Pharmacogenomic genome-wide association studies: lessons learned thus far.
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ABSTRACT:
SUBMITTER: Crowley JJ
PROVIDER: S-EPMC3893087 | biostudies-literature | 2009 Feb
REPOSITORIES: biostudies-literature
Publications
Pharmacogenomic genome-wide association studies: lessons learned thus far.
Crowley James J JJ Sullivan Patrick F PF McLeod Howard L HL
Pharmacogenomics 20090201 2
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Project description:Purpose of reviewThe purpose of this review is to describe the recent knowledge gathered from the identification of seven genomic regions that have been linked to the risk of developing malignant glioma.Recent findingsThe recent novel discoveries in fine mapping and genotype-phenotype studies will be highlighted. Through imputation and next-generation sequencing a novel genetic variant, rs55705857, with a strong association at 8q24 has been discovered and validated in two studies. This locus is specifically associated with IDH1-mutated and IDH2-mutated tumors and oligodendroglial tumors, albeit the specific mechanism of tumor development is not understood. The genetic variants associated with the risk of glioma in the EGFR gene have also been associated with specific somatic aberrations, including loss at the CDKN2A/B locus and allele specific loss of EGFR in the tumors. A specific TP53 low frequency variant has also been associated with glioma risk and validated in a separate data set. The genetic risk in the telomere regulating genes TERT and RTEL appear to be associated with higher grade tumors without IDH mutations.SummaryThe link of genetic loci to specific tumor subtypes may have relevance for understanding glioma biology, and for developing new diagnostic tools and targeted therapy for glioma.
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Project description:In a common pharmacogenomic scenario, outcome measures are compared for treated and untreated subjects across genotype-defined subgroups. The key question is whether treatment benefit (or harm) is particularly strong in certain subgroups, and therefore the statistical analysis focuses on the interaction between treatment and genotype. However, genome-wide analysis in such scenarios requires careful statistical thought as, in addition to the usual problems of multiple testing, the marker-defined sample sizes, and therefore power, vary across the individual genotypes being evaluated. The variability in power means that the usual practice of using a common P-value threshold across tests has difficulties. The reason is that the use of a fixed threshold, with variable power, implies that the costs of type I and type II errors vary across tests in a manner that is implicit rather than dictated by the analyst. In this paper we discuss this problem and describe an easily implementable solution based on Bayes factors. We pay particular attention to the specification of priors, which is not a straightforward task. The methods are illustrated using data from a randomized controlled clinical trial in which homocysteine levels are compared in individuals receiving low and high doses of folate supplements and across marker subgroups. The method we describe is implemented in the R computing environment with code available from http://faculty.washington.edu/jonno/cv.html.
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Project description:BackgroundAlcohol dependence (AD) is a complex psychiatric disorder and a significant public health problem. Twin and family-based studies have consistently estimated its heritability to be approximately 50%, and many studies have sought to identify specific genetic variants associated with susceptibility to AD. These studies have been primarily linkage or candidate gene based and have been mostly unsuccessful in identifying replicable risk loci. Genome-wide association studies (GWAS) have improved the detection of specific loci associated with complex traits, including AD. However, findings from GWAS explain only a small proportion of phenotypic variance, and alternative methods have been proposed to investigate the associations that do not meet strict genome-wide significance criteria.MethodsThis review summarizes all published AD GWAS and post-GWAS analyses that have sought to exploit GWAS data to identify AD-associated loci.ResultsFindings from AD GWAS have been largely inconsistent, with the exception of variants encoding the alcohol-metabolizing enzymes. Analyses of GWAS data that go beyond standard association testing have demonstrated the polygenic nature of AD and the large contribution of common variants to risk, nominating novel genes and pathways for AD susceptibility.ConclusionsFindings from AD GWAS and post-GWAS analyses have greatly increased our understanding of the genetic etiology of AD. However, it is clear that larger samples will be necessary to detect loci in addition to those that encode alcohol-metabolizing enzymes, which may only be possible through consortium-based efforts. Post-GWAS approaches to studying the genetic influences on AD are increasingly common and could greatly increase our knowledge of both the genetic architecture of AD and the specific genes and pathways that influence risk.
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Project description:The investigation of the genetic basis of refractive error and myopia entered a new stage with the introduction of genome-wide association studies (GWAS). Multiple GWAS on many ethnic groups have been published over the years, providing new insight into the genetic architecture and pathophysiology of refractive error. This is a review of the GWAS published to date, the main lessons learned, and future possible directions of genetic studies of myopia and refractive error.
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Project description:Genetic Analysis Workshop 17 (GAW17) focused on the transition from genome-wide association study designs and methods to the study designs and statistical genetic methods that will be required for the analysis of next-generation sequence data including both common and rare sequence variants. In the 166 contributions to GAW17, a wide variety of statistical methods were applied to simulated traits in population- and family-based samples, and results from these analyses were compared to the known generating model. In general, many of the statistical genetic methods used in the population-based sample identified causal sequence variants (SVs) when the estimated locus-specific heritability, as measured in the population-based sample, was greater than about 0.08. However, SVs with locus-specific heritabilities less than 0.03 were rarely identified consistently. In the family-based samples, many of the methods detected SVs that were rarer than those detected in the population-based sample, but the estimated locus-specific heritabilities for these rare SVs, as measured in the family-based samples, were substantially higher (>0.2) than their corresponding heritabilities in the population-based samples. Substantial inflation of the type I error rate was observed across a wide variety of statistical methods. Although many of the contributions found little inflation in type I error for Q4, a trait with no causal SVs, type I error rates for Q1 and Q2 were well above their nominal levels with the inflation for Q1 being higher than that for Q2. It seems likely that this inflation in type I error is due to correlations among SVs.
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Project description:Genome-wide association studies (GWAS) have been the focus of considerable effort in psychiatry. These efforts have markedly increased knowledge of the genetic basis of psychiatric disorders, and yielded empirical data on genetic architecture critical to addressing long-standing debates in the field. There is a now a clear path to increased knowledge of the 'parts lists' for these disorders.
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Project description:The past 3 years have witnessed a dramatic expansion in our knowledge of the genetic determinants of estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD). However, heritability estimates of eGFR indicate that we have only identified a small proportion of the total heritable contribution to the phenotypic variation. The majority of associations reported from genome-wide association studies identify genomic regions of interest and further work will be required to identify the causal variants responsible for a specific phenotype. Progress in this area is likely to stem from the identification of novel risk genotypes, which will offer insight into the pathogenesis of disease and potential novel therapeutic targets. Follow-up studies stimulated by findings from genome-wide association studies of kidney disease are already yielding promising results, such as the identification of an association between urinary uromodulin levels and incident CKD. Although this work is at an early stage, prospects for progress in our understanding of CKD and its treatment look more promising now than at any point in the past.
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