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 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 PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations

2019 ◽  
Vol 35 (22) ◽  
pp. 4851-4853 ◽  
Author(s):  
Mihir A Kamat ◽  
James A Blackshaw ◽  
Robin Young ◽  
Praveen Surendran ◽  
Stephen Burgess ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Association Studies ◽  
1000 Genomes Project ◽  
Online Tool ◽  
Protein Levels ◽  
1000 Genomes ◽  
Different Types ◽  
Genomic Regions

Abstract Summary PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates ‘phenome scans’, where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner (‘PhenoScanner V2’), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants. Availability and implementation PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.

scholarly journals Non-Mendelian etiologic factors in neuropsychiatric illness: Pleiotropy, epigenetics, and convergence

2012 ◽  
Vol 35 (5) ◽  
pp. 363-364 ◽  
Author(s):  
Curtis K. Deutsch ◽  
William J. McIlvane
Keyword(s):  
Genetic Association ◽  
Behavior Genetics ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Neuropsychiatric Disorders ◽  
Behavioral Phenotypes ◽  
Causal Factors ◽  
Multiple Phenotypes ◽  
Target Article ◽  
Neuropsychiatric Illness

AbstractThe target article by Charney on behavior genetics/genomics discusses how numerous molecular factors can inform heritability estimations and genetic association studies. These factors find application in the search for genes for behavioral phenotypes, including neuropsychiatric disorders. We elaborate upon how single causal factors can generate multiple phenotypes, and discuss how multiple causal factors may converge on common neurodevelopmental mechanisms.

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.

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