Assessment of cumulative evidence on genetic associations: interim guidelines

Meta-research: The art of getting it wrong

Meta-analysis has major strengths, but sometimes it can often lead to wrong and misleading answers. In this SRSM presidential address, I discuss some case studies that exemplify these problems, including examples from meta-analyses of both clinical trials and observational associations. I also discuss issues of effect size estimation, bias (in particular significance-chasing biases), and credibility in meta-research. I examine the factors that affect the credibility of meta-analyses, including magnitude of effects, multiplicity of analyses, scale of data, flexibility of analyses, reporting, and conflicts of interest. Under the current circumstances, a survey of expert meta-analysts attending the SRSM meeting showed that most of them believe that the true effect is practically equally likely to lie within the 95% confidence interval of a meta-analysis or outside of it. Finally, I address the placement of meta-analysis in the wider current research agenda and make a plea for adoption of more prospective meta-designs. In many/most/all fields, all primary original research may be designed, executed, and interpreted as a prospective meta-analysis. Copyright © 2011 John Wiley & Sons, Ltd.

Recommendations for standardization and phenotype definitions in genetic studies of osteoarthritis: the TREAT-OA consortium

OBJECTIVE

To address the need for standardization of osteoarthritis (OA) phenotypes by examining the effect of heterogeneity among symptomatic (SOA) and radiographic osteoarthritis (ROA) phenotypes.

METHODS

Descriptions of OA phenotypes of the 28 studies involved in the TREAT-OA consortium were collected. We investigated whether different OA definitions result in different association results by creating various hip OA definitions in one large population based cohort (the Rotterdam Study I (RSI)) and testing those for association with gender, age and body mass index using one-way ANOVA. For ROA, we standardized the hip-, knee- and hand ROA definitions and calculated prevalence's of ROA before and after standardization in nine cohort studies. This procedure could only be performed in cohort studies and standardization of SOA definitions was not feasible at this moment.

RESULTS

In this consortium, all studies with SOA phenotypes (knee, hip and hand) used a different definition and/or assessment of OA status. For knee-, hip- and hand ROA five, four and seven different definitions were used, respectively. Different hip ROA definitions do lead to different association results. For example, we showed in the RSI that hip OA defined as "at least definite joint space narrowing (JSN) and one definite osteophyte" was not associated with gender (P =0.22), but defined as "at least one definite osteophyte" was significantly associated with gender (P=3×10(-9)). Therefore, a standardization process was undertaken for ROA definitions. Before standardization a wide range of ROA prevalence's was observed in the nine cohorts studied. After standardization the range in prevalence of knee- and hip ROA was small.

CONCLUSION

Phenotype definitions influence the prevalence of OA and association with clinical variables. ROA phenotypes within the TREAT-OA consortium were standardized to reduce heterogeneity and improve power in future genetics studies.

Application of systems biology approach identifies and validates GRB2 as a risk gene for schizophrenia in the Irish Case Control Study of Schizophrenia (ICCSS) sample

Recently, we prioritized 160 schizophrenia candidate genes (SZGenes) by integrating multiple lines of evidence and subsequently identified twenty-four pathways in which these 160 genes are overrepresented. Among them, four neurotransmitter-related pathways were top ranked. In this study, we extended our previous pathway analysis by applying a systems biology approach to identifying candidate genes for schizophrenia. We constructed protein-protein interaction subnetworks for four neurotransmitter-related pathways and merged them to obtain a general neurotransmitter network, from which five candidate genes stood out. We tested the association of four genes (GRB2, HSPA5, YWHAG, and YWHAZ) in the Irish Case-Control Study of Schizophrenia (ICCSS) sample (1021 cases and 626 controls). Interestingly, six of the seven tested SNPs in GRB2 showed significant signal, two of which (rs7207618 and rs9912608) remained significant after permutation test or Bonferroni correction, suggesting that GRB2 might be a risk gene for schizophrenia in Irish population. To our knowledge, this is the first report of GRB2 being significantly associated with schizophrenia in a specific population. Our results suggest that the systems biology approach is promising for identification of candidate genes and understanding the etiology of complex diseases.

Evaluation of the association studies of single nucleotide polymorphisms and hepatocellular carcinoma: a systematic review

PURPOSE

For decades of years, hundreds of candidate gene-based association studies explored the relationship between single nucleotide polymorphisms (SNPs) and hepatocellular carcinoma (HCC). There was no systematic review summarized the results of these association studies of candidate SNPs and HCC to date. In order to summarize the results of the association studies, we conducted a concise systematic review.

METHODS

By searching Pubmed database before October 2010, we reviewed all the association studies about candidate SNPs and HCC. If the eligible study number on a given SNP was more than three, we conducted a meta-analysis. We reported here only the overall positive-association results with statistical significance and evaluated the reliability of the associations by using false-positive report probability (FPRP) analysis and the Venice guidelines on genetic epidemiology studies.

RESULTS

Six SNPs of five genes (rs1800562 of HFE, rs17868323 and rs11692021 of UGT1A7, rs2279744 of MDM2, rs1143627 of IL-1B, and rs4880 of MnSOD) showed overall significant associations with HCC. The eligible number of the studies varied from three to nine. Two SNPs (rs1800562 of HFE and rs2279744 of MDM2) passed the FPRP threshold (FPRP < 0.20). According to the Venice guidelines, the associations between the two SNPs (rs1800562 and rs2279744) and HCC were of moderate evidence.

CONCLUSIONS

Two SNPs (rs1800562 of HFE and rs2279744 of MDM2) were associated with HCC with moderate epidemiological evidence and deserve further study and additional biological and clinical assessment.

The factor XII -4C>T variant and risk of common thrombotic disorders: A HuGE review and meta-analysis of evidence from observational studies

Coagulation factor XII is involved in thrombus formation and therefore may play a role in the etiology of thrombotic disorders. A common variant in the factor XII (F12) gene (-4C>T, rs1801020) results in decreased plasma levels of this coagulation factor. The existence of associations between low factor XII levels or F12 variants and thrombotic outcomes has been debated for more than a decade. The authors conducted a review and meta-analysis to evaluate the evidence for an association between F12 -4C>T and 2 common thrombotic outcomes: venous thromboembolism and myocardial infarction, which are hypothesized to share some etiologic pathways. MEDLINE, EMBASE, and HuGE Navigator were searched through July 2009 to identify relevant epidemiologic studies, and data were summarized using random-effects meta-analysis. Sixteen candidate gene studies (4,386 cases, 40,089 controls) were analyzed. None of the investigated contrasts reached statistical significance at P < 0.05, apart from a very weak association with myocardial infarction for the TT + CT versus CC contrast (odds ratio = 1.13, 95% confidence interval: 1.00, 1.27). Overall, based on the synthesis of observational studies, the evidence for an association between F12 -4C>T and venous thromboembolism and myocardial infarction is weak.

IL28B genomic-based treatment paradigms for patients with chronic hepatitis C infection: the future of personalized HCV therapies

Genome-wide association studies (GWAS) have recently identified host genetic variation to be critical for predicting treatment response and spontaneous clearance in patients infected with hepatitis C virus (HCV). These important new studies are reviewed and their future clinical implications discussed. Single-nucleotide polymorphisms in the region of the IL28B gene on chromosome 19, coding for the interferon (IFN)-λ-3 or IL28B gene, are strongly associated with treatment response to pegylated IFN and ribavirin in patients infected with genotype 1 HCV. The good response variant is associated with a twofold increase in the rate of cure. Allele frequencies differ between ethnic groups, largely explaining the observed differences in response rates between Caucasians, African Americans and Asians. IL28B polymorphism is also strongly associated with spontaneous clearance of HCV. The biological mechanisms responsible for these genetic associations remain unknown and are the focus of ongoing research. Knowledge of a patient's IL28B genotype is likely to aid in clinical decision making with standard of care regimens. Future studies will investigate the possibility of individualizing treatment duration and novel regimens according to IL28B type.

Arg72Pro TP53 polymorphism and cancer susceptibility: a comprehensive meta-analysis of 302 case-control studies

Arg72Pro is a common polymorphism in TP53, showing differences in its biological functions. Case-control studies have been performed to elucidate the role of Arg72Pro in cancer, although the results are conflicting and heterogeneous. Here, we analyzed pooled data from case-control studies to determine the role of Arg72Pro in different cancer sites. We performed a systematic review and meta-analysis of 302 case-control studies that analyzed Arg72Pro in cancer susceptibility. Odds ratios were estimated for different tumor sites using distinct genetic models, and the heterogeneity between studies was explored using I(2) values and meta-regression. We adopted quality criteria to classify the studies. Subgroup analyses were done for tumor sites according to ethnicity, histological, and anatomical sites. Results indicated that Arg72Pro is associated with higher susceptibility to cancer in some tumor sites, mainly hepatocarcinoma. For some tumor sites, quality of studies was associated with the size of genetic association, mainly in cervical, head and neck, gastric, and lung cancer. However, study quality did not explain the observed heterogeneity substantially. Meta-regression showed that ethnicity, allelic frequency and genotyping method were responsible for a substantial part of the heterogeneity observed. Our results suggest ethnicity and histological and anatomical sites may modulate the penetrance of Arg72Pro in cancer susceptibility. This meta-analysis denotes the importance for more studies with good quality and that the covariates responsible for heterogeneity should be controlled to obtain a more conclusive response about the function of Arg72Pro in cancer.

The genetics of Alzheimer disease: back to the future

Three decades of genetic research in Alzheimer disease (AD) have substantially broadened our understanding of the pathogenetic mechanisms leading to neurodegeneration and dementia. Positional cloning led to the identification of rare, disease-causing mutations in APP, PSEN1, and PSEN2 causing early-onset familial AD, followed by the discovery of APOE as the single most important risk factor for late-onset AD. Recent genome-wide association approaches have delivered several additional AD susceptibility loci that are common in the general population, but exert only very small risk effects. As a result, a large proportion of the heritability of AD continues to remain unexplained by the currently known disease genes. It seems likely that much of this "missing heritability" may be accounted for by rare sequence variants, which, owing to recent advances in high-throughput sequencing technologies, can now be assessed in unprecedented detail.

Invited commentary: Gene X lifestyle interactions and complex disease traits--inferring cause and effect from observational data, sine qua non

Observational epidemiology has made outstanding contributions to the discovery and elucidation of relations between lifestyle factors and common complex diseases such as type 2 diabetes. Recent major advances in the understanding of the human genetics of this disease have inspired studies that seek to determine whether the risk conveyed by bona fide risk loci might be modified by lifestyle factors such as diet composition and physical activity levels. A major challenge is to determine which of the reported findings are likely to represent causal interactions and which might be explained by other factors. The authors of this commentary use the Bradford-Hill criteria, a set of tried-and-tested guidelines for causal inference, to evaluate the findings of a recent study on interaction between variation at the cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 (CDKAL1) locus and total energy intake with respect to prevalent metabolic syndrome and hemoglobin A₁(c) levels in a cohort of 313 Japanese men. The current authors conclude that the study, while useful for hypothesis generation, does not provide overwhelming evidence of causal interactions. They overview ways in which future studies of gene × lifestyle interactions might overcome the limitations that motivated this conclusion.

Genotator: a disease-agnostic tool for genetic annotation of disease

BACKGROUND

Disease-specific genetic information has been increasing at rapid rates as a consequence of recent improvements and massive cost reductions in sequencing technologies. Numerous systems designed to capture and organize this mounting sea of genetic data have emerged, but these resources differ dramatically in their disease coverage and genetic depth. With few exceptions, researchers must manually search a variety of sites to assemble a complete set of genetic evidence for a particular disease of interest, a process that is both time-consuming and error-prone.

METHODS

We designed a real-time aggregation tool that provides both comprehensive coverage and reliable gene-to-disease rankings for any disease. Our tool, called Genotator, automatically integrates data from 11 externally accessible clinical genetics resources and uses these data in a straightforward formula to rank genes in order of disease relevance. We tested the accuracy of coverage of Genotator in three separate diseases for which there exist specialty curated databases, Autism Spectrum Disorder, Parkinson's Disease, and Alzheimer Disease. Genotator is freely available at http://genotator.hms.harvard.edu.

RESULTS

Genotator demonstrated that most of the 11 selected databases contain unique information about the genetic composition of disease, with 2514 genes found in only one of the 11 databases. These findings confirm that the integration of these databases provides a more complete picture than would be possible from any one database alone. Genotator successfully identified at least 75% of the top ranked genes for all three of our use cases, including a 90% concordance with the top 40 ranked candidates for Alzheimer Disease.

CONCLUSIONS

As a meta-query engine, Genotator provides high coverage of both historical genetic research as well as recent advances in the genetic understanding of specific diseases. As such, Genotator provides a real-time aggregation of ranked data that remains current with the pace of research in the disease fields. Genotator's algorithm appropriately transforms query terms to match the input requirements of each targeted databases and accurately resolves named synonyms to ensure full coverage of the genetic results with official nomenclature. Genotator generates an excel-style output that is consistent across disease queries and readily importable to other applications.

Null genotypes of GSTM1 and GSTT1 contribute to hepatocellular carcinoma risk: evidence from an updated meta-analysis

BACKGROUND & AIMS

Studies investigating the associations between glutathione S-transferase (GST) genetic polymorphisms and hepatocellular carcinoma (HCC) risk have reported controversial results. Thus, a meta-analysis was performed to clarify the effects of GSTM1 and GSTT1 polymorphisms on HCC risk.

METHODS

We identified 132 relevant records through a literature search up to November 22, 2009, and 24 individual case-control studies from 23 publications were finally included, involving a total of 3349 HCC cases and 5609 controls. Subgroup analyses were performed by ethnicity, or by area according to the incidence rate and hepatitis virus status.

RESULTS

Analyses of total relevant studies showed an increased HCC risk was significantly associated with null genotypes of GSTM1 (OR=1.26, 95% CI 1.03-1.54, p(OR)=0.027) and GSTT1 (OR=1.28, 95% CI 1.09-1.51, p(OR)=0.002). In addition, the GSTM1-GSTT1 interaction analysis showed that the dual null genotype of GSTM1/GSTT1 was significantly associated with increased HCC risk (OR=1.89, 95% CI 1.38-2.60, p(OR)<0.001). Subgroup analyses showed that the associations above were still statistically significant in Asians (p(GSTM1)=0.017, p(GSTT1)=0.001, p(Dual null genotype)<0.001), high-rate areas (p(GSTM1)=0.012, p(GSTT1)=0.006, p(Dual null genotype)<0.001), and HBV-dominant areas (p(GSTM1)=0.003, p(GSTT 1)=0.003, p(Dual null genotype)<0.001).

CONCLUSIONS

This meta-analysis suggests null genotypes of GSTM1 and GSTT1 are both associated with increased HCC risk in Asians, and individuals with the dual null genotype of GSTM1/GSTT1 are particularly susceptible to developing HCC.

The emergence of translational epidemiology: from scientific discovery to population health impact

Recent emphasis on translational research (TR) is highlighting the role of epidemiology in translating scientific discoveries into population health impact. The authors present applications of epidemiology in TR through 4 phases designated T1-T4, illustrated by examples from human genomics. In T1, epidemiology explores the role of a basic scientific discovery (e.g., a disease risk factor or biomarker) in developing a "candidate application" for use in practice (e.g., a test used to guide interventions). In T2, epidemiology can help to evaluate the efficacy of a candidate application by using observational studies and randomized controlled trials. In T3, epidemiology can help to assess facilitators and barriers for uptake and implementation of candidate applications in practice. In T4, epidemiology can help to assess the impact of using candidate applications on population health outcomes. Epidemiology also has a leading role in knowledge synthesis, especially using quantitative methods (e.g., meta-analysis). To explore the emergence of TR in epidemiology, the authors compared articles published in selected issues of the Journal in 1999 and 2009. The proportion of articles identified as translational doubled from 16% (11/69) in 1999 to 33% (22/66) in 2009 (P = 0.02). Epidemiology is increasingly recognized as an important component of TR. By quantifying and integrating knowledge across disciplines, epidemiology provides crucial methods and tools for TR.

Meta-analysis of epidemiological studies of association of P53 codon 72 polymorphism with bladder cancer

Although there have been many studies investigating a possible association between p53 codon 72 polymorphism and risk of bladder cancer, the results have been inconsistent. We conducted a meta-analysis of six epidemiological studies, which included 597 bladder cancer cases and 731 controls. Patients with bladder cancer had a significantly lower frequency of Pro/Arg [odds ratio (OR) = 0.80, 95% confidence interval (CI) = 0.64-0.99], when compared to controls. Stratifying for race, we found that among Caucasians, patients with bladder cancer had a significantly higher frequency of Arg/Arg (OR = 1.64, 95%CI = 1.18-2.28) and a lower frequency of Pro/Arg (OR = 0.62, 95%CI = 0.44-0.86), compared to controls. Stratifying various studies by the stage of bladder cancer, we found that invasive bladder cancers had a significantly lower frequency of Arg/Arg (OR = 0.58, 95%CI = 0.36-0.93) and a higher frequency of Pro/Arg (OR = 0.62, 95%CI = 0.44-0.86) than did non-invasive bladder cancers. No significant association was found between this genotype and human papilloma virus. Based on our meta-analysis, we suggest that p53 codon 72 polymorphism is associated with bladder cancer and that genotypic distribution of this polymorphism varies with the stage of bladder cancer.

Genetic epidemiology of age-related osteoporosis and its clinical applications

Osteoporosis is an important and complex disorder that is highly prevalent worldwide. This disease poses a major challenge to modern medicine and its treatment is associated with high costs. Numerous studies have endeavored to decipher the pathogenesis of this disease. The clinical assessment of patients often incorporates information about a family history of osteoporotic fractures. Indeed, the observation of an increased risk of fracture in an individual with a positive parental history of hip fracture provides strong evidence for the heritability of osteoporosis. The onset and progression of osteoporosis are generally controlled by multiple genetic and environmental factors, as well as interactions between them, with rare cases determined by a single gene. In an attempt to identify the genetic markers of complex diseases such as osteoporosis, there has been a move away from traditional linkage mapping studies and candidate gene association studies to higher-density genome-wide association studies. The advent of high-throughput technology enables genotyping of millions of DNA markers in the human genome, and consequently the identification and characterization of causal variants and loci that underlie osteoporosis. This Review presents an overview of the major findings since 2007 and clinical applications of these genome-wide linkage and association studies.

Possible risk modifications in the association between MnSOD Ala-9Val polymorphism and breast cancer risk: subgroup analysis and evidence-based sample size calculation for a future trial

Manganese superoxide dismutase (MnSOD) has been identified as an important scavenger of reactive oxygen species (ROS), which can cause oxidative stress followed by breast cancer. A number of subsequent population-based studies have investigated the association between MnSOD Ala-9Val polymorphism and the risk of breast cancer. However, these studies have yielded conflicting results. This fact implies that the effect of MnSOD Ala-9Val polymorphism on the susceptibility to breast cancer may be modified by other risk factors. To provide a more definitive conclusion, a full meta-analysis combining and summarizing 16 studies was first performed using both traditional and Bayesian approaches. During this step, a recessive inheritance mode was determined after a biological justification. The capability of the Bayesian method was highlighted in the estimation of a pooled odds ratio and 95% confidence interval. As a result, no significant association was observed (OR = 0.978, CI = 0.914-1.046). Bayesian meta-regression and subgroup analysis were then conducted to find possible risk modifications by other factors, including menopausal status, ethnicity effect, use of oral contraceptives, use of hormone replacement therapy, fruits and vegetables intake, vitamin supplement, and body mass index. While the power of most subgroups may be insufficient to make a statistical statement, an evidence-based sample size calculation based upon updated meta-analysis was performed to power a future trial. For example, approximately 5,000 subjects are required for a new Asian study (2,500 cases and 2,500 controls) to achieve 80% power.

Coriell Personalized Medicine Collaborative®: a prospective study of the utility of personalized medicine

There is a dearth of large prospective studies to determine if genetic risk factors are useful predictors of health outcomes and if reporting them to individuals or physicians changes health behavior. The Coriell Personalized Medicine Collaborative® (CPMC, NJ, USA) is a prospective observational study with three cohorts – community, cancer and chronic disease cohorts. Participants provide detailed medical history through a dynamic internet-based portal. DNA is tested and personalized risk reports are provided for potentially actionable health conditions. To date, the community cohort has enrolled 4372 participants. The internet-based portal supplies educational content, captures phenotypic data and delivers customized risk reports. The Informed Cohort Oversight Board has approved 16 health conditions to date, and risk reports with genetic and nongenetic risks for six conditions have been released. The majority (87%) of participants who completed requisite questionnaires viewed at least one report. The CPMC is a cohort study delivering customized risk reports for actionable conditions using a web interface and measuring outcomes longitudinally.

Glutathione-S-transferase genes and asthma phenotypes: a Human Genome Epidemiology (HuGE) systematic review and meta-analysis including unpublished data

BACKGROUND

Oxidative stress is thought to be involved in the pathogenesis of asthma. Glutathione-S-transferase (GST) enzymes, which play an important role in antioxidant defences, may therefore influence asthma risk. Two common deletion polymorphisms of GSTM1 and GSTT1 genes and the GSTP1 Ile105Val polymorphism have been associated with asthma in children and adults, but results are inconsistent across studies.

METHODS

Systematic review and meta-analysis of the effects of GST genes on asthma, wheezing and bronchial hyper-responsiveness (BHR), with inclusion of unpublished data from three studies, including the large Avon Longitudinal Study of Parents and Children (ALSPAC). Random effect or fixed effect models were used as appropriate, and sensitivity analyses were performed to assess the impact of study characteristics and quality on pooled results.

RESULTS

The meta-analyses of GSTM1 (n = 22 studies) and GSTT1 (n = 19) showed increased asthma risk associated with the null genotype, but there was extreme between-study heterogeneity and publication bias and the association disappeared when meta-analysis was restricted to the largest studies. Meta-analysis of GSTP1 Ile105Val (n = 17) and asthma suggested a possible protective effect of the Val allele, but heterogeneity was extreme. Few studies evaluated wheezing and BHR and most reported no associations, although weak evidence was found for positive associations of GSTM1 null and GSTP1 Val allele with wheezing and a negative association of GSTP1 Val allele with BHR.

CONCLUSIONS

Our findings do not support a substantial role of GST genes alone in the development of asthma. Future studies of large size should focus on interactions of GST genes with environmental oxidative exposures and with other genes involved in antioxidant pathways. Quality of study conduct and reporting needs to be improved to increase credibility of the evidence accumulating over time.

Approaches to advancing quantitative human health risk assessment of environmental chemicals in the post-genomic era

The contribution of genomics and associated technologies to human health risk assessment for environmental chemicals has focused largely on elucidating mechanisms of toxicity, as discussed in other articles in this issue. However, there is interest in moving beyond hazard characterization to making more direct impacts on quantitative risk assessment (QRA)--i.e., the determination of toxicity values for setting exposure standards and cleanup values. We propose that the evolution of QRA of environmental chemicals in the post-genomic era will involve three, somewhat overlapping phases in which different types of approaches begin to mature. The initial focus (in Phase I) has been and continues to be on "augmentation" of weight of evidence--using genomic and related technologies qualitatively to increase the confidence in and scientific basis of the results of QRA. Efforts aimed towards "integration" of these data with traditional animal-based approaches, in particular quantitative predictors, or surrogates, for the in vivo toxicity data to which they have been anchored are just beginning to be explored now (in Phase II). In parallel, there is a recognized need for "expansion" of the use of established biomarkers of susceptibility or risk of human diseases and disorders for QRA, particularly for addressing the issues of cumulative assessment and population risk. Ultimately (in Phase III), substantial further advances could be realized by the development of novel molecular and pathway-based biomarkers and statistical and in silico models that build on anticipated progress in understanding the pathways of human diseases and disorders. Such efforts would facilitate a gradual "reorientation" of QRA towards approaches that more directly link environmental exposures to human outcomes.

Cluster analysis in severe emphysema subjects using phenotype and genotype data: an exploratory investigation

Background

Numerous studies have demonstrated associations between genetic markers and COPD, but results have been inconsistent. One reason may be heterogeneity in disease definition. Unsupervised learning approaches may assist in understanding disease heterogeneity.

Methods

We selected 31 phenotypic variables and 12 SNPs from five candidate genes in 308 subjects in the National Emphysema Treatment Trial (NETT) Genetics Ancillary Study cohort. We used factor analysis to select a subset of phenotypic variables, and then used cluster analysis to identify subtypes of severe emphysema. We examined the phenotypic and genotypic characteristics of each cluster.

Results

We identified six factors accounting for 75% of the shared variability among our initial phenotypic variables. We selected four phenotypic variables from these factors for cluster analysis: 1) post-bronchodilator FEV₁ percent predicted, 2) percent bronchodilator responsiveness, and quantitative CT measurements of 3) apical emphysema and 4) airway wall thickness. K-means cluster analysis revealed four clusters, though separation between clusters was modest: 1) emphysema predominant, 2) bronchodilator responsive, with higher FEV₁; 3) discordant, with a lower FEV₁ despite less severe emphysema and lower airway wall thickness, and 4) airway predominant. Of the genotypes examined, membership in cluster 1 (emphysema-predominant) was associated with TGFB1 SNP rs1800470.

Conclusions

Cluster analysis may identify meaningful disease subtypes and/or groups of related phenotypic variables even in a highly selected group of severe emphysema subjects, and may be useful for genetic association studies.

The association between the peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala gene variant and type 2 diabetes mellitus: a HuGE review and meta-analysis

The peroxisome proliferator-activated receptor-γ gene (PPARG) has been implicated in the etiology of type 2 diabetes mellitus and has been investigated in numerous epidemiologic studies. In this Human Genome Epidemiology review, the authors assessed this relation in an updated meta-analysis of 60 association studies. Electronic literature searches were conducted on September 14, 2009. Population-based cohort, case-control, cross-sectional, or genome-wide association studies reporting associations between the PPARG Pro12Ala gene variant (rs1801282) and type 2 diabetes were included. An updated literature-based meta-analysis involving 32,849 type 2 diabetes cases and 47,456 controls in relation to the PPARG Pro12Ala variant was conducted. The combined overall odds ratio, calculated by per-allele genetic model random-effects meta-analysis for type 2 diabetes and the Pro12Ala polymorphism, was 0.86 (95% confidence interval: 0.81, 0.90). The analysis indicated a moderate level of heterogeneity attributable to genuine variation in gene effect size (I² = 37%). This may reflect the variation observed between ethnic populations and/or differences in body mass index. Work on PPARG Pro12Ala should now focus on the observed heterogeneity in the magnitude of the association between populations. Further investigations into gene-gene and gene-environment interactions may prove enlightening.

Population description and its role in the interpretation of genetic association

Despite calls for greater clarity and precision of population description, studies have documented persistent ambiguity in the use of race/ethnicity terms in genetic research. It is unclear why investigators tolerate such ambiguity, or what effect these practices have on the evaluation of reported associations. To explore the way that population description is used to replicate and/or extend previously reported genetic observations, we examined articles describing the association of the peroxisome proliferator-activated receptor-gamma-gamma Pro12Ala polymorphism with type 2 diabetes mellitus and related phenotypes, published between 1997 and 2005. The 80 articles identified were subjected to a detailed content analysis to determine (1) how sampled populations were described, (2) whether and how the choice of sample was explained, and (3) how the allele frequency and genetic association findings identified were contextualized and interpreted. In common with previous reports, we observed a variety of sample descriptions and little explanation for the choice of population investigated. Samples of European origin were typically described with greater specificity than samples of other origin. However, findings from European samples were nearly always compared to samples described as "Caucasian" and sometimes generalized to all Caucasians or to all humans. These findings suggest that care with population description, while important, may not fully address analytical concerns regarding the interpretation of variable study outcomes or ethical concerns regarding the attribution of genetic observations to broad social groups. Instead, criteria which help investigators better distinguish justified and unjustified forms of population generalization may be required.

Causal relationship of susceptibility genes to ischemic stroke: comparison to ischemic heart disease and biochemical determinants

Interrelationships between genetic and biochemical factors underlying ischemic stroke and ischemic heart disease are poorly understood. We: 1) undertook the most comprehensive meta-analysis of genetic polymorphisms in ischemic stroke to date; 2) compared genetic determinants of ischemic stroke with those of ischemic heart disease, and 3) compared effect sizes of gene-stroke associations with those predicted from independent biochemical data using a mendelian randomization strategy. Electronic databases were searched up to January 2009. We identified: 1) 187 ischemic stroke studies (37,481 cases; 95,322 controls) interrogating 43 polymorphisms in 29 genes; 2) 13 meta-analyses testing equivalent polymorphisms in ischemic heart disease; and 3) for the top five gene-stroke associations, 146 studies (65,703 subjects) describing equivalent gene-biochemical relationships, and 28 studies (46,928 subjects) describing biochemical-stroke relationships. Meta-analyses demonstrated positive associations with ischemic stroke for factor V Leiden Gln506, ACE I/D, MTHFR C677T, prothrombin G20210A, PAI-1 5G allele and glycoprotein IIIa Leu33Pro polymorphisms (ORs: 1.11 – 1.60). Most genetic associations show congruent levels of risk comparing ischemic stroke with ischemic heart disease, but three genes—glycoprotein IIIa, PAI-1 and angiotensinogen—show significant dissociations. The magnitudes of stroke risk observed for factor V Leiden, ACE, MTHFR and prothrombin, but not PAI-1, polymorphisms, are consistent with risks associated with equivalent changes in activated protein C resistance, ACE activity, homocysteine, prothrombin, and PAI-1 levels, respectively. Our results demonstrate causal relationships for four of the most robust genes associated with stroke while also showing that PAI-1 4G/5G polymorphism influences cardiovascular risk via a mechanism not simply related to plasma levels of PAI-1 (or tPA) alone.

The neurobiology of D-amino acid oxidase and its involvement in schizophrenia

D-amino acid oxidase (DAO) is a flavoenzyme that metabolizes certain D-amino acids, notably the endogenous N-methyl D-aspartate receptor (NMDAR) co-agonist, D-serine. As such, it has the potential to modulate the function of NMDAR and to contribute to the widely hypothesized involvement of NMDAR signalling in schizophrenia. Three lines of evidence now provide support for this possibility: DAO shows genetic associations with the disorder in several, although not all, studies; the expression and activity of DAO are increased in schizophrenia; and DAO inactivation in rodents produces behavioural and biochemical effects, suggestive of potential therapeutic benefits. However, several key issues remain unclear. These include the regional, cellular and subcellular localization of DAO, the physiological importance of DAO and its substrates other than D-serine, as well as the causes and consequences of elevated DAO in schizophrenia. Herein, we critically review the neurobiology of DAO, its involvement in schizophrenia, and the therapeutic value of DAO inhibition. This review also highlights issues that have a broader relevance beyond DAO itself: how should we weigh up convergent and cumulatively impressive, but individually inconclusive, pieces of evidence regarding the role that a given gene may have in the aetiology, pathophysiology and pharmacotherapy of schizophrenia?

The pursuit of susceptibility genes for Alzheimer's disease: progress and prospects

The recent discoveries in genome-wide association studies (GWAS) of novel susceptibility loci (CLU, CR1 and PICALM) for Alzheimer's disease (AD) have elicited considerable interest in the AD community. But what are the implications of these purely epidemiological findings for our understanding of disease etiology and patient care? In this review, we attempt to place these findings in the context of current and future AD genetics research. CLU, CR1 and PICALM support existing hypotheses about the amyloid, lipid, chaperone and chronic inflammatory pathways in AD pathogenesis. We discuss how these and future findings can be translated into efforts to ameliorate patient care by genetic profiling for risk prediction and pharmacogenetics and by guiding drug development.

The Epilepsy Genetic Association Database (epiGAD): analysis of 165 genetic association studies, 1996-2008

We have created the Epilepsy Genetic Association Database (epiGAD, http://www.epigad.org, an online database of epilepsy genetic association studies. A systematic search using several search engines identified 165 studies. Herein we analyze the types of studies available, the sample sizes used, and the strength of the findings. Common questions examined were susceptibility to idiopathic generalized epilepsy, focal epilepsy, or febrile seizures, and pharmacogenomic approaches to drug-resistant epilepsy. Sample sizes were generally small; 80% of studies had 200 or fewer cases, although more recent studies published from 2005-2008 incorporated slightly larger sample sizes. No association was judged as "strong" using current criteria for assessing genetic associations--this is probably due to inadequate sample sizes. Sample sizes need to increase, either by research collaboration or via systematic reviews and meta-analyses. We believe epiGAD will facilitate future meta-analyses.

Invited commentary: genetic variants and individual- and societal-level risk factors

Over the past decade, leading epidemiologists have noted the importance of social factors in studying and understanding the distribution and determinants of disease in human populations; but to what extent are epidemiologic studies integrating genetic information and other biologic variables with information about individual-level risk factors and group-level or societal factors related to the broader residential, behavioral, or cultural context? There remains a need to consider ways to integrate genetic information with social and contextual information in epidemiologic studies, partly to combat the overemphasis on the importance of genetic factors as determinants of disease in human populations. Even in genome-wide association studies of coronary heart disease and other common complex diseases, only a small proportion of heritability is explained by the genetic variants identified to date. It is possible that familial clustering due to genetic factors has been overestimated and that important environmental or social influences (acting alone or in combination with genetic variants) have been overlooked. The accompanying article by Bressler et al. (Am J Epidemiol. 2010;171(1):14-23) highlights some of these important issues.

Rare diseases epidemiology research

Rare Diseases Epidemiology is a novel action field still largely unexplored. However, Rare Diseases is a topic of growing interest at world level. The aims of this chapter are to revise useful epidemiological tools and define areas where epidemiology can help improve the rare disease knowledge, and facilitate policy decisions taking into account the real burden of rare diseases in society. This chapter also seeks to describe: the problems of coding and classification of diseases, measuring disease frequency, the study designs and association studies, the causality, the evolution from descriptive to epigenetic epidemiology and the natural history of disease. One of the major challenges facing analytical epidemiology and clinical epidemiological research into rare diseases is that genes can be involved in both aetiology and prognosis. Despite the many similarities between genetic association studies and classic observational epidemiological studies, the former pose several specific limitations, including an unprecedented volume of new data and the likelihood of very small individual effects, as well other limitations. Selecting the appropriate pathway from among all those available, i.e. the one that best relates genes from the various known regions and disease mechanisms, is crucial for the success of this type of studies.

Improved reporting of statistical design and analysis: guidelines, education, and editorial policies

A majority of original articles published in biomedical journals include some form of statistical analysis. Unfortunately, many of the articles contain errors in statistical design and/or analysis. These errors are worrisome, as the misuse of statistics jeopardizes the process of scientific discovery and the accumulation of scientific knowledge. To help avoid these errors and improve statistical reporting, four approaches are suggested: (1) development of guidelines for statistical reporting that could be adopted by all journals, (2) improvement in statistics curricula in biomedical research programs with an emphasis on hands-on teaching by biostatisticians, (3) expansion and enhancement of biomedical science curricula in statistics programs, and (4) increased participation of biostatisticians in the peer review process along with the adoption of more rigorous journal editorial policies regarding statistics. In this chapter, we provide an overview of these issues with emphasis to the field of molecular biology and highlight the need for continuing efforts on all fronts.

Steroid 5-{alpha}-reductase Type 2 (SRD5a2) gene polymorphisms and risk of prostate cancer: a HuGE review

Steroid 5-alpha-reductase type 2 (SRD5a2) is a critical enzyme in androgen metabolism. Two polymorphisms in the SRD5a2 gene, V89L (rs523349) and A49T (rs9282858), have been studied for associations with prostate cancer risk, with conflicting results. The authors conducted a systematic review and meta-analysis (1997-2007) to examine these associations and compared the results with findings from genome-wide association studies of prostate cancer. The meta-analysis included 24 case-control studies (10,088 cases and 10,120 controls for V89L and 4,998 cases and 5,451 controls for A49T). The authors found that prostate cancer was not associated with V89L (L allele vs. V allele: odds ratio = 0.99, 95% confidence interval: 0.94, 1.05) and was probably not associated with A49T (T allele vs. A allele: odds ratio = 1.10, 95% confidence interval: 0.86, 1.40). These results could have been distorted by spectrum-of-disease bias, convenience sampling of cases and controls, genotype misclassification, and/or confounding. Neither V89L nor A49T was included in microarray chips used for published genome-wide association studies. Analysis of well-designed population-based studies with pathway-based arrays containing common genetic variants could be useful for identifying genetic factors in prostate cancer.

APOA2, dietary fat, and body mass index: replication of a gene-diet interaction in 3 independent populations

BACKGROUND

Nutrigenetics studies the role of genetic variation on interactions between diet and health, aiming to provide more personalized dietary advice. However, replication has been low. Our aim was to study interaction among a functional APOA2 polymorphism, food intake, and body mass index (BMI) in independent populations to replicate findings and to increase their evidence level.

METHODS

Cross-sectional, follow-up (20 years), and case-control analyses were undertaken in 3 independent populations. We analyzed gene-diet interactions between the APOA2 -265T>C polymorphism and saturated fat intake on BMI and obesity in 3462 individuals from 3 populations in the United States: the Framingham Offspring Study (1454 whites), the Genetics of Lipid Lowering Drugs and Diet Network Study (1078 whites), and Boston-Puerto Rican Centers on Population Health and Health Disparities Study (930 Hispanics of Caribbean origin).

RESULTS

Prevalence of the CC genotype in study participants ranged from 10.5% to 16.2%. We identified statistically significant interactions between the APOA2 -265T>C and saturated fat regarding BMI in all 3 populations. Thus, the magnitude of the difference in BMI between the individuals with the CC and TT+TC genotypes differed by saturated fat. A mean increase in BMI of 6.2% (range, 4.3%-7.9%; P = .01) was observed between genotypes with high- (> or =22 g/d) but not with low- saturated fat intake in all studies. Likewise, the CC genotype was significantly associated with higher obesity prevalence in all populations only in the high-saturated fat stratum. Meta-analysis estimations of obesity for individuals with the CC genotype compared with the TT+TC genotype were an odds ratio of 1.84 (95% confidence interval, 1.38-2.47; P < .001) in the high-saturated fat stratum, but no association was detected in the low-saturated fat stratum (odds ratio, 0.81; 95% confidence interval, 0.59-1.11; P = .18).

CONCLUSION

For the first time to our knowledge, a gene-diet interaction influencing BMI and obesity has been strongly and consistently replicated in 3 independent populations.

Genome-wide association studies in Alzheimer's disease

Genome-wide association studies (GWAS) have gained considerable momentum over the last couple of years for the identification of novel complex disease genes. In the field of Alzheimer's disease (AD), there are currently eight published and two provisionally reported GWAS, highlighting over two dozen novel potential susceptibility loci beyond the well-established APOE association. On the basis of the data available at the time of this writing, the most compelling novel GWAS signal has been observed in GAB2 (GRB2-associated binding protein 2), followed by less consistently replicated signals in galanin-like peptide (GALP), piggyBac transposable element derived 1 (PGBD1), tyrosine kinase, non-receptor 1 (TNK1). Furthermore, consistent replication has been recently announced for CLU (clusterin, also known as apolipoprotein J). Finally, there are at least three replicated loci in hitherto uncharacterized genomic intervals on chromosomes 14q32.13, 14q31.2 and 6q24.1 likely implicating the existence of novel AD genes in these regions. In this review, we will discuss the characteristics and potential relevance to pathogenesis of the outcomes of all currently available GWAS in AD. A particular emphasis will be laid on findings with independent data in favor of the original association.

Fulfilling the promise of personalized medicine? Systematic review and field synopsis of pharmacogenetic studies

BACKGROUND

Studies of the genetic basis of drug response could help clarify mechanisms of drug action/metabolism, and facilitate development of genotype-based predictive tests of efficacy or toxicity (pharmacogenetics).

OBJECTIVES

We conducted a systematic review and field synopsis of pharmacogenetic studies to quantify the scope and quality of available evidence in this field in order to inform future research.

DATA SOURCES

Original research articles were identified in Medline, reference lists from 24 meta-analyses/systematic reviews/review articles and U.S. Food and Drug Administration website of approved pharmacogenetic tests. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS, AND INTERVENTION CRITERIA: We included any study in which either intended or adverse response to drug therapy was examined in relation to genetic variation in the germline or cancer cells in humans.

STUDY APPRAISAL AND SYNTHESIS METHODS

Study characteristics and data reported in abstracts were recorded. We further analysed full text from a random 10% subset of articles spanning the different subclasses of study.

RESULTS

From 102,264 Medline hits and 1,641 articles from other sources, we identified 1,668 primary research articles (1987 to 2007, inclusive). A high proportion of remaining articles were reviews/commentaries (ratio of reviews to primary research approximately 25 ratio 1). The majority of studies (81.8%) were set in Europe and North America focussing on cancer, cardiovascular disease and neurology/psychiatry. There was predominantly a candidate gene approach using common alleles, which despite small sample sizes (median 93 [IQR 40-222]) with no trend to an increase over time, generated a high proportion (74.5%) of nominally significant (p<0.05) reported associations suggesting the possibility of significance-chasing bias. Despite 136 examples of gene/drug interventions being the subject of >or=4 studies, only 31 meta-analyses were identified. The majority (69.4%) of end-points were continuous and likely surrogate rather than hard (binary) clinical end-points.

CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

The high expectation but limited translation of pharmacogenetic research thus far may be explained by the preponderance of reviews over primary research, small sample sizes, a mainly candidate gene approach, surrogate markers, an excess of nominally positive to truly positive associations and paucity of meta-analyses. Recommendations based on these findings should inform future study design to help realise the goal of personalised medicines. SYSTEMATIC REVIEW REGISTRATION NUMBER: Not Registered.

Association between the ubiquitin carboxyl-terminal esterase L1 gene (UCHL1) S18Y variant and Parkinson's Disease: a HuGE review and meta-analysis

The ubiquitin carboxyl-terminal esterase L1 gene, UCHL1, located on chromosome 4p14, has been studied as a potential candidate gene for Parkinson's disease risk. The authors conducted a Human Genome Epidemiology review and meta-analysis of published case-control studies of the UCHL1 S18Y variant and Parkinson's disease in Asian and Caucasian samples. The meta-analysis of studies in populations of Asian ancestry showed a statistically significant association between the Y allele and reduced risk of Parkinson's disease under a recessive model (odds ratio (OR) for YY vs. SY + SS = 0.79, 95% confidence interval (CI): 0.67, 0.94; P = 0.006). For a dominant model, the association was not significant in Asian populations (OR for YY + SY vs. SS = 0.88, 95% CI: 0.68, 1.14; P = 0.33). For populations of European ancestry, the meta-analysis showed a significant association between the Y allele and decreased risk of Parkinson's disease under a dominant model (OR = 0.89, 95% CI: 0.81, 0.98; P = 0.02) but not under a recessive model (OR = 0.92, 95% CI: 0.66, 1.30; P = 0.65). Using the Venice criteria, developed by the Human Genome Epidemiology Network Working Group on the assessment of cumulative evidence, the authors concluded that moderate evidence exists for an association between the S18Y variant and Parkinson's disease.

Evidence-based medicine and practice guidelines: application to genetics

The Professional Practice and Guidelines Committee of the American College of Medical Genetics has the responsibility of overseeing the development of guidelines for the practice of clinical genetics. In the past, most, if not all, guidelines were primarily based on expert opinion. However, recently the goal has become to develop guidelines that are more evidence-based, or at least, to recognize the level of evidence available to the authors of these documents. This article reviews the challenges that are faced by geneticists who are charged with the development of practice guidelines.

Discovery properties of genome-wide association signals from cumulatively combined data sets

Genetic effects for common variants affecting complex disease risk are subtle. Single genome-wide association (GWA) studies are typically underpowered to detect these effects, and combination of several GWA data sets is needed to enhance discovery. The authors investigated the properties of the discovery process in simulated cumulative meta-analyses of GWA study-derived signals allowing for potential genetic model misspecification and between-study heterogeneity. Variants with null effects on average (but also between-data set heterogeneity) could yield false-positive associations with seemingly homogeneous effects. Random effects had higher than appropriate false-positive rates when there were few data sets. The log-additive model had the lowest false-positive rate. Under heterogeneity, random-effects meta-analyses of 2-10 data sets averaging 1,000 cases/1,000 controls each did not increase power, or the meta-analysis was even less powerful than a single study (power desert). Upward bias in effect estimates and underestimation of between-study heterogeneity were common. Fixed-effects calculations avoided power deserts and maximized discovery of association signals at the expense of much higher false-positive rates. Therefore, random- and fixed-effects models are preferable for different purposes (fixed effects for initial screenings, random effects for generalizability applications). These results may have broader implications for the design and interpretation of large-scale multiteam collaborative studies discovering common gene variants.

Replication in genome-wide association studies

Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss prerequisites for exact replication; issues of heterogeneity; advantages and disadvantages of different methods of data synthesis across multiple studies; frequentist vs. Bayesian inferences for replication; and challenges that arise from multi-team collaborations. While consistent replication can greatly improve the credibility of a genotype-phenotype association, it may not eliminate spurious associations due to biases shared by many studies. Conversely, lack of replication in well-powered follow-up studies usually invalidates the initially proposed association, although occasionally it may point to differences in linkage disequilibrium or effect modifiers across studies.