Background: Malaria within the Greater Mekong sub-region is extremely heterogeneous. While China and Thailand have been relatively successful in controlling malaria, Myanmar continues to see high prevalence. Coupled with the recent emergence of artemisinin-resistant malaria along the Thai-Myanmar border, this makes Myanmar an important focus of malaria within the overall region. However, accurate epidemiological data from Myanmar have been lacking, in part because of ongoing and emerging conflicts between the government and various ethnic groups. Here the results are reported from a risk analysis of malaria slide positivity in a conflict zone along the China-Myanmar border. Methods. Surveys were conducted in 13 clinics and hospitals around Laiza City, Myanmar between April 2011 and October 2012. Demographic, occupational and educational information, as well as malaria infection history, were collected. Logistic models were used to assess risk factors for slide positivity. Results: Age patterns in Plasmodium vivax infections were younger than those with Plasmodium falciparum. Furthermore, males were more likely than females to have falciparum infections. Patients who reported having been infected with malaria during the previous year were much more likely to have a current vivax infection. During the second year of the study, falciparum infections among soldiers increased signficiantly. Conclusions: These results fill some knowledge gaps with regard to risk factors associated with malaria slide positivity in this conflict region of north-eastern Myanmar. Since epidemiological studies in this region have been rare or non-existent, studies such as the current are crucial for understanding the dynamic nature of malaria in this extremely heterogeneous epidemiological landscape. © 2013 Li et al.; licensee BioMed Central Ltd.

MOTIVATION:Polygenic risk score (PRS) methods based on genome-wide association studies (GWAS) have a potential for predicting the risk of developing complex diseases and are expected to become more accurate with larger training datasets and innovative statistical methods. The area under the ROC curve (AUC) is often used to evaluate the performance of PRSs, which requires individual genotypic and phenotypic data in an independent GWAS validation dataset. We are motivated to develop methods for approximating AUC of PRSs based on the summary level data of the validation dataset, which will greatly facilitate the development of PRS models for complex diseases. RESULTS:We develop statistical methods and an R package SummaryAUC for approximating the AUC and its variance of a PRS when only the summary level data of the validation dataset are available. SummaryAUC can be applied to PRSs with SNPs either genotyped or imputed in the validation dataset. We examined the performance of SummaryAUC using a large-scale GWAS of schizophrenia. SummaryAUC provides accurate approximations to AUCs and their variances. The bias of AUC is typically <0.5% in most analyses. SummaryAUC cannot be applied to PRSs that use all SNPs in the genome because it is computationally prohibitive. AVAILABILITY AND IMPLEMENTATION:https://github.com/lsncibb/SummaryAUC. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.