Fotini K. Kavvoura

Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies

Bone mineral density (BMD) is a heritable complex trait used in the clinical diagnosis of osteoporosis and the assessment of fracture risk. We performed meta-analysis of five genome-wide association studies of femoral neck and lumbar spine BMD in 19,195 subjects of Northern European descent. We identified 20 BMD loci that reached genome-wide significance (GWS; P < 5 × 10−8), of which 13 map to regions not previously associated with this trait: 1p31.3 (GPR177), 2p21 (SPTBN1), 3p22 (CTNNB1), 4q21.1 (MEPE), 5q14 (MEF2C), 7p14 (STARD3NL), 7q21.3 (FLJ42280), 11p11.2 (LRP4, ARHGAP1, F2), 11p14.1 (DCDC5), 11p15 (SOX6), 16q24 (FOXL1), 17q21 (HDAC5) and 17q12 (CRHR1). The meta-analysis also confirmed at GWS level seven known BMD loci on 1p36 (ZBTB40), 6q25 (ESR1), 8q24 (TNFRSF11B), 11q13.4 (LRP5), 12q13 (SP7), 13q14 (TNFSF11) and 18q21 (TNFRSF11A). The many SNPs associated with BMD map to genes in signaling pathways with relevance to bone metabolism and highlight the complex genetic architecture that underlies osteoporosis and variation in BMD.

Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics : The PDGene database

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinsons disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10−8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10−8). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls.

Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider in the appraisal of evidence and potential biases that may interfere. We describe the relative merits and caveats for common methods used to trace inconsistency across studies along with possible reasons for non-replication of proposed associations. Different statistical models may be employed to combine data and some common misconceptions may arise in the process. Several meta-analysis diagnostics are often applied or misapplied in the literature, and we comment on their use and limitations. An alternative to overcome limitations arising from retrospective combination of data from published studies is to create networks of research teams working in the same field and perform collaborative meta-analyses of individual participant data, ideally on a prospective basis. We discuss the advantages and the challenges inherent in such collaborative approaches. Meta-analysis can be a useful tool in dissecting the genetics of complex diseases and traits, provided its methods are properly applied and interpreted.

Local literature bias in genetic epidemiology: an empirical evaluation of the Chinese literature.

Background Postulated epidemiological associations are subject to several biases. We evaluated whether the Chinese literature on human genome epidemiology may offer insights on the operation of selective reporting and language biases. Methods and Findings We targeted 13 gene-disease associations, each already assessed by meta-analyses, including at least 15 non-Chinese studies. We searched the Chinese Journal Full-Text Database for additional Chinese studies on the same topics. We identified 161 Chinese studies on 12 of these gene-disease associations; only 20 were PubMed-indexed (seven English full-text). Many studies (14–35 per topic) were available for six topics, covering diseases common in China. With one exception, the first Chinese study appeared with a time lag (2–21 y) after the first non-Chinese study on the topic. Chinese studies showed significantly more prominent genetic effects than non-Chinese studies, and 48% were statistically significant per se, despite their smaller sample size (median sample size 146 versus 268, p < 0.001). The largest genetic effects were often seen in PubMed-indexed Chinese studies (65% statistically significant per se). Non-Chinese studies of Asian-descent populations (27% significant per se) also tended to show somewhat more prominent genetic effects than studies of non-Asian descent (17% significant per se). Conclusion Our data provide evidence for the interplay of selective reporting and language biases in human genome epidemiology. These biases may not be limited to the Chinese literature and point to the need for a global, transparent, comprehensive outlook in molecular population genetics and epidemiologic studies in general.

Selection in reported epidemiological risks: an empirical assessment.

Background Epidemiological studies may be subject to selective reporting, but empirical evidence thereof is limited. We empirically evaluated the extent of selection of significant results and large effect sizes in a large sample of recent articles. Methods and Findings We evaluated 389 articles of epidemiological studies that reported, in their respective abstracts, at least one relative risk for a continuous risk factor in contrasts based on median, tertile, quartile, or quintile categorizations. We examined the proportion and correlates of reporting statistically significant and nonsignificant results in the abstract and whether the magnitude of the relative risks presented (coined to be consistently ≥1.00) differs depending on the type of contrast used for the risk factor. In 342 articles (87.9%), ≥1 statistically significant relative risk was reported in the abstract, while only 169 articles (43.4%) reported ≥1 statistically nonsignificant relative risk in the abstract. Reporting of statistically significant results was more common with structured abstracts, and was less common in US-based studies and in cancer outcomes. Among 50 randomly selected articles in which the full text was examined, a median of nine (interquartile range 5–16) statistically significant and six (interquartile range 3–16) statistically nonsignificant relative risks were presented (p = 0.25). Paradoxically, the smallest presented relative risks were based on the contrasts of extreme quintiles; on average, the relative risk magnitude was 1.41-, 1.42-, and 1.36-fold larger in contrasts of extreme quartiles, extreme tertiles, and above-versus-below median values, respectively (p < 0.001). Conclusions Published epidemiological investigations almost universally highlight significant associations between risk factors and outcomes. For continuous risk factors, investigators selectively present contrasts between more extreme groups, when relative risks are inherently lower.

Reporting of human genome epidemiology (HuGE) association studies: an empirical assessment

BackgroundSeveral thousand human genome epidemiology association studies are published every year investigating the relationship between common genetic variants and diverse phenotypes. Transparent reporting of study methods and results allows readers to better assess the validity of study findings. Here, we document reporting practices of human genome epidemiology studies.MethodsArticles were randomly selected from a continuously updated database of human genome epidemiology association studies to be representative of genetic epidemiology literature. The main analysis evaluated 315 articles published in 2001–2003. For a comparative update, we evaluated 28 more recent articles published in 2006, focusing on issues that were poorly reported in 2001–2003.ResultsDuring both time periods, most studies comprised relatively small study populations and examined one or more genetic variants within a single gene. Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification. Statistical power, the use of unrelated study participants, and the use of replicate samples were reported more often in articles published during 2006 when compared with the earlier sample.ConclusionWe conclude that many items needed to assess error and bias in human genome epidemiology association studies are not consistently reported. Although some improvements were seen over time, reporting guidelines and online supplemental material may help enhance the transparency of this literature.

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 Alzheimers 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.

Exposure to pesticides and diabetes: A systematic review and meta-analysis

BACKGROUND Diabetes mellitus has a multifactorial pathogenesis with a strong genetic component as well as many environmental and lifestyle influences. Emerging evidence suggests that environmental contaminants, including pesticides, might play an important role in the pathogenesis of diabetes. OBJECTIVES We performed a systematic review and meta-analysis of observational studies that assessed the association between exposure to pesticides and diabetes and we examined the presence of heterogeneity and biases across available studies. METHODS A comprehensive literature search of peer-reviewed original research pertaining to pesticide exposure and diabetes, published until 30st May 2015, with no language restriction, was conducted. Eligible studies were those that investigated potential associations between pesticides and diabetes without restrictions on diabetes type. We included cohort studies, case-control studies and cross-sectional studies. We extracted information on study characteristics, type of pesticide assessed, exposure assessment, outcome definition, effect estimate and sample size. RESULTS We identified 22 studies assessing the association between pesticides and diabetes. The summary OR for the association of top vs. bottom tertile of exposure to any type of pesticide and diabetes was 1.58 (95% CI: 1.32-1.90, p=1.21×10(-6)), with large heterogeneity (I(2)=66.8%). Studies evaluating Type 2 diabetes in particular (n=13 studies), showed a similar summary effect comparing top vs. bottom tertiles of exposure: 1.61 (95% CI 1.37-1.88, p=3.51×10(-9)) with no heterogeneity (I(2)=0%). Analysis by type of pesticide yielded an increased risk of diabetes for DDE, heptachlor, HCB, DDT, and trans-nonachlor or chlordane. CONCLUSIONS The epidemiological evidence, supported by mechanistic studies, suggests an association between exposure to organochlorine pesticides and Type 2 diabetes.

Concordance of functional in vitro data and epidemiological associations in complex disease genetics

Purpose: We aimed to assess whether epidemiological evidence on genetic associations for complex diseases concord with in vitro functional data.Methods: We examined 36 studies on bi-allelic markers and 23 studies on haplotypes where investigators had addressed both epidemiological associations and the functional effect of the same gene variants in luciferase reporter systems in vitro.Results: There was no correlation between epidemiological odds ratios and luciferase activity ratios (−0.09, P = 0.60). Luciferase activity ratios could not tell whether a probed epidemiologic association would be significant or not (area under receiver operating characteristics curve, 0.52). Luciferase results usually were qualitatively similar across cell lines and experimental conditions, with some exceptions. A luciferase activity ratio of 1.44 adequately separated statistically significant from non-significant functional differences (area under receiver operating characteristics curve, 0.95). Binary and continuous disease outcomes usually gave concordant results; other in vitro methods, in particular EMSA, agreed with luciferase results. Selective reporting and use of different variants and contrasts between functional and epidemiological analyses were common in these studies.Conclusions: In vitro biological data and epidemiology provide independent lines of evidence on complex diseases. We provide suggestions for improving the design and reporting of studies addressing both in vitro and epidemiological effects.

Ala45Thr polymorphism of the NEUROD1 gene and diabetes susceptibility: a meta-analysis.

A meta-analysis assessed whether the Ala45Thr polymorphism of the neurogenic differentiation 1 (NEUROD1) gene is associated with increased risk of diabetes mellitus type 1 (T1D) or type 2 (T2D). Fourteen case-control studies were analyzed, including genotype data on 3,057 patients with diabetes (T1D n=1,213, T2D n=1,844) and 2,446 controls. Overall and race-specific summary odds ratios (ORs) were obtained with fixed and random effects models. The Thr allele did not significantly increase the overall risk for T1D (OR 1.27 [0.94–1.71], P=0.12) or T2D (OR 1.07 [0.90–1.28], P=0.46). The Thr allele conferred increased susceptibility in subjects of Asian racial descent to T1D (OR 1.88 [1.10–3.21], P=0.020), but not to T2D (OR 1.08 [0.74–1.56], P=0.70). There was no association in subjects of European descent (OR 0.97 [0.76–1.23], P=0.80 for T1D; OR 1.03 [0.88–1.21], P=0.68 for T2D). Larger studies seemed to show more conservative estimates for the association with T1D (P=0.083). The Ala45Thr polymorphism of the NEUROD1 gene has no effect on susceptibility to T2D. It may however be a risk factor for susceptibility to T1D, in particular for subjects of Asian descent, although bias cannot be totally excluded.