Background: Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). Results: In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. Conclusion: iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.

Non-random patterns of genetic variation exist among individuals in a population owing to a variety of evolutionary factors. Therefore, populations are structured into genetically distinct subpopulations. As genotypic datasets become ever larger, it is increasingly difficult to correctly estimate the number of subpopulations and assign individuals to them. The computationally efficient non-parametric, chiefly Principal Components Analysis (PCA)-based methods are thus becoming increasingly relied upon for population structure analysis. Current PCA-based methods can accurately detect structure; however, the accuracy in resolving subpopulations and assigning individuals to them is wanting. When subpopulations are closely related to one another, they overlap in PCA space and appear as a conglomerate. This problem is exacerbated when some subpopulations in the dataset are genetically far removed from others. We propose a novel PCA-based framework which addresses this shortcoming.

The prevalence of Plasmodium vivax is increasing in the border regions of Thailand; one potential problem confounding the control of malaria in these regions is the emergence and spread of drug resistance. The aim of this study was to determine the genetic diversity in genes potentially linked to drug resistance in P. vivax parasites isolated from four different border regions of Thailand; Thai-Myanmar (Tak, Mae Hong Son and Prachuap Khiri Khan Provinces), and Thai-Cambodian borders (Chanthaburi Province). Isolates were collected from 345 P. vivax patients in 2008 and 2014, and parasite DNA extracted and subjected to nucleotide sequencing at five putative drug-resistance loci (Pvdhfr, Pvdhps, Pvmdr1, Pvcrt-o and Pvk12). The prevalence of mutations in Pvdhfr, Pvdhps and Pvmdr1 were markedly different between the Thai-Myanmar and Thai-Cambodian border areas and also varied between sampling times. All isolates carried the Pvdhfr (58R and 117N/T) mutation, however, whereas the quadruple mutant allele (I57R58M61T117) was the most prevalent (69.6%) in the Thai-Myanmar border region, the double mutant allele (F57R58T61N117) was at fixation on the Thai-Cambodian border (100%). The most prevalent genotypes of Pvdhps and Pvmdr1 were the double mutant (S382G383K512G553) (65.1%) and single mutant (M958Y976F1076) (46.5%) alleles, respectively on the Thai-Myanmar border while the single Pvdhps mutant (S382G383K512A553) (52.7%) and the triple Pvmdr1 mutant (M958F976L1076) (81%) alleles were dominant on the Thai-Cambodian border. No mutations were observed in the Pvcrt-o gene in either region. Novel mutations in the Pvk12 gene, the P. vivax orthologue of PfK13, linked to artemisinin resistance in Plasmodium falciparum, were observed with three nonsynonymous and three synonymous mutations in six isolates (3.3%).

MicroRNAs are generally understood to regulate gene expression through binding to target sequences within 3′-UTRs of mRNAs. Therefore, computational prediction of target sites is usually restricted to these gene regions. Recent experimental studies though have suggested that microRNAs may alternatively modulate gene expression by interacting with promoters. A database of potential microRNA target sites in promoters would stimulate research in this field leading to more understanding of complex microRNA regulatory mechanism. We developed a database hosting predicted microRNA target sites located within human promoter sequences and their associated genomic features, called microPIR (microRNA-Promoter Interaction Resource). microRNA seed sequences were used to identify perfect complementary matching sequences in the human promoters and the potential target sites were predicted using the RNAhybrid program. >15 million target sites were identified which are located within 5000 bp upstream of all human genes, on both sense and antisense strands. The experimentally confirmed argonaute (AGO) binding sites and EST expression data including the sequence conservation across vertebrate species of each predicted target are presented for researchers to appraise the quality of predicted target sites. The microPIR database integrates various annotated genomic sequence databases, e.g. repetitive elements, transcription factor binding sites, CpG islands, and SNPs, offering users the facility to extensively explore relationships among target sites and other genomic features. Furthermore, functional information of target genes including gene ontologies, KEGG pathways, and OMIM associations are provided. The built-in genome browser of microPIR provides a comprehensive view of multidimensional genomic data. Finally, microPIR incorporates a PCR primer design module to facilitate experimental validation. The proposed microPIR database is a useful integrated resource of microRNA-promoter target interactions for experimental microRNA researchers and computational biologists to study the microRNA regulation through gene promoter. The database can be freely accessed from: http://www4a.biotec.or.th/micropir.

Background: The ever increasing sizes of population genetic datasets pose great challenges for population structure analysis. The Tracy-Widom (TW) statistical test is widely used for detecting structure. However, it has not been adequately investigated whether the TW statistic is susceptible to type I error, especially in large, complex datasets. Non-parametric, Principal Component Analysis (PCA) based methods for resolving structure have been developed which rely on the TW test. Although PCA-based methods can resolve structure, they cannot infer ancestry. Model-based methods are still needed for ancestry analysis, but they are not suitable for large datasets. We propose a new structure analysis framework for large datasets. This includes a new heuristic for detecting structure and incorporation of the structure patterns inferred by a PCA method to complement STRUCTURE analysis. Results: A new heuristic called EigenDev for detecting population structure is presented. When tested on simulated data, this heuristic is robust to sample size. In contrast, the TW statistic was found to be susceptible to type I error, especially for large population samples. EigenDev is thus better-suited for analysis of large datasets containing many individuals, in which spurious patterns are likely to exist and could be incorrectly interpreted as population stratification. EigenDev was applied to the iterative pruning PCA (ipPCA) method, which resolves the underlying subpopulations. This subpopulation information was used to supervise STRUCTURE analysis to infer patterns of ancestry at an unprecedented level of resolution. To validate the new approach, a bovine and a large human genetic dataset (3945 individuals) were analyzed. We found new ancestry patterns consistent with the subpopulations resolved by ipPCA. Conclusions: The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca.

Control of malaria is threatened by emerging parasite resistance to artemisinin and derivative drug (ART) therapies. The molecular detail of how Plasmodium malaria parasites respond to ART and how this could contribute to resistance are not well understood. To address this question, we performed a transcriptomic study of dihydroartemisinin (DHA) response in P. falciparum K1 strain and in P. berghei ANKA strain using microarray and RNA-seq technology. Microarray data from DHA-treated P. falciparum trophozoite stage parasites revealed a response pattern that is overall less trophozoite-like and more like the other stages of asexual development. A meta-analysis of these data with previously published data from other ART treatments revealed a set of common differentially expressed genes. Notably, ribosomal protein genes are down-regulated in response to ART. A similar pattern of trophozoite transcriptomic change was observed from RNA-seq data. RNA-seq data from DHA-treated P. falciparum rings reveal a more muted response, although there is considerable overlap of differentially expressed genes with DHA-treated trophozoites. No genes are differentially expressed in DHA-treated P. falciparum schizonts. The transcriptional response of P. berghei to DHA treatment in vivo in infected mice is similar to the P. falciparum in vitro culture ring and trophozoite responses, in which ribosomal protein genes are notably down-regulated. Ring and trophozoite stage Plasmodium respond to ART by arresting metabolic processes such as protein synthesis and glycolysis. This response can be protective in rings, as shown by the phenomenon of dormancy. In contrast, this response is not as protective in trophozoites owing to their commitment to a highly active and vulnerable metabolic state. The lower metabolic demands of schizonts could explain why they are less sensitive and unresponsive to ART. The ART response pattern is revealed clearly from RNA-seq data, suggesting that this technology is of great utility for studying drug response in Plasmodium.