In our attempt to comprehensively understand the nature of association of variants at 11q23.3 apolipoprotein gene cluster region, we genotyped a prioritized set of 96 informative SNPs using Fluidigm customized SNP genotyping platform in a sample of 508 coronary artery disease (CAD) cases and 516 controls. We found 12 SNPs as significantly associated with CAD at P <0.05, albeit only four (rs2849165, rs17440396, rs6589566 and rs633389) of these remained significant after Benjamin Hochberg correction. Of the four, while rs6589566 confers risk to CAD, the other three SNPs reduce risk for the disease. Interaction of variants that belong to regulatory genes BUD13 and ZPR1 with APOA5-APOA4 intergenic variants is also observed to significantly increase the risk towards CAD. Further, ROC analysis of the risk scores of the 12 significant SNPs suggests that our study has substantial power to confer these genetic variants as predictors of risk for CAD, as illustrated by AUC (0.763; 95% CI: 0.729–0.798, p = <0.0001). On the other hand, the protective SNPs of CAD are associated with elevated Low Density Lipoprotein Cholesterol and Total Cholesterol levels, hence with dyslipidemia, in our sample of controls, which may suggest distinct effects of the variants at 11q23.3 chromosomal region towards CAD and dyslipidemia. It may be necessary to replicate these findings in the independent and ethnically heterogeneous Indian samples in order to establish this as an Indian pattern. However, only functional analysis of the significant variants identified in our study can provide more precise understanding of the mechanisms involved in the contrasting nature of their effects in manifesting dyslipidemia and CAD.

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Breast cancer incidences is steadily increasing in Fiji and accurate forecasting can have major implications in controlling this deadly illness. The best forecasting model is the one that does not underestimates or overestimates the true number of breast cancer cases and gives minimal prediction errors. This paper proposes Linear Regression model for forecasting breast cancer cases using the reported number of cases in Fijian population from the year 1995 to 2016. The proposed model is also compared with the Naïve Forecast Method as the benchmark. The performances of the two models were analyzed based on measures such as the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The proposed model was then further validated using the diagnostic measures such as Goodness-of-fit (R 2 ), Tracking Signal (TS) and Bias. The results showed that the proposed Linear Regression model outperformed the Naïve Forecast Method. It also satisfies the validity diagnostic measures and is a better tool in forecasting Fiji's yearly breast cancer cases.