scholarly journals Phenotype-Based Genetic Association Studies (PGAS)—Towards Understanding the Contribution of Common Genetic Variants to Schizophrenia Subphenotypes

Genes ◽  
2014 ◽  
Vol 5 (1) ◽  
pp. 97-105 ◽  
Author(s):  
Hannelore Ehrenreich ◽  
Klaus-Armin Nave
Keyword(s):  
Genetic Association ◽  
Genetic Variants ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Common Genetic Variants

scholarly journals Efficient inference for genetic association studies with multiple outcomes

Biostatistics ◽  
2017 ◽  
Vol 18 (4) ◽  
pp. 618-636 ◽  
Author(s):  
Helene Ruffieux ◽  
Anthony C. Davison ◽  
Jorg Hager ◽  
Irina Irincheeva
Keyword(s):  
Genetic Association ◽  
Genetic Variants ◽  
Association Studies ◽  
Computational Cost ◽  
Genetic Association Studies ◽  
Joint Modeling ◽  
Molecular Data ◽  
Joint Analysis ◽  
Modern Biology ◽  
Bayesian Procedures

SUMMARY Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes.

Genetic association studies in patients with traumatic brain injury

2010 ◽  
Vol 28 (1) ◽  
pp. E9 ◽  
Author(s):  
Efthimios Dardiotis ◽  
Kostas N. Fountas ◽  
Maria Dardioti ◽  
Georgia Xiromerisiou ◽  
Eftychia Kapsalaki ◽  
...  
Keyword(s):  
Health Care ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Genetic Association ◽  
Genetic Variants ◽  
Association Studies ◽  
Head Injuries ◽  
Genetic Association Studies ◽  
Considerable Variability ◽  
Current Review

Traumatic brain injury (TBI) constitutes a major cause of mortality and disability worldwide, especially among young individuals. It is estimated that despite all the recent advances in the management of TBI, approximately half of the patients suffering head injuries still have unfavorable outcomes, which represents a substantial health care, social, and economic burden to societies. Considerable variability exists in the clinical outcome after TBI, which is only partially explained by known factors. Accumulating evidence has implicated various genetic elements in the pathophysiology of brain trauma. The extent of brain injury after TBI seems to be modulated to some degree by genetic variants. The authors' current review focuses on the up-to-date state of knowledge regarding genetic association studies in patients sustaining TBI, with particular emphasis on the mechanisms underlying the implication of genes in the pathophysiology of TBI.

scholarly journals The impact of rare variation on gene expression across tissues

Nature ◽  
2017 ◽  
Vol 550 (7675) ◽  
pp. 239-243 ◽  
Author(s):  
Xin Li ◽  
◽  
Yungil Kim ◽  
Emily K. Tsang ◽  
Joe R. Davis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Rare Variants ◽  
Disease Risk ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Sequencing Data ◽  
Common Genetic Variants ◽  
Coding Variants ◽  
The Impact

Abstract Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk1,2,3,4. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants1,5. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles1,6,7, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues8,9,10,11, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release12. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

scholarly journals Role of CGRP pathway polymorphisms in migraine: a systematic review and impact on CGRP mAbs migraine therapy

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Damiana Scuteri ◽  
Maria Tiziana Corasaniti ◽  
Paolo Tonin ◽  
Pierluigi Nicotera ◽  
Giacinto Bagetta
Keyword(s):  
Systematic Review ◽  
Human Genome ◽  
Genetic Association ◽  
Systematic Reviews ◽  
Genetic Variants ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Response To Therapy ◽  
Gene Encoding ◽  
Meta Analyses

Abstract Background the interest of clinical reaseach in polymorphisms and epigenetics in migraine has been growing over the years. Due to the new era of preventative migraine treatment opened by monoclonal antibodies (mAbs) targeting the signaling of the calcitonin-gene related peptide (CGRP), the present systematic review aims at identifying genetic variants occurring along the CGRP pathway and at verifying whether these can affect the clinical features and the course of disease and the responsiveness of patients to therapy. Methods the literature search has been conducted consulting the most relevant scientific databases, i.e. PubMed/MEDLINE, Scopus, Web of Science, the Human Genome Epidemiology (HuGE) Published Literature database (Public Health Genomics Knowledge Base) and Clinicaltrials.gov from database inception until April 1, 2021. The process of identification and selection of the studies included in the analysis has followed the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) criteria for systematic reviews and meta-analyses and the guidance from the Human Genome Epidemiology Network for reporting gene-disease associations. Results the search has retrieved 800 results, among which only 7 studies have met the eligibility criteria for inclusion in the analysis. The latter are case-control studies of genetic association and an exploratory analysis and two polymorphisms have been detected as the most recurring: the rs3781719 (T > C) of the CALC A gene encoding CGRP and the rs7590387 of the gene encoding the receptor activity-modifying protein (RAMP) 1 (C > G). Only one study assessing the methylation pattern with regard to CGRP pathway has been found from the search. No genetic association studies investigating the possible effect of genetic variants affecting CGRP signaling on the responsiveness to the most recent pharmacological approaches, i.e. anti-CGRP(R) mAbs, gepants and ditans, have been published. According to the Human Genome Epidemiology (HuGE) systematic reviews and meta-analyses risk-of-bias score for genetic association studies, the heterogeneity between and across studies and the small sample size do not allow to draw conclusions and prompt future studies. Conclusions adequately powered, good quality genetic association studies are needed to understand the impact of genetic variants affecting the pathway of CGRP on migraine susceptibility and clinical manifestation and to predict the response to therapy in terms of efficacy and safety.

scholarly journals Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Qiong Yang ◽  
Yuanjia Wang
Keyword(s):  
Genetic Association ◽  
Genetic Variants ◽  
Method Development ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Multiple Phenotypes ◽  
Different Types ◽  
Single Marker ◽  
Inference Methods ◽  
Multivariate Phenotypes

Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical) or different types of components (e.g., some are continuous and others are categorical). We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

scholarly journals Kernel-Based Measure of Variable Importance for Genetic Association Studies

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Vicente Gallego ◽  
M. Luz Calle ◽  
Ramon Oller
Keyword(s):  
Genetic Association ◽  
Genetic Variants ◽  
Disease Risk ◽  
Association Studies ◽  
Univariate Analysis ◽  
Genetic Association Studies ◽  
Genetic Effect ◽  
Variable Importance ◽  
Genetic Interactions ◽  
Nucleotide Polymorphisms

Abstract The identification of genetic variants that are associated with disease risk is an important goal of genetic association studies. Standard approaches perform univariate analysis where each genetic variant, usually Single Nucleotide Polymorphisms (SNPs), is tested for association with disease status. Though many genetic variants have been identified and validated so far using this univariate approach, for most complex diseases a large part of their genetic component is still unknown, the so called missing heritability. We propose a Kernel-based measure of variable importance (KVI) that provides the contribution of a SNP, or a group of SNPs, to the joint genetic effect of a set of genetic variants. KVI can be used for ranking genetic markers individually, sets of markers that form blocks of linkage disequilibrium or sets of genetic variants that lie in a gene or a genetic pathway. We prove that, unlike the univariate analysis, KVI captures the relationship with other genetic variants in the analysis, even when measured at the individual level for each genetic variable separately. This is specially relevant and powerful for detecting genetic interactions. We illustrate the results with data from an Alzheimer’s disease study and show through simulations that the rankings based on KVI improve those rankings based on two measures of importance provided by the Random Forest. We also prove with a simulation study that KVI is very powerful for detecting genetic interactions.

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