Assessment of cumulative evidence on genetic associations: interim guidelines

Tumor necrosis factor polymorphisms and alcoholic liver disease: a HuGE review and meta-analysis

The association between alcoholic liver disease (ALD) and tumor necrosis factor-alpha gene (TNFA) polymorphisms has been analyzed in several studies, but results have been conflicting. The main purpose of this study was to integrate previous findings and explore whether these polymorphisms are associated with susceptibility to ALD. The authors surveyed studies on the relation between TNFA gene polymorphisms and ALD by means of an electronic database search. A meta-analysis was conducted in a random-effects model. The association between ALD and the -238G>A or -308G>A polymorphism of the TNFA gene has been analyzed in 11 studies. Concerning the -238G>A polymorphism, the authors found a significant association between possession of the A allele and risk of alcoholic liver cirrhosis (odds ratio = 1.47, 95% confidence interval: 1.05, 2.07). Meta-analysis of the relation between the -308G>A polymorphism and ALD did not show any significant association. Given the limited number of studies and the potential biases, more data are needed to confirm the association described for the -238A allele.

Angiotensins in Alzheimer's disease - friend or foe?

The renin-angiotensin system (RAS) is an important regulator of blood pressure. Observational and experimental studies suggest that alterations in blood pressure and components of the brain RAS contribute to the development and progression of Alzheimer's disease (AD), resulting in changes that can lead or contribute to cognitive decline. The complexity of the RAS and diversity of its interactions with neurological processes have recently become apparent but large gaps in our understanding still remain. Modulation of activity of components of the brain RAS offers substantial opportunities for the treatment and prevention of dementia, including AD. This paper reviews molecular, genetic, experimental and clinical data as well as the therapeutic opportunities that relate to the involvement of the RAS in AD.

An empirical comparison of meta-analyses of published gene-disease associations versus consortium analyses

PURPOSE

Consortia of investigators currently compile sufficiently large sample sizes to investigate the effects of low-risk susceptibility genetic variants. It is not clear how the results obtained by consortia compare with those derived from meta-analyses of published studies.

METHODS

We performed meta-analyses of published data for 16 genetic polymorphisms investigated by the Breast Cancer Association Consortium, and compared sample sizes, heterogeneity, and effect sizes. PubMed, Web of Science, and Human Genome Epidemiology Network databases were searched for breast cancer case-control association studies.

RESULTS

We found that meta-analyses of published data and consortium analyses were based on substantially different data. Published data by non-consortium teams amounted on average to 26.9% of all available data (range 3.0 -50.0%). Both approaches showed statistically significant decreased breast cancer risks for CASP8 D302H. The meta-analyses of published data demonstrated statistically significant results for five other genes and the consortium analyses for two other genes, but the strength of this evidence, evaluated on the basis of the Venice criteria, was not strong.

CONCLUSIONS

Because both approaches identified the same gene out of 16 candidates, the methods can be complimentary. The expense and complexity of consortium-based studies should be considered vis-à-vis the potential methodological limitations of synthesis of published studies.

Association of the TLR4 Asp299Gly polymorphism with lung function in relation to body mass index

Background

Previous studies have shown conflicting results for the association between TLR4 polymorphism (Asp299Gly) and lung function. We investigated the influence of TLR4 Asp299Gly, a polymorphism, on lung function in a community population.

Methods

In 2003, a cross-sectional survey was conducted to assess the respiratory health of residents living in and around the town of Humboldt, Saskatchewan, Canada. There were 2090 adults age 18-79 years who completed a questionnaire that included a medical and smoking history, as well as socio-economic and lifestyle variables. Genetic information and lung function test measurements were available on 1725 subjects (754 males and 971 females) of the 2090 respondents. These subjects were selected for further analysis to investigate the association between TLR4 Asp299Gly genotype and forced expiratory volume in the first second in liters (FEV₁), forced vital capacity in liters (FVC), FEV₁/FVC ratio, and forced expiratory flow rate in liters/second (FEF_25-75). Multivariable linear regression analysis was used to investigate associations.

Results

Adjusted mean values of FEV₁ and FVC were significantly different between TLR4 wild type and TLR4 variant groups [Mean ± S.E.: (TLR4 wild type - FEV₁: 3.18 ± 0.02, FVC: 3.95 ± 0.03; TLR4 variant - FEV₁: 3.31 ± 0.06, FVC: 4.14 ± 0.07)]. Based on multivariable regression analysis, we observed that body mass index (BMI) was associated with decreased FEV₁/FVC ratio and FEF_25-75 in TLR4 variant group but not in wild type group.

Conclusion

BMI may modify the associations of TLR4 Asp299Gly polymorphism with FEV₁/FVC ratio and FEF_25-75.

Enabling personalized medicine through the use of healthcare information technology

To achieve the goal of personalized medicine, advances in healthcare information technology (HIT) must be accompanied by similar advances in data analysis and translation into clinical care. For healthcare providers and their patients, advances in genomics and other related fields must be captured by sophisticated decision support systems that integrate information from multiple sources of data and facilitate their use in the clinic. In addition to addressing privacy concerns, regulators will also need to work toward establishing evidentiary standards for the use of data from electronic clinical sources in areas such as comparative effectiveness and safety, while addressing the complexity behind the genetic basis of the exceptions to their general findings. Overall, the increased use of HIT will allow for a more integrated, efficient system for collecting and analyzing clinical data, providing a fruitful environment for hypothesis testing and generation, as well as leading to more personalized and effective healthcare.

Large-scale analysis of association between GDF5 and FRZB variants and osteoarthritis of the hip, knee, and hand

OBJECTIVE

GDF5 and FRZB have been proposed as genetic loci conferring susceptibility to osteoarthritis (OA); however, the results of several studies investigating the association of OA with the rs143383 polymorphism of the GDF5 gene or the rs7775 and rs288326 polymorphisms of the FRZB gene have been conflicting or inconclusive. To examine these associations, we performed a large-scale meta-analysis of individual-level data.

METHODS

Fourteen teams contributed data on polymorphisms and knee, hip, and hand OA. For rs143383, the total number of cases and controls, respectively, was 5,789 and 7,850 for hip OA, 5,085 and 8,135 for knee OA, and 4,040 and 4,792 for hand OA. For rs7775, the respective sample sizes were 4,352 and 10,843 for hip OA, 3,545 and 6,085 for knee OA, and 4,010 and 5,151 for hand OA, and for rs288326, they were 4,346 and 8,034 for hip OA, 3,595 and 6,106 for knee OA, and 3,982 and 5,152 for hand OA. For each individual study, sex-specific odds ratios (ORs) were calculated for each OA phenotype that had been investigated. The ORs for each phenotype were synthesized using both fixed-effects and random-effects models for allele-based effects, and also for haplotype effects for FRZB.

RESULTS

A significant random-effects summary OR for knee OA was demonstrated for rs143383 (1.15 [95% confidence interval 1.09-1.22]) (P=9.4x10(-7)), with no significant between-study heterogeneity. Estimates of effect sizes for hip and hand OA were similar, but a large between-study heterogeneity was observed, and statistical significance was borderline (for OA of the hip [P=0.016]) or absent (for OA of the hand [P=0.19]). Analyses for FRZB polymorphisms and haplotypes did not reveal any statistically significant signals, except for a borderline association of rs288326 with hip OA (P=0.019).

CONCLUSION

Evidence of an association between the GDF5 rs143383 polymorphism and OA is substantially strong, but the genetic effects are consistent across different populations only for knee OA. Findings of this collaborative analysis do not support the notion that FRZB rs7775 or rs288326 has any sizable genetic effect on OA phenotypes.

Genome-wide association studies, field synopses, and the development of the knowledge base on genetic variation and human diseases

Genome-wide association studies (GWAS) have led to a rapid increase in available data on common genetic variants and phenotypes and numerous discoveries of new loci associated with susceptibility to common complex diseases. Integrating the evidence from GWAS and candidate gene studies depends on concerted efforts in data production, online publication, database development, and continuously updated data synthesis. Here the authors summarize current experience and challenges on these fronts, which were discussed at a 2008 multidisciplinary workshop sponsored by the Human Genome Epidemiology Network. Comprehensive field synopses that integrate many reported gene-disease associations have been systematically developed for several fields, including Alzheimer's disease, schizophrenia, bladder cancer, coronary heart disease, preterm birth, and DNA repair genes in various cancers. The authors summarize insights from these field synopses and discuss remaining unresolved issues—especially in the light of evidence from GWAS, for which they summarize empirical P-value and effect-size data on 223 discovered associations for binary outcomes (142 with P < 10⁻⁷). They also present a vision of collaboration that builds reliable cumulative evidence for genetic associations with common complex diseases and a transparent, distributed, authoritative knowledge base on genetic variation and human health. As a next step in the evolution of Human Genome Epidemiology reviews, the authors invite investigators to submit field synopses for possible publication in the American Journal of Epidemiology.

The Scientific Foundation for personal genomics: recommendations from a National Institutes of Health-Centers for Disease Control and Prevention multidisciplinary workshop

The increasing availability of personal genomic tests has led to discussions about the validity and utility of such tests and the balance of benefits and harms. A multidisciplinary workshop was convened by the National Institutes of Health and the Centers for Disease Control and Prevention to review the scientific foundation for using personal genomics in risk assessment and disease prevention and to develop recommendations for targeted research. The clinical validity and utility of personal genomics is a moving target with rapidly developing discoveries but little translation research to close the gap between discoveries and health impact. Workshop participants made recommendations in five domains: (1) developing and applying scientific standards for assessing personal genomic tests; (2) developing and applying a multidisciplinary research agenda, including observational studies and clinical trials to fill knowledge gaps in clinical validity and utility; (3) enhancing credible knowledge synthesis and information dissemination to clinicians and consumers; (4) linking scientific findings to evidence-based recommendations for use of personal genomics; and (5) assessing how the concept of personal utility can affect health benefits, costs, and risks by developing appropriate metrics for evaluation. To fulfill the promise of personal genomics, a rigorous multidisciplinary research agenda is needed.

Genome-wide association studies, field synopses, and the development of the knowledge base on genetic variation and human diseases

Genome-wide association studies (GWAS) have led to a rapid increase in available data on common genetic variants and phenotypes and numerous discoveries of new loci associated with susceptibility to common complex diseases. Integrating the evidence from GWAS and candidate gene studies depends on concerted efforts in data production, online publication, database development, and continuously updated data synthesis. Here the authors summarize current experience and challenges on these fronts, which were discussed at a 2008 multidisciplinary workshop sponsored by the Human Genome Epidemiology Network. Comprehensive field synopses that integrate many reported gene-disease associations have been systematically developed for several fields, including Alzheimer's disease, schizophrenia, bladder cancer, coronary heart disease, preterm birth, and DNA repair genes in various cancers. The authors summarize insights from these field synopses and discuss remaining unresolved issues -- especially in the light of evidence from GWAS, for which they summarize empirical P-value and effect-size data on 223 discovered associations for binary outcomes (142 with P < 10(-7)). They also present a vision of collaboration that builds reliable cumulative evidence for genetic associations with common complex diseases and a transparent, distributed, authoritative knowledge base on genetic variation and human health. As a next step in the evolution of Human Genome Epidemiology reviews, the authors invite investigators to submit field synopses for possible publication in the American Journal of Epidemiology.

XRCC1 Arg399Gln, Arg194Trp and Arg280His polymorphisms in breast cancer risk: a meta-analysis

X-ray repair cross-complementing group 1 (XRCC1) plays an important role in base excision and single-strand break repair, as a scaffold protein that brings together proteins of the DNA repair complex, and appears to be a candidate for cancer risk. However, studies on the association between polymorphisms in this protein and cancer have yielded conflicting results. We performed a meta-analysis to investigate the association between the breast cancer and the XRCC1 polymorphisms Arg194Trp (9411 cases and 9783 controls), Arg399Gln (22 481 cases and 23 905 controls) and Arg280His (6062 cases and 5864 controls) in different inheritance models. Our analysis suggested that Arg399Gln was associated with a trend of increased breast cancer risk when using both dominant [odds ratio (OR) = 1.06, 95% confidence interval (CI): 1.00-1.13] and recessive models (OR = 1.12, 95% CI: 1.02-1.23) to analyse the data. In ethnic subgroups and using recessive model analysis: Arg399Gln increased breast cancer risk in Asians (OR = 1.26, 95% CI: 0.96-1.64) and Africans (OR = 1.80, 95% CI: 0.97-3.32), and also while only slightly increasing the breast cancer risk in Caucasians (OR = 1.08, 95% CI: 0.95-1.22). However, Arg194Trp (recessive model, OR = 0.95, 95% CI: 0.75-1.20) and Arg280His (recessive model, OR = 1.28, 95% CI: 0.64-2.55) did not appear to be risk factors for breast cancer. Larger scale primary studies are required to further evaluate the interaction of XRCC1 polymorphisms and breast cancer risk in specific populations.

A primate-specific, brain isoform of KCNH2 affects cortical physiology, cognition, neuronal repolarization and risk of schizophrenia

Organized neuronal firing is critical for cortical processing and is disrupted in schizophrenia. Using 5’ RACE in human brain, we identified a primate-specific isoform (3.1) of the K⁺-channel KCNH2 that modulates neuronal firing. KCNH2-3.1 mRNA levels are comparable to KCNH2-1A in brain, but 1000-fold lower in heart. In schizophrenic hippocampus, KCNH2-3.1 expression is 2.5-fold greater than KCNH2-1A. A meta-analysis of 5 clinical samples (367 families, 1158 unrelated cases, 1704 controls) shows association of SNPs in KCNH2 with schizophrenia. Risk-associated alleles predict lower IQ scores and speed of cognitive processing, altered memory-linked fMRI signals, and increased KCNH2-3.1 expression in post-mortem hippocampus. KCNH2-3.1 lacks a domain critical for slow channel deactivation. Overexpression of KCNH2-3.1 in primary cortical neurons induces a rapidly deactivating K⁺ current and a high-frequency, non-adapting firing pattern. These results identify a novel KCNH2 channel involved in cortical physiology, cognition, and psychosis, providing a potential new psychotherapeutic drug target.

Investigation of DNA polymorphisms in SMAD genes for genetic predisposition to diabetic nephropathy in patients with type 1 diabetes mellitus

AIMS/HYPOTHESIS

SMAD proteins are involved in multiple signalling pathways and are key modulators of gene expression. We hypothesised that genetic variation in selected SMAD genes contributes to susceptibility to diabetic nephropathy.

METHODS

We selected 13 haplotype tag (ht) single nucleotide polymorphisms (SNPs) from 67 variants identified by resequencing the SMAD2 and SMAD3 genes. For SMAD1, SMAD4 and SMAD5 genes, genotype data were downloaded for 217 SNPs from Phase II of the International HapMap project. Of these, 85 SNPs met our inclusion criteria, resulting in the selection of 13 tag SNPs for further investigation. A case-control approach was employed, using 267 nephropathic patients and 442 controls with type 1 diabetes from Ireland. Two further populations (totalling 1,407 patients, 2,238 controls) were genotyped to validate initial findings. Genotyping was conducted using iPLEX, TaqMan and gel electrophoresis.

RESULTS

The distribution of genotypes was in Hardy-Weinberg equilibrium. Analysis by the chi(2) test of genotype and allele frequencies in patients versus controls in the Irish population (n = 709) revealed evidence for the association of one allele at 5% level of significance (rs10515478, p(uncorrected) = 0.006; p(corrected) = 0.04). This finding represents a relatively small difference in allele frequency of 6.4% in the patient group compared with 10.7% in the control group; this difference was not supported in subsequent investigations using DNA from European individuals with similar phenotypic characteristics.

CONCLUSIONS/INTERPRETATION

We selected an appropriate subset of variants for the investigation of common genetic risk factors and assessed SMAD1 to SMAD5 genes for association with diabetic nephropathy. We conclude that common polymorphisms in these genes do not strongly influence genetic susceptibility to diabetic nephropathy in white individuals with type 1 diabetes mellitus.

Systematic reviews of genetic association studies. Human Genome Epidemiology Network

Gurdeep S. Sagoo and colleagues describe key components of the methodology for undertaking systematic reviews and meta-analyses of genetic association studies.

Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts

BACKGROUND

The primary aim of genome-wide association studies is to identify novel genetic loci associated with interindividual variation in the levels of risk factors, the degree of subclinical disease, or the risk of clinical disease. The requirement for large sample sizes and the importance of replication have served as powerful incentives for scientific collaboration. Methods- The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium was formed to facilitate genome-wide association studies meta-analyses and replication opportunities among multiple large population-based cohort studies, which collect data in a standardized fashion and represent the preferred method for estimating disease incidence. The design of the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium includes 5 prospective cohort studies from the United States and Europe: the Age, Gene/Environment Susceptibility-Reykjavik Study, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study. With genome-wide data on a total of about 38 000 individuals, these cohort studies have a large number of health-related phenotypes measured in similar ways. For each harmonized trait, within-cohort genome-wide association study analyses are combined by meta-analysis. A prospective meta-analysis of data from all 5 cohorts, with a properly selected level of genome-wide statistical significance, is a powerful approach to finding genuine phenotypic associations with novel genetic loci.

CONCLUSIONS

The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and collaborating non-member studies or consortia provide an excellent framework for the identification of the genetic determinants of risk factors, subclinical-disease measures, and clinical events.

Meta-analysis in genome-wide association studies

The advent of genome-wide association studies has allowed considerable progress in the identification and robust replication of common gene variants that confer susceptibility to common diseases and other phenotypes of interest. These genetic effect sizes are almost invariably moderate to small in magnitude and single studies, even if large, are underpowered to detect them with confidence. Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations. In this review, we discuss the key methodological issues in the set-up, information gathering and processing, and analysis of meta-analyses of genome-wide association datasets. We illustrate, as an example, the application of meta-analysis methods in the elucidation of common genetic variants associated with Type 2 diabetes. Finally, we discuss the prospects and caveats for future application of meta-analysis methods in the genome-wide setting.

Genetic aspects of vasovagal syncope: a systematic review of current evidence

Knowledge on the aetiology of vasovagal syncope (VVS) is of great importance to optimize its diagnostic and therapeutic options. To unravel the largely unknown pathophysiology, studies on genetic aspects of VVS can be of use. This systematic review on all available literature aims to provide an overview of the current knowledge of VVS genetics. The MEDLINE and EMBASE database were systematically searched for all studies discussing genetic factors as a cause of VVS. Hereditary aspects of VVS were studied in 19 studies. Six studies determined a positive family history in, respectively, 19-90% of the VVS patients. These numbers, however, are not higher than the cumulative incidence of VVS in the general population (35-39%). Four studies examined potential genetic polymorphisms associated with VVS. Only a Gly389 allele was more frequently present in VVS patients with a positive HUT test, although the significance level was set much higher than usual in genetic studies, and this result has not been replicated so far. Knowledge on genetic aspects of VVS could be very useful in clinical practice and research, but the current evidence that it has a genetic basis is not very strong.

New findings from genetic association studies of schizophrenia

In the past 20 years, association studies of schizophrenia have evolved from analyses in lesser than 100 subjects of one or two markers in candidate genes to systematic analyses of association at a genome-wide level in samples of thousands of subjects. During this process, much of the emergent literature has been difficult to interpret and definitive findings that have met with universal acceptance have been elusive, largely because studies have been underpowered for such conclusions to be drawn. Nevertheless, in the course of the past few years, a few promising candidate genes have been reported for which the evidence is positive across multiple studies, and more recently, genome-wide association studies have yielded findings of a compelling nature. It is clear that genetic studies in schizophrenia have borne fruit, a process that can be expected to accelerate in the next few years, and that these findings are providing new avenues for research into the pathophysiology of this poorly understood disorder.

A field synopsis on low-penetrance variants in DNA repair genes and cancer susceptibility

BACKGROUND

Several genes encoding for DNA repair molecules implicated in maintaining genomic integrity have been proposed as cancer-susceptibility genes. Although efforts have been made to create synopses for specific fields that summarize the data from genetic association studies, such an overview is not available for genes involved in DNA repair.

METHODS

We have created a regularly updated database of studies addressing associations between DNA repair gene variants (excluding highly penetrant mutations) and different types of cancer. Using 1087 datasets and publicly available data from genome-wide association platforms, meta-analyses using dominant and recessive models were performed on 241 associations between individual variants and specific cancer types that had been tested in two or more independent studies. The epidemiological strength of each association was graded with Venice criteria that assess amount of evidence, replication, and protection from bias. All statistical tests were two-sided.

RESULTS

Thirty-one nominally statistically significant (ie, P < .05 without adjustment for multiple comparisons) associations were recorded for 16 genes in dominant and/or recessive model analyses (BRCA2, CCND1, ERCC1, ERCC2, ERCC4, ERCC5, MGMT, NBN, PARP1, POLI, TP53, XPA, XRCC1, XRCC2, XRCC3, and XRCC4). XRCC1, XRCC2, TP53, and ERCC2 variants were each nominally associated with several types of cancer. Three associations were graded as having "strong" credibility, another four had modest credibility, and 24 had weak credibility based on Venice criteria. Requiring more stringent P values to account for multiplicity of comparisons, only the associations of ERCC2 codon 751 (recessive model) and of XRCC1 -77 T>C (dominant model) with lung cancer had P <or= .0001 and retained P <or= .001 even when the first published studies on the respective associations were excluded.

CONCLUSIONS

We have conducted meta-analyses of 241 associations between variants in DNA repair genes and cancer and have found sparse association signals with strong epidemiological credibility. This synopsis offers a model to survey the current status and gaps in evidence in the field of DNA repair genes and cancer susceptibility, may indicate potential pleiotropic activity of genes and gene pathways, and may offer mechanistic insights in carcinogenesis.

Seven lipoprotein lipase gene polymorphisms, lipid fractions, and coronary disease: a HuGE association review and meta-analysis

Lipoprotein lipase (LPL) is a key enzyme in lipoprotein metabolism and a major candidate gene for coronary heart disease (CHD). The authors assessed associations between 7 LPL polymorphisms and lipid fractions and CHD risk in population-based cohort, case-control, and cross-sectional studies published by January 2007. Meta-analyses of 22,734 CHD cases and 50,177 controls in 89 association studies focused on the relations of the T-93G (rs1800590), D9N (rs1801177), G188E, N291S (rs268), PvuII (rs285), HindIII (rs320), and S447X (rs328) polymorphisms to high density lipoprotein cholesterol, triglycerides, myocardial infarction, or coronary stenosis. Carriers of 9N or 291S had modestly adverse lipid profiles. Carriers of the less common allele of HindIII or of 447X had modestly advantageous profiles. The combined odds ratio for CHD among carriers was 1.33 (95% confidence interval (CI): 1.14, 1.56) for 9N, 1.07 (95% CI: 0.96, 1.20) for 291S, 0.89 (95% CI: 0.81, 0.98) for the less common HindIII allele, and 0.84 (95% CI: 0.75, 0.94) for 447X. For T-93G (odds ratio (OR) = 1.22, 95% CI: 0.98, 1.52) and PvuII (OR = 0.96, 95% CI: 0.89, 1.04), there were null associations with lipid levels or CHD risk; information on G188E was limited (OR = 2.80, 95% CI: 0.88, 8.87). The study of LPL genotypes confirms the existence of close interrelations between high density lipoprotein cholesterol and triglyceride pathways. The influence of these genotypes on CHD risk warrants further investigation.

NQO1 polymorphisms and de novo childhood leukemia: a HuGE review and meta-analysis

Polymorphisms in NQO1, a gene coding for the phase II enzyme involved in the detoxification of quinone carcinogens, have been associated with childhood leukemia in some studies, although the observed direction and magnitude of effects have been inconsistent. Therefore, the authors systematically reviewed all published reports describing the effect of NQO1 in de novo childhood leukemia and conducted a meta-analysis of 7 case-control studies that examined the association between NQO1*2 and childhood leukemia. Although a family-based study previously demonstrated over-transmission of this allele among childhood acute lymphoblastic leukemia cases, the meta-analysis showed that the presence of a NQO1*2 variant allele, which reduces the activity of the enzyme NAD(P)H:quinone oxidoreductase 1 (NQO1), had no significant effect on childhood leukemia. However, there was an increased risk associated with having at least 1 copy of the NQO1*2 allele in a subset of cases with MLL translocations (summary odds ratio = 1.39, 95% confidence interval: 0.98, 1.97). Heterogeneity between studies may be due to differences in population exposures to NQO1 substrates and small sample sizes, as well as potential population stratification in non-family-based studies. Therefore, further research is warranted on the role of NQO1 polymorphisms in the etiology of childhood leukemia, especially among MLL-positive leukemias.

Systems Epidemiology in Cancer

Prospective studies in cancer epidemiology have conserved their study design over the last decades. In this context, current epidemiologic studies investigating gene-environment interactions are based on biobank for the analysis of genetic variation and biomarkers, using notified cancer as outcome. These studies result from the use of high-throughput technologies rather than from the development of novel design strategies. In this article, we propose the globolomic design to run integrated analyses of cancer risk covering the major -omics in blood and tumor tissue. We defined this design as an extension of the existing prospective design by collecting tissue and blood samples at time of diagnosis, including biological material suitable for transcriptome analysis. The globolomic design opens up for several new analytic strategies and, where gene expression profiles could be used to verify mechanistic information from experimental biology, adds a new dimension to causality in epidemiology. This could improve, for example, the interpretation of risk estimates related to single nucleotide polymorphisms in gene-environment studies by changing the criterion of biological plausibility from a subjective discussion of in vitro information to observational data of human in vivo gene expression. This ambitious design should consider the complexity of the multistage carcinogenic process, the latency time, and the changing lifestyle of the cohort members. This design could open the new research discipline of systems epidemiology, defined in this article as a counterpart to systems biology. Systems epidemiology with a focus on gene functions challenges the current concept of biobanking, which focuses mainly on DNA analyses. (Cancer Epidemiol Biomarkers Prev 2008;17(11):2954–7)

Recognition of genetic factors influencing the progression of hepatitis C : potential for personalized therapy

Infection with hepatitis C virus (HCV) is a major cause of chronic liver disease. Hepatic fibrosis may develop in subjects with chronic HCV infection, culminating in cirrhosis and an increased risk of hepatocellular carcinoma. The rate of development of fibrosis varies substantially between individuals; while it is influenced by a number of demographic and environmental factors, these account for only a small proportion of the variability. There are no clinical markers or tests that predict the rate of fibrosis progression in an individual subject. Thus, there has been increasing interest in the influence of host genetic factors on the rate of disease progression, and whether a genetic signature can be developed to reliably identify individuals at risk of severe disease. Numerous case-control, candidate gene, allele-association studies have examined the relationship between host single nucleotide polymorphisms or other genetic mutations and fibrosis in patients with chronic HCV infection. However, these studies have generally been irreproducible and disappointing. As seen with genetic studies for other diseases, small study cohorts and poor study design have contributed to limited meaningful findings. The successful determination of genetic signatures for fibrosis progression in chronic HCV will require multicenter collaborations using genome-wide association studies, with large, phenotypically well-defined sample sets. While these studies will require a significant financial commitment, a successful outcome offers the potential for personalized therapy and better patient management.

Thirty years of Alzheimer's disease genetics: the implications of systematic meta-analyses

The genetic underpinnings of Alzheimer's disease (AD) remain largely elusive despite early successes in identifying three genes that cause early-onset familial AD (those that encode amyloid precursor protein (APP) and the presenilins (PSEN1 and PSEN2)), and one genetic risk factor for late-onset AD (the gene that encodes apolipoprotein E (APOE)). A large number of studies that aimed to help uncover the remaining disease-related loci have been published in recent decades, collectively proposing or refuting the involvement of over 500 different gene candidates. Systematic meta-analyses of these studies currently highlight more than 20 loci that have modest but significant effects on AD risk. This Review discusses the putative pathogenetic roles and common biochemical pathways of some of the most genetically and biologically compelling of these potential AD risk factors.

Evaluation of the potential excess of statistically significant findings in published genetic association studies: application to Alzheimer's disease

The authors evaluated whether there is an excess of statistically significant results in studies of genetic associations with Alzheimer's disease reflecting either between-study heterogeneity or bias. Among published articles on genetic associations entered into the comprehensive AlzGene database (www.alzgene.org) through January 31, 2007, 1,348 studies included in 175 meta-analyses with 3 or more studies each were analyzed. The number of observed studies (O) with statistically significant results (P = 0.05 threshold) was compared with the expected number (E) under different assumptions for the magnitude of the effect size. In the main analysis, the plausible effect size of each association was the summary effect presented in the respective meta-analysis. Overall, 19 meta-analyses (all with eventually nonsignificant summary effects) had a documented excess of O over E: Typically single studies had significant effects pointing in opposite directions and early summary effects were dissipated over time. Across the whole domain, O was 235 (17.4%), while E was 164.8 (12.2%) (P < 10(-6)). The excess showed a predilection for meta-analyses with nonsignificant summary effects and between-study heterogeneity. The excess was seen for all levels of statistical significance and also for studies with borderline P values (P = 0.05-0.10). The excess of significant findings may represent significance-chasing biases in a setting of massive testing.

Calibration of credibility of agnostic genome-wide associations

Genome-wide testing platforms are increasingly used to promote "agnostic" approaches to the discovery of gene variants associated with the risk of many common diseases and quantitative traits. The early track record of genome-wide association (GWA) studies suggests that some proposed associations are replicated quite consistently with large-scale subsequent evidence from multiple studies, others have a more inconsistent replication record, some have failed to be replicated by independent investigators and many more early proposed associations await further replication. An important question is how to calibrate the credibility of these postulated associations. A simple Bayesian method is applied here to achieve such calibration. The variability of the estimated credibility is examined under different assumptions. Empirical examples are drawn from existing GWA studies. It is demonstrated that the credibility of different proposed associations can cover a very wide range. The credibility of specific associations usually remains relatively robust when different plausible assumptions are made (within a reasonable range) for the prior odds of an association being true, or the magnitude of the anticipated effect size for genetic associations. Heterogeneity and bias assumptions can have a more major impact on the credibility estimates and thus they need very careful consideration in each case. Credibility calibration may be used in conjunction with qualitative criteria for the appraisal of the cumulative evidence that take into consideration the amount, consistency, and protection from bias in the data.

Genetic research in schizophrenia: new tools and future perspectives

Genetically, schizophrenia is a complex disease whose pathogenesis is likely governed by a number of different risk factors. While substantial efforts have been made to identify the underlying susceptibility alleles over the past 2 decades, they have been of only limited success. Each year, the field is enriched with nearly 150 additional genetic association studies, each of which either proposes or refutes the existence of certain schizophrenia genes. To facilitate the evaluation and interpretation of these findings, we have recently created a database for genetic association studies in schizophrenia ("SzGene"; available at http://www.szgene.org). In addition to systematically screening the scientific literature for eligible studies, SzGene also reports the results of allele-based meta-analyses for polymorphisms with sufficient genotype data. Currently, these meta-analyses highlight not only over 20 different potential schizophrenia genes, many of which represent the "usual suspects" (eg, various dopamine receptors and neuregulin 1), but also several that were never meta-analyzed previously. All the highlighted loci contain at least one variant showing modest (summary odds ratios approximately 1.20 [range 1.06-1.45]) but nominally significant risk effects. This review discusses some of the strengths and limitations of the SzGene database, which could become a useful bioinformatics tool within the schizophrenia research community.

Systematic meta-analyses and field synopsis of genetic association studies in schizophrenia: the SzGene database

In an effort to pinpoint potential genetic risk factors for schizophrenia, research groups worldwide have published over 1,000 genetic association studies with largely inconsistent results. To facilitate the interpretation of these findings, we have created a regularly updated online database of all published genetic association studies for schizophrenia ('SzGene'). For all polymorphisms having genotype data available in at least four independent case-control samples, we systematically carried out random-effects meta-analyses using allelic contrasts. Across 118 meta-analyses, a total of 24 genetic variants in 16 different genes (APOE, COMT, DAO, DRD1, DRD2, DRD4, DTNBP1, GABRB2, GRIN2B, HP, IL1B, MTHFR, PLXNA2, SLC6A4, TP53 and TPH1) showed nominally significant effects with average summary odds ratios of approximately 1.23. Seven of these variants had not been previously meta-analyzed. According to recently proposed criteria for the assessment of cumulative evidence in genetic association studies, four of the significant results can be characterized as showing 'strong' epidemiological credibility. Our project represents the first comprehensive online resource for systematically synthesized and graded evidence of genetic association studies in schizophrenia. As such, it could serve as a model for field synopses of genetic associations in other common and genetically complex disorders.

Genome-wide association studies for complex traits: consensus, uncertainty and challenges

The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.

A critical appraisal of the scientific basis of commercial genomic profiles used to assess health risks and personalize health interventions

Predictive genomic profiling used to produce personalized nutrition and other lifestyle health recommendations is currently offered directly to consumers. By examining previous meta-analyses and HuGE reviews, we assessed the scientific evidence supporting the purported gene-disease associations for genes included in genomic profiles offered online. We identified seven companies that offer predictive genomic profiling. We searched PubMed for meta-analyses and HuGE reviews of studies of gene-disease associations published from 2000 through June 2007 in which the genotypes of people with a disease were compared with those of a healthy or general-population control group. The seven companies tested at least 69 different polymorphisms in 56 genes. Of the 56 genes tested, 24 (43%) were not reviewed in meta-analyses. For the remaining 32 genes, we found 260 meta-analyses that examined 160 unique polymorphism-disease associations, of which only 60 (38%) were found to be statistically significant. Even the 60 significant associations, which involved 29 different polymorphisms and 28 different diseases, were generally modest, with synthetic odds ratios ranging from 0.54 to 0.88 for protective variants and from 1.04 to 3.2 for risk variants. Furthermore, genes in cardiogenomic profiles were more frequently associated with noncardiovascular diseases than with cardiovascular diseases, and though two of the five genes of the osteogenomic profiles did show significant associations with disease, the associations were not with bone diseases. There is insufficient scientific evidence to conclude that genomic profiles are useful in measuring genetic risk for common diseases or in developing personalized diet and lifestyle recommendations for disease prevention.

Genetic association studies in nursing practice and scholarship

PURPOSE

To explore the role of genetic association studies in risk assessment for common complex diseases.

ORGANIZING FRAMEWORK

An introduction to the types of genetic association studies is followed by a discussion of their potential use in risk assessment for age-related macular degeneration and type 2 diabetes mellitus. The benefits and limitations of this burgeoning technology are explored and related to nursing practice and scholarship.

CONCLUSIONS

Nurses in practice must be prepared to assist clients with decisions about seeking and interpreting results from genetic association studies and nurse researchers must apply current guidelines for conducting robust studies and applying the results of such studies in clinical practice.

CLINICAL RELEVANCE

Data collected from genetic association studies will increasingly be used to identify novel prevention and treatment strategies for many complex diseases. An understanding of the principles that underlie this new science is essential for nurses in all areas of clinical practice as they design, test, and implement appropriate intervention and prevention strategies based on genetic association studies.

Pharmacogenetics of major depression: insights from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial

Major depression is a serious mental illness frequently associated with devastating consequences for those affected. Suicide rates are significantly elevated, creating a sense of urgency to identify effective yet safe treatment options. A plethora of antidepressants are available on the market today, designed to act on different neurotransmitter systems in the brain, providing the clinician with several treatment strategies. There is, however, very little guidance as to which antidepressant may be most successful in a certain individual. Biomarkers that can predict treatment outcome would thus be of great value, shortening the time until remission and reducing costs for the healthcare system by reducing unsuccessful treatment attempts. The proven contribution of heredity to major depression risk suggests that genetic markers may be good biomarkers for treatment outcome.The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study and a large ancillary pharmacogenetic study in 1953 STAR*D participants constitute the largest effort to date to identify genetic predictors of antidepressant treatment outcome. In this review, the results of candidate gene studies carried out so far are summarized and discussed, and some future directions are proposed.