scholarly journals Large-scale multivariate multi-ancestry Interaction analyses point towards different genetic mechanisms by population and exposure

2019 ◽  
Author(s):  
Vincent Laville ◽  
Timothy Majarian ◽  
Yun J Sung ◽  
Karen Schwander ◽  
Mary F Feitosa ◽  
...  
Keyword(s):  
Complex Traits ◽  
Large Scale ◽  
Biological Significance ◽  
Genetic Effect ◽  
Cell Types ◽  
Interaction Effects ◽  
Phenotypic Variance ◽  
Genetic Associations ◽  
Genetic Heritability ◽  
Meta Analyses

AbstractTheCHARGE Gene-Lifestyle Interactions Working Groupis a unique initiative formed to improve our understanding of the role and biological significance of gene-environment interactions in human traits and diseases. The consortium published several multi-ancestry genome-wide interaction studies (GWIS) involving up to 610,475 individuals for three lipids and four blood pressure traits while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and GxE interactions across phenotype-exposure-population trios, and to derive new insights on the potential mechanistic underlying GxE through in-silico functional analyses. Our comparative analysis shows first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted a negligible correlation between main and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell-types. In these analyses, we found multiple instances of heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work we identified potential biases in methods used to jointly meta-analyses genetic and interaction effects. We performed a series of simulations to characterize these limitations and to provide the community with guideline for future GxE studies.

scholarly journals Identifying small-effect genetic associations overlooked by the conventional fixed-effect model in a large-scale meta-analysis of coronary artery disease

2019 ◽  
Author(s):  
Lerato E Magosi ◽  
Anuj Goel ◽  
Jemma C Hopewell ◽  
Martin Farrall
Keyword(s):  
Random Effects ◽  
Complex Traits ◽  
Large Scale ◽  
Meta Analysis ◽  
Genetic Effect ◽  
High Sensitivity ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Supplementary Information ◽  
Genetic Associations

Abstract Motivation Common small-effect genetic variants that contribute to human complex traits and disease are typically identified using traditional fixed-effect (FE) meta-analysis methods. However, the power to detect genetic associations under FE models deteriorates with increasing heterogeneity, so that some small-effect heterogeneous loci might go undetected. A modified random-effects meta-analysis approach (RE2) was previously developed that is more powerful than traditional fixed and random-effects methods at detecting small-effect heterogeneous genetic associations, the method was updated (RE2C) to identify small-effect heterogeneous variants overlooked by traditional fixed-effect meta-analysis. Here, we re-appraise a large-scale meta-analysis of coronary disease with RE2C to search for small-effect genetic signals potentially masked by heterogeneity in a FE meta-analysis. Results Our application of RE2C suggests a high sensitivity but low specificity of this approach for discovering small-effect heterogeneous genetic associations. We recommend that reports of small-effect heterogeneous loci discovered with RE2C are accompanied by forest plots and standardized predicted random-effects statistics to reveal the distribution of genetic effect estimates across component studies of meta-analyses, highlighting overly influential outlier studies with the potential to inflate genetic signals. Availability and implementation Scripts to calculate standardized predicted random-effects statistics and generate forest plots are available in the getspres R package entitled from https://magosil86.github.io/getspres/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics

2015 ◽  
Author(s):  
Guillaume Pare ◽  
Shihong Mao ◽  
Wei Q. Deng
Keyword(s):  
Complex Traits ◽  
Regression Models ◽  
Phenotypic Variance ◽  
Genetic Associations ◽  
Multiple Regression Models ◽  
Novel Method ◽  
Combine Information ◽  
Regional Associations ◽  
Retirement Study ◽  
Variance Explained

AbstractDespite considerable efforts, known genetic associations only explain a small fraction of predicted heritability. Regional associations combine information from multiple contiguous genetic variants and can improve variance explained at established association loci. However, regional associations are not easily amenable to estimation using summary association statistics because of sensitivity to linkage disequilibrium (LD). We now propose a novel method to estimate phenotypic variance explained by regional associations using summary statistics while accounting for LD. Our method is asymptotically equivalent to multiple regression models when no interaction or haplotype effects are present. It has multiple applications, such as ranking of genetic regions according to variance explained or comparison of variance explained by to or more regions. Using height and BMI data from the Health Retirement Study (N=7,776), we show that most genetic variance lies in a small proportion of the genome and that previously identified linkage peaks have higher than expected regional variance.

scholarly journals Carotid Artery Atherosclerosis: A Review on Heritability and Genetics

2018 ◽  
Vol 21 (5) ◽  
pp. 333-346 ◽  
Author(s):  
Bianka Forgo ◽  
Emanuela Medda ◽  
Anita Hernyes ◽  
Laszlo Szalontai ◽  
David Laszlo Tarnoki ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Intima Media Thickness ◽  
Carotid Intima Media Thickness ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Carotid Artery Atherosclerosis ◽  
Meta Analyses

Carotid atherosclerosis (CAS) is associated with increased cardiovascular risk, and therefore, assessing the genetic versus environmental background of CAS traits is of key importance. Carotid intima-media-thickness and plaque characteristics seem to be moderately heritable, with remarkable differences in both heritability and presence or severity of these traits among ethnicities. Although the considerable role of additive genetic effects is obvious, based on the results so far, there is an important emphasis on non-shared environmental factors as well. We aimed to collect and summarize the papers that investigate twin and family studies assessing the phenotypic variance attributable to genetic associations with CAS. Genes in relation to CAS markers were overviewed with a focus on genetic association studies and genome-wide association studies. Although the role of certain genes is confirmed by studies conducted on large populations and meta-analyses, many of them show conflicting results. A great focus should be on future studies elucidating the exact pathomechanism of these genes in CAS in order to imply them as novel therapeutic targets.

scholarly journals Field synopsis and systematic meta-analyses of genetic association studies in isolated dystonia

2018 ◽  
Author(s):  
Olena Ohlei ◽  
Valerija Dobricic ◽  
Katja Lohmann ◽  
Christine Klein ◽  
Christina Lill ◽  
...  
Keyword(s):  
Genetic Association ◽  
Large Scale ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Sufficient Information ◽  
Genetic Associations ◽  
Pubmed Database ◽  
The Status ◽  
Meta Analyses

AbstractBackground and objectivesDystonia is a genetically complex disease with both monogenic and polygenic causes. For the latter, numerous genetic associations studies have been performed with largely inconsistent results. The aim of this study was to perform a field synopsis including systematic meta-analyses of genetic association studies in isolated dystoniaMethodsFor the field synopsis we systematically screened and scrutinized the published literature using NCBI’s PubMed database. For genetic variants with sufficient information in at least two independent datasets, random-effects meta-analyses were performed, including meta-analyses stratified by ethnic descent and dystonia subtypes.ResultsA total of 3,575 articles were identified and scrutinized resulting in the inclusion of 42 independent publications allowing 134 meta-analyses on 45 variants across 17 genes. While our meta-analyses pinpointed several significant association signals with variants in TOR1A, DRD1, and ARSG, no single variant displayed compelling association with dystonia in the available data.ConclusionsOur study provides an up-to-date summary of the status of dystonia genetic association studies. Additional large-scale studies are needed to better understand the genetic causes of isolated dystonia.

scholarly journals General Statistics of Stochastic Process of Gene Expression in Eukaryotic Cells

Genetics ◽  
2002 ◽  
Vol 161 (3) ◽  
pp. 1321-1332 ◽  
Author(s):  
V A Kuznetsov ◽  
G D Knott ◽  
R F Bonner
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Phenotypic Diversity ◽  
Biological Significance ◽  
Cell Types ◽  
Distribution Model ◽  
Monoallelic Expression ◽  
Considerable Uncertainty ◽  
Number Of Genes

Abstract Thousands of genes are expressed at such very low levels (≤1 copy per cell) that global gene expression analysis of rarer transcripts remains problematic. Ambiguity in identification of rarer transcripts creates considerable uncertainty in fundamental questions such as the total number of genes expressed in an organism and the biological significance of rarer transcripts. Knowing the distribution of the true number of genes expressed at each level and the corresponding gene expression level probability function (GELPF) could help resolve these uncertainties. We found that all observed large-scale gene expression data sets in yeast, mouse, and human cells follow a Pareto-like distribution model skewed by many low-abundance transcripts. A novel stochastic model of the gene expression process predicts the universality of the GELPF both across different cell types within a multicellular organism and across different organisms. This model allows us to predict the frequency distribution of all gene expression levels within a single cell and to estimate the number of expressed genes in a single cell and in a population of cells. A random “basal” transcription mechanism for protein-coding genes in all or almost all eukaryotic cell types is predicted. This fundamental mechanism might enhance the expression of rarely expressed genes and, thus, provide a basic level of phenotypic diversity, adaptability, and random monoallelic expression in cell populations.

scholarly journals Functional enrichments of disease variants across thousands of independent loci in eight diseases

2016 ◽  
Author(s):  
Abhishek K. Sarkar ◽  
Lucas D. Ward ◽  
Manolis Kellis
Keyword(s):  
Complex Traits ◽  
Target Genes ◽  
Cell Types ◽  
Biological Pathways ◽  
Joint Analysis ◽  
Genetic Associations ◽  
Narrow Sense Heritability ◽  
Regulatory Regions ◽  
Downstream Target ◽  
Common Diseases

AbstractFor most complex traits, known genetic associations only explain a small fraction of the narrow sense heritability prompting intense debate on the genetic basis of complex traits. Joint analysis of all common variants together explains much of this missing heritability and reveals that large numbers of weakly associated loci are enriched in regulatory regions, but fails to identify specific regions or biological pathways. Here, we use epigenomic annotations across 127 tissues and cell types to investigate weak regulatory associations, the specific enhancers they reside in, their downstream target genes, their upstream regulators, and the biological pathways they disrupt in eight common diseases. We show weak associations are significantly enriched in disease-relevant regulatory regions across thousands of independent loci. We develop methods to control for LD between weak associations and overlap between annotations. We show that weak non-coding associations are additionally enriched in relevant biological pathways implicating additional downstream target genes and upstream disease-specific master regulators. Our results can help guide the discovery of biologically meaningful, but currently undetectable regulatory loci underlying a number of common diseases.

scholarly journals Predicting cell-type-specific non-coding RNA transcription from genome sequence

2020 ◽  
Author(s):  
Masaru Koido ◽  
Chung-Chau Hon ◽  
Satoshi Koyama ◽  
Hideya Kawaji ◽  
Yasuhiro Murakawa ◽  
...  
Keyword(s):  
Complex Traits ◽  
Complex Trait ◽  
Cell Types ◽  
Dependent Manner ◽  
Genetic Associations ◽  
Cell Type ◽  
Non Coding Rna ◽  
Causal Variants ◽  
A Cell ◽  
Cell Type Specific

SUMMARYTranscription is regulated through complex mechanisms involving non-coding RNAs (ncRNAs). However, because transcription of ncRNAs, especially enhancer RNAs, is often low and cell type-specific, its dependency on genotype remains largely unexplored. Here, we developed mutation effect prediction on ncRNA transcription (MENTR), a quantitative machine learning framework reliably connecting genetic associations with expression of ncRNAs, resolved to the level of cell type. MENTR-predicted mutation effects on ncRNA transcription were concordant with estimates from previous genetic studies in a cell type-dependent manner. We inferred reliable causal variants from 41,223 GWAS variants, and proposed 7,775 enhancers and 3,548 long-ncRNAs as complex trait-associated ncRNAs in 348 major human primary cells and tissues, including plausible enhancer-mediated functional alterations in single-variant resolution in Crohn’s disease. In summary, we present new resources for discovering causal variants, the biological mechanisms driving complex traits, and the sequence-dependency of ncRNA regulation in relevant cell types.

scholarly journals Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications

2018 ◽  
Author(s):  
Simon Haworth ◽  
Ruth Mitchell ◽  
Laura Corbin ◽  
Kaitlin H Wade ◽  
Tom Dudding ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Data ◽  
Population Based ◽  
Risk Scores ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Common Genetic Variants ◽  
The Uk

Introductory paragraphThe inclusion of genetic data in large studies has enabled the discovery of genetic contributions to complex traits and their application in applied analyses including those using genetic risk scores (GRS) for the prediction of phenotypic variance. If genotypes show structure by location and coincident structure exists for the trait of interest, analyses can be biased. Having illustrated structure in an apparently homogeneous collection, we aimed to a) test for geographical stratification of genotypes in UK Biobank and b) assess whether stratification might induce bias in genetic association analysis.We found that single genetic variants are associated with birth location within UK Biobank and that geographic structure in genetic data could not be accounted for using routine adjustment for study centre and principal components (PCs) derived from genotype data. We found that GRS for complex traits do appear geographically structured and analysis using GRS can yield biased associations. We discuss the likely origins of these observations and potential implications for analysis within large-scale population based genetic studies.

scholarly journals Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes

2019 ◽  
Author(s):  
Ana Viñuela ◽  
Arushi Varshney ◽  
Martijn van de Bunt ◽  
Rashmi B. Prasad ◽  
Olof Asplund ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Association Studies ◽  
Cell Types ◽  
Genome Wide Association Studies ◽  
Selective Enrichment ◽  
Gwas Signal

AbstractMost signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development.

scholarly journals Heritability and genetic variance of dementia with Lewy bodies

2018 ◽  
Author(s):  
Rita Guerreiro ◽  
Valentina Escott-Price ◽  
Dena G. Hernandez ◽  
Celia Kun-Rodrigues ◽  
Owen A. Ross ◽  
...  
Keyword(s):  
Genetic Variability ◽  
Complex Traits ◽  
Dementia With Lewy Bodies ◽  
Large Scale ◽  
Lewy Bodies ◽  
Significant Snps ◽  
Risk Scores ◽  
Phenotypic Variance ◽  
Appreciable Effect ◽  
Risk Variants

AbstractRecent large-scale genetic studies have allowed for the first glimpse of the effects of common genetic variability in dementia with Lewy bodies (DLB), identifying risk variants with appreciable effect sizes. However, it is currently well established that a substantial portion of the genetic heritable component of complex traits is not captured by genome-wide significant SNPs. To overcome this issue, we have estimated the proportion of phenotypic variance explained by genetic variability (SNP heritability) in DLB using a method that is unbiased by allele frequency or linkage disequilibrium properties of the underlying variants. This shows that the heritability of DLB is nearly twice as high as previous estimates based on common variants only (31% vs 59.9%). We also determine the amount of phenotypic variance in DLB that can be explained by recent polygenic risk scores from either Parkinson’s disease (PD) or Alzheimer’s disease (AD), and show that, despite being highly significant, they explain a low amount of variance. Additionally, to identify pleiotropic events that might improve our understanding of the disease, we performed genetic correlation analyses of DLB with over 200 diseases and biomedically relevant traits. Our data shows that DLB has a positive correlation with education phenotypes, which is opposite to what occurs in AD. Overall, our data suggests that novel genetic risk factors for DLB should be identified by larger GWAS and these are likely to be independent from known AD and PD risk variants.

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