scholarly journals Variably methylated regions in the newborn epigenome: environmental, genetic and combined influences

2018 ◽  
Author(s):  
Darina Czamara ◽  
Gökçen Eraslan ◽  
Jari Lahti ◽  
Christian M. Page ◽  
Marius Lahti-Pulkkinen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Environmental Factors ◽  
Genetic Variants ◽  
Disease Risk ◽  
Association Studies ◽  
Information Criterion ◽  
Genome Wide Association Studies ◽  
Complex Disorders ◽  
Genetic And Environmental Factors ◽  
Genomic Regions

AbstractBackgroundEpigenetic processes, including DNA methylation (DNAm), are among the mechanisms allowing integration of genetic and environmental factors to shape cellular function. While many studies have investigated either environmental or genetic contributions to DNAm, few have assessed their integrated effects. We examined the relative contributions of prenatal environmental factors and genotype on DNA methylation in neonatal blood at variably methylated regions (VMRs), defined as consecutive CpGs showing the highest variability of DNAm in 4 independent cohorts (PREDO, DCHS, UCI, MoBa, N=2,934).ResultsWe used Akaike’s information criterion to test which factors best explained variability of methylation in the cohort-specific VMRs: several prenatal environmental factors (E) including maternal demographic, psychosocial and metabolism related phenotypes, genotypes in cis (G), or their additive (G+E) or interaction (GxE) effects. G+E and GxE models consistently best explained variability in DNAm of VMRs across the cohorts, with G explaining the remaining sites best. VMRs best explained by G, GxE or G+E, as well as their associated functional genetic variants (predicted using deep learning algorithms), were located in distinct genomic regions, with different enrichments for transcription and enhancer marks. Genetic variants of not only G and G+E models, but also of variants in GxE models were significantly enriched in genome wide association studies (GWAS) for complex disorders.ConclusionGenetic and environmental factors in combination best explain DNAm at VMRs. The CpGs best explained by G, G+E or GxE are functionally distinct. The enrichment of GxE variants in GWAS for complex disorders supports their importance for disease risk.

scholarly journals Schizophrenia-associated differential DNA methylation in the superior temporal gyrus is distributed to many sites across the genome and annotated by the risk gene MAD1L1

2020 ◽  
Author(s):  
Brandon C McKinney ◽  
Christopher M Hensler ◽  
Yue Wei ◽  
David A Lewis ◽  
Jiebiao Wang ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Environmental Factors ◽  
Genetic Variants ◽  
Association Studies ◽  
Superior Temporal Gyrus ◽  
Differential Methylation ◽  
Composition Analysis ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
The Individual

Background: Many genetic variants and multiple environmental factors increase risk for schizophrenia (SZ). SZ-associated genetic variants and environmental risk factors have been associated with altered DNA methylation (DNAm), the addition of a methyl group to a cytosine in DNA. DNAm changes, acting through effects on gene expression, represent one potential mechanism by which genetic and environmental factors confer risk for SZ and alter neurobiology. Methods: We investigated the hypothesis that DNAm in superior temporal gyrus (STG) is altered in SZ. We measured genome-wide DNAm in postmortem STG from 44 SZ subjects and 44 non-psychiatric comparison (NPC) subjects using Illumina Infinium MethylationEPIC BeadChip microarrays. We applied tensor composition analysis to extract cell type-specific DNAm signals. Results: We found that DNAm levels differed between SZ and NPC subjects at 242 sites, and 44 regions comprised of two or more sites, with a false discovery rate cutoff of q=0.1. We determined differential methylation at nine of the individual sites were driven by neuron-specific DNAm alterations. Glia-specific DNAm alterations drove the differences at two sites. Notably, we identied SZ-associated differential methylation within within mitotic arrest deficient 1-like 1 (MAD1L1), a gene strongly associated with SZ through genome-wide association studies. Conclusions: This study adds to a growing number of studies that implicate DNAm, and epigenetic pathways more generally, in SZ. Our findings suggest differential methylation may contribute to STG dysfunction in SZ. Future studies to identify the mechanisms by which altered DNAm, especially within MAD1L1, contributes to SZ neurobiology are warranted.

scholarly journals Molecular Variants in Human Trace Amine-Associated Receptors and Their Implications in Mental and Metabolic Disorders

2019 ◽  
Vol 40 (2) ◽  
pp. 239-255 ◽  
Author(s):  
Grazia Rutigliano ◽  
Riccardo Zucchi
Keyword(s):  
Genetic Variants ◽  
Affective Disorders ◽  
Metabolic Disorders ◽  
Association Studies ◽  
Single Gene ◽  
Genome Wide Association Studies ◽  
Complex Disorders ◽  
Linkage Analyses ◽  
Genome Wide ◽  
Trace Amine

Abstract We provide a comprehensive review of the available evidence on the pathophysiological implications of genetic variants in the human trace amine-associated receptor (TAAR) superfamily. Genes coding for trace amine-associated receptors (taars) represent a multigene family of G-protein-coupled receptors, clustered to a small genomic region of 108 kb located in chromosome 6q23, which has been consistently identified by linkage analyses as a susceptibility locus for schizophrenia and affective disorders. Most TAARs are expressed in brain areas involved in emotions, reward and cognition. TAARs are activated by endogenous trace amines and thyronamines, and evidence for a modulatory action on other monaminergic systems has been reported. Therefore, linkage analyses were followed by fine mapping association studies in schizophrenia and affective disorders. However, none of these reports has received sufficient universal replication, so their status remains uncertain. Single nucleotide polymorphisms in taars have emerged as susceptibility loci from genome-wide association studies investigating migraine and brain development, but none of the detected variants reached the threshold for genome-wide significance. In the last decade, technological advances enabled single-gene or whole-exome sequencing, thus allowing the detection of rare genetic variants, which may have a greater impact on the risk of complex disorders. Using these approaches, several taars (especially taar1) variants have been detected in patients with mental and metabolic disorders, and in some cases, defective receptor function has been demonstrated in vitro. Finally, with the use of transcriptomic and peptidomic techniques, dysregulations of TAARs (especially TAAR6) have been identified in brain disorders characterized by cognitive impairment.

scholarly journals Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of neurological function and disease

2018 ◽  
Author(s):  
Charlie Hatcher ◽  
Caroline L. Relton ◽  
Tom R. Gaunt ◽  
Tom G. Richardson
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Histone Acetylation ◽  
Genetic Variants ◽  
Large Scale ◽  
Genetic Variant ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Epigenetic Mechanisms ◽  
Trait Variation

AbstractIntegrative approaches which harness large-scale molecular datasets can help develop mechanistic insight into findings from genome-wide association studies (GWAS). We have performed extensive analyses to uncover transcriptional and epigenetic processes which may play a role in neurological trait variation.This was undertaken by applying Bayesian multiple-trait colocalization systematically across the genome to identify genetic variants responsible for influencing intermediate molecular phenotypes as well as neurological traits. In this analysis we leveraged high dimensional quantitative trait loci data derived from prefrontal cortex tissue (concerning gene expression, DNA methylation and histone acetylation) and GWAS findings for 5 neurological traits (Neuroticism, Schizophrenia, Educational Attainment, Insomnia and Alzheimer’s disease).There was evidence of colocalization for 118 associations suggesting that the same underlying genetic variant influenced both nearby gene expression as well as neurological trait variation. Of these, 73 associations provided evidence that the genetic variant also influenced proximal DNA methylation and/or histone acetylation. These findings support previous evidence at loci where epigenetic mechanisms may putatively mediate effects of genetic variants on traits, such as KLC1 and schizophrenia. We also uncovered evidence implicating novel loci in neurological disease susceptibility, including genes expressed predominantly in brain tissue such as MDGA1, KIRREL3 and SLC12A5.An inverse relationship between DNA methylation and gene expression was observed more than can be accounted for by chance, supporting previous findings implicating DNA methylation as a transcriptional repressor. Our study should prove valuable in helping future studies prioritise candidate genes and epigenetic mechanisms for in-depth functional follow-up analyses.

scholarly journals Progress and promise in understanding the genetic basis of common diseases

2015 ◽  
Vol 282 (1821) ◽  
pp. 20151684 ◽  
Author(s):  
Alkes L. Price ◽  
Chris C. A. Spencer ◽  
Peter Donnelly
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Common Disease ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Common Diseases ◽  
Multiple Phenotypes ◽  
Disease Associations ◽  
Genetic And Environmental Factors ◽  
Genetic Signal

Susceptibility to common human diseases is influenced by both genetic and environmental factors. The explosive growth of genetic data, and the knowledge that it is generating, are transforming our biological understanding of these diseases. In this review, we describe the technological and analytical advances that have enabled genome-wide association studies to be successful in identifying a large number of genetic variants robustly associated with common disease. We examine the biological insights that these genetic associations are beginning to produce, from functional mechanisms involving individual genes to biological pathways linking associated genes, and the identification of functional annotations, some of which are cell-type-specific, enriched in disease associations. Although most efforts have focused on identifying and interpreting genetic variants that are irrefutably associated with disease, it is increasingly clear that—even at large sample sizes—these represent only the tip of the iceberg of genetic signal, motivating polygenic analyses that consider the effects of genetic variants throughout the genome, including modest effects that are not individually statistically significant. As data from an increasingly large number of diseases and traits are analysed, pleiotropic effects (defined as genetic loci affecting multiple phenotypes) can help integrate our biological understanding. Looking forward, the next generation of population-scale data resources, linking genomic information with health outcomes, will lead to another step-change in our ability to understand, and treat, common diseases.

scholarly journals Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics

2021 ◽  
Author(s):  
Karthik A. Jagadeesh ◽  
Kushal K Dey ◽  
Daniel T. Montoro ◽  
Steven Gazal ◽  
Jesse M Engreitz ◽  
...  
Keyword(s):  
Single Cell ◽  
Disease Progression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Disease Risk ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Cascade Process ◽  
Cellular Processes

Cellular dysfunction is a hallmark of disease. Genome-wide association studies (GWAS) have provided a powerful means to identify loci and genes contributing to disease risk, but in many cases the related cell types/states through which genes confer disease risk remain unknown. Deciphering such relationships is important both for our understanding of disease, and for developing therapeutic interventions. Here, we introduce a framework for integrating single-cell RNA-seq (scRNA-seq), epigenomic maps and GWAS summary statistics to infer the underlying cell types and processes by which genetic variants influence disease. We analyzed 1.6 million scRNA-seq profiles from 209 individuals spanning 11 tissue types and 6 disease conditions, and constructed gene programs capturing cell types, disease progression in cell types, and cellular processes both within and across cell types. We evaluated these gene programs for disease enrichment by transforming them to SNP annotations with tissue-specific epigenomic maps and computing enrichment scores across 60 diseases and complex traits (average N=297K). The inferred disease enrichments recapitulated known biology and highlighted novel relationships for different conditions, including GABAergic neurons in major depressive disorder (MDD), disease progression programs in M cells in ulcerative colitis, and a disease-specific complement cascade process in multiple sclerosis. Our framework provides a powerful approach for identifying the cell types and cellular processes by which genetic variants influence disease.

scholarly journals The contribution of tissue-grouped BMI-associated gene sets to cardiometabolic-disease risk: a Mendelian randomization study

2020 ◽  
Vol 49 (4) ◽  
pp. 1246-1256
Author(s):  
Inge Verkouter ◽  
Renée de Mutsert ◽  
Roelof A J Smit ◽  
Stella Trompet ◽  
Frits R Rosendaal ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Mendelian Randomization ◽  
Disease Risk ◽  
Expression Profiles ◽  
Association Studies ◽  
Tissue Expression ◽  
Cardiometabolic Disease ◽  
Genome Wide Association Studies ◽  
Gene Sets ◽  
Artery Disease

Abstract Background Body mass index (BMI)-associated loci are used to explore the effects of obesity using Mendelian randomization (MR), but the contribution of individual tissues to risks remains unknown. We aimed to identify tissue-grouped pathways of BMI-associated loci and relate these to cardiometabolic disease using MR analyses. Methods Using Genotype-Tissue Expression (GTEx) data, we performed overrepresentation tests to identify tissue-grouped gene sets based on mRNA-expression profiles from 634 previously published BMI-associated loci. We conducted two-sample MR with inverse-variance-weighted methods, to examine associations between tissue-grouped BMI-associated genetic instruments and type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD), with use of summary-level data from published genome-wide association studies (T2DM: 74 124 cases, 824 006 controls; CAD: 60 801 cases, 123 504 controls). Additionally, we performed MR analyses on T2DM and CAD using randomly sampled sets of 100 or 200 BMI-associated genetic variants. Results We identified 17 partly overlapping tissue-grouped gene sets, of which 12 were brain areas, where BMI-associated genes were differentially expressed. In tissue-grouped MR analyses, all gene sets were similarly associated with increased risks of T2DM and CAD. MR analyses with randomly sampled genetic variants on T2DM and CAD resulted in a distribution of effect estimates similar to tissue-grouped gene sets. Conclusions Overrepresentation tests revealed differential expression of BMI-associated genes in 17 different tissues. However, with our biology-based approach using tissue-grouped MR analyses, we did not identify different risks of T2DM or CAD for the BMI-associated gene sets, which was reflected by similar effect estimates obtained by randomly sampled gene sets.

Genetics in anxiety disorders - an update

2011 ◽  
Vol 26 (S2) ◽  
pp. 2097-2097
Author(s):  
K. Domschke
Keyword(s):  
Panic Disorder ◽  
Anxiety Disorders ◽  
Candidate Genes ◽  
Genetic Variants ◽  
Emotional Processing ◽  
Disease Risk ◽  
Association Studies ◽  
Imaging Genetics ◽  
Genome Wide Association Studies ◽  
Increased Risk

Twin studies propose a strong genetic contribution to the pathogenesis of anxiety disorders with a heritability of about 50%. The dissection of the complex-genetic underpinnings of anxiety disorders requires a multi-level approach using molecular genetic, imaging genetic, (cognitive)-behavioral genetic and pharmacogenetic techniques linking basic and clinical research.The present talk will first give an overview of results from linkage and association studies yielding support for several candidate genes contributing to the genetic risk for anxiety and panic disorder in particular such as the adenosine 2A receptor, the catechol-O-methyltransferase, the neuropeptide S receptor and the serotonin receptor 1A genes. Results from the first genome-wide association studies in the field of anxiety disorders will be discussed. Additionally, studies on gene-environment interactions between anxiety disorder risk variants and environmental factors will be presented. Imaging genetics approaches have yielded evidence for several risk genes to crucially impact activation in brain regions critical for emotional processing. Gene variation has furthermore been found to potentially confer an increased risk for panic disorder via elevated autonomic arousal and dysfunctional cognitions regarding bodily sensations. Finally, there is first evidence for genetic variants impacting treatment response to antidepressant pharmacotherapy in anxiety disorders.Thus, converging lines of evidence will be presented for several candidate genes of anxiety to exert an increased disease risk potentially via a distorted cortico-limbic interaction during emotional processing, increased physiological arousal or dysfunctional cognition. Additionally, a possible impact of genetic variants on pharmacoresponse in anxiety disorders and its potential clinical implications will be discussed.

scholarly journals Finding genetic variants in plants without complete genomes

2019 ◽  
Author(s):  
Yoav Voichek ◽  
Detlef Weigel
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Structural Variants ◽  
Sequencing Data ◽  
New Associations ◽  
Maize Populations ◽  
Genome Wide ◽  
Genomic Regions

AbstractStructural variants and presence/absence polymorphisms are common in plant genomes, yet they are routinely overlooked in genome-wide association studies (GWAS). Here, we expand the genetic variants detected in GWAS to include major deletions, insertions, and rearrangements. We first use raw sequencing data directly to derive short sequences, k-mers, that mark a broad range of polymorphisms independently of a reference genome. We then link k-mers associated with phenotypes to specific genomic regions. Using this approach, we re-analyzed 2,000 traits measured in Arabidopsis thaliana, tomato, and maize populations. Associations identified with k-mers recapitulate those found with single-nucleotide polymorphisms (SNPs), however, with stronger statistical support. Moreover, we identified new associations with structural variants and with regions missing from reference genomes. Our results demonstrate the power of performing GWAS before linking sequence reads to specific genomic regions, which allow detection of a wider range of genetic variants responsible for phenotypic variation.

scholarly journals In Search of Complex Disease Risk through Genome Wide Association Studies

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3083
Author(s):  
Lorena Alonso ◽  
Ignasi Morán ◽  
Cecilia Salvoro ◽  
David Torrents
Keyword(s):  
Genetic Variants ◽  
Complex Disease ◽  
Disease Risk ◽  
Association Studies ◽  
Personalised Medicine ◽  
Genome Wide Association ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Treatment Protocols ◽  
Genome Wide

The identification and characterisation of genomic changes (variants) that can lead to human diseases is one of the central aims of biomedical research. The generation of catalogues of genetic variants that have an impact on specific diseases is the basis of Personalised Medicine, where diagnoses and treatment protocols are selected according to each patient’s profile. In this context, the study of complex diseases, such as Type 2 diabetes or cardiovascular alterations, is fundamental. However, these diseases result from the combination of multiple genetic and environmental factors, which makes the discovery of causal variants particularly challenging at a statistical and computational level. Genome-Wide Association Studies (GWAS), which are based on the statistical analysis of genetic variant frequencies across non-diseased and diseased individuals, have been successful in finding genetic variants that are associated to specific diseases or phenotypic traits. But GWAS methodology is limited when considering important genetic aspects of the disease and has not yet resulted in meaningful translation to clinical practice. This review presents an outlook on the study of the link between genetics and complex phenotypes. We first present an overview of the past and current statistical methods used in the field. Next, we discuss current practices and their main limitations. Finally, we describe the open challenges that remain and that might benefit greatly from further mathematical developments.

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