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BackgroundClassifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data.New methodOur method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models.ResultsWe validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity.Comparison with existing methodsFor the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002).ConclusionsWe have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders.

Many disease-susceptible SNPs exhibit significant disparity in ancestral and derived allele frequencies across worldwide populations. While previous studies have examined population differentiation of alleles at specific SNPs, global ethnic patterns of ensembles of disease risk alleles across human diseases are unexamined. To examine these patterns, we manually curated ethnic disease association data from 5,065 papers on human genetic studies representing 1,495 diseases, recording the precise risk alleles and their measured population frequencies and estimated effect sizes. We systematically compared the population frequencies of cross-ethnic risk alleles for each disease across 1,397 individuals from 11 HapMap populations, 1,064 individuals from 53 HGDP populations, and 49 individuals with whole-genome sequences from 10 populations. Type 2 diabetes (T2D) demonstrated extreme directional differentiation of risk allele frequencies across human populations, compared with null distributions of European-frequency matched control genomic alleles and risk alleles for other diseases. Most T2D risk alleles share a consistent pattern of decreasing frequencies along human migration into East Asia. Furthermore, we show that these patterns contribute to disparities in predicted genetic risk across 1,397 HapMap individuals, T2D genetic risk being consistently higher for individuals in the African populations and lower in the Asian populations, irrespective of the ethnicity considered in the initial discovery of risk alleles. We observed a similar pattern in the distribution of T2D Genetic Risk Scores, which are associated with an increased risk of developing diabetes in the Diabetes Prevention Program cohort, for the same individuals. This disparity may be attributable to the promotion of energy storage and usage appropriate to environments and inconsistent energy intake. Our results indicate that the differential frequencies of T2D risk alleles may contribute to the observed di

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.

Many DNA variants have been identified on more than 300 diseases and traits using Genome-Wide Association Studies (GWASs). Some have been validated using deep sequencing, but many fewer have been validated functionally, primarily focused on non-synonymous coding SNPs (nsSNPs). It is an open question whether synonymous coding SNPs (sSNPs) and other non-coding SNPs can lead to as high odds ratios as nsSNPs. We conducted a broad survey across 21,429 disease-SNP associations curated from 2,113 publications studying human genetic association, and found that nsSNPs and sSNPs shared similar likelihood and effect size for disease association. The enrichment of disease-associated SNPs around the 80(th) base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3' untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies.

BackgroundThe amount of gene expression data in the public repositories, such as NCBI Gene Expression Omnibus (GEO) has grown exponentially, and provides a gold mine for bioinformaticians, but has not been easily accessible by biologists and clinicians.ResultsWe developed an automated approach to annotate and analyze all GEO data sets, including 1,515 GEO data sets from 231 microarray types across 42 species, and performed 12,658 group versus group comparisons of 24 GEO-specified types. We then built GeneChaser, a web server that enables biologists and clinicians without bioinformatics skills to easily identify biological and clinical conditions in which a gene or set of genes was differentially expressed. GeneChaser displays these conditions in graphs, gives statistical comparisons, allows sort/filter functions and provides access to the original studies.We performed a single gene search for Nanog and a multiple gene search for Nanog, Oct4, Sox2 and LIN28, confirmed their roles in embryonic stem cell development, identified several drugs that regulate their expression, and suggested their potential roles in sex determination, abnormal sperm morphology, malaria infection, and cancer.ConclusionWe demonstrated that GeneChaser is a powerful tool to elucidate information on function, transcriptional regulation, drug-response and clinical implications for genes of interest.

BackgroundCandidate single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWASs) were often selected for validation based on their functional annotation, which was inadequate and biased. We propose to use the more than 200,000 microarray studies in the Gene Expression Omnibus to systematically prioritize candidate SNPs from GWASs.ResultsWe analyzed all human microarray studies from the Gene Expression Omnibus, and calculated the observed frequency of differential expression, which we called differential expression ratio, for every human gene. Analysis conducted in a comprehensive list of curated disease genes revealed a positive association between differential expression ratio values and the likelihood of harboring disease-associated variants. By considering highly differentially expressed genes, we were able to rediscover disease genes with 79% specificity and 37% sensitivity. We successfully distinguished true disease genes from false positives in multiple GWASs for multiple diseases. We then derived a list of functionally interpolating SNPs (fitSNPs) to analyze the top seven loci of Wellcome Trust Case Control Consortium type 1 diabetes mellitus GWASs, rediscovered all type 1 diabetes mellitus genes, and predicted a novel gene (KIAA1109) for an unexplained locus 4q27. We suggest that fitSNPs would work equally well for both Mendelian and complex diseases (being more effective for cancer) and proposed candidate genes to sequence for their association with 597 syndromes with unknown molecular basis.ConclusionsOur study demonstrates that highly differentially expressed genes are more likely to harbor disease-associated DNA variants. FitSNPs can serve as an effective tool to systematically prioritize candidate SNPs from GWASs.