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Type-2 diabetes is a complex disease modified by a number of environmental and genetic factors that contribute at varying degrees to the final phenotype and possibly interact with each other. Deciphering the genetic background of the disease serves multiple goals ranging from expanding our knowledge on the disease pathogenesis and identifying future targets for drug development to successfully personalizing clinical disease prediction and prognosis. In the present review, we aimed to systematically appraise the current evidence from genome-wide association studies (GWAS) on type- 2 diabetes, identify the gene targets that have been assessed to-date, and discuss issues that stem from the rapid growth of this literature. Our search identified more than 60 recently identified loci implicated with type-2 diabetes and related traits assessed in populations of European and Asian ancestry. A considerable number of the proposed genes seem to be related to beta-cell development and function, but there are several genes identified as "diabetes-genes" whose underlying pathway linked to diabetes remains poorly understood. Despite the increasing numbers of identified genetic markers, a large proportion of the observed type-2 diabetes heritability remains unexplained; larger GWAS on enhanced genotyping platforms, refined ascertainment of the characteristics of the populations under study and additional information from whole-genome sequencing will contribute to a more comprehensive view of the genetic architecture of the disease. This information is also anticipated to improve the predictive ability of multiple-loci genetic risk scores that will eventually be able to identify disease susceptibility over and above the traditional non-genetic risk factors.

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 x 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.

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.

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.

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 This work was made possible by a grant to ECNIS (Environmental Cancer Risk, Nutrition and Individual Susceptibility), a network of excellence operating within the European Union 6th Framework Program, Priority 5: “Food Quality and Safety” (contract no. 513943), and by a grant of the compagnia di San Paolo, of the Italian Association for Cancer Research, Italy, and of the Piedmont Region Progetti di Ricerca Sanitaria Finalizzata. F. K. Kavvoura is supported by a PENED training grant cofi nanced by EU — European Social Fund (75%) and the Greek Ministry of Development – General Secretariat of Research and Technology (25%).

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.