The role of the fat mass and obesity associated gene (FTO) in breast cancer risk

<p>Abstract</p> <p>Background</p> <p>Obesity has been shown to increase breast cancer risk. <it>FTO </it>is a novel gene which has been identified through genome wide association studies (GWAS) to be related to obesity. Our objective was to evaluate tissue expression of FTO in breast and the role of FTO SNPs in predicting breast cancer risk.</p> <p>Methods</p> <p>We performed a case-control study of 354 breast cancer cases and 364 controls. This study was conducted at Northwestern University. We examined the role of single nucleotide polymorphisms (SNPs) of intron 1 of <it>FTO </it>in breast cancer risk. We genotyped cases and controls for four SNPs: rs7206790, rs8047395, rs9939609 and rs1477196. We also evaluated tissue expression of FTO in normal and malignant breast tissue.</p> <p>Results</p> <p>We found that all SNPs were significantly associated with breast cancer risk with rs1477196 showing the strongest association. We showed that FTO is expressed both in normal and malignant breast tissue. We found that <it>FTO </it>genotypes provided powerful classifiers to predict breast cancer risk and a model with epistatic interactions further improved the prediction accuracy with a receiver operating characteristic (ROC) curves of 0.68.</p> <p>Conclusion</p> <p>In conclusion we have shown a significant expression of FTO in malignant and normal breast tissue and that <it>FTO </it>SNPs in intron 1 are significantly associated with breast cancer risk. Furthermore, these <it>FTO </it>SNPs are powerful classifiers in predicting breast cancer risk.</p

<Book Reviews> Ingemar Fagerlind and Lawrence J Saha Education and National Development : A Comparative Perspective

textabstractVarious modeling methods have been proposed to estimate the potential predictive ability of polygenic risk variants that predispose to various common diseases. However, it is unknown whether differences between them affect their conclusions on predictive ability. We reviewed input parameters, assumptions and output of the five most common methods and compared their estimates of the area under the receiver operating characteristic (ROC) curve (AUC) using hypothetical data representing effect sizes and frequencies of genetic variants, population disease risk and number of variants. To assess the accuracy of the estimated AUCs, we aimed to reproduce the AUCs of published empirical studies. All methods assumed that the combined effect of genetic variants on disease risk followed a multiplicative risk model of independent genetic effects, but they either assumed per allele, per genotype or dominant/recessive effects for the genetic variants. Modeling strategy and input parameters differed. Methods used simulation analysis or analytical formulas with effect sizes quantified by odds ratios (ORs) or relative risks. Estimated AUC values were similar for lower ORs (0.7) due to variants with strong effects, differences in estimated AUCs between methods increased. The simulation methods accurately reproduced the AUC values of empirical studies, but the analytical methods did not. We conclude that despite differences in input parameters, the modeling methods estimate similar AUC for realistic values of the ORs. When one or more variants have stronger effects and AUC values are higher, the simulation methods tend to be more accurate