to determine if genomewide prediction is an effective breeding approach for fruit quality traits in an apple scion breeding program. Representative apple scion breeding germplasm (nindividuals= 955), high-quality single nucleotide polymorphism (SNP) data (nSNPs= 977), and breeding program fruit quality trait data at harvest were analyzed. Breeding parents `Honeycrisp' and `Minneiska' were highly represented. Moderate to high predictive abilities were observed for most fruit quality traits at harvest. For example, when 25% random subsets of the germplasm set were used as training sets, mean predictive abilities ranged from 0.35 to 0.54 across traits. Trait, training and test sets, family size for within family prediction, and number of SNPs per chromosome affected model predictive ability. Inclusion of large-effect QTLs as fixed effects resulted in higher predictive abilities for some traits (e.g. percent red overcolor). Postdiction (i.e. retrospective) analyses demonstrated the impact of culling threshold on selection decisions. The results of this study demonstrate that genomewide selection is a useful breeding approach for certain fruit quality traits in apple.

First genome-wide association study for lymphatic filariasis in a West African population points to a human leukocyte antigen-mediated disease pathophysiology

ObjectivesLymphatic filariasis (LF) represents a parasitic disease caused by filarial nematodes. Although some infected individuals present an asymptomatic course, others suffer severe chronic lymphatic pathology, including lymphedema, hydrocele, and elephantiasis. Several studies have shown that host genetic factors influence LF susceptibility and chronic pathology. The current study aimed to conduct the first genome-wide association study to systematically determine LF susceptibility. MethodsWe analyzed genome-wide single-nucleotide polymorphism data from 1459 LF cases and 1492 asymptomatic controls of West African (Ghanaian) descent. ResultsWe identified two independent genome-wide significant associated genetic variants near the genes HLA-DQB2 (rs7742085) and HLA-DQA1 (rs4959107) contributing to LF and/or lymphedema susceptibility (P <5.0 × 10−8, odds ratios [ORs] >1.30). We also observed suggestive evidence of LF associations (P <1.0 × 10−6) at two non-HLA loci, near the genes ZFHX4-AS1 (rs79562145) and CHP2 (rs12933387). In contrast, we could not replicate any previously reported LF associations drawn from candidate gene association studies. On the polygenic level, we show that our genome-wide association study data explain 24-42% of LF heritability, depending on an assumed population prevalence of 0.5-5.0%. ConclusionOur findings point to an involvement of HLA-mediated immune mechanisms in LF pathophysiology.

DeepCGP: A Deep Learning Method to Compress Genome-Wide Polymorphisms for Predicting Phenotype of Rice.

Genomic selection (GS) is expected to accelerate plant and animal breeding. During the last decade, genome-wide polymorphism data have increased, which has raised concerns about storage cost and computational time. Several individual studies have attempted to compress the genome data and predict phenotypes. However, compression models lack adequate quality of data after compression, and prediction models are time consuming and use original data to predict the phenotype. Therefore, a combined application of compression and genomic prediction modeling using deep learning could resolve these limitations. A Deep Learning Compression-based Genomic Prediction (DeepCGP) model that can compress genome-wide polymorphism data and predict phenotypes of a target trait from compressed information was proposed. The DeepCGP model contained two parts: (i) an autoencoder model based on deep neural networks to compress genome-wide polymorphism data, and (ii) regression models based on random forests (RF), genomic best linear unbiased prediction (GBLUP), and Bayesian variable selection (BayesB) to predict phenotypes from compressed information. Two datasets with genome-wide marker genotypes and target trait phenotypes in rice were applied. The DeepCGP model obtained up to 99% prediction accuracy to the maximum for a trait after 98% compression. BayesB required extensive computational time among the three methods, and showed the highest accuracy; however, BayesB could only be used with compressed data. Overall, DeepCGP outperformed state-of-the-art methods in terms of both compression and prediction. Our code and data are available at https://github.com/tanzilamohita/DeepCGP.

Differential Methylation of Telomere-Related Genes Is Associated with Kidney Disease in Individuals with Type 1 Diabetes.

Diabetic kidney disease (DKD) represents a major global health problem. Accelerated ageing is a key feature of DKD and, therefore, characteristics of accelerated ageing may provide useful biomarkers or therapeutic targets. Harnessing multi-omics, features affecting telomere biology and any associated methylome dysregulation in DKD were explored. Genotype data for nuclear genome polymorphisms in telomere-related genes were extracted from genome-wide case-control association data (n = 823 DKD/903 controls; n = 247 end-stage kidney disease (ESKD)/1479 controls). Telomere length was established using quantitative polymerase chain reaction. Quantitative methylation values for 1091 CpG sites in telomere-related genes were extracted from epigenome-wide case-control association data (n = 150 DKD/100 controls). Telomere length was significantly shorter in older age groups (p = 7.6 × 10-6). Telomere length was also significantly reduced (p = 6.6 × 10-5) in DKD versus control individuals, with significance remaining after covariate adjustment (p = 0.028). DKD and ESKD were nominally associated with telomere-related genetic variation, with Mendelian randomisation highlighting no significant association between genetically predicted telomere length and kidney disease. A total of 496 CpG sites in 212 genes reached epigenome-wide significance (p ≤ 10-8) for DKD association, and 412 CpG sites in 193 genes for ESKD. Functional prediction revealed differentially methylated genes were enriched for Wnt signalling involvement. Harnessing previously published RNA-sequencing datasets, potential targets where epigenetic dysregulation may result in altered gene expression were revealed, useful as potential diagnostic and therapeutic targets for intervention.

Multiomics Analyses Reveal Microbiome-Gut-Brain Crosstalk Centered on Aberrant Gamma-Aminobutyric Acid and Tryptophan Metabolism in Drug-Naïve Patients with First-Episode Schizophrenia.

Schizophrenia (SCZ) is associated with complex crosstalk between the gut microbiota and host metabolism, but the underlying mechanism remains elusive. Investigating the aberrant neurotransmitter processes reflected by alterations identified using multiomics analysis is valuable to fully explain the pathogenesis of SCZ. We conducted an integrative analysis of multiomics data, including the serum metabolome, fecal metagenome, single nucleotide polymorphism data, and neuroimaging data obtained from a cohort of 127 drug-naïve, first-episode SCZ patients and 92 healthy controls to characterize the microbiome-gut-brain axis in SCZ patients. We used pathway-based polygenic risk score (PRS) analyses to determine the biological pathways contributing to genetic risk and mediation effect analyses to determine the important neuroimaging features. Additionally, a random forest model was generated for effective SCZ diagnosis. We found that the altered metabolome and dysregulated microbiome were associated with neuroactive metabolites, including gamma-aminobutyric acid (GABA), tryptophan, and short-chain fatty acids. Further structural and functional magnetic resonance imaging analyses highlighted that gray matter volume and functional connectivity disturbances mediate the relationships between Ruminococcus_torgues and Collinsella_aerofaciens and symptom severity and the relationships between species Lactobacillus_ruminis and differential metabolites l-2,4-diaminobutyric acid and N-acetylserotonin and cognitive function. Moreover, analyses of the Polygenic Risk Score (PRS) support that alterations in GABA and tryptophan neurotransmitter pathways are associated with SCZ risk, and GABA might be a more dominant contributor. This study provides new insights into systematic relationships among genes, metabolism, and the gut microbiota that affect brain functional connectivity, thereby affecting SCZ pathogenesis.