Plausible Genes Research Articles

Multi-omics approaches define novel aphid effector candidates associated with virulence and avirulence phenotypes

BackgroundCompatibility between plant parasites and their hosts is genetically determined {Citation}both interacting organisms. For example, plants may carry resistance (R) genes or deploy chemical defences. Aphid saliva contains many proteins that are secreted into host tissues. Subsets of these proteins are predicted to act as effectors, either subverting or triggering host immunity. However, associating particular effectors with virulence or avirulence outcomes presents challenges due to the combinatorial complexity. Here we use defined aphid and host genetics to test for co-segregation of expressed aphid transcripts and proteins with virulent or avirulent phenotypes.ResultsWe compared virulent and avirulent pea aphid parental genotypes, and their bulk segregant F1 progeny on Medicago truncatula genotypes carrying or lacking the RAP1 (Resistance to Acyrthosiphon pisum 1) resistance quantitative trait locus. Differential gene expression analysis of whole body and head samples, in combination with proteomics of saliva and salivary glands, enabled us to pinpoint proteins associated with virulence or avirulence phenotypes. There was relatively little impact of host genotype, whereas large numbers of transcripts and proteins were differentially expressed between parental aphids, likely a reflection of their classification as divergent biotypes within the pea aphid species complex. Many fewer transcripts intersected with the equivalent differential expression patterns in the bulked F1 progeny, providing an effective filter for removing genomic background effects. Overall, there were more upregulated genes detected in the F1 avirulent dataset compared with the virulent one. Some genes were differentially expressed both in the transcriptome and in the proteome datasets, with aminopeptidase N proteins being the most frequent differentially expressed family. In addition, a substantial proportion (27%) of salivary proteins lack annotations, suggesting that many novel functions remain to be discovered.ConclusionsEspecially when combined with tightly controlled genetics of both insect and host plant, multi-omics approaches are powerful tools for revealing and filtering candidate lists down to plausible genes for further functional analysis as putative aphid effectors.

Predicting tumour resistance to paclitaxel and carboplatin utilising genome-wide screening in haploid human embryonic stem cells.

Taxanes and platinum molecules, specifically paclitaxel and carboplatin, are widely used anticancer drugs that induce cell death and serve as first-line chemotherapy for various cancer types. Despite the efficient effect of both drugs on cancer cell proliferation, many tumours have innate resistance against paclitaxel and carboplatin, which leads to inefficient treatment and poor survival rates. Haploid human embryonic stem cells (hESCs) are a novel and robust platform for genetic screening. To gain a comprehensive view of genes that affect or regulate paclitaxel and carboplatin resistance, genome-wide loss-of-function screens in haploid hESCs were performed. Both paclitaxel and carboplatin screens have yielded selected plausible gene lists and pathways relevant to resistance prediction. The effects of mutations in selected genes on the resistance to the drugs were demonstrated. Based on the results, an algorithm that can predict resistance to paclitaxel or carboplatin was developed. Applying the algorithm to the DNA mutation profile of patients' tumours enabled the separation of sensitive versus resistant patients, thus, providing a prediction tool. As the anticancer drugs arsenal can offer alternatives in case of resistance to either paclitaxel or carboplatin, an early prediction can provide a significant advantage and should improve treatment. The algorithm assists this unmet need and helps predict whether a patient will respond to the treatment and may have an immediate clinically actionable application.

Abstract C055: Unraveling pancreatic cancer susceptibility at 5p15.33: Functional characterization of a novel VNTR element

Abstract Genome-wide association studies (GWAS) have identified independent signals at 5p15.33 across numerous cancers, with protective alleles for one cancer often conferring risk for another. Many of these associations are thought to act via allele-specific alterations in the cis- regulation of target genes. Transcriptomic analyses in multiple tissue types implicate two plausible target genes: TERT and CLPTM1L. However, the mechanisms linking cancer susceptibility alleles to their presumed target genes, along with the observed antagonistic pleiotropy between different cancers remain elusive. With this in mind, we employed a comprehensive approach, integrating statistical fine-mapping of GWAS summary statistics with massively parallel reporter assays (MPRA) and a densely tiled CRISPRi screen performed across eight cell lines, representative of four distinct cancers associated with 5p15.33 alleles – pancreatic, melanoma, lung, and bladder cancer. Integrative screening identified up to 30 variants across multiple signals in the locus with putative allele-specific transcriptional activity. Furthermore, a variable number tandem repeat (VNTR) in intron 9 of CLPTM1L emerged as a potent transcriptional enhancer in CRISPRi screening. Leveraging PacBio sequencing, VNTR alleles of European ancestry samples from the 1000 Genomes Project were genotyped and found to be highly polymorphic, with longer VNTRs linked to the haplotype tagged by rs31490-A (risk increasing for pancreatic cancer). Subsequent imputation into a pancreatic cancer GWAS of 9,040 cases and 12,496 controls revealed an association between longer VNTRs and risk of pancreatic cancer (OR for VNTR in the 75th percentile = 1.17, 95% CI = 1.13-1.22, P = 2.21x10-12). The significance of the association for the lead SNP rs31490 after conditioning on the VNTR genotype dropped to P= 2.20x10-5, suggesting that VNTR alleles merit consideration as part of the credible variant set for pancreatic cancer. Targeted CRISPRi assays suggested VNTR-mediated cis-regulation of both TERT and CLPTM1L in PANC-1 and MIA PaCa-2 pancreatic cancer cell lines, while dual luciferase reporter assays in the same cell lines revealed that VNTR enhancer activity is mediated by Hippo pathway transcription factors. In ongoing experiments, we aim to expand on luciferase reporter assays and DNA-protein binding assays in relevant cell lines with the aim of not only deciphering how longer VNTRs differentially exert their transcriptional effects compared to shorter alleles but also characterizing the divergent directions of effect between different cancer types. These findings underscore the proposition that pancreatic cancer susceptibility at the 5p15.33 locus may be mediated by both SNPs and an uncharted class of variants, yet to be characterized in the post-GWAS landscape. Citation Format: Aidan O'Brien, Hyunkyung Kong, Minal Patel, Katelyn E Connelly, Mai Xu, Irene Collins, Jun Zhong, Jason Hoskins, Michelle Ho, Brenen Papenburg, Mark M Iles, Matthew H Law, Maria-Teresa Landi, Rachael Stolzenber- Solomon, Brian M Wolpin, Alison P Klein, Nick Orr, Stephen J Chanock, Sara Lindström, Marc-Henri Stern, Ludmila Prokunina-Olsson, Jieyon Choi, Kevin M Brown, Laufey T Amundadottir. Unraveling pancreatic cancer susceptibility at 5p15.33: Functional characterization of a novel VNTR element [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C055.

Deciphering early responsive signature genes in rice blast disease: an integrated temporal transcriptomic study.

Rice blast disease, caused by Magnaporthe oryzae, reigns as the top-most cereal killer, jeopardizing global food security. This necessitates the timely scouting of pathogen stress-responsive genes during the early infection stages. Thus, we integrated time-series microarray (GSE95394) and RNA-Seq (GSE131641) datasets to decipher rice transcriptome responses at 12- and 24-h post-infection (Hpi). Our analysis revealed 1580 differentially expressed genes (DEGs) overlapped between datasets. We constructed a protein-protein interaction (PPI) network for these DEGs and identified significant subnetworks using the MCODE plugin. Further analysis with CytoHubba highlighted eight plausible hub genes for pathogenesis: RPL8 (upregulated) and RPL27, OsPRPL3, RPL21, RPL9, RPS5, OsRPS9, and RPL17 (downregulated). We validated the expression levels of these hub genes in response to infection, finding that RPL8 exhibited significantly higher expression compared with other downregulated genes. Remarkably, RPL8 formed a distinct cluster in the co-expression network, whereas other hub genes were interconnected, with RPL9 playing a central role, indicating its pivotal role in coordinating gene expression during infection. Gene Ontology highlighted the enrichment of hub genes in the ribosome and protein translation processes. Prior studies suggested that plant immune defence activation diminishes the energy pool by suppressing ribosomes. Intriguingly, our study aligns with this phenomenon, as the identified ribosomal proteins (RPs) were suppressed, while RPL8 expression was activated. We anticipate that these RPs could be targeted to develop new stress-resistant rice varieties, beyond their housekeeping role. Overall, integrating transcriptomic data revealed more common DEGs, enhancing the reliability of our analysis and providing deeper insights into rice blast disease mechanisms.

Integrative bioinformatics analysis for the identification of hub genes and Virtual screening of phytochemicals to inhibit AURKA in HepatoCellular carcinoma

HepatoCellular Carcinoma (HCC) is one of the most deadly and prevalent neoplasia, accounting for nearly 830,180 mortalities and 905,677 fresh occurrences worldwide annually. Aggressive malignancy with multifaceted etiologies increases in occurrence due to inadequate early diagnosis and ineffective treatment outcomes. Hence the present study aims to identify novel HCC associated biomarkers and inhibit the plausible genes through phytocompounds. Herein, we have implemented the meta-analysis of GSE36376, GSE57957 and GSE84598 micro-array profiles by utilizing GEO2R which resulted in identification of 1683 aberrantly expressed genes. The predicted DEGs were further subjected to Functional annotation and pathway enrichment analysis by using Blast2GO and ExpressAnalyst respectively. Successively, Protein-Protein Interaction analysis was performed by Cytoscape software, and the top 11 most significant hub nodes were identified. The most frequently occurring hub gene Aurora Kinase A (AURKA) was considered as plausible target for subsequent identification of inhibitors. The plant-derived small molecules retrieved from NPACT database were subjected to molecular docking, Molecular dynamic simulations and MMGBSA analysis against AURKA. Conclusively, findings from our study postulates Garcinone C and Silymarin targeting elevated AURKA levels which may contribute as potential inhibitors for HCC patients. However, these outcomes provide only computational insights for targeted HCC-therapeutics but for clinical application of Garcinone C and Silymarin in vitro and in vivo molecular validations are still warranted.

GREM2 inactivation increases trabecular bone mass in mice

Osteoporosis is a common skeletal disease affecting millions of individuals world-wide, with an increased risk of fracture, and a decreased quality of life. Despite its well-known consequences, the etiology of osteoporosis and optimal treatment methods are not fully understood. Human genetic studies have identified genetic variants within the FMN2/GREM2 locus to be associated with trabecular volumetric bone mineral density (vBMD) and vertebral and forearm fractures, but not with cortical bone parameters. GREM2 is a bone morphogenetic protein (BMP) antagonist. In this study, we employed Grem2-deficient mice to investigate whether GREM2 serves as the plausible causal gene for the fracture signal at the FMN2/GREM2 locus. We observed that Grem2 is moderately expressed in bone tissue and particularly in osteoblasts. Complete Grem2 gene deletion impacted mouse survival and body growth. Partial Grem2 inactivation in Grem2+/− female mice led to increased trabecular BMD of femur and increased trabecular bone mass in tibia due to increased trabecular thickness, with an unchanged cortical thickness, as compared with wildtype littermates. Furthermore, Grem2 inactivation stimulated osteoblast differentiation, as evidenced by higher alkaline phosphatase (Alp), osteocalcin (Bglap), and osterix (Sp7) mRNA expression after BMP-2 stimulation in calvarial osteoblasts and osteoblasts from the long bones of Grem2−/− mice compared to wildtype littermates. These findings suggest that GREM2 is a possible target for novel osteoporotic treatments, to increase trabecular bone mass and prevent osteoporotic fractures.

Brain cell-type shifts in Alzheimer's disease, autism, and schizophrenia interrogated using methylomics and genetics.

Few neuropsychiatric disorders have replicable biomarkers, prompting high-resolution and large-scale molecular studies. However, we still lack consensus on a more foundational question: whether quantitative shifts in cell types-the functional unit of life-contribute to neuropsychiatric disorders. Leveraging advances in human brain single-cell methylomics, we deconvolve seven major cell types using bulk DNA methylation profiling across 1270 postmortem brains, including from individuals diagnosed with Alzheimer's disease, schizophrenia, and autism. We observe and replicate cell-type compositional shifts for Alzheimer's disease (endothelial cell loss), autism (increased microglia), and schizophrenia (decreased oligodendrocytes), and find age- and sex-related changes. Multiple layers of evidence indicate that endothelial cell loss contributes to Alzheimer's disease, with comparable effect size to APOE genotype among older people. Genome-wide association identified five genetic loci related to cell-type composition, involving plausible genes for the neurovascular unit (P2RX5 and TRPV3) and excitatory neurons (DPY30 and MEMO1). These results implicate specific cell-type shifts in the pathophysiology of neuropsychiatric disorders.

Towards Biologically Plausible and Private Gene Expression Data Generation

Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, however, primarily focuses on basic benchmarking datasets and tends to report promising results only for elementary metrics and relatively simple data distributions. In this paper, we initiate a systematic analysis of how DP generative models perform in their natural application scenarios, specifically focusing on real-world gene expression data. We conduct a comprehensive analysis of five representative DP generation methods, examining them from various angles, such as downstream utility, statistical properties, and biological plausibility. Our extensive evaluation illuminates the unique characteristics of each DP generation method, offering critical insights into the strengths and weaknesses of each approach, and uncovering intriguing possibilities for future developments. Perhaps surprisingly, our analysis reveals that most methods are capable of achieving seemingly reasonable downstream utility, according to the standard evaluation metrics considered in existing literature. Nevertheless, we find that none of the DP methods are able to accurately capture the biological characteristics of the real dataset. This observation suggests a potential over-optimistic assessment of current methodologies in this field and underscores a pressing need for future enhancements in model design.

Screening of the Combined Risk of Genetics and Epidemiology on Infertility Among Indian Men: Synergistic Effect of AZFc Partial Deletions and Habits of Smokeless Chewing Tobacco.

The AZFc partial deletions of Y chromosome and lifestyle/epidemiological factors such as the use of smokeless chewing tobacco (SCT) exhibit intriguing variations in their association with male infertility across the population, ethnicity, and genetic background. Here, a pioneering attempt has been made to elucidate the interactions of such deletions with the habits of SCT consumption among the participating individuals, using their large epidemiological data. This screening program was conducted among Bengali-speaking men in West Bengal, India. We screened the prevalence and association of distinct partial deletions (gr/gr, b1/b3, and b2/b3) of the AZFc region using locus-specific sequence-tagged site (STS) markers among 728 case subjects and compared them with 264 ethnicity- and age-matched proven-fertile control men. The recorded epidemiological data of the study group and the outcome of partial deletion analysis were compiled to frame the plausible Gene × Epidemiological factor (G × E) interactions. The gr/gr deletion was reported to be significantly associated with azoospermic (p = .0015, odds ratio [OR] = 3.413) and oligozoospermic (p = .0382, OR = 3.012) case subgroups, and b1/b3 deletions were also detected among the infertile persons only. The G × E model revealed that men who carried microdeletions as well as were SCT users had an elevated risk of infertility (p = .002, OR = 6.38). The study highlights the fact that AZFc partial deletions and SCT, when co-occurred, synergistically increase the risk of infertility among men. This work helps to get more insight into the etiology of male infertility in the light of gene-environmental interaction.

Genetic basis of Arabidopsis thaliana responses to infection by naïve and adapted isolates of turnip mosaic virus.

Plant viruses account for enormous agricultural losses worldwide, and the most effective way to combat them is to identify genetic material conferring plant resistance to these pathogens. Aiming to identify genetic associations with responses to infection, we screened a large panel of Arabidopsis thaliana natural inbred lines for four disease-related traits caused by infection by A. thaliana-naïve and -adapted isolates of the natural pathogen turnip mosaic virus (TuMV). We detected a strong, replicable association in a 1.5 Mb region on chromosome 2 with a 10-fold increase in relative risk of systemic necrosis. The region contains several plausible causal genes as well as abundant structural variation, including an insertion of a Copia transposon into a Toll/interleukin receptor (TIR-NBS-LRR) coding for a gene involved in defense, that could be either a driver or a consequence of the disease-resistance locus. When inoculated with TuMV, loss-of-function mutant plants of this gene exhibited different symptoms than wild-type plants. The direction and severity of symptom differences depended on the adaptation history of the virus. This increase in symptom severity was specific for infections with the adapted isolate. Necrosis-associated alleles are found worldwide, and their distribution is consistent with a trade-off between resistance during viral outbreaks and a cost of resistance otherwise, leading to negative frequency-dependent selection.

Genetic basis of Arabidopsis thaliana responses to infection by naïve and adapted isolates of turnip mosaic virus

Plant viruses account for enormous agricultural losses worldwide, and the most effective way to combat them is to identify genetic material conferring plant resistance to these pathogens. Aiming to identify genetic associations with responses to infection, we screened a large panel of Arabidopsis thaliana natural inbred lines for four disease-related traits caused by infection by A. thaliana-naïve and -adapted isolates of the natural pathogen turnip mosaic virus (TuMV). We detected a strong, replicable association in a 1.5 Mb region on chromosome 2 with a 10-fold increase in relative risk of systemic necrosis. The region contains several plausible causal genes as well as abundant structural variation, including an insertion of a Copia transposon into a Toll/interleukin receptor (TIR-NBS-LRR) coding for a gene involved in defense, that could be either a driver or a consequence of the disease-resistance locus. When inoculated with TuMV, loss-of-function mutant plants of this gene exhibited different symptoms than wild-type plants. The direction and severity of symptom differences depended on the adaptation history of the virus. This increase in symptom severity was specific for infections with the adapted isolate. Necrosis-associated alleles are found worldwide, and their distribution is consistent with a trade-off between resistance during viral outbreaks and a cost of resistance otherwise, leading to negative frequency-dependent selection.

Identification of diagnostic molecules and potential traditional Chinese medicine components for Alzheimer's disease by single cell RNA sequencing combined with a systematic framework for network pharmacology.

Single-cell RNA sequencing (scRNA-Seq) provides new perspectives and ideas to investigate the interactions between different cell types and organisms. By integrating scRNA-seq with new computational frameworks or specific technologies, better Alzheimer's disease (AD) treatments may be developed. The single-cell sequencing dataset GSE158234 was obtained from the GEO database. Preprocessing, quality control, dimensionality-reducing clustering, and annotation to identify cell types were performed on it. RNA-seq profiling dataset GSE238013 was used to determine the components of specific cell subpopulations in diverse samples. A set of genes included in the OMIM, Genecards, CTD, and DisGeNET databases were selected as highly plausible AD-related genes. Then, ROC curves were created to predict the diagnostic value using the significantly expressed genes in the KO group as hub genes. The genes mentioned above were mapped to the Coremine Medical database to forecast prospective therapeutic Chinese medicines, and a "Chinese medicine-ingredient-target" network was constructed to screen for potential therapeutic targets. The last step was to undertake Mendelian randomization research to determine the causal link between the critical gene IL1B and AD in the genome-wide association study. Using the scRNA-seq dataset, five unique cell clusters were discovered. These clusters were further subdivided into four distinct cell types using marker genes. The KO group showed a more substantial differential subgroup of macrophages than the WT group. By using the available datasets and PPI network analysis, 54 common genes were discovered. Four clusters were identified using the MCODE approach, and correlation analysis showed that seven genes in those four clusters had a significantly negative correlation with macrophages. Six genes in four sets had a significantly positive correlation. Five genes had different levels of expression in the WT and KO groups. The String database was used to identify the regulatory relationships between the four genes (IL10, CX3CR1, IL1B, and IL6) that were finally selected as AD hub genes. Screening identified potential traditional Chinese medicine to intervene in the transformation process of AD, including Radix Salviae, ginseng, Ganoderma, licorice, Coptidis Rhizoma, and Scutellariae Radix, in addition to promising therapeutic targets, such as PTGS1, PTGS2, and RXRA. Finally, it was shown that IL1B directly correlated with immune cell infiltration in AD. In inverse variance weighting, we found that IL1B was associated with a higher risk of AD, with an OR of 1.003 (95% CI = 1.001-1.006, p = 0.038). Our research combined network pharmacology and the scRNA-seq computational framework to uncover pertinent hub genes and prospective traditional Chinese medicine potential therapeutic targets for AD. These discoveries may aid in understanding the molecular processes behind AD genes and the development of novel medications to treat the condition.

MULTI-OMICS INTEGRATION IDENTIFIES GENES INFLUENCING TRAITS ASSOCIATED WITH CARDIOVASCULAR RISKS

Abstract The Long Life Family Study (LLFS) enrolled 4,953 participants in 539 pedigrees displaying exceptional longevity. To identify genetic mechanisms that protect LLFS participants against age-related cardiovascular risks, we developed a freely available multi-omics integration pipeline and applied it to 11 traits associated with cardiovascular risks. Using our pipeline, we aggregated gene-level statistics from Rare-Variant Analysis, GWAS, and gene expression-trait association by Correlated Meta-Analysis (CMA). Across all traits, CMA identified 51 significant genes after Bonferroni correction (P ≤ 2.8×10-7). CETP, NLRC5, SLC45A3, and TOMM40 lie within 50 Kb of a known trait-associated variant (previously associated genes). Analysis of protein-protein interaction (PPI) networks identified another 63 genes (passing genes) that (1) have CMA p-value ≤ 5×10-3, (2) lie in a PPI module (highly connected subnetwork) enriched for genes with low P-values, and (3) are annotated with a biological process that is enriched among module genes, ten of which were previously associated with the same traits. Permutation analysis showed that passing genes have a false positive rate of 1 in 14876 and are more likely to be previously known than non-passing genes with similar p-values. CMA improved on the 3 input analyses by producing the largest number of modules enriched for genes with low P-values and highly enriched for genes participating in shared biological processes. Overall, module analysis identified highly plausible candidate causal genes whose P-values after CMA alone were merely suggestive.

Meta-analysis of genome-wide association studies of gestational duration and spontaneous preterm birth identifies new maternal risk loci.

Preterm birth (<37 weeks of gestation) is a major cause of neonatal death and morbidity. Up to 40% of the variation in timing of birth results from genetic factors, mostly due to the maternal genome. We conducted a genome-wide meta-analysis of gestational duration and spontaneous preterm birth in 68,732 and 98,370 European mothers, respectively. The meta-analysis detected 15 loci associated with gestational duration, and four loci associated with preterm birth. Seven of the associated loci were novel. The loci mapped to several biologically plausible genes, for example HAND2 whose expression was previously shown to decrease during gestation, associated with gestational duration, and GC (Vitamin D-binding protein), associated with preterm birth. Downstream in silico-analysis suggested regulatory roles as underlying mechanisms for the associated loci. LD score regression found birth weight measures as the most strongly correlated traits, highlighting the unique nature of spontaneous preterm birth phenotype. Tissue expression and colocalization analysis revealed reproductive tissues and immune cell types as the most relevant sites of action. We report novel genetic risk loci that associate with preterm birth or gestational duration, and reproduce findings from previous genome-wide association studies. Altogether, our findings provide new insight into the genetic background of preterm birth. Better characterization of the causal genetic mechanisms will be important to public health as it could suggest new strategies to treat and prevent preterm birth.

Towards In Silico Identification of Genes Contributing to Similarity of Patients' Multi-Omics Profiles: A Case Study of Acute Myeloid Leukemia.

We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients' similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software. For each cluster, we rank multi-omics features, ensuring the best separation between clusters, and select the top-ranked features that preserve clustering. To find genes targeted by DNA methylation and miRNAs found in the top-ranked features, we use chromosome-conformation capture data and miRNet2.0 software, respectively. To identify informative genes, these combined sets of target genes are analyzed in terms of their enrichment in somatic/germline mutations, GO biological processes/pathways terms and known sets of genes considered to be important in relation to a given disease, as recorded in the Molecular Signature Database from GSEA. The protein-protein interaction (PPI) networks were analyzed to identify genes that are hubs of PPI networks. We used data recorded in The Cancer Genome Atlas for patients with acute myeloid leukemia to demonstrate our approach, and discuss our findings in the context of results in the literature.

Deciphering the genetic landscape of obesity: a data-driven approach to identifying plausible causal genes and therapeutic targets

ObjectivesGenome-wide association studies (GWAS) have successfully revealed numerous susceptibility loci for obesity. However, identifying the causal genes, pathways, and tissues/cell types responsible for these associations remains a challenge, and standardized analysis workflows are lacking. Additionally, due to limited treatment options for obesity, there is a need for the development of new pharmacological therapies. This study aimed to address these issues by performing step-wise utilization of knowledgebase for gene prioritization and assessing the potential relevance of key obesity genes as therapeutic targets.Methods and resultsFirst, we generated a list of 28,787 obesity-associated SNPs from the publicly available GWAS dataset (approximately 800,000 individuals in the GIANT meta-analysis). Then, we prioritized 1372 genes with significant in silico evidence against genomic and transcriptomic data, including transcriptionally regulated genes in the brain from transcriptome-wide association studies. In further narrowing down the gene list, we selected key genes, which we found to be useful for the discovery of potential drug seeds as demonstrated in lipid GWAS separately. We thus identified 74 key genes for obesity, which are highly interconnected and enriched in several biological processes that contribute to obesity, including energy expenditure and homeostasis. Of 74 key genes, 37 had not been reported for the pathophysiology of obesity. Finally, by drug-gene interaction analysis, we detected 23 (of 74) key genes that are potential targets for 78 approved and marketed drugs.ConclusionsOur results provide valuable insights into new treatment options for obesity through a data-driven approach that integrates multiple up-to-date knowledgebases.

Colocalization of corneal resistance factor GWAS loci with GTEx e/sQTLs highlights plausible candidate causal genes for keratoconus postnatal corneal stroma weakening.

Background: Genome-wide association studies (GWAS) for corneal resistance factor (CRF) have identified 100s of loci and proved useful to uncover genetic determinants for keratoconus, a corneal ectasia of early-adulthood onset and common indication of corneal transplantation. In the current absence of studies to probe the impact of candidate causal variants in the cornea, we aimed to fill some of this knowledge gap by leveraging tissue-shared genetic effects. Methods: 181 CRF signals were examined for evidence of colocalization with genetic signals affecting steady-state gene transcription and splicing in adult, non-eye, tissues of the Genotype-Tissue Expression (GTEx) project. Expression of candidate causal genes thus nominated was evaluated in single cell transcriptomes from adult cornea, limbus and conjunctiva. Fine-mapping and colocalization of CRF and keratoconus GWAS signals was also deployed to support their sharing causal variants. Results and discussion: 26.5% of CRF causal signals colocalized with GTEx v8 signals and nominated genes enriched in genes with high and specific expression in corneal stromal cells amongst tissues examined. Enrichment analyses carried out with nearest genes to all 181 CRF GWAS signals indicated that stromal cells of the limbus could be susceptible to signals that did not colocalize with GTEx's. These cells might not be well represented in GTEx and/or the genetic associations might have context specific effects. The causal signals shared with GTEx provide new insights into mediation of CRF genetic effects, including modulation of splicing events. Functionally relevant roles for several implicated genes' products in providing tensile strength, mechano-sensing and signaling make the corresponding genes and regulatory variants prime candidates to be validated and their roles and effects across tissues elucidated. Colocalization of CRF and keratoconus GWAS signals strengthened support for shared causal variants but also highlighted many ways into which likely true shared signals could be missed when using readily available GWAS summary statistics.

Comprehensive analysis of geographic and breed-purpose influences on genetic diversity and inherited disease risk in the Doberman dog breed

BackgroundPublicly available phenotype data and genotyping array data from two citizen science projects: “Doberman Health Surveys” and “The Doberman Diversity Project” were analyzed to explore relative homozygosity, diversity, and disorder risk according to geographical locale and breeding purpose in the Doberman.ResultsFrom the phenotypic data cohort, life expectancy of a Doberman at birth is 9.1 years. The leading causes of death were heart disease (accounting for 28% of deaths) and cancers (collectively accounting for 14% of deaths).By genotyping, the world Doberman population exists as four major cohorts (European exhibition-bred, Americas exhibition-bred, European work, Americas pet/informal). Considering the entire Doberman population, four genomic regions longer than 500 Kb are fixed in 90% or more of 3,226 dogs included in this study. The four fixed regions reside on two autosomal chromosomes: CFA3:0.8–2.3 Mb (1.55 Mb); CFA3: 57.9–59.8 Mb (1.8 Mb); CFA31:0–1.2 Mb (1.2 Mb); and CFA31:4.80–6.47 Mb (1.67 Mb). Using public variant call files including variants for eight Doberman pinschers, we observed 30 potentially functional alternate variants that were evolutionarily diverged relative to the wider sequenced dog population within the four strongly homozygous chromosomal regions.Effective population size (Ne) is a statistical measure of breed diversity at the time of sampling that approximates the number of unique individuals. The major identified sub-populations of Dobermans demonstrated Ne in the range 70–236. The mean level of inbreeding in the Doberman breed is 40% as calculated by the number of array variants in runs of homozygosity divided by the assayed genome size (excluding the X chromosome). The lowest observed level of inbreeding in the Dobermans assayed was 15% in animals that were first generation mixes of European and USA bred Dobermans. Array variant analysis shows that inter-crossing between European and USA-bred Dobermans has capacity to re-introduce variation at many loci that are strongly homozygous.ConclusionsWe conclude that efforts to improve breed diversity first should focus on regions with the highest fixation levels, but managers must ensure that mutation loads are not worsened by increasing the frequencies of rarer haplotypes in the identified regions. The analysis of global data identified regions of strong fixation that might impact known disorder risks in the breed. Plausible gene candidates for future analysis of the genetic basis of cardiac disease and cancer were identified in the analysis.

Cis- and trans-eQTM analysis reveals novel epigenetic and transcriptomic immune markers of atopic asthma in airway epithelium

Expression quantitative trait methylation (eQTM) analyses uncover associations between DNA methylation markers and gene expression. Most eQTM analyses of complex diseases have focused on cis-eQTM pairs (within 1 megabase). This study sought to identify cis- and trans-methylation markers associated with gene expression in airway epithelium from youth with and without atopic asthma. In this study, the investigators conducted both cis- and trans-eQTM analyses in nasal (airway) epithelial samples from 158 Puerto Rican youth with atopic asthma and 100 control subjects without atopy or asthma. The investigators then attempted to replicate their findings in nasal epithelial samples from 2 studies of children, while also examining whether their results in nasal epithelium overlap with those from an eQTM analysis in white blood cells from the Puerto Rican subjects. This study identified 9,108 cis-eQTM pairs and 2,131,500 trans-eQTM pairs. Trans-associations were significantly enriched for transcription factor and microRNA target genes. Furthermore, significant cytosine-phosphate-guanine sites (CpGs) were differentially methylated in atopic asthma and significant genes were enriched for genes differentially expressed in atopic asthma. In this study, 50.7% to 62.6% of cis- and trans-eQTM pairs identified in Puerto Rican youth were replicated in 2 smaller cohorts at false discovery rate-adjusted P< .1. Replicated genes in the trans-eQTM analysis included biologically plausible asthma-susceptibility genes (eg, HDC, NLRP3, ITGAE, CDH26, and CST1) and are enriched in immune pathways. Studying both cis- and trans-epigenetic regulation of airway epithelial gene expression can identify potential causal and regulatory pathways or networks for childhood asthma. Trans-eQTM CpGs may regulate gene expression in airway epithelium through effects on transcription factor and microRNA target genes.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.