scholarly journals Bayesian reassessment of the epigenetic architecture of complex traits

2018 ◽  
Author(s):  
Daniel Trejo Banos ◽  
Daniel L. McCartney ◽  
Tom Battram ◽  
Gibran Hemani ◽  
Rosie M. Walker ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Complex Traits ◽  
Disease Risk ◽  
Study Finding ◽  
Cigarette Consumption ◽  
Blood Cholesterol ◽  
Common Disease ◽  
Inclusion Probability ◽  
Methylation Array ◽  
Wide Range

1AbstractEpigenetic DNA modification is partly under genetic control, and occurs in response to a wide range of environmental exposures. Linking epigenetic marks to clinical outcomes may provide greater insight into underlying molecular processes of disease, assist in the identification of therapeutic targets, and improve risk prediction. Here, we present a statistical approach, based on Bayesian inference, that estimates associations between disease risk and all measured epigenetic probes jointly, automatically controlling for both data structure (including cell-count effects, relatedness, and experimental batch effects) and correlations among probes. We benchmark our approach in simulation study, finding improved estimation of probe associations across a wide range of scenarios over existing approaches. Our method estimates the total proportion of disease risk captured by epigenetic probe variation, and when we applied it to measures of body mass index (BMI) and cigarette consumption behaviour in 5,101 individuals, we find that 66.7% (95% CI 60.0-72.8) of the variation in BMI and 67.7% (95% CI 58.4-76.9) of the variation in cigarette consumption can be captured by methylation array data from whole blood, independent of the variation explained by single nucleotide polymorphism markers. We find novel associations, with smoking behaviour associated with a methylation probe at the MNDA gene with >95% posterior inclusion probability, which is a myeloid cell nuclear differentiation antigen gene previously implicated as a biomarker for inflammation and non-Hodgkin lymphoma risk. We conduct unique genome-wide enrichment analyses, identifying blood cholesterol, lipid transport and sterol metabolism pathways for BMI, and response to xenobiotic stimulus and negative regulation of RNA polymerase II promoter transcription for smoking, all with >95% posterior inclusion probability of having methylation probes with associations >1.5 times larger than the average. Finally, we improve phenotypic prediction in two independent cohorts by 28.7% and 10.2% for BMI and smoking respectively over a LASSO model. These results imply that probe measures may capture large amounts of variance because they are likely a consequence of the phenotype rather than a cause. As a result, ‘omics’ data may enable accurate characterization of disease progression and identification of individuals who are on a path to disease. Our approach facilitates better understanding of the underlying epigenetic architecture of complex common disease and is applicable to any kind of genomics data.

scholarly journals Bayesian reassessment of the epigenetic architecture of complex traits

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Trejo Banos ◽  
Daniel L. McCartney ◽  
Marion Patxot ◽  
Lucas Anchieri ◽  
Thomas Battram ◽  
...  
Keyword(s):  
Complex Traits ◽  
Complex Disease ◽  
Target Identification ◽  
Blood Cholesterol ◽  
Methylation Array ◽  
Single Nucleotide ◽  
Epigenetic Effects ◽  
Probability Prediction ◽  
Stimuli Response ◽  
Insight Into

Abstract Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70–79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3–51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal.

scholarly journals Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

2021 ◽  
Author(s):  
Steven Gazal ◽  
Omer Weissbrod ◽  
Farhad Hormozdiari ◽  
Kushal Dey ◽  
Joseph Nasser ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Common Disease ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Functional Interpretation ◽  
Genome Wide

Although genome-wide association studies (GWAS) have identified thousands of disease-associated common SNPs, these SNPs generally do not implicate the underlying target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis, but it is unclear how these strategies should be applied in the context of interpreting common disease risk variants. We developed a framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk, leveraging polygenic analyses of disease heritability to define and estimate their precision and recall. We applied our framework to GWAS summary statistics for 63 diseases and complex traits (average N=314K), evaluating 50 S2G strategies. Our optimal combined S2G strategy (cS2G) included 7 constituent S2G strategies (Exon, Promoter, 2 fine-mapped cis-eQTL strategies, EpiMap enhancer-gene linking, Activity-By-Contact (ABC), and Cicero), and achieved a precision of 0.75 and a recall of 0.33, more than doubling the precision and/or recall of any individual strategy; this implies that 33% of SNP-heritability can be linked to causal genes with 75% confidence. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 7,111 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. Finally, we applied cS2G to genome-wide fine-mapping results for these traits (not restricted to GWAS loci) to rank genes by the heritability linked to each gene, providing an empirical assessment of disease omnigenicity; averaging across traits, we determined that the top 200 (1%) of ranked genes explained roughly half of the heritability linked to all genes. Our results highlight the benefits of our cS2G strategy in providing functional interpretation of GWAS findings; we anticipate that precision and recall will increase further under our framework as improved functional assays lead to improved S2G strategies. 

scholarly journals Schizophrenia: a common disease caused by multiple rare alleles

2007 ◽  
Vol 190 (3) ◽  
pp. 194-199 ◽  
Author(s):  
Jon M. McClellan ◽  
Ezra Susser ◽  
Mary-Claire King
Keyword(s):  
Complex Traits ◽  
Disease Risk ◽  
Gene Discovery ◽  
Alternative View ◽  
Common Disease ◽  
Combined Effects ◽  
Locus Heterogeneity ◽  
Human Genomics ◽  
Discovery Research ◽  
Rare Alleles

SummarySchizophrenia is widely held to stem from the combined effects of multiple common polymorphisms, each with a small impact on disease risk. We suggest an alternative view: that schizophrenia is highly heterogeneous genetically and that many predisposing mutations are highly penetrant and individually rare, even specific to single cases or families. This ‘common disease – rare alleles' hypothesis is supported by recent findings in human genomics and by allelic and locus heterogeneity for other complex traits. We review the implications of this model for gene discovery research in schizophrenia.

Chronic inflammation and cancer: The role of endothelial dysfunction and vascular inflammation

2021 ◽  
Vol 27 ◽  
Author(s):  
Lara J. Bou Malhab ◽  
Maha M. Saber-Ayad ◽  
Ranyah Al-Hakm ◽  
Vidhya A Nair ◽  
Panagiotis Paliogiannis ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Endothelial Dysfunction ◽  
Cancer Progression ◽  
Disease Risk ◽  
Vascular Inflammation ◽  
Causal Link ◽  
Cancer Development ◽  
Common Disease ◽  
Wide Range

: Long-lasting subclinical inflammation is associated with a wide range of human diseases, particularly at middle and older age. Recent reports showed that there is a direct causal link between inflammation and cancer development, as several cancers were found to be associated with chronic inflammatory conditions. In patients with cancer, healthy endothelial cells regulate vascular homeostasis, and it is believed that they can limit tumor growth, invasiveness, and metastasis. Conversely, dysfunctional endothelial cells that have been exposed to the inflammatory tumor microenvironment can support cancer progression and metastasis. Dysfunctional endothelial cells can exert these effects via diverse mechanisms including dysregulated adhesion, permeability, and activation of NF-κB and STAT3 signaling. In this review, we highlight the role of vascular inflammation in predisposition to cancer within the context of two common disease risk factors: obesity and smoking. In addition, we discuss the molecular triggers, pathophysiological mechanisms, and the biological consequences of vascular inflammation during cancer development and metastasis. Finally, we summarize the current therapies and pharmacological agents that target vascular inflammation and endothelial dysfunction.

scholarly journals Genome-wide maps of enhancer regulation connect risk variants to disease genes

2020 ◽  
Author(s):  
Joseph Nasser ◽  
Drew T. Bergman ◽  
Charles P. Fulco ◽  
Philine Guckelberger ◽  
Benjamin R. Doughty ◽  
...  
Keyword(s):  
Complex Traits ◽  
Disease Risk ◽  
Association Studies ◽  
Cell Types ◽  
Common Disease ◽  
Genome Wide Association Studies ◽  
Abc Model ◽  
Risk Variants ◽  
Genome Wide ◽  
Causal Variants

AbstractGenome-wide association studies have now identified tens of thousands of noncoding loci associated with human diseases and complex traits, each of which could reveal insights into biological mechanisms of disease. Many of the underlying causal variants are thought to affect enhancers, but we have lacked genome-wide maps of enhancer-gene regulation to interpret such variants. We previously developed the Activity-by-Contact (ABC) Model to predict enhancer-gene connections and demonstrated that it can accurately predict the results of CRISPR perturbations across several cell types. Here, we apply this ABC Model to create enhancer-gene maps in 131 cell types and tissues, and use these maps to interpret the functions of fine-mapped GWAS variants. For inflammatory bowel disease (IBD), causal variants are >20-fold enriched in enhancers in particular cell types, and ABC outperforms other regulatory methods at connecting noncoding variants to target genes. Across 72 diseases and complex traits, ABC links 5,036 GWAS signals to 2,249 unique genes, including a class of 577 genes that appear to influence multiple phenotypes via variants in enhancers that act in different cell types. Guided by these variant-to-function maps, we show that an enhancer containing an IBD risk variant regulates the expression of PPIF to tune mitochondrial membrane potential. Together, our study reveals insights into principles of genome regulation, illuminates mechanisms that influence IBD, and demonstrates a generalizable strategy to connect common disease risk variants to their molecular and cellular functions.

Understanding Schizophrenia: Genetic Causes and Treatment

2019 ◽  
Vol 1 (1) ◽  
pp. 6-12
Author(s):  
Fatima Javeria ◽  
Shazma Altaf ◽  
Alishah Zair ◽  
Rana Khalid Iqbal
Keyword(s):  
Copy Number ◽  
Drug Targets ◽  
Disease Risk ◽  
Mental Disease ◽  
Copy Number Variants ◽  
Aminobutyric Acid ◽  
Epigenetic Mechanisms ◽  
Gamma Aminobutyric Acid ◽  
Wide Range ◽  
Severe Mental Disease

Schizophrenia is a severe mental disease. The word schizophrenia literally means split mind. There are three major categories of symptoms which include positive, negative and cognitive symptoms. The disease is characterized by symptoms of hallucination, delusions, disorganized thinking and speech. Schizophrenia is related to many other mental and psychological problems like suicide, depression, hallucinations. Including these, it is also a problem for the patient’s family and the caregiver. There is no clear reason for the disease, but with the advances in molecular genetics; certain epigenetic mechanisms are involved in the pathophysiology of the disease. Epigenetic mechanisms that are mainly involved are the DNA methylation, copy number variants. With the advent of GWAS, a wide range of SNPs is found linked with the etiology of schizophrenia. These SNPs serve as ‘hubs’; because these all are integrating with each other in causing of schizophrenia risk. Until recently, there is no treatment available to cure the disease; but anti-psychotics can reduce the disease risk by minimizing its symptoms. Dopamine, serotonin, gamma-aminobutyric acid, are the neurotransmitters which serve as drug targets in the treatment of schizophrenia. Due to the involvement of genetic and epigenetic mechanisms, drugs available are already targeting certain genes involved in the etiology of the disease.

scholarly journals Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 991
Author(s):  
Erik Widen ◽  
Timothy G. Raben ◽  
Louis Lello ◽  
Stephen D. H. Hsu
Keyword(s):  
Disease Risk ◽  
Smoking Status ◽  
Glycated Haemoglobin ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Disease ◽  
Atherosclerotic Cardiovascular Disease ◽  
Uk Biobank ◽  
Lipoprotein A ◽  
The Uk

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

scholarly journals Cardiovascular Disease Risk in Bears and Other Gay Men: A Descriptive Study from Poland

2021 ◽  
Vol 18 (3) ◽  
pp. 1044
Author(s):  
Magdalena Mijas ◽  
Karolina Koziara ◽  
Andrzej Galbarczyk ◽  
Grazyna Jasienska
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Gay Men ◽  
Disease Risk ◽  
Older Age ◽  
Analysis Of Covariance ◽  
Control Group ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Wide Range

A risk of cardiovascular disease (CVD) is increased by multiple factors including psychosocial stress and health behaviors. Sexual minority men who identify as Bears form a subculture distinguished by characteristics associated with increased CVD risk such as elevated stress and high body weight. However, none of the previous studies comprehensively investigated CVD risk in this population. Our study compared Bears (N = 31) with other gay men (N = 105) across a wide range of CVD risk factors. Logistic regression and analysis of covariance (ANCOVA) models were performed to compare both groups concerning behavioral (e.g., physical activity), medical (e.g., self-reported hypertension), and psychosocial (e.g., depressiveness) CVD risk factors. Bears were characterized by older age and higher body mass index (BMI) than the control group. We also observed higher resilience, self-esteem, as well as greater prevalence of self-reported hypertension, diabetes, and hypercholesterolemia in Bears. None of these differences remained statistically significant after adjusting for age and, in the case of self-reported diagnosis of diabetes, both age and BMI. Our study demonstrates that Bears are characterized by increased CVD risk associated predominantly with older age and higher BMI. Health promotion interventions addressed to this community should be tailored to Bears’ subcultural norms and should encourage a healthier lifestyle instead of weight loss.

scholarly journals EWAS Data Hub: a resource of DNA methylation array data and metadata

2019 ◽  
Vol 48 (D1) ◽  
pp. D890-D895 ◽  
Author(s):  
Zhuang Xiong ◽  
Mengwei Li ◽  
Fei Yang ◽  
Yingke Ma ◽  
Jian Sang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Complex Traits ◽  
Cell Types ◽  
Great Promise ◽  
Methylation Array ◽  
Array Data ◽  
The Past ◽  
Comprehensive Collection ◽  
Dna Methylation Array ◽  
Brain Parts

Abstract Epigenome-Wide Association Study (EWAS) has become an effective strategy to explore epigenetic basis of complex traits. Over the past decade, a large amount of epigenetic data, especially those sourced from DNA methylation array, has been accumulated as the result of numerous EWAS projects. We present EWAS Data Hub (https://bigd.big.ac.cn/ewas/datahub), a resource for collecting and normalizing DNA methylation array data as well as archiving associated metadata. The current release of EWAS Data Hub integrates a comprehensive collection of DNA methylation array data from 75 344 samples and employs an effective normalization method to remove batch effects among different datasets. Accordingly, taking advantages of both massive high-quality DNA methylation data and standardized metadata, EWAS Data Hub provides reference DNA methylation profiles under different contexts, involving 81 tissues/cell types (that contain 25 brain parts and 25 blood cell types), six ancestry categories, and 67 diseases (including 39 cancers). In summary, EWAS Data Hub bears great promise to aid the retrieval and discovery of methylation-based biomarkers for phenotype characterization, clinical treatment and health care.

scholarly journals Dietary Fiber, Atherosclerosis, and Cardiovascular Disease

Nutrients ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 1155 ◽  
Author(s):  
Ghada A. Soliman
Keyword(s):  
Cardiovascular Disease ◽  
Dietary Fiber ◽  
Water Solubility ◽  
Disease Risk ◽  
Current Knowledge ◽  
Digestive Enzyme ◽  
Water Soluble ◽  
Blood Cholesterol ◽  
Fiber Intake ◽  
Intestinal Transit Time

Observational studies have shown that dietary fiber intake is associated with decreased risk of cardiovascular disease. Dietary fiber is a non-digestible form of carbohydrates, due to the lack of the digestive enzyme in humans required to digest fiber. Dietary fibers and lignin are intrinsic to plants and are classified according to their water solubility properties as either soluble or insoluble fibers. Water-soluble fibers include pectin, gums, mucilage, fructans, and some resistant starches. They are present in some fruits, vegetables, oats, and barley. Soluble fibers have been shown to lower blood cholesterol by several mechanisms. On the other hand, water-insoluble fibers mainly include lignin, cellulose, and hemicellulose; whole-grain foods, bran, nuts, and seeds are rich in these fibers. Water-insoluble fibers have rapid gastric emptying, and as such may decrease the intestinal transit time and increase fecal bulk, thus promoting digestive regularity. In addition to dietary fiber, isolated and extracted fibers are known as functional fiber and have been shown to induce beneficial health effects when added to food during processing. The recommended daily allowances (RDAs) for total fiber intake for men and women aged 19–50 are 38 gram/day and 25 gram/day, respectively. It is worth noting that the RDA recommendations are for healthy people and do not apply to individuals with some chronic diseases. Studies have shown that most Americans do not consume the recommended intake of fiber. This review will summarize the current knowledge regarding dietary fiber, sources of food containing fiber, atherosclerosis, and heart disease risk reduction.

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