Integrating Mouse and Human Genetic Data to Move beyond GWAS and Identify Causal Genes in Cholesterol Metabolism.
Ontology highlight
ABSTRACT:
SUBMITTER: Li Z
PROVIDER: S-EPMC7184639 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:IntroductionRice (Oryza sativa L.) production is being challenged by global warming. Identifying new loci and favorable alleles associated with heat tolerance is crucial to developing rice heat-tolerant varieties.MethodsWe evaluated the heat tolerance at the seedling stage using 620 diverse rice accessions. A total of six loci associated with heat tolerance were identified by a genome-wide association study (GWAS) with ~2.8 million single nucleotide polymorphisms (SNPs).ResultsAmong the six detected loci, qHT7 harbored the strongest association signal and the most associated SNPs. By comparing the transcriptomes of two representative accessions with contrasting heat tolerance, LOC_Os07g48710 (OsVQ30) was selected as a promising candidate gene in qHT7 due to the significant difference in its expression level between the two accessions. Haplotype 4 (Hap4) of LOC_Os07g48710 was determined as the favorable haplotype for heat tolerance via the gene-based haplotype analysis. The heat-tolerant haplotype LOC_Os07g48710Hap4 is highly enriched in the tropical Geng/Japonica accessions, and its frequency has decreased significantly during the improvement process of rice varieties.DiscussionBased on the GWAS and transcriptomics integrated results, a hypothetical model modulated by qHT7 in response to heat stress was proposed. Our results provide valuable candidate genes for improving rice heat tolerance through molecular breeding.
| S-EPMC9868562 | biostudies-literature
Project description:Two new gene-based association analysis methods, called PrediXcan and TWAS for GWAS individual-level and summary data, respectively, were recently proposed to integrate GWAS with eQTL data, alleviating two common problems in GWAS by boosting statistical power and facilitating biological interpretation of GWAS discoveries. Based on a novel reformulation of PrediXcan and TWAS, we propose a more powerful gene-based association test to integrate single set or multiple sets of eQTL data with GWAS individual-level data or summary statistics. The proposed test was applied to several GWAS datasets, including two lipid summary association datasets based on [Formula: see text] and [Formula: see text] samples, respectively, and uncovered more known or novel trait-associated genes, showcasing much improved performance of our proposed method. The software implementing the proposed method is freely available as an R package.
| S-EPMC5676241 | biostudies-literature
Project description:Thyroid carcinoma (TC) is the most common endocrine malignancy. The incidence rate of thyroid cancer has increased rapidly in recent years. The occurrence and development of thyroid cancers are highly related to the massive genetic and epigenetic changes. Therefore, it is essential to explore the mechanism of thyroid cancer pathogenesis. Genome-Wide Association Studies (GWAS) have been widely used in various diseases. Researchers have found multiple single nucleotide polymorphisms (SNPs) are significantly related to TC. However, the biological mechanism of these SNPs is still unknown. In this paper, we used one GWAS dataset and two eQTL datasets, and integrated GWAS with expression quantitative trait loci (eQTL) in both thyroid and blood to explore the mechanism of mutations and causal genes of thyroid cancer. Finally, we found rs1912998 regulates the expression of IGFALS (P = 1.70E-06) and HAGH (P = 5.08E-07) in thyroid, which is significantly related to thyroid cancer. In addition, KEGG shows that these genes participate in multiple thyroid cancer-related pathways.
| S-EPMC7889963 | biostudies-literature
Project description:Cell packs a lot of genetic and regulatory information through a structure known as chromatin, i.e. DNA is wrapped around histone proteins and is tightly packed in a remarkable way. To express a gene in a specific coding region, the chromatin would open up and DNA loop may be formed by interacting enhancers and promoters. Furthermore, the mediator and cohesion complexes, sequence-specific transcription factors, and RNA polymerase II are recruited and work together to elaborately regulate the expression level. It is in pressing need to understand how the information, about when, where, and to what degree genes should be expressed, is embedded into chromatin structure and gene regulatory elements. Thanks to large consortia such as Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomic projects, extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell types. This rich data offer an exciting opportunity to model the causal regulatory relationship. Here, we will review the current experimental approaches, foundational data, computational problems, interpretive frameworks, and integrative models that will enable the accurate interpretation of regulatory landscape. Particularly, we will discuss the efforts to organize, analyze, model, and integrate the DNA accessibility data, transcriptional data, and functional genomic regions together. We believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.
| S-EPMC5501464 | biostudies-other
Project description:Inference about the causal structure that induces correlations between two traits can be achieved by combining genetic associations with a mediation-based approach, as is done in the causal inference test (CIT). However, we show that measurement error in the phenotypes can lead to the CIT inferring the wrong causal direction, and that increasing sample sizes has the adverse effect of increasing confidence in the wrong answer. This problem is likely to be general to other mediation-based approaches. Here we introduce an extension to Mendelian randomisation, a method that uses genetic associations in an instrumentation framework, that enables inference of the causal direction between traits, with some advantages. First, it can be performed using only summary level data from genome-wide association studies; second, it is less susceptible to bias in the presence of measurement error or unmeasured confounding. We apply the method to infer the causal direction between DNA methylation and gene expression levels. Our results demonstrate that, in general, DNA methylation is more likely to be the causal factor, but this result is highly susceptible to bias induced by systematic differences in measurement error between the platforms, and by horizontal pleiotropy. We emphasise that, where possible, implementing MR and appropriate sensitivity analyses alongside other approaches such as CIT is important to triangulate reliable conclusions about causality.
| S-EPMC5711033 | biostudies-literature
Project description:Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.
| S-EPMC10776627 | biostudies-literature
Project description:Health risk factors such as body mass index (BMI) and serum cholesterol are associated with many common diseases. It often remains unclear whether the risk factors are cause or consequence of disease, or whether the associations are the result of confounding. We develop and apply a method (called GSMR) that performs a multi-SNP Mendelian randomization analysis using summary-level data from genome-wide association studies to test the causal associations of BMI, waist-to-hip ratio, serum cholesterols, blood pressures, height, and years of schooling (EduYears) with common diseases (sample sizes of up to 405,072). We identify a number of causal associations including a protective effect of LDL-cholesterol against type-2 diabetes (T2D) that might explain the side effects of statins on T2D, a protective effect of EduYears against Alzheimer's disease, and bidirectional associations with opposite effects (e.g., higher BMI increases the risk of T2D but the effect of T2D on BMI is negative).
| S-EPMC5768719 | biostudies-literature
Project description:Background: Nasal polyps (NP) are benign inflammatory growths of nasal and paranasal sinus mucosa that can substantially impair patients' quality of life by various symptoms such as nasal obstruction, insomnia, and anosmia. NP often relapse even after surgical treatment, and the curative therapy would be challenging without understanding the underlying mechanisms. Genome wide association studies (GWASs) on NP have been conducted; however, few genes that are causally associated with NP have been identified. Methods: We aimed to prioritize NP associated genes for functional follow-up studies using the summary data-based Mendelian Randomization (SMR) and Bayesian colocalization (COLOC) methods to integrate the summary-level data of the GWAS on NP and the expression quantitative trait locus (eQTL) study in blood. We utilized the GWAS data including 5,554 NP cases and 258,553 controls with 34 genome-wide significant loci from the FinnGen consortium (data freeze 8) and the eQTL data from 31,684 participants of predominantly European ancestry from the eQTLGen consortium. Results: The SMR analysis identified several genes including TNFRSF18, CTSK, and IRF1 that were associated with NP due to not linkage but pleiotropy or causality. The COLOC analysis strongly suggested that these genes and the trait of NP were affected by shared causal variants, and thus were colocalized. An enrichment analysis by Metascape suggested that these genes might be involved in the biological process of cellular response to cytokine stimulus. Conclusion: We could prioritize several NP associated genes including TNFRSF18, CTSK, and IRF1 for follow-up functional studies in future to elucidate the underlying disease mechanisms.
| S-EPMC10326843 | biostudies-literature
Project description:Schizophrenia (SCZ) is a polygenic disease with a heritability approaching 80%. Over 100 SCZ-related loci have so far been identified by genome-wide association studies (GWAS). However, the risk genes associated with these loci often remain unknown. We present a new risk gene predictor, rGAT-omics, that integrates multi-omics data under a Bayesian framework by combining the Hotelling and Box-Cox transformations. The Bayesian framework was constructed using gene ontology, tissue-specific protein-protein networks, and multi-omics data including differentially expressed genes in SCZ and controls, distance from genes to the index single-nucleotide polymorphisms (SNPs), and de novo mutations. The application of rGAT-omics to the 108 loci identified by a recent GWAS study of SCZ predicted 103 high-risk genes (HRGs) that explain a high proportion of SCZ heritability (Enrichment = 43.44 and [Formula: see text]). HRGs were shown to be significantly ([Formula: see text]) enriched in genes associated with neurological activities, and more likely to be expressed in brain tissues and SCZ-associated cell types than background genes. The predicted HRGs included 16 novel genes not present in any existing databases of SCZ-associated genes or previously predicted to be SCZ risk genes by any other method. More importantly, 13 of these 16 genes were not the nearest to the index SNP markers, and them would have been difficult to identify as risk genes by conventional approaches while ten out of the 16 genes are associated with neurological functions that make them prime candidates for pathological involvement in SCZ. Therefore, rGAT-omics has revealed novel insights into the molecular mechanisms underlying SCZ and could provide potential clues to future therapies.
| S-EPMC7969765 | biostudies-literature
Project description:BackgroundTo evaluate the discordance in frailty classification between the frailty index (FI) and the physical frailty phenotype (PFP) and identify factors discriminating those with discordant frailty classification from each other and from those for whom the assessments agree.MethodsA prospective observational study of older adults aged 65 and older selected from Medicare eligibility lists in four U.S. communities (n = 5,362). The PFP was measured by the Cardiovascular Health Study PFP. Participants meeting three or more of the five criteria were deemed frail. The FI was calculated as the proportion of deficits in an a priori selected set of 48 measures, and participants were classified as frail if FI is greater than 0.35.ResultsThe prevalence of frailty was 7.0% by the PFP and 8.3% by the FI. Of the 730 deemed frail by either instrument, only 12% were in agreement, whereas 39% were classified as frail by the PFP, but not the FI, and 48% were classified as frail by the FI, but not the PFP. Participants aged 65-72 years or with greater disease burden were most likely to be characterized as being FI-frail, but not PFP-frail. The associations of frailty with age and mortality were stronger when frailty was measured by the PFP rather than the FI.ConclusionsDespite comparable frailty prevalence between the PFP and the FI, there was substantial discordance in individual-level classification, with highest agreement existing only in the most vulnerable subset. These findings suggest that there are clinically important contexts in which the PFP and the FI cannot be used interchangeably.
| S-EPMC7176056 | biostudies-literature