scholarly journals Quantifying the impact of genetically regulated expression on complex traits and diseases

2019 ◽  
Author(s):  
Mingxuan Cai ◽  
Lin Chen ◽  
Jin Liu ◽  
Can Yang
Keyword(s):  
Complex Traits ◽  
Wide Spectrum ◽  
Association Studies ◽  
Strong Association ◽  
Significant Proportion ◽  
Reference Panel ◽  
Genome Wide Association Studies ◽  
Regulated Expression ◽  
Phenotypic Variations ◽  
The Impact

About 90% of risk variants identified from genome-wide association studies (GWAS) are located in non-coding regions, highlighting the regulatory role of genetic variants. We propose a unified statistical framework, IGREX, for quantifying the impact of genetically regulated expression (GREX). This is achieved by estimating proportion of phenotypic variations that can be explained by the GREX component. IGREX only requires summary-level GWAS data and a gene expression reference panel as input. In real data analysis, using 48 tissues from the GTEx project as the reference panel, we applied IGREX to a wide spectrum of phenotypes in GWAS, and observed a significant proportion of phenotypic variations could be attributed to the GREX component. In particular, the results given by IGREX revealed tissue-across and tissue-specific patterns of the GREX effects. We also observed strong association between GREX effect and immune-related proteins, further supporting the relevance between GREX and the immune processes.

scholarly journals Extensive impact of low-frequency variants on the phenotypic landscape at population-scale

2019 ◽  
Author(s):  
T. Fournier ◽  
O. Abou Saada ◽  
J. Hou ◽  
J. Peter ◽  
E. Caudal ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Growth Traits ◽  
Association Studies ◽  
Significant Proportion ◽  
Low Frequency ◽  
Population Level ◽  
Initial Population ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

AbstractGenome-wide association studies (GWAS) allows to dissect the genetic basis of complex traits at the population level1. However, despite the extensive number of trait-associated loci found, they often fail to explain a large part of the observed phenotypic variance2–4. One potential source of this discrepancy could be the preponderance of undetected low-frequency genetic variants in natural populations5,6. To increase the allele frequency of those variants and assess their phenotypic effects at the population level, we generated a diallel panel consisting of 3,025 hybrids, derived from pairwise crosses between a subset of natural isolates from a completely sequenced 1,011 Saccharomyces cerevisiae population. We examined each hybrid across a large number of growth traits, resulting in a total of 148,225 cross/trait combinations. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a significant proportion (29%) is governed by non-additive effects. This is confirmed by the fact that a majority of complete dominance is observed in 25% of the traits. By performing GWAS on the diallel panel, we detected 1,723 significantly associated genetic variants, with 16.3% of them being low-frequency variants in the initial population. These variants, which would not be detected using classical GWAS, explain 21% of the phenotypic variance on average. Altogether, our results demonstrate that low-frequency variants should be accounted for as they contribute to a large part of the phenotypic variation observed in a population.

scholarly journals The Impact of the ‘Mis-Peptidome’ on HLA Class I-Mediated Diseases: Contribution of ERAP1 and ERAP2 and Effects on the Immune Response

2020 ◽  
Vol 21 (24) ◽  
pp. 9608
Author(s):  
Valentina Tedeschi ◽  
Giorgia Paldino ◽  
Fabiana Paladini ◽  
Benedetta Mattorre ◽  
Loretta Tuosto ◽  
...  
Keyword(s):  
Inflammatory Diseases ◽  
Association Studies ◽  
Strong Association ◽  
Hla Class I ◽  
Class I ◽  
Mass Spectrometry Analysis ◽  
Genome Wide Association Studies ◽  
Spectrometry Analysis ◽  
Hla Class I Molecules ◽  
The Impact

The strong association with the Major Histocompatibility Complex (MHC) class I genes represents a shared trait for a group of autoimmune/autoinflammatory disorders having in common immunopathogenetic basis as well as clinical features. Accordingly, the main risk factors for Ankylosing Spondylitis (AS), prototype of the Spondyloarthropathies (SpA), the Behçet’s disease (BD), the Psoriasis (Ps) and the Birdshot Chorioretinopathy (BSCR) are HLA-B*27, HLA-B*51, HLA-C*06:02 and HLA-A*29:02, respectively. Despite the strength of the association, the HLA pathogenetic role in these diseases is far from being thoroughly understood. Furthermore, Genome-Wide Association Studies (GWAS) have highlighted other important susceptibility factors such as Endoplasmic Reticulum Aminopeptidase (ERAP) 1 and, less frequently, ERAP2 that refine the peptidome presented by HLA class I molecules to CD8+ T cells. Mass spectrometry analysis provided considerable knowledge of HLA-B*27, HLA-B*51, HLA-C*06:02 and HLA-A*29:02 immunopeptidome. However, the combined effect of several ERAP1 and ERAP2 allelic variants could generate an altered pool of peptides accounting for the “mis-immunopeptidome” that ranges from suboptimal to pathogenetic/harmful peptides able to induce non-canonical or autoreactive CD8+ T responses, activation of NK cells and/or garbling the classical functions of the HLA class I molecules. This review will focus on this class of epitopes as possible elicitors of atypical/harmful immune responses which can contribute to the pathogenesis of chronic inflammatory diseases.

MR-Corr2: a two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants

2021 ◽  
Author(s):  
Qing Cheng ◽  
Tingting Qiu ◽  
Xiaoran Chai ◽  
Baoluo Sun ◽  
Yingcun Xia ◽  
...  
Keyword(s):  
Complex Traits ◽  
Error Control ◽  
Type I Error ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Bivariate Normal Distribution ◽  
Supplementary Information ◽  
Type I ◽  
Genome Wide Association Studies ◽  
The Impact

Abstract Motivation Mendelian randomization (MR) is a valuable tool to examine the causal relationships between health risk factors and outcomes from observational studies. Along with the proliferation of genome-wide association studies, a variety of two-sample MR methods for summary data have been developed to account for horizontal pleiotropy (HP), primarily based on the assumption that the effects of variants on exposure (γ) and HP (α) are independent. In practice, this assumption is too strict and can be easily violated because of the correlated HP. Results To account for this correlated HP, we propose a Bayesian approach, MR-Corr2, that uses the orthogonal projection to reparameterize the bivariate normal distribution for γ and α, and a spike-slab prior to mitigate the impact of correlated HP. We have also developed an efficient algorithm with paralleled Gibbs sampling. To demonstrate the advantages of MR-Corr2 over existing methods, we conducted comprehensive simulation studies to compare for both type-I error control and point estimates in various scenarios. By applying MR-Corr2 to study the relationships between exposure–outcome pairs in complex traits, we did not identify the contradictory causal relationship between HDL-c and CAD. Moreover, the results provide a new perspective of the causal network among complex traits. Availability and implementation The developed R package and code to reproduce all the results are available at https://github.com/QingCheng0218/MR.Corr2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals IGREX for quantifying the impact of genetically regulated expression on phenotypes

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Mingxuan Cai ◽  
Lin S Chen ◽  
Jin Liu ◽  
Can Yang
Keyword(s):  
Wide Spectrum ◽  
Association Studies ◽  
Related Protein ◽  
Protein Biomarkers ◽  
Individual Level ◽  
Cell Tissue ◽  
Regulated Expression ◽  
Eqtl Data ◽  
The Impact ◽  
Trait Associations

Abstract By leveraging existing GWAS and eQTL resources, transcriptome-wide association studies (TWAS) have achieved many successes in identifying trait-associations of genetically regulated expression (GREX) levels. TWAS analysis relies on the shared GREX variation across GWAS and the reference eQTL data, which depends on the cellular conditions of the eQTL data. Considering the increasing availability of eQTL data from different conditions and the often unknown trait-relevant cell/tissue-types, we propose a method and tool, IGREX, for precisely quantifying the proportion of phenotypic variation attributed to the GREX component. IGREX takes as input a reference eQTL panel and individual-level or summary-level GWAS data. Using eQTL data of 48 tissue types from the GTEx project as a reference panel, we evaluated the tissue-specific IGREX impact on a wide spectrum of phenotypes. We observed strong GREX effects on immune-related protein biomarkers. By incorporating trans-eQTLs and analyzing genetically regulated alternative splicing events, we evaluated new potential directions for TWAS analysis.

scholarly journals RAD51B (rs8017304 and rs2588809), TRIB1 (rs6987702, rs4351379, and rs4351376), COL8A1 (rs13095226), and COL10A1 (rs1064583) Gene Variants with Predisposition to Age-Related Macular Degeneration

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Alvita Vilkeviciute ◽  
Loresa Kriauciuniene ◽  
Romanas Chaleckis ◽  
Vytenis Pranas Deltuva ◽  
Rasa Liutkeviciene
Keyword(s):  
Macular Degeneration ◽  
Association Studies ◽  
Strong Association ◽  
Age Related Macular Degeneration ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Exudative Amd ◽  
Age Related ◽  
Significant Difference ◽  
The Impact

Background. Age-related macular degeneration (AMD) is a progressive neurodegenerative disease of a central part of the neural retina (macula) and a leading cause of blindness in elderly people. While it is known that the AMD is a multifactorial disease, genetic factors involved in lipid metabolism, inflammation, and neovascularization are currently being widely studied in genome-wide association studies (GWAS). The aim of our study was to evaluate the impact of new single nucleotide polymorphisms (SNPs) in RAD51B, TRIB1, COL8A1, and COL10A1 genes on AMD development. Methods. Case-control study involved 254 patients diagnosed with early AMD, 244 patients with exudative AMD, and 942 control subjects. The genotyping of RAD51B (rs8017304 and rs2588809), TRIB1 (rs6987702, rs4351379, and rs4351376), COL8A1 (rs13095226), and COL10A1 (rs1064583) was carried out using TaqMan assays by a real-time polymerase chain reaction (RT-PCR) method. Results. Statistically significant difference was found in genotype (TT, TC, and CC) distribution of COL8A1 rs13095226 between exudative AMD and control groups (60.2%, 33.6%, and 6.1% vs. 64.9%, 32.3%, and 2.9%, respectively, p=0.036). Also, comparing with TT+TC, rs13095226 CC genotype was associated with 3.5-fold increased odds of exudative AMD development (OR = 3.540; 95% CI: 1.415-8.856; p=0.007). Conclusion. Our study revealed a strong association between a variant in COL8A1 (rs13095226) and exudative AMD development.

scholarly journals A practical view of fine-mapping and gene prioritization in the post-genome-wide association era

Open Biology ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 190221 ◽  
Author(s):  
R. V. Broekema ◽  
O. B. Bakker ◽  
I. H. Jonkers
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Population Based ◽  
Gene Prioritization ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Underlying Mechanisms ◽  
The Impact

Over the past 15 years, genome-wide association studies (GWASs) have enabled the systematic identification of genetic loci associated with traits and diseases. However, due to resolution issues and methodological limitations, the true causal variants and genes associated with traits remain difficult to identify. In this post-GWAS era, many biological and computational fine-mapping approaches now aim to solve these issues. Here, we review fine-mapping and gene prioritization approaches that, when combined, will improve the understanding of the underlying mechanisms of complex traits and diseases. Fine-mapping of genetic variants has become increasingly sophisticated: initially, variants were simply overlapped with functional elements, but now the impact of variants on regulatory activity and direct variant-gene 3D interactions can be identified. Moreover, gene manipulation by CRISPR/Cas9, the identification of expression quantitative trait loci and the use of co-expression networks have all increased our understanding of the genes and pathways affected by GWAS loci. However, despite this progress, limitations including the lack of cell-type- and disease-specific data and the ever-increasing complexity of polygenic models of traits pose serious challenges. Indeed, the combination of fine-mapping and gene prioritization by statistical, functional and population-based strategies will be necessary to truly understand how GWAS loci contribute to complex traits and diseases.

scholarly journals Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock

2020 ◽  
Author(s):  
Biaty Raymond ◽  
Loic Yengo ◽  
Roy Costilla ◽  
Chris Schrooten ◽  
Aniek C. Bouwman ◽  
...  
Keyword(s):  
Complex Traits ◽  
Prior Information ◽  
Genetic Variance ◽  
Association Studies ◽  
Mammalian Species ◽  
Significant Proportion ◽  
Significant Snps ◽  
Genome Wide Association Studies ◽  
Randomized Sampling ◽  
Human Height

Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p=0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p=0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p=0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.

scholarly journals MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations

2016 ◽  
Author(s):  
Gibran Hemani ◽  
Jie Zheng ◽  
Kaitlin H Wade ◽  
Charles Laurin ◽  
Benjamin Elsworth ◽  
...  
Keyword(s):  
Complex Traits ◽  
Disease Risk ◽  
Drug Repositioning ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Lipid Lowering ◽  
Genome Wide Association Studies ◽  
Causal Relationships ◽  
Genetic Associations ◽  
The Impact

AbstractPublished genetic associations can be used to infer causal relationships between phenotypes, bypassing the need for individual-level genotype or phenotype data. We have curated complete summary data from 1094 genome-wide association studies (GWAS) on diseases and other complex traits into a centralised database, and developed an analytical platform that uses these data to perform Mendelian randomization (MR) tests and sensitivity analyses (MR-Base, http://www.mrbase.org). Combined with curated data of published GWAS hits for phenomic measures, the MR-Base platform enables millions of potential causal relationships to be evaluated. We use the platform to predict the impact of lipid lowering on human health. While our analysis provides evidence that reducing LDL-cholesterol, lipoprotein(a) or triglyceride levels reduce coronary disease risk, it also suggests causal effects on a number of other non-vascular outcomes, indicating potential for adverse-effects or drug repositioning of lipid-lowering therapies.

Strong Association between Serotonin Transporter 5-HTTVNTR Variant and Psychoactive Substance (Nicotine) Use in the Turkish Cypriot Population

2020 ◽  
Vol 21 (6) ◽  
pp. 466-470
Author(s):  
Emine Kandemis ◽  
Gulten Tuncel ◽  
Ozen Asut ◽  
Sehime G. Temel ◽  
Mahmut C. Ergoren
Keyword(s):  
Serotonin Transporter ◽  
Association Studies ◽  
Strong Association ◽  
Smoking Behavior ◽  
Population Based ◽  
Genome Wide Association Studies ◽  
Genotype Distribution ◽  
Psychological Traits ◽  
Wide Range ◽  
Turkish Cypriot

Background: The use of psychoactive substances is one of the most dangerous social problems worldwide. Nicotine dependence results from the interaction between neurobiological, environmental and genetic factors. Serotonin is a neurotransmitter that has a wide range of central nervous system activities. The serotonin transporter gene has been previously linked to psychological traits. Objective: A variable number of tandem repeats within the serotonin transporter-linked polymorphic gene region are believed to alter the transcriptional efficiency of the 5-HTT gene. Therefore, we aimed to investigate the association between this polymorphic site and smoking behavior in the Turkish Cypriot population. Methods: A total of 259 (100 smokers, 100 non-smokers and 59 ex-smokers) Turkish Cypriots were included in this population-based cross-sectional study. Genomic DNA was extracted from peripheral blood samples and the 5-HTTVNTR2 polymorphisms were determined by the PCR-RFLP. Results: The allelic frequency and genotype distribution results of this study showed a strong association (P<0.0001) between smokers and non-smokers. No statistical significance was found between non-smokers and ex-smokers. Conclusion: This is the first genetic epidemiology study to investigate the allelic frequencies of 5-HTTVNTR2 polymorphisms associated with smoking behavior in the Turkish Cypriot population. Based on the results of this study, genome-wide association studies should be designed for preventive medicine in this population.

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