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BackgroundThe pathogenesis of obesity is reportedly related to variations in the fat mass and an obesity-associated gene (FTO); however, as the number of reports increases, particularly with respect to varying ethnicities, there is a need to determine more precisely the effect sizes in each ethnic group. In addition, some reports have claimed ethnic-specific associations with alternative SNPs, and to that end there has been a degree of confusion.MethodsWe searched PubMed, MEDLINE, Web of Science, EMBASE, and BIOSIS Preview to identify studies investigating the associations between the five polymorphisms and obesity risk. Individual study odds ratios (OR) and their 95% confidence intervals (CI) were estimated using per-allele comparison. Summary ORs were estimated using a random effects model.ResultsWe identified 59 eligible case-control studies in 27 articles, investigating 41,734 obesity cases and 69,837 healthy controls. Significant associations were detected between obesity risk and the five polymorphisms: rs9939609 (OR: 1.31, 95% CI: 1.26 to 1.36), rs1421085 (OR: 1.43, 95% CI: 1.33 to 1.53), rs8050136 (OR: 1.25, 95% CI: 1.13 to 1.38), rs17817449 (OR: 1.54, 95% CI: 1.41 to 1.68), and rs1121980 (OR: 1.34, 95% CI: 1.10 to 1.62). Begg's and Egger's tests provided no evidence of publication bias for the polymorphisms except rs1121980. There is evidence of higher heterogeneity, with I2 test values ranging from 38.1% to 84.5%.ConclusionsThis meta-analysis suggests that FTO may represent a low-penetrance susceptible gene for obesity risk. Individual studies with large sample size are needed to further evaluate the associations between the polymorphisms and obesity risk in various ethnic populations.

Simultaneous multiclass classi¢cation of tumor types is essential for future clinical implementations of microarraybased cancer diagnosis. In this study, we have combined genetic algorithms (GAs) and all paired support vector machines (SVMs) for multiclass cancer identi¢cation. The predictive features have been selected through iterative SVMs/GAs, and recursive feature elimination post-processing steps, leading to a very compact cancer-related predictive gene set. Leave-one-out cross-validations yielded accuracies of 87.93% for the eightclass and 85.19% for the fourteen-class cancer classi¢cations, outperforming the results derived from previously published methods. ß

We have combined genetic algorithm (GA) and all paired (AP) support vector machine (SVM) methods for multiclass cancer categorization. Predictive features can be automatically determined through iterative GA/SVM, leading to very compact sets of non-redundant cancer-relevant genes with the best classification performance reported to date. Interestingly, these different classifier sets harbor only modest overlapping gene features but have similar levels of accuracy in leave-one-out cross-validations (LOOCV). Further characterization of these optimal tumor discriminant features, including the use of nearest shrunken centroids (NSC), analysis of annotations and literature text mining, reveals previously unappreciated tumor subclasses and a series of genes that could be used as cancer biomarkers. With this approach, we believe that microarray-based multiclass molecular analysis can be an effective tool for cancer biomarker discovery and subsequent molecular cancer diagnosis.

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To address the association between variants and breast cancer, an increasing number of articles on genetic association studies, genome-wide association studies (GWASs), and related meta- and pooled analyses have been published. Such studies have prompted an updated assessment of the associations between gene variants and breast cancer risk. We searched PubMed, Medline, and Web of Science and retrieved a total of 87 meta- and pooled analyses, which addressed the associations between 145 gene variants and breast cancer. Analyses met the following criteria: (1) breast cancer was the outcome, (2) the articles were all published in English, and (3) in the recent published meta- and pooled analyses, the analyses with more subjects were selected. Among the 145 variants, 46 were significantly associated with breast cancer and the other 99 (in 62 genes) were not significantly associated with breast cancer. The summary ORs for the 46 significant associations (P < 0.05) were further assessed by the method of false-positive report probability (FPRP). Our results demonstrated that 10 associations were noteworthy: CASP8 (D302H), CHEK2 (*1100delC), CTLA4 (+49G>A), FGFR2 (rs2981582, rs1219648, and rs2420946), HRAS (rare alleles), IL1B (rs1143627), LSP1 (rs3817198), and MAP3K1 (rs889312). In addition, eight GWASs were identified, in which 25 loci were obtained (14 in nine genes, six near a gene or genes, and five intergenic loci). Of the 25 SNPs, 20 were noteworthy: C6orf97 (rs2046210 and rs3757318), FGFR2 (rs2981579, rs1219648, and rs2981582), LSP1 (rs909116), RNF146 (rs2180341), SLC4A7 (rs4973768), MRPS30 (rs7716600), TOX3 (rs3803662 and rs4784227), ZNF365 (rs10995190), rs889312, rs614367, rs13281615, rs13387042, rs11249433, rs1011970, rs614367, and rs1562430. In summary, in this review of genetic association studies, 31.7% of the gene-variant breast cancer associations were significant, and 21.7% of these significant associations were noteworthy. However, in GWASs, 80% of the significant associations were noteworthy.

Recently, many new loci associated with type 2 diabetes have been uncovered by genetic association studies and genome-wide association studies. As more reports are made, particularly with respect to varying ethnicities, there is a need to determine more precisely the effect sizes in each major racial group. In addition, some reports have claimed ethnic-specific associations with alternative single-nucleotide polymorphisms (SNPs), and to that end there has been a degree of confusion. We conducted a meta-analysis using an additive genetic model. Eight polymorphisms in 155 studies with 121174 subjects (53385 cases and 67789 controls) were addressed in this meta-analysis. Significant associations were found between type 2 diabetes and rs7903146, rs12255372, rs11196205, rs7901695, rs7895340 and rs4506565, with summary odds ratios (ORs) (95% confidence interval) of 1.39 (1.34-1.45), 1.33 (1.27-1.40), 1.20 (1.14-1.26), 1.32 (1.25-1.39), 1.21 (1.13-1.29) and 1.39 (1.29-1.49), respectively. In addition, no significant associations were found between the two polymorphisms (rs290487 and rs11196218) and type 2 diabetes. The summary ORs for the six statistically significant associations (P < 0.05) were further evaluated by estimating the false-positive report probability, with results indicating that all of the six significant associations were considered noteworthy, and may plausibly be true associations. Significant associations were found between the six polymorphisms (rs7903146, rs12255372, rs11196205, rs7901695, rs7895340 and rs4506565) in the TCF7L2 gene and type 2 diabetes risk, and the other two polymorphisms (rs11196218 and rs290487) were not found to be significantly associated with type 2 diabetes. Subgroups analyses show that significant associations are not found between the six SNPs (rs7903146, rs12255372, rs11196205, rs7901695, rs7895340, and rs4506565) and the type 2 diabetes in some ethnic populations.