Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimer's disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases. Herein, we describe our collective datasets comprised of GWAS data from 2,099 subjects; microarray gene expression data from 773 brain samples, 186 of which also have RNAseq; and an independent cohort of 556 brain samples with RNAseq. We expect that these datasets, which are available to all qualified researchers, will enable investigators to explore and identify transcriptional mechanisms contributing to neurodegenerative diseases. Despite this progress, a comprehensive understanding of the molecular mechanisms underlying these complex conditions remains elusive. This is partly due to the inability of the disease GWAS approach to identify the actual disease gene and the functional disease risk variants. We10 and others12 utilized combined gene expression GWAS (eGWAS) and disease GWAS to identify loci which harbor regulatory variants that confer disease risk and to nominate the actual disease genes at these loci. The underlying premise of these studies is that genetic variants that modulate expression levels of genes, which encode critical members of disease molecular pathways, will also influence disease risk13. If this is correct, then there should be significant overlap between disease GWAS and eGWAS variants, especially if assessed in the disease-relevant tissue. Indeed, in an eGWAS of brain tissue from subjects with AD and non-AD, comprised largely of other neurodegenerative diagnoses, we identified significant enrichment for disease GWAS variants for AD and other diseases10. We14–18 and others19–22 determined that many of the risk variants for AD and other neurodegenerative diseases influence brain levels of genes that are nearby in the genome. These studies implicate the genes that are likely to be involved in disease pathways, nominate regulatory variants as the functional disease risk factors and provide testable hypotheses for their downstream effects.In the past decade GWAS identified risk loci for human diseases, including AD23 utilize microarray-based gene or exon arrays. Despite the versatility, cost-effectiveness and large-scale utility, this approach has limitations, including restricted dynamic range, lack of probes for all known gene isoforms and confinement of assays to known transcripts. RNA sequencing (RNAseq) provides an attractive alternative that can surpass these limitations and provide much more in-depth information about the human transcriptome in a high-throughput manner24. To expand our prior work on the human transcriptome based on microarray approaches and to evaluate gene/exon/isoform levels in a comparative fashion between AD and other neurodegenerative diseases, we have generated RNAseq data on brain samples from both a subset of the subjects that underwent microarray transcriptome studies18 and also an independent cohort. These datasets will be of utility in performing expression quantitative trait loci (eQTL), expression profiling and network analyses to facilitate interpretation of genetic associations and further understanding of disease-mediated changes in transcriptional regulation.Most large-scale gene expression studies in human brains published to date25 from Sage Bionetworks as part of a series of datasets developed in support of the AMP-AD Target Identification and Preclinical Validation Project. The AMP-AD consortium includes six academic teams that will be generating genomic data from human brain or blood samples collected from more than 10 cohorts. Datasets are hosted in a common environment with standardized meta-data and annotations to facilitate cross-cohort query, access, and analysis. Each dataset provides a unique perspective on AD; therefore, datasets differ in types, generation protocols, and underlying patient characteristics. Together, this collection represents to date the most comprehensive collection of human genomic data in the field and, as such, it will be invaluable to a broad set of researchers.The present report is a description of the large-scale human genetic, and both microarray- and RNAseq-based transcriptome datasets we generated. The datasets described in this report have been made available to the research community through the Accelerating Medicines Partnership in Alzheimer’s Disease (AMP-AD) Knowledge Portal (Data Citation 1). The portal is hosted in the Synapse software platform1 (Mayo LOAD GWAS) on 2,099 subjects (Data Citation 2); (2) Mayo eGWAS10 on 773 samples from the cerebellum (CER) and temporal cortex (TCX) brain regions from a subset of Mayo LOAD GWAS participants ; (3) Mayo Pilot RNAseq18 generated on a subset of 186 TCX samples from the Mayo eGWAS (Data Citation 5); (4) Mayo RNAseq on an independent cohort of 556 TCX26 (Data Citation 6) and CER (Data Citation 7) samples from subjects with AD, progressive supranuclear palsy (PSP), pathologic aging and elderly controls without neurodegenerative diseases. This report provides a comprehensive understanding of these cohorts, a detailed description of subjects, samples, data generation, and quality control (QC) as well as instructions to access these rich datasets by the scientific community.The datasets described herein include the following: (1) late-onset AD GWASMayo LOAD GWAS1 (Data Citation 2) and microarray-based whole transcriptome data from the Mayo eGWAS10 . Next-generation RNA-sequencing (RNAseq) data from a subset of the patients from the Mayo Clinic eGWAS, referred to as the ‘Mayo Pilot RNAseq’ (Data Citation 5), was published in part18. A non-overlapping cohort with RNAseq-based transcriptome data named ‘Mayo RNAseq’  has also been published in part26. For a comprehensive description of the overall repository, the data from the published studies are also described herein, albeit in an abbreviated fashion. These four study cohorts will be referred to by their names as mentioned above, preceded by letters A-D 28. Age for the clinically diagnosed LOAD cases is defined as age at first diagnosis of AD, since age at onset is not always available. Age at entry into the study is used for the clinically diagnosed controls. Age at death is utilized for the cases and controls in the postmortem Mayo Clinic Brain Bank series, given that for this cohort, age at clinical diagnosis/ evaluation is not always available. Illumina Hap300 microarray genotypes from the subjects in these three case-control series were utilized to conduct a GWAS of LOAD risk1.The characteristics of the cohort for this study (Data Citation 2) were previously described in detailication1 . Subject4.0 ref. , while c10. All subjects in the Mayo eGWAS  are a subset of the Mayo Clinic Brain Bank series from the Mayo LOAD GWAS (Data Citation 2) (29. Transcriptome measurements were obtained from TCX of 202 AD subjects and from CER of 197 AD (n=107); Lewy body disease ; corticobasal degeneration ; frontotemporal lobar degeneration ; multiple system atrophy , vascular dementia ; other (n=10). There were 177 non-AD subjects with CER transcriptome measurements that had the following neuropathologies: PSP (n=98); LBD (n=23); CBD (n=22); FTLD (n=15); MSA (n=7); VaD (n=4); other (n=8). Eighty-five percent of the subjects in the TCX cohort overlapped with those in the CER cohort. Demographics for the Mayo eGWAS subjects and samples, including RNA quality as assessed by RNA Integrity Numbers (RIN) are shown in This cohort was previously described in detailation 2) . The Mayf 197 AD . This st26. PSP is a primary tauopathy characterized neuropathologically by neurofibrillary tangles (NFT) and tau-positive glial lesions30; and often presents clinically as a parkinsonian disorder. All PSP subjects were diagnosed neuropathologically by a single neuropathologist (DWD). For this study, only TCX samples were assessed (All subjects in the Mayo Pilot RNAseq study (Data Citation 5) are a subset of the Mayo eGWAS , and are therefore also participants of the Mayo Clinic Brain Bank series that was included in the Mayo LOAD GWAS (Data Citation 2) . The diaassessed .26 (Data Citation 6) and 278 CER (Data Citation 7) samples. Two hundred thirty-eight subjects had both CER and TCX RNAseq and the rest had either CER or TCX RNAseq measurements based on tissue availability. CER samples were from the following diagnostic categories: 86 AD, 84 PSP, 28 pathologic aging and 80 controls without neurodegenerative diagnoses. TCX samples had the following diagnostic groups: 84 AD, 84 PSP, 30 pathologic aging and 80 controls. Control subjects each had Braak28 NFT stage of 3.0 or less, CERAD31 neuritic and cortical plaque densities of 0 (none) or 1 (sparse) and lacked any of the following pathologic diagnoses: AD, Parkinson’s disease (PD), DLB, VaD, PSP, motor neuron disease (MND), CBD, Pick’s disease (PiD), Huntington’s disease (HD), FTLD, hippocampal sclerosis (HipScl) or dementia lacking distinctive histology (DLDH). Subjects with pathologic aging also lacked the above diagnoses and had Braak NFT stage of 3.0 or less, but had CERAD neuritic and cortical plaque densities of 2 or more. None of the pathologic aging subjects had a clinical diagnosis of dementia or mild cognitive impairment. Given the presence of amyloid plaques, but not tangles and the absence of dementia, pathologic aging is considered to be either a prodrome of AD or a condition, in which there is resistance to the development of NFT and/or dementia32.The subjects from this cohort are non-overlapping with the cohorts described above. The Mayo RNAseq cohort was utilized to generate RNAseq-based whole transcriptome data from 278 TCXWithin the Mayo RNAseq cohort , all AD and PSP subjects were from the Mayo Clinic Brain Bank, and all pathologic aging subjects were obtained from the Banner Sun Health Institute. Thirty-four control CER and 31 control TCX samples were from the Mayo Clinic Brain Bank, and the remaining control tissue was from the Banner Sun Health Institute. All subjects were North American Caucasians. All but control subjects, had ages at death ≥60, and a more relaxed lower age cutoff of ≥50 was applied for normal controls to achieve sample sizes similar to that of AD and PSP subjects. No upper age limit was imposed on this cohort, however when subjects had ages at death of ≥90, their ages were recorded as ‘90_or_above’ and shown as ‘90’ in APOE4 positive subjects in the AD group, followed by pathologic aging, then PSP and control subjects. AD and pathologic aging subjects had greater female sex frequency (57%), followed by controls (49%), then PSP subjects (39%). RIN for all samples were selected to be ≥5.0. Pathologic aging and control samples had slightly lower RINs than AD and PSP samples, due to limitations in availability of samples in these former diagnostic categories.1. For the antemortem Mayo Clinic Jacksonville and Mayo Clinic Rochester series, whole blood samples were collected in 10 ml EDTA tubes followed by DNA extraction using AutoGenFlex STAR instrument (AutoGen), whereas cerebellar tissue was used for DNA extraction from the postmortem Mayo Clinic Brain Bank series using the Wizard Genomic DNA purification kit (Promega). Given limited amounts of DNA from samples in the Mayo Clinic Rochester series and Mayo Clinic Brain Bank series, whole genome amplification (WGA) was applied using the Illustra GenomiPhi V2 DNA Amplification Kit , in four 5 ml reactions that utilized 5–15 ng genomic DNA as a template. Subsequent to the pooling of these reaction products, WGA DNA was subjected to quality control (QC) using SNP genotyping as previously described.For the Mayo LOAD GWAS (A) (Data Citation 2), DNA samples were collected and processed as previously described10 (B)  and Mayo Pilot RNAseq18 (C) (Data Citation 5) were previously described. Total RNA was extracted from frozen brain samples using the Ambion RNAqueous kit  according to the manufacturer’s instructions. Brain samples for the Mayo RNAseq (D)  study underwent RNA extractions via the Trizol/chloroform/ethanol method, followed by DNase and Cleanup of RNA using Qiagen RNeasy Mini Kit and Qiagen RNase -Free DNase Set. The quantity and quality of all RNA samples were determined by the Agilent 2100 Bioanalyzer using the Agilent RNA 6000 Nano Chip . Samples had to have an RNA Integrity Number (RIN) ≥5.0 for inclusion in either study  according to the manufacturer’s protocols. Two samples were genotyped per chip for 318,237 SNPs across the genome. Genotype calls were made using the auto-calling algorithm in Illumina’s BeadStudio 2.0 software.The genotype data for the Mayo LOAD GWAS (A) (Data Citation 2) was generated using HumanHap300-Duo Genotyping BeadChips10. Probe annotations were done based on NCBI RefSeq, Build 36.2. The RNA samples were randomized across the chips and plates using a stratified approach to ensure balance with respect to diagnosis, age, gender, RIN and APOE genotype. Raw probe mRNA expression data were exported from GenomeStudio software (Illumina Inc.) and preprocessed for background correction, variance stabilizing transformation, quantile normalization and probe filtering using the lumi package of BioConductor33.For the Mayo eGWAS study (B) , transcript levels were measured using the Whole Genome DASL assay  as previously described26 and CER samples  underwent RNAseq at different times. These samples were randomized across flowcells, taking into account age at death, sex, RIN, Braak stage and diagnosis. The TruSeq RNA Sample Prep Kit  was used for library preparation from all samples. The library concentration and size distribution was determined on an Agilent Bioanalyzer DNA 1000 chip. All samples were run in triplicates using barcoding (3 samples per flowcell lane). For Mayo Pilot RNAseq (C) (Data Citation 5) samples, 50 base-pair, paired-end sequencing was done, whereas Mayo RNAseq (D)  samples underwent 101 bp, paired-end sequencing.Samples for both Mayo Pilot RNAseq (C) (Data Citation 5) and Mayo RNAseq (D)  studies were randomized prior to transfer to the Mayo Clinic MGF Gene Expression Core for library preparation and then the Sequencing Core for RNA sequencing. Mayo Pilot RNAseq (C) (Data Citation 5) AD and PSP samples were randomized across flowcells, taking into account age at death, sex and RIN. These samples underwent library preparation and sequencing at different times and therefore should be considered as separate datasets. Likewise, Mayo RNAseq (D) of TCX34 and converted to binary ped (.bed) and map (.bim) files, which are deposited together with PLINK format fam and covariate files (DOI and descriptions for each these files are provided in Mayo LOAD GWAS (A) (Data Citation 2) genotypes from Illumina BeadStudio 2.0 software were utilized to generate lgen, map and fam files that were imported into PLINK10 and described above. The Mayo eGWAS ‘eSNP Results’ (Data Citation 4) are the eQTL results from the test of association between the Mayo LOAD GWAS (Data Citation 2) genotypes and the WG-DASL gene expression measures analyzed by multivariable linear regression using an additive model in PLINK34, as published previously10 , age at death, sex, PCR plate, RIN and adjusted RIN squared (RIN-RINmean)2. Analyses were limited to SNP-probe pairs that were in-cis, defined as +/−100 kb of the targeted gene according to NCBI Build 36. The ADs and non–ADs were analyzed both separately and jointly. The joint analyses included diagnosis as an additional covariate . Results of analyses for both the genotyped SNPs as well as genotypes imputed to HapMap2 reference are provided. HapMap2 imputations were done as described10. The eGWAS results were previously made available through the NIAGADS repository (https://www.niagads.org/datasets/ng00025).The Mayo eGWAS WG-DASL microarray expression dataset from TCX and CER (B) includes covariates and probe expression levels (Data Citation 3), which are preprocessed as published18 (Data Citation 5), Mayo RNAseq TCX26 and CER data  were processed using the same analytic pipeline. Read alignments were done using the SNAPR software35, an RNA sequence aligner based on SNAP, using GRCh38 reference and Ensembl v77 gene models. Outputs include per-sample gene and transcript counts, which are merged into a single file per data type (gene or transcript) that contains data for all samples across all genes/transcripts  normalization method to calculate counts per million (CPM). These normalized counts are saved for both gene and transcript levels  (Data Citation 2) QC methods were previously published10 (B)  data was generated as follows: We annotated probes for presence of genetic variants by comparing their positions according to NCBI RefSeq, Build 36.3 to those of all variants within dbSNP131 and identified the list of probes that have ≥1 variants within their sequence. We depict this information in the files for the Mayo eGWAS, ‘eSNP Results’ (Data Citation 4)  samples were also run on each PCR plate as part of QC. The expression phenotypes include results from only one of the replicate subjects selected randomly and exclude UHR results. It should be noted that 3 AD and 9 non-AD subjects for TCX, and 4 AD subjects for CER, do not have associated GWAS genotypes as they did not pass≥1 GWAS QC parameter described above.The Mayo eGWAS18 (C) (Data Citation 5) data principal components analysis (PCA) identified 2 outliers in the AD and 4 in the PSP cohort. The covariates for these subjects were set to missing (=NA) in the respective covariate files (DOI and descriptions for these files are provide in 26 samples (Data Citation 6) but 2 such subjects in the CER analyses (Data Citation 7). The covariate data in the relevant CER files for these two subjects were set to missing. We likewise assessed the RNASeq data for sex discrepancies based on Y chromosome gene expression and documented sex and identified 2 subjects with mis-matched sex for both TCX and CER, plus a third subject in the CER cohort. These were also set to missing in the covariate files. At the time of this publication, the Mayo RNAseq subjects did not have GWAS genotypes deposited on Synapse.For the Mayo Pilot RNAseqwww.niagads.org).The data described herein is available for use by the research community and has been deposited in the AMP-AD Knowledge Portal . 25 for backend support, providing users with both web-based and programmatic access to data files. All data files in the portal are annotated using a standard vocabulary to enable users to search for relevant content across the AMP-AD datasets using programmatic queries. Data is stored in a cloud based manner hosted by Amazon web services (AWS), which enables user to execute cloud-based compute. Detailed descriptions including data processing, QC metrics, and assay and cohort specific variables are provided for each file as applicable.The AMP-AD Knowledge Portal hosts data derived from multiple cohorts that were generated as part of or used in support of the AMP-AD Target Identification and Preclinical Validation project . The portal uses the Synapse software platformAccess for the data described herein is controlled in a manner set forth by the institutional review board (IRB) at the Mayo Clinic. All data use terms include: (1) maintenance of data in a secure and confidential manner, (2) respect for the privacy of study participants, (3) citation of the data contributors in any publications resulting from data use, and (4) informing data contributors of resultant publications. Specific data use terms are provided for each dataset  under the header ‘Terms of use’; users must register for a Synapse account and provide electronic agreement to these terms prior to accessing the study files. Access to the Mayo LOAD GWAS data (A)  requires a data use certificate (doi:10.7303/syn2954402.2). User approvals are managed by the Synapse Access and Compliance Team (ACT).Data on the AMP-AD Knowledge Portal are annotated with a common dictionary of terms (doi:10.7303/syn5478487.2) to enable querying of the data using the Synapse analytical clients . Fields, their allowable values specific to the datasets described herein and the dictionary of annotations are shown in How to cite this article: Allen, M et al. Human whole genome genotype and transcriptome data for Alzheimer's and other neurodegenerative diseases. Sci. Data 3:160089 doi: 10.1038/sdata.2016.89 (2016).


Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual’s response to environmental challenges. Individuals with different genotypes may respond differently to environmental variation. Here, Favé et al. find substantial impacts of different environment exposures on the transcriptome and clinical endophenotypes when controlling for genetic ancestry by analyzing data from ∼1000 individuals from a founder population in Quebec. Individuals with different genotypes may respond differently to environmental variation and generate an array of phenotypic landscape14. Such gene-by-environment interactions are thought to be pervasive and may be responsible for a large fraction of the unexplained variance in heritability and disease risk16. Yet, disease risk, owing to either environmental exposures and/or their interactions with genotype, remains poorly understood18.Environmental exposures, coupled with genetic variation, influence disease susceptibility, and deconstructing their respective contributions remains one of the principal challenges in understanding complex diseaseshttp://www.partnershipfortomorrow.ca) is a cohort comprising over 315,000 Canadians, and captures over 700 variables, ranging from longitudinal health information to environmental exposures, to determine genetic and environmental factors contributing to chronic disease. The program includes the Quebec regional cohort, CARTaGENE, which has enrolled over 40,000, predominantly French-Canadian (FC) individuals between 40 and 70 years of age21, to date. Drawing from this founding population of individuals with largely French ancestry, we selected 1007 individuals to determine mechanisms by which genomes, the environment, and their interactions contribute to phenotypic variation. After attributing a regional and/or continental ancestry to each individual using genome-wide polymorphism data, we are able to capture the effect of different environmental exposures on gene expression and health-related traits, while simultaneously controlling for genetic relatedness and migration. Further, in order to capture gene-by-environment interactions through eQTL analyses, we combine whole-transcriptome RNA-Sequencing profiles with whole-genome genotyping and extensive fine-scale environmental exposure data.Canada’s precision medicine initiative, the Canadian Partnership for Tomorrow Project (CPTP: 2); Quebec City, a smaller urban center ; and Saguenay-Lac-Saint-Jean, a less urbanized region . Differences in the regional environment within and across these cities, including ambient pollutant concentrations, are known to be associated with various health outcomes23. The majority of the Quebec population is of FC descent; a group of individuals descending from French settlers that colonized the Saint-Lawrence Valley from 1608 to the British conquest of 175924. Despite considerable expansion, the population remained linguistically and religiously isolated while remote regions were colonized by small numbers of settlers, such as SAG26 and contributed to the establishment of subpopulations. These sequential population bottlenecks impacted the genome of FCs through increasing the relative deleterious mutations load27, while reducing overall genetic diversity in the population relative to the European population28. Using high-density whole-genome genotyping assays (Illumina Omni 2.5), we confirm that FCs (n = 689) form a distinct genetic cluster relative to those of European descent (n = 136)  Fig. , as has bec Fig. , consist29 (Methods). Using whole-genome genotyping, we are able to distinguish between “FC-locals” and “FC-regional migrants” . Additionally, a greater number of genes are differentially expressed between individuals having the same regional ancestry but who reside in different regions , and we find this pattern in nearly all pairwise comparisons of this nature  ), community land-based measures (ozone (O3) and sulfur dioxide (SO2) for air pollution) are collated. Community level estimates of socio-economic indices , and built environment features  are also incorporated. A total of 12 environmental exposures, all of them measured or estimated at the level of three-digit postal code  , however, higher concentrations of SO2 and O3, (land-based measures) are observed in SAG  to our multidimensional data to capture associations between 57 clinical endophenotypes , respiratory function (FEV1), as well as liver enzyme levels (Alanine aminotransferase level (ALT), aspartate aminotransferase level (AST), and gamma-glutamyl transferase (GGT)) show the strongest associations with annual SO2 and O3 ambient levels , we focus our high-resolution analyses on the participant’s weekly SO2 exposure.We apply coinertia analyses2 pollution, obtained from averaging over a 14-day window preceding the time-point of each individual blood sampling , and MTL individuals exposed to high levels of SO2 , or vice-versa. In that way, we can single out the effect of the local environment itself, predominantly attributable to SO2 exposure, to the broader regional effect detected earlier. Using a robust resampling approach to balance the number of individuals in each category, we are able to identify with confidence 170 differentially expressed genes between high- and low-SO2-exposure individuals; these are also found to be differentially expressed between regions , suggesting that SO2 exposure, rather than the region itself, is modulating these associations. We find that the expression of the 170 DEGs (between high- and low-exposure to SO2) is also associated with four key clinical traits (Forced expiratory volume (FEV1), lung disease, liver enzymes, and arterial stiffness)  are well documented to be associated with air pollution levels37. Gamma-glutamyltransferase (GGT) has been reported to occur in atherosclerotic plaques40, is elevated following pollution exposure42, and is predictive in a dose-dependent manner of cardiovascular risk43. Interestingly, we find GGT levels to be associated with the differentially expressed genes across SO2 exposure environments, in particular those genes enriched in blood coagulation and platelet regulation , are consistently reported as influenced by air pollution by other studies45. To discover gene-by-environment interactions in both FCs and Europeans, we identify eQTLs for which the effect size is modulated by exposure with one of four ambient air pollutants (env-eQTLs): PM2.5, NO2, O3, and SO2. First, we identify canonical eQTLs using 5,313,384 genotypes and show a high replication for proximal canonical eQTLs (cis-eQTLs) with previously discovered cis-eQTLs  to perform regressions of gene expression levels (eGenes) on cis-SNPs (eSNPs), pollution level, and the interaction between eSNP and pollution  we retain the lowest Bonferroni-corrected p value for each eGene and transform this set into q values46 to determine statistical significance . This results in the identification of ten unique significant eSNP–eGene pairs (with nine unique eGenes). (3) We then examine these candidate pairs in our replication cohort (n = 417), where four out of the ten pairs are significantly replicated  with the same direction of effect in both the discovery and replication cohorts. Last, (4) all four replicated eSNP–eGenes associations  remain significant using empirical p value estimates through permutations on the combined cohort  (Supplementary Table To identify gene-by-environment interactions with air pollution (env-eQTLs), we use a randomly generated discovery cohort  modulates the association between the genotype of at least one eSNP and the eGene expression  or assisting in chromatin and histone binding of transcription factors (atad2)48.Following the application of this stringent filtering, we identify and replicate three eGenes (four eSNP–eGene pairs) for which air pollution  , and less common (MAF between 0.05 and 0.1) and, for each endophenotype, calculate the odds ratios of observing less common variants (compared to common) for stronger endophenotypic associations. We find that less common variants are overrepresented for stronger associations between eSNPs and some endophenotypic traits  is observed in our cohort .Further information and requests for reagents may be directed to the Biobank CARTaGENE which regulates the access to the data and biological materials  and Framingham risk scores, ensuring an equal representation of ages and gender. Two-hundred-and-ninety-two additional samples were subsequently selected from CARTaGENE (freeze 2) based on their RNA and complete arterial stiffness (AIx) measures availability. These samples were selected for having high AIx values as well as average AIx values to complement the first freeze of samples with the intention of achieving a broad range of arterial stiffness values across the complete study cohort. All samples were collected in the same year, with a standardized protocol in all sampling clinicsp value > 0.001, MAF > 5% and percent of missing data < 1%).In total, 928 samples with RNA-Seq profiles that passed quality control (QC) thresholds were genotyped on the Illumina Omni2.5 array to obtain high-density SNP genotyping data. A total of 1,213,103 SNP were retained after filtering and QC (Hardy–Weinberg 56. TruSeq RNA Sample Prep kit v2 (Illumina) was used to construct paired-end RNA-Seq libraries with 500 ng of globin-depleted total RNA. Recommended Illumina protocols were followed for quantification and quality control of RNASeq libraries prior to sequencing. Paired-end RNA sequencing was performed on a HiSeq 2000 platform at the Genome Quebec Innovation Center . Sequencing was performed for freeze 1 (708 samples) using three samples per lane, and for freeze 2 (292 samples) using six samples per lane yielding about 60 million reads per samples. All RNA-seq experimental steps following blood draw were conducted in the same central laboratory, and samples were distributed randomly over sequencing lanes  and a globin mRNA-depletion was performed using the GLOBINclear-Human kit (ThermoFisher Scientific). The quality and integrity of the RNA samples were verified using an Agilent Bioanalyzer 2100 and all samples had an RNA integrity number (RIN) > 7.5. A RIN above 7.5 is indicative of high quality RNA in the sample and for which RNA degradation is minimal, indicating optimal transport and preservation conditions. Our RIN threshold is more stringent than other large-scale consortium studying gene expression in tissues57 using the two-pass protocol, as recommended by the Broad Institute. The two-pass protocol consists in two consecutive mappings steps having the same set of parameters with only the reference that is optimized in the second mapping procedure. The first mapping is done using the reference gene definition coming from ENSEMBL (release 75). Then, using the splicing junction database files formed by the first pass mapping step for all the samples combined together and the same gene definition file, a second reference is indexed and optimized and is used for the second mapping step. The number of mismatches allowed across pair is five and a soft-clipping step that optimizes alignment scores is also done automatically by STAR. The PCR duplicates were conserved as it was shown that quantification of highly expressed genes were disproportionately affected by PCR duplicates removal58. Only properly paired reads were kept (using samtools59) for the analysis, according to STAR parameters. After these steps, HTseq (v.0.6.1p1)60 was launched separately on each alignment file using the same gene reference file that was used for the alignments.Reads were trimmed for adapters and bad quality bases first using Trim Galore and were then assembled to a reference genome  Major Allele release) using STAR (v2.3.1z15)All analyses downstream were conducted using R 3.1.2 and R 3.2.2 and Bioconductor R packages.61, a haplotype-based method powerful enough to detect fine-scale genetic structure. Original genotyping data was used apart from singletons, yielding to 1,908,336 SNPs. Singletons were removed as they are non-informative for phasing and contribute to computation burden for the step of haplotypes sharing inference performed with ChromoPainter. Genotypic data was phased with SHAPEIT (v2.r644)62 using the HapMap genetic maps. Coancestry matrices were obtained from ChromoPainter with parameters estimation step done with ten iterations on four chromosomes only. ChromoPainter method performs a reconstruction of every individual genome using chunks of DNA donated by the other individuals and report matrices of the number and length of those chunks. We used the chunk count matrix to (1) run FineSTRUCTURE algorithm to build a tree  63, and the fineSTRUCTURE tree, a PCA of FC individuals reveals groupings of sub-populations of individuals that follow a North–South structure  and considering their region of residence  a PCA on the chunk count matrix reveals three groups corresponding to region of residence, with the Montreal and Quebec groups overlapping to a greater extent, in line with their greater geographic proximity 64 to perform the imputation using the 1000 Genomes Phase I integrated haplotypes (Dec 2013). We used the parameters Ne = 11418 and call thresh = 0.9. We removed variants with a call rate <90%, MAF > 1%, and Hardy–Weinberg p value > 0.0001. A total of 9,157,622 variants passed the filters. Of these, 8,877,297 variants were found on the autosomes and included 779,579 insertion-deletion polymorphisms (indels) (8.78%) and 8,097,718 SNPs (91.22%). 280,325 variants were found on the chromosome X, which included 28,504 indels  and 251,821 SNPs (89.84%).To increase the power for the association study with gene expression levels, variant imputation was conducted on 968 individuals for which the genotyping was available from the Illumina Omni2.5 array. We pre-phased the genotypes with SHAPEIT (v2.r64410)2 > 0.2 and 50 SNPs window shifting every five SNPs)  with SNPs pruned for LD  were removed from the analysis for a total of 15,632 genes retained for all downstream analyses. Individuals that showed obvious outlier after visual inspection of principal component plots were removed .The deep phenotyping of the CARTaGENE cohort allow for a thorough exploration of the biological and environmental factors that may influence genome-wide gene expression patterns. As most statistical procedures assume a normal distribution to the underlying data, we transformed the normalized counts from freeze 1 to a Gaussian distribution using a log2cpm transformation using edgeR. We summarize the gene expression levels by performing a PCA on the normalized expression matrix (ePCA). To identify variables that contribute to genome-wide gene expression variation, we performed a stepwise regression (stepwise search from both directions) on ePC1 and ePC2. Results of the stepwise regression are given in Supplementary Table S1, as well as the results from the replication analyses using freeze 2. We included the following low level endophenotypes in the stepwise procedure: set, region of residence, cell counts , arterial stiffness, age, and sex.Using the freeze 1 data set of 708 individuals, we quantified the proportion of the variance in expression attributable to cell counts, age, sex, region, and arterial stiffness  by using principal variance component analysis (PVCA), and found that the region of residence explains ∼16% of the variance in gene expression, while the effects of age, sex, and cell counts were much lower Fig. . These an = 136) and Enfant-Jesus . QUE individuals expression profiles from the combined data set show that individuals from STS and EF form a single cluster on a ePCA plot  and biological  effects on gene expression 67, we performed a differential gene expression analysis using the 15,632 genes that passed the QC filters established above. We performed the differential expression modeling using the following statistical model:B is the surrogate variable, representing the batch effect estimated by SVA, and S represent the freeze effect that is included in the final (see below for further details).Because of the large proportion of the variance in gene expression explained by region of residence, we identified genes that are differentially expressed between pairwise comparisons between the FC-locals from the three regions . Using edgeRp value below the Bonferroni-corrected threshold of 3.20 × 10−6 . The SVA corrected expression levels retained the variation associated with region, but removed any effects of cell counts that was present in the uncorrected expression levels  in the comparison between Mtl-FC-locals and Sag-FC-locals. Using the 12 origin-living groups and the 6649 genes, we performed an unsupervised clustering and visualized the groupings using a heatmap . Reactome enrichment analyses were conducted with R the package reactomePA, and here again, the background set of genes was defined as the 15,632 genes expressed in blood that pass our filters  or national LUR models estimates (PM 2.5 and NO2) incorporating information from land use data and satellite remote sensing70. Built environment variables  and social and material deprivation indicators were accessed through the Quebec government data portal (https://www.inspq.qc.ca/environnement-bati). All environmental data sources are described in Supplementary Table We obtained air quality measures in the year of sampling (2010) from either land-based stations , or was reformatted to this geographic scale. Postal code districts in Canada are small geographic areas which assist in delivering mail. Postal codes are a series of six digits that identify a small geographic area in a municipality, usually grouping just a few houses together or a small neighborhood. Three-level digits are larger areas that include several houses, a small neighborhood, or a small village. The population of FSAs in Canada range from a few hundreds to tens of thousands of individuals. Three-digit postal code districts can be of different areas, and are smaller in densely populated areas, and larger in areas of low population density. Maps in Fig. 71 is a multivariate statistical part of the large family of ordination methods, such as PCA, redundancy analysis (RDA), or canonical correlation analysis (CCA). CoIA is a general approach and existing methods such as the ones mentioned above appear as special cases of it31. These methods have been widely used in ecological research, including CoIA which has been more recently developed. Collectively, these methods allow for detecting an underlying data structure between two data tables. CoIA uses a combination of PCA and multivariate linear regressions to detect linear combinations of variables from one data table that explain the variance in the second data table. CoIA is more flexible than RDA or CCA, and overcomes their limitations by allowing for more variables than the number of samples to be tested71, which is generally the case in genome-wide scale analyses . This makes CoIA a method of choice to integrate data of diverse types, and of high-throughput like most omics data.Coinertia analysis (CoIA)imputePCA from the R package missMDA. The coinertia analysis performs a double inertia analysis of each data set and then project the variables of the original environmental and gene expression data sets on the new co-inertia axes. Relationships between the two matrices were assessed by comparing the CoIA estimated from the real data set with the CoIA distribution estimated after bootstrapping. Two sets of 500 of CoIAs were computed independently between gene expression and fine-scale environmental data. Supplementary Fig. n = 497 for each) a total of 10,000× resampling of 200 individuals (without replacement) were performed. We performed a CoIA for each resampling step. We report the median value of the distribution of each environment–gene expression pair cross-tabulated values for each group. Gene enrichment were performed using gProfiler72, and using the 15,632 expressed genes that passed our filters in whole blood as the background gene set .To increase our resolution in air pollution exposures, we used daily SO2 exposure. We combined the results of DGE analysis in a list of 468 differentially expressed genes, and from these candidates, 170 genes were also identified as differentially expressed between regions  of exposure, we resampled 100 times 108 individuals with replacement from the low- and high-exposure category and performed the DGE pipeline. We performed the SVA while retaining variation associated with SOons Fig. . Those s2 exposure. We then built multivariate models using the SO2, O3, and PM2.5 14-day exposures, as well as the remaining 9 non-pollution environmental exposures : PM2.5, NO2, O3, and SO2. We categorized the participants using a k-means algorithm73 into two categories, high or low exposure, irrespective of the pollutant type (Table 1). A k-means algorithm attempts to partition the individuals into k groups , such that the sum of squared Euclidean distances from points to the assigned centroid (cluster mean) is minimized.Environmental factors not only directly affect phenotypic variation, but can also modulate associations between segregating genetic variants and phenotypesp value associated with the Student’s t-statistic for the interaction term SijkEijk. We applied a Bonferroni correction to the interaction p values for SNP-wise multiple testing within gene and retained the most significant putative eSNP–eGene pair from each gene. We then assessed this set of “best” eSNP–eGene p values for significance across all 15,632 genes at the false discovery rate (FDR) threshold of 0.05 by transforming the set into q values46). This represented the set of significant discovery eSNP–eGene pairs to be tested in the replication set. We then reported the environmental eSNP–eGene pairs that were significant (replicated) in the replication cohort  and had the same direction of effect in both cohorts (n = 4 out of the 10).We adopted a strategy  using permutation: for each eSNP–eGene pair we performed the same eQTL modeling (Yijk ~ Sijk + Eijk + SijkEijk) and permuted the expression values (Y) before obtaining the test statistic (Student’s t) for the interaction term. By repeating this procedure 1000 times for each eSNP–eGene pair, we built null distributions to assess the original observed (not permuted) t-statistics. The empirical permutation p value for each eSNP–eGene pair was taken as the proportion of permutation t-statistics larger than the observed t-statistic  for stronger endophenotypic associations  or the CPTP portal  upon request. The built environment data set is publicly available from the Quebec government data portal. The air pollution data set is available upon request to Air Health Effects division, Government of Canada. All environmental data sources are detailed in Supplementary Table Genotyping, expression, health phenotypes, and exposure data used in this study are available from CARTaGENE (Supplementary InformationPeer Review File


Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation.Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods.Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.The online version of this article (doi:10.1186/s12918-017-0514-1) contains supplementary material, which is available to authorized users. It is known that 13% of deaths all over the world are caused by cancer . Early dIn cancer research, cDNA Microarrays and high density oligonucleotide chips are increasingly used and in the meantime they raise numerous excellent and challenging research problems in fields. By monitoring expression levels in cells for tens of thousands of genes simultaneously, microarray experiments may lead to a better understanding of the molecular variations among tumors and hence to a more informative classification . Over thMaglogiannis et al.  proposedIn the current perspective, SVM demonstrates as a benchmark for various disciplines in particular for dealing with small sample problems. The effectiveness of SVMs depends on the choice of kernels. In , we propIn this paper, we propose a parsimonious kernel named Hadamard Kernel for breast cancer outcome predictions. The remainder of this paper is structured as follows. In “Preliminariesn data instances with corresponding class annotations: xi∈Rp,yi∈{−1,1}. SVM constructs a hyperplane to ensure good separation having largest distance from it to the nearest data points in each class category , where n data instances X={x1,x2,…,xn}, kernel matrix K is a n×n matrix which is symmetric, i.e., x,x′∈X.When the data sets are nonlinearly separable, one can construct a nonlinear mapping for input vectors into feature space of higher dimensionality . DiffereThere are a number of popular kernels, the most straightforward one is:x and x′ in Rp.Linear Kernel. Another popularly used kernel matrix is polynomial kernel that is expressed as d is parameter. If the distance between x and x′ is small, the kernel value would be large; on the contrary, if x is far away from x′ in terms of Euclidean distance, the kernel value would be small. Hence this kernel provides a similarity measure between data points.Gaussian Radial Basis Function (RBF) kernel is defined as Hadamard Kernelϕ(x) explicitly as long as constructing appropriate kernel matrix. The Positive Semi-Definite (PSD) property T,x2=T, then the kernel matrix has the following structure: Assume ∘However, in our particular case, where all the gene expression values are positive valued, the kernel here is positive semi-definite. We give the proof in the subsequent statement.K0 is positive semi-definite when that data matrix X is positive valued.Kernel A=(aij), we define the Hadamard inverse of A by For a positive matrix by Bapat  and refoby Bapat , we haveA is a positive symmetric matrix with only one positive eigenvalue, then A∘(−1) is positive semi-definite.If K is always positive semi-definite is equivalent to show that VX is always positive semi-definite.Let VX is positive semi-definite, we only need to show that wi as we have wi=T, then we have To show that wie+(wie)T is a positive symmetric matrix of rank 2 and it is not positive semi-definite , hence it has exactly one positive eigenvalue. Therefore by the result of Reams, Note that X as well.We can generalize the result to any non-negative matrix K0 is positive semi-definite when data matrix X is non-negative.Kernel x,y≥0, we define the binary operation For UX is positive semi-definite, we only need to show that Uwe is positive semi-definite for any nonnegative column vector w.Then w has zero entries. Without loss of generality, write w=T where y>0, then Uye is positive semi-definite. Hence it suffices to show that Uye is positive semi-definite for any positive column vector y.Suppose y′ to be the Hadamard inverse of y, then Uye=(y′e+(y′e)T)∘(−1). Note that y′e+(y′e)T is a positive symmetric matrix of rank 2 and it is not positive semi-definite , hence it has exactly one positive eigenvalue. Therefore by the result of Reams [Uwe is positive semi-definite. □Take of Reams , Uwe isWe now proceed to prove the first theorem.X, we can generate a corresponding new matrix Xα by For any data sample within matrix Models for comparisonIn this paper, we consider breast cancer outcome predictions based on high dimensional gene expression profiles. Hence the number of samples is relatively small. In the literature it is shown that SVM is statistically better than other machine learning algorithms. We therefore confine our research in the framework of SVMs and exclude other algorithms from our scope of research. Other kernels for a comparison are listed below.SVM Linear Kernel SVM Quadratic Kernel SVM RBF Kernel SVM Correlation KernelThis kernel construction can be decomposed into three steps.KCB where V is the matrix composed of eigenvectors, P is the diagonal matrix where diagonal entries are eigenvalues. P to another diagonal matrix 1. Based on the correlation matrix, we first construct a preliminary kernel. We obtained a number of real-world data sets from National Center for Biotechnology Information . The firEstrogen Receptor-Positive (ER+) and ER- breast cancers tend to show different patterns of metastasis. In this data set where the access number is GSE32394, glycan structure analyse by Custom Affymetrix Glyco v4 GeneChip was conducted to compare the two types of breast cancer. There are 19 samples in total, of which 9 are ER+, the number of attributes is 1259.The third data set is used to differentiate non-invasive breast cancer and invasive breast cancer, the access number is GSE59246. mRNA, miRNA and DNA copy number profiles are generated to measure the expression of different samples. Arrays consist of 3 normal controls, 46 ductal carcinoma in situ lesions, and 56 small invasive breast cancers. We discard the 3 normal controls, so we have 102 samples in total. In this data set, the number of attributes is 62976.Studies show that circulating miRNAs have the potential to become biomarkers. This data set involves 78 samples in total, 1205 circulating miRNAs for measurements. 26 of the 78 samples are negative. Identification number for this data set in NCBI is GSE59993.One more data set is related to breast cancer prognosis, GSE25055 is the identification number. A total number of 310 breast cancer patients is involved. The number of attributes is 22283. This study is conducted with Affymetrix Human Genome U133A Array. It is a neoadjuvant study of HER2-negative breast cancer cases treated with taxane-anthracycline chemotherapy pre-operatively and endocrine therapy if ER-positive. Response was assessed at the end of neoadjuvant treatment. Using 5 years as a cutoff, we conduct the outcome prediction.The last data set contains 60 patients with ER-positive primary breast cancer and treated with tamoxifen monotherapy for 5 years , the idek-fold cross validation is performed as follows: first of all, the training data set k subsets k−1 subsets and the remaining subset is treated as the test set. Repeating this process k times such that each subset is tested once, all the prediction results are recorded for the computation of prediction accuracy. In our case, we conduct 5-fold cross validation for model evaluations.Cross validation is a standard way to evaluate the supervised learning model. The P (positive) and N (negative), while the predicted positive and negative classes are P′ and N′, respectively. This is illustrated by Table x and y axes, respectively, for a binary classifier system as its discrimination threshold is varied. FPR and TPR are defined as follow: In the context of classification, suppose the two true classes are The area under the ROC curve (AUC) , 23 is aIn this section, we will show the performance of the Hadamard Kernel in conjunction with SVM and the other 4 kernels for breast cancer outcome predictions as tested on the five data sets. We employed the AUC measured by 5-fold cross-validation run 10 times to evaluate the performance. All the experiments are conducted using Matlab R2012 under Window 7 Operations System.σ before model training. Therefore, we initially conduct 10 time 5-fold cross validation solely on RBF kernel with σ∈{10−2,10−1,1,10,100,1000}. Averaged AUC Values with corresponding standard deviations are shown in Table σ. The best σ of RBF kernel for GSE32394 and GSE59246 breast cancer prediction is 1000 whereas the best σ for GSE59993 is 10. Particular case can also arise when RBF kernel is insensitive to values of σ. We can draw the conclusion that there is no optimal σ for all the considered data sets and different data sets may have different best σ.In RBF kernel, we have to specify the parameter α. Figures S1 to S6  with step size 0.1. Optimal α in Hadamard Kernel varies in different data sets. For example, one can see a steady decrement in performance when α>1.3 in GSE1872 and when α>2.8 in GSE59246. There is no obvious pattern detected in Additional file 1: Figure S2 in GSE32394, the performance is unstable with respect to α. But we can see a tendency of decrement in an overall manner. For GSE59993, the performance is firstly increasing, achieving the best for α=0.5. The performance is then decreasing steadily. In GSE25055, the performance of hadamard kernel stays in a stable range when α<2.8, it then decreases drastically. For GSE1379, the performance of hadamard kernel gradually increases when α>2. It can be seen that different datasets may fit for different best α in Hadamard Kerne, the optimal α determination becomes an interesting problem.For hadamard kernel, we would like to see the performance of Hadamard Kernel in relation with parameter x-axis, 1 represents the first 5-fold cross-validation. The corresponding values in y axis are the AUC values for the considered 5 methods. For example, in Additional file 1: Figure S7 for GSE1872, the best performance is shown in Hadamard Kernel and Correlation Kernel in the first round, achieving 100% in accuracy. The performances of Linear Kernel, RBF Kernel and Quadratic Kernel are not satisfactory. RBF Kernel shows the worst performance, the AUC values are below 30%. Similar patterns can be detected in the remaining 9 round 5-fold cross-validations. In summary, Hadamard Kernel and Correlation Kernel show the best performance regarding the 10 runs 5-fold cross-validations.Figures S7 to S12 Figures. Additional file 1 includes 12 figures. Figure S1 to S6 describe the performance of Hadamard kernel with different values of Additional file 2Results on RNAseq data. Additional file 2 contains results on RNAseq data for breast cancer outcome predictions. (DOCX 157 kb)


RankComp to detect differentially expressed genes (DEGs) for individual disease samples measured by a particular platform. In this paper, with 461 normal lung tissue samples separately measured by four commonly used platforms, we demonstrated that tens of millions of gene pairs with significantly stable REOs in normal lung tissue can be consistently detected in samples measured by different platforms. However, about 20% of stable REOs commonly detected by two different platforms  showed inconsistent REO patterns due to the differences in probe design principles. Based on the significantly stable REOs (FDR<0.01) for normal lung tissue consistently detected by the four platforms, which tended to have large rank differences, RankComp detected averagely 1184, 1335 and 1116 DEGs per sample with averagely 96.51%, 95.95% and 94.78% precisions in three evaluation datasets with 25, 57 and 58 paired lung cancer and normal samples, respectively. Individualized pathway analysis revealed some common and subtype-specific functional mechanisms of lung cancer. Similar results were observed for colorectal cancer. In conclusion, based on the cross-platform significantly stable REOs for a particular normal tissue, differentially expressed genes and pathways in any disease sample measured by any of the platforms can be readily and accurately detected, which could be further exploited for dissecting the heterogeneity of cancer.The highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue are widely reversed in the cancer condition. Based on this finding, we have recently proposed an algorithm named  RankComp RankComp RankComp  and RanPRankComp , the indRankComp . Especia samples .RankComp obviates the need of batch effect adjustment and inter-sample normalization. As validated in our previous studies, the within-sample REOs are highly reproducible and comparable between data produced by different laboratories using the same or similar platforms [RankComp algorithm is that it is based on REOs that are highly stable in a pre-defined percentage  of normal samples, which is lack of statistical control and may limit the detection power of DEGs in individual samples. Thus, it is also necessary to evaluate the performance of RankComp when using significantly stable gene pairs, selected with statistical control rather than a pre-defined percentage, in a particular type of normal tissue as the basis for the individual-level differential expression analysis.It is well known that gene expression profiling is susceptible to various technical artifacts or ‘batch effects’ introduced by differences in laboratory conditions, reagent lots and personals –13, espelatforms . HoweverRankComp based on gene pairs with highly stable REOs in at least 99% normal samples, RankComp based on significantly stable REOs in normal samples can detect much more DEGs for each disease sample with slightly decrease of precision. Finally, based on the individual-level differential expression analysis, we applied the individual-level pathway analysis to reveal some common and subtype-specific functional mechanisms of lung adenocarcinoma and colon adenocarcinoma, respectively.In this article, we firstly compared gene expression profiles generated with four commonly used gene expression profiling platforms  for normal lung and colorectal tissues, respectively. For each type of normal tissue, we showed that tens of millions of gene pairs with significantly stable REOs, especially those with large expression differences, can be consistently detected in samples measured by different platforms. Then, we showed that, comparing with For a particular type of normal tissue, we focused on evaluating the consistency between the within-sample relative expression orderings (REOs) in samples separately measured by four platforms, including three commonly used microarray platforms  and a RNA-sequencing platform. All the data used in this study are described in Table p<1.0-16). As shown in Table Firstly, for the Affymetrix platform, we collected a set of 150 normal lung tissue samples from four datasets  and another set of 65 normal lung tissue samples from the GSE19188 dataset, referred to as SetA and SetB, respectively. From SetA and SetB, 197,546,446 and 195,767,556 significantly stable REOs  were identified, respectively. The two lists of significantly stable REOs had 190,118,028 overlaps, among which 98% showed the same REO patterns in SetA and SetB, indicating that the significantly stable REOs of gene pairs were highly reproducible , as shown in Figure The above results together indicated that the REOs of gene pairs are widely stable in a particular type of human normal tissue and most of them could be found with only about 20 samples.Then, we evaluated the consistency between the significantly stable REOs detected by different platforms. For each of the three microarray platforms, we defined the REOs consistently detected in SetA and SetB measured by the platform for a type of normal tissue as the stable REOs of the platform for this type of normal tissue. Especially, we only analyzed the gene pairs consisting of genes commonly detected by all the four platforms.p<1.0-16). For the 66,305,728 significantly stable REOs consistently detected by the above two platforms, 79.91% were included in the 77,825,426 significantly stable REOs found in the data measured by the Agilent platform and the consistency increased to 92.1%. Furthermore, for the 48,802,858 significantly stable REOs consistently detected by the three microarray platforms, 98.01% were included in the 99,202,212 significantly stable REOs  detected by the RNA-sequencing platform and the consistency further increased to 96.79%. Totally 46,295,854 gene pairs with significantly stable REOs (FDR<0.01) were consistently detected by the four platforms for the normal lung samples. Similar results were also observed for normal colorectal tissue, as described in Table For the 94,145,902 significantly stable REOs detected from the normal lung tissue samples measured by the Affymetrix platform, 85.50% were also detected from the data measured by the Illumina platform, among which 82.37% showed the same REO patterns in the samples measured by the two platforms .The above results indicated that the cross-platform ability of significantly stable REOs increases as the significantly stable REOs can be consistently detected in increasing number of platforms. A possible explanation could be that the REOs kept across multiple platforms with different probe design principles tend to have large rank differences which are difficult to be reversed by the probe detection biases of various platforms. To illustrate this possibility, for the significantly stable REOs commonly detected by the Affymetrix and Illumina platforms for normal lung tissue, we compared the rank differences between the gene pairs with consistent REOs and the gene pairs with inconsistent REOs detected by the two platforms using the GSE19188 dataset. As expected, the median of the rank differences of gene pairs with consistent REOs was 6855, which was significantly larger than that (median=3144) of the gene pairs with inconsistent REOs  between the cancer sample and the paired adjacent normal sample as the benchmark (see Methods). Considering that other individual-specific factors irrelevant to the cancer condition may induce transcriptional alternations in the cancer sample, we focused on individualizing the population-level DEGs predetermined to ensure the DEGs identified in individual cancer samples to be associated with cancer. We also evaluated the performance of RankComp for individualized differential expression analysis based on the highly stable REOs (stable in at least 99% of the samples) consistently detected in the normal lung and colorectal tissue samples measured by the four platforms, respectively.As described above, totally 46,295,854 gene pairs with significantly stable REOs FDR<0.01) were consistently detected in the normal lung samples separately measured by the four platforms. Based on these significantly stable REOs, we applied  were conp<1.0-16). We defined the 6,822 reproducible DEGs as the population-level DEGs for lung cancer. Then, using three datasets with paired lung cancer and adjacent normal samples separately measured by three different platforms, we evaluated the performance of RankComp in individualizing the population-level DEGs. For each cancer sample, we performed RankComp based on the 46,295,854 gene pairs with significantly stable REOs (FDR<0.01) consistently detected in the normal lung samples measured by the four platforms. For the 25 lung cancer samples of the GSE27262 dataset measured by the Affymetrix platform, RankComp identified averagely 1,184 DEGs per sample with averagely 96.51% precision according to the observed expression differences between the cancer samples and their paired adjacent normal samples. We also evaluated RankComp using the GSE32863 and TCGA-luad (lung adenocarcinoma samples from TCGA) datasets which included 57 and 58 paired cancer and adjacent normal samples measured by the Illumina microarray platform and the Illumina HiSeq 2000 platform, respectively. Averagely 1,335 and 1,116 DEGs were identified per cancer sample and the average precisions were 95.95% and 94.78% for the two datasets, respectively. In contrast, based on the 21,789,916 highly stable REOs (stable in at least 99% of the samples) consistently detected in the normal lung samples measured by the four platforms, the average precision increased to 98.96%, 98.97 % and 95.65% but averagely only 392, 542 and 474 DEGs were identified per sample in the three datasets, respectively. Similar results were also observed for colorectal cancer, as shown in Figure For lung cancer, using the GSE19188 and GSE19804 datasets, we firstly detected 12,359 and 8,681 DEGs  between the cancer and normal tissues, respectively. The two lists of DEGs had 6,929 overlaps, among which 98.46% showed the same deregulation directions in the cancer tissues in the two datasets  of the 515 samples. In contrast, some pathways could be subtype-specific. For example, the ‘Fanconi anemia pathway’ significantly enriched with up-regulated genes in about 20% or 65% (with FDR<0.1 or p<0.05) of the 515 samples, indicating that it might be associated with cancer prognosis [p<0.05) of the 515 lung cancer samples, indicating that it might also be associated with cancer prognosis [First, we detected pathways separately enriched with up- or down-regulated genes for each of the 515 lung adenocarcinoma samples  . As showl cycle’  pathway rognosis . For anorognosis .p<0.05). Thus, these pathways, such as the ‘metabolic’ [Similarly, we performed pathway enrichment analysis for each of the 285 colon adenocarcinoma samples. As shown in Figure tabolic’  and ‘oxitabolic’  pathwaystabolic’  and ‘Tyrtabolic’  pathwaystabolic’ . In additabolic’ , 35.The above results suggested that individualized pathway analysis could provide hints for revealing common and subtype-specific functional mechanisms of cancer. The functional analysis results also provided extra evidence for the authenticity of individualized DEGs at the functional level.RankComp based on highly stable REOs , RankComp based on significantly stable REOs can detect much more DEGs with slightly decrease of precision for each disease sample, as demonstrated by the results for both lung cancer and colorectal cancer. Individual-level DEGs analysis naturally enables us to perform pathway analysis at the individual level, which could reveal common functional mechanisms as well as subtype-specific functional mechanisms of cancer. This is totally different from the traditional population-level pathway analysis which cannot discriminate whether a significant pathway is altered in a group of patients  or all patients. Furthermore, our results showed that almost all gene pairs had significantly stable REOs across samples for a given normal tissue. This indicated that the relative ordering of gene expression is overall stable in a particular type of normal human tissue, indicating that genes may need to express in a comprehensive coordination structure to carry normal function systematically [As demonstrated in this article, tens of millions of gene pairs with significantly stable REOs in a particular type of normal tissue, especially those with large expression differences, can be consistently detected by different platforms. This provides the basis for individual-level differential expression analysis for cancer samples measured by different platforms. Compared with atically .Based on the significantly stable REOs consistently detected by multiple platforms for a particular type of tissue, DEGs and deregulated pathways for any disease sample measured by any of these platforms can be readily detected. This could be of particular valuable when we need to analyze multiple datasets of disease samples measured by different platforms to identify and validate various cancer signatures (such as prognostic signatures) . MoreoveRankComp is an economic and efficient method which can identify DEGs for individual disease samples measured by different platforms. Moreover, the individual-level analysis of DEGs can also provide the possibility to identify robust diagnostic and prognostic biomarker for precision medicine [In summary, a large fraction of the widely stable REOs in a particular type of normal tissue can be consistently detected in samples measured by different platforms. By fully using previously accumulated gene expression data of normal samples, medicine .http://www.ncbi.nlm.nih.gov/geo/) and the mRNA-seq data measured by RNA-sequencing platform were downloaded from GEO and TCGA [http://cancergenome.nih.gov/).The gene expression profiles analyzed in this study are described in Table and TCGA   and used the Robust Multi-array Average algorithm for background adjustment . For theFor array-based data, each probeset ID was mapped to Entrez gene ID with the corresponding platform file. If a probeset was mapped to multiple or zero gene, then the data of this probeset was deleted. If multiple probesets were mapped to the same gene, the expression value for the gene was defined as the arithmetic mean of the value of multiple probesets. For the sequence-based data form GEO, we directly downloaded the processed data and each Gene Symbol was mapped to Entrez gene ID with biological DataBase network (bioDBnet). For the sequence-based data of level 3 from TCGA, we removed genes whose expression measurements were at or below a noise threshold of 0.2 normalized counts in at least 75% of samples .The REO of two genes, A and B, is denoted as A >B (or B<A) if gene A has a higher (or lower) expression level than gene B. The significance of a REO is determined by a binomial test  as follon denotes the total number of normal samples, k denotes the number of samples that have a certain REO pattern  in n normal samples and 0p (0p=0.5) is the probability of observing a certain REO pattern in a normal sample by chance. Then, the p-values were adjusted using the Benjamini and Hochberg method [where g method .1L and list 2 with length 2L, have n overlaps, among which k have the same REO patterns, then the POG score from list 1 (or list 2) to list 2 (or list 1), denoted as POG12 (or POG21), is calculated as 1k/L (or 2k/L), and the concordance score is calculated as k/n. The probability of observing the concordance score by chance is calculated with the binomial distribution model as described above, where 0p (0p =0.5) is the probability of a gene pair having the same REO patterns in the two lists by chance.We used the POG (Percentage of Overlapping Gene pairs) score , 44 to eRankComp algorithm is described in [RankComp. Then, to determine whether a given gene A is differentially expressed in a given disease sample, Fisher's exact test [The detail of the ribed in . Brieflyact test  is used RankComp, assuming that the unknown previously normal state of a cancer tissue could be approximately represented by the adjacent normal tissue of the cancer tissue. After identifying DEGs for one cancer sample, if the deregulation directions (up- or down-regulations) of DEGs are consistent with the deregulation directions observed in the cancer sample compared with its own adjacent normal sample, then they are defined as true positives (TP); otherwise, false positives (FP). The precision rate is calculated as TP/(TP+FP) for each cancer sample. To ensure the association between the individualized DEGs and cancer, we restricted our evaluation to the reproducible population-level DEGs predetermined using two datasets for each type of cancer. For each cancer, the statistical significance of the concordance score between two lists of DEGs between cancer samples and normal controls, detected by Student's t-test, is calculated by the binomial distribution model as described above. Finally, the DEGs reproducibly detected in the two datasets for each cancer are defined as the population-level DEGs associated with the cancer.We used paired cancer and adjacent normal samples to evaluate the performance of p-values are adjusted using the Benjamini and Hochberg procedure.Data of 223 pathways covering 6290 unique genes were extracted from the Kyoto Encyclopedia of Genes and Genomes (KEGG)  on 10 Ma


Over the past few years, emergent threats posed by infectious diseases and bioterrorism have become public health concerns that have increased the need for prompt disease outbreak warnings. In most of the existing disease surveillance systems, disease outbreak risk is assessed by the detection of disease outbreaks. However, this is a retrospective approach that impacts the timeliness of the warning. Some disease surveillance systems can predict the probabilities of infectious disease outbreaks in advance by determining the relationship between a disease outbreak and the risk factors. However, this process depends on the availability of risk factor data. In this article, we propose a Bayesian belief network (BBN) method to assess disease outbreak risks at different spatial scales based on cases or virus detection rates. Our experimental results show that this method is more accurate than traditional methods and can make uncertainty estimates, even when some data are unavailable. With the appearance of a variety of new infectious diseases  and the renewed prevalence of existing diseases 2Handbook of Bio-surveillance: “spatial and spatial-temporal”). To this end, many new techniques have been introduced over recent decades, including classical time-series models, as well as methods for detecting spatial and spatio-temporal clusters7et al.Thus far, organizations at the national, state, and local level, including the American Centers for Disease Control and Prevention (CDC)BBNs use a combination of graph theory and probability theory based on Thomas Bayes’theoremA BBN represents the hierarchical and mutual relationships among variables, in contrast to the regression model, which can only represent the outcome variables’ dependencies based on a predictor variable. With the BBN, the joint distribution of all variables is reflected in the directed acyclic graph(DAG), which is a finite directed graph with no directed cycles, where marginal and conditional probabilities can be computed for each node. Therefore, the BBN is more appropriate for prognostic and diagnostic applications. There are many BBN-based risk analysis applications, including analyses of risk in maritime transportation systems’ organizational factorsA BBN uses a directed acyclic graph (DAG)Generally, three methods — the expert knowledge method, the network structure learning algorithm, and an integration of these two methods — are applied to build the Bayesian belief network. The expert knowledge method involves building a network by setting nodes, ordering, giving constraints to the structure, and drawing the directed acyclic graph completely according to experts’ comments and the results of lecture review; however, the network structure learning algorithm is based on the independence test proposed by Cheng JieTo introduce the approach, we first review the concept of d-separation, which plays an important role in our algorithm. For any three disjoint node sets X, Y, and Z in a belief network, X is said to be d-separated from Y by Z if there is no active undirected path bath between X and Y. A path between X and Y is active if the following conditions are met: (1) every node in the path has head-to-head arrows in Z or has a descendant in Z; and (2) every other node in the path is outside Z. To understand d-separation, we can use an analogy that is similar to the one suggested by (Waidyanatha 2010)Xi, Xj is defined asIn information theory, the mutual information of two nodes and conditional mutual information is defined asXi, Xj are two nodes and C is a set of nodes. In our algorithm, we use conditional mutual information as CI tests to measure the average information between two nodes when the statuses of some valves are changed by the condition-set C. When I is smaller than a certain threshold value ε, we say that Xi, Xj are d-separated by the condition-set C, and they are conditionally independent.where This algorithm also makes the following assumptions: (1) the database attributes have discrete values and there are no missing values in all the records; (2) the volume of data is large enough for reliable CI tests; and (3) the ordering of the attributes is available before the network construction, i.e., a node’s parents’ nodes should appear earlier in the order.The algorithm has three phases: drafting, thickening, and thinning. In the first phase, the algorithm computes mutual information of each pair of nodes as a measure of closeness, and creates a draft based on this information. In the second phase, the algorithm adds arcs when the pairs of nodes cannot be d-separated. The result of phase II is an independence map (I-map) of the underlying dependency model. In the third phase, each arc of the I-map is examined using CI tests and will be removed if the two nodes of the arc can be d-separated. The result of Phase III is the minimal I-map.Phase I: (Drafting)1) Initialize the graph structure G < V, E> ; where V = nodes , E = {} , and S = Φ, R = Φ.vi, vj) where vi, vj ∈ V, compute mutual information I. For the pairs of nodes that have mutual information greater than a certain small value ε, sort them by their mutual information from large to small, and put them into an ordered set S.2) For each pair of nodes (3) Get the first two pairs of nodes in S and remove them from S. Add the corresponding arcs to E. .4) Get the first pair of nodes remaining in S and remove it from S. If there is no open path between the two nodes (these two nodes are d-separated given the empty set), add the corresponding arc to E. Otherwise, add the pair of nodes to the end of an ordered set R.5) Repeat step 4 until S is empty.Phase II: (Thickening)6) Get the first pair of nodes in R and remove it from R.7) Find a block set that blocks each open path between these two nodes by a set of minimum number of nodes. Conduct a CI test. If these two nodes are still dependent on each other given the block set, connect them by an arc.8) Go to step 6 until R is empty.Phase III: (Thinning)9) For each edge in E, if an open path separate from the current edge remains, it will be brought from E. Then, the independence of the corresponding block set is tested. If the edge and corresponding block set are still correlated, the edge returns to E; otherwise, it is deleted.Friedman proved theoretically that the method based on independence testing more closely resembles the semantics of the Bayesian networking principle, and it achieves effective results in practiceAfter constructing a Bayesian belief network, a probabilistic inference is taken to elucidate the dependence relationships between variable nodes in the network. In our study, probabilistic inference was achieved by calculating the joint conditional probability distributions among variables based on their cause and consequence relationships. For each BBN node, a conditional probability formula or table, which represents the probabilities of each value of the node given the conditions of its parents, is supplied. In addition, the distributions of the parent nodes can be calculated given the values of their children. After the conditional probability table is obtained, the joint probability distributions can be decomposed into local probability distributions.The joint probability distribution formula in BBN can be expressed asx1, …, xn) refers to the probability of a specificcombination of values x1, …, xn from the set of variables , where Parent (Xi) refers to the set of P(xi|Parents(Xi)) reflects the conditionalprobability, which is related to the node Xi based on its parent nodes.where P.et al.et al.et al.40 (2010) utilized rough set to explore spatial decision rules in neural-tube birth defects and predict where high rates of the disease would occur.The rough set method, proposed by Pawlak in 1982Rough set theory is data-driven, and the advantage of this approach is that it does not require the user to make any a priori assumptions about the data. When using this method for epidemiological warnings, the whole analysis process can be divided into four steps: (1) data discretization, which, in this case, in order to ensure the comparability of different models, is the same as the data preparation which has been used in the Bayesian Network model; (2) find minimal reducts of conditional attributes, remove redundant attributes, and reduce system complexity; (3) generate rules according to the reducts, which can provide a rule base for inference and prediction; and (4) apply the rules to predict the epidemic outbreak risk level.As a comparison, a traditional linear regression model was used to estimate the HFMD outbreak risks. Regression analysis is a strong scientific method, which is an important branch of modern applied statistics. It has been widely used in many scientific fields. It can not only extract the information hidden in massive raw data groups and grasp the main characteristics of the population data to obtain the mathematical expression of the relationship between variables, but it can also use one or more variables to predict or forecast another dependent variable value. Regression forecasting method is the use of regression analysis method, through the statistical processing and analysis of a large number of observation data, to find out the relationship between the number of predictive objects and influencing factors. The theory of multiple linear regression requires data that generally follows a multivariate normal distribution with the same covariance, but it is usually difficult to meet such conditions with epidemiological data, where a lot of influence factors have 0/1 distribution. At this time, we can use a logistic regression model to predict the disease. For example, the most commonly used predictive method in the study of cancer epidemiology is the logistic regression method.Model performance was evaluated with receiver operating characteristic (ROC) analysis, which has been widely useful for evaluating the performance of binary classifier. A typical ROC curve plots true positive rate (sensitivity) against false positive rate (1-specificity) for the entire range of possible thresholds, therefore providing a unified representation for assessing the overall model performance. The area under the curve (AUC) was used as a single performance measure to decide whether the model prediction was better than random (0.5). A perfect model would yield an AUC value of 1. Training AUC values were fairly high across models and higher than the testing AUC values, as anticipated . The testing AUC values, which demonstrate the actual model predictive powers, suggest whether the model predictions are better than random.The risk of disease outbreak is the probability or chance that a specified disease outbreak will occur. Existing outbreak definitions are composed of two components: (i) an endemic channel that aims to replicate historical trends of expected cases; and (ii) a set of criteria that determines what level of variation above this endemic frequency should be classified as an outbreakIn the existing study, factors that affect the risks of disease outbreak in different areas have been classified as demographic, socioeconomic, and meteorological27Because BBNs are optimized for discrete variables, all continuous variables should be discretized before data is put into the network after determining risk factors. At present, there are three primary methodsTo test the feasibility and effectiveness of BBN in assessing infectious disease outbreak risk, we respectively applied BBN to assess the hand, foot, and mouth disease (HFMD) and measles outbreak risks based on disease cases and symptoms at different spatial scales.In this case, a HFMD outbreak was defined as an abnormal increase in the severity and mortality of HFMD within the next month. BBN was used to assess HFMD outbreak risk in Hunan Province, China based on virus detection rate and demographic, socioeconomic and meteorological factors. It should be noted that the study period was during the peak months  of HFMD in HunanThe disease data incorporated in the in BBN were the rate, severity, and mortality of HFMD and the rate of virus EV71 detection in mild cases. People older than five years were ignored in the study because HFMD mainly infects children under the age of 5Hunan Statistical Yearbook33343536The environmental factors in this study were socioeconomic and meteorological factors. The socioeconomic factors included the density of the susceptible population  and urbanization levels. These data were extracted from the P50, P80, as the indicator of potential outbreak risks. When the severity and mortality rates of HFMD in the current observation period were less than P50, the risk level was set as level 0 (low risk). When the rate fell within , the risk level was level 1(moderate risk). If the rate was more than P80, outbreak risk was high, and the risk level was set as level 2. The current observation period and historical data block were dynamically shifted forward by month.In this study, the moving percentile methodWhen all data were collected, discretization was performed prior to constructing the network. In this study, we used 70% of the data (98 samples) as the training data to build the network, and the remaining 42 samples were used as test data.In this case, there were three different ways to construct the Bayesian belief network: (1) only the network structure learning algorithm proposed by Cheng JieTo evaluate the performances of different Bayesian belief networks, the percentage of samples correctly predicted from all test samples was taken as the index of the risk assessment accuracy. As seen in the conditional probability table , the effTo compare with the risk assessment accuracy of the rough set method with BBN, we applied the rough set method in the HFMD and measles outbreak risks prediction experiments. Both the training data and the test sample data were input to each method. In the rough set method, we obtained 179 reducts from the decision system. The smallest set contains four attributes: the rate of virus EV71 detection in mild cases for children aged 0–5 years old, urbanization level, average temperature, and average wind speed. This means that at least four spatial attributes should be used to synthesize the decision rules. According to the 179 reducts, we next generated 17,165 rules. These rules, which discovered the relationship between the attributes and decision, are the decision rules. According to the decision rules, the risk levels of the HFMD outbreak were estimated. 2. In these statistic models, the general form was as follows:As a comparison, we also used a tradition linear regression model to estimate the HFMD outbreak risk. In this case, as the dependent variable is a discrete classification, we used the logistic regression model, which is the most commonly used method in epidemiological prediction research, to predict the HFMD outbreak risk level . As was done before when constructing Bayesian Belief Networks and using Rough Set methods, the training data were input to build the logistic regression model, and the test data were criterion. The 30 experiments were carried out, and 30 fitting results were obtained. When we modelled each data set, we chose the optimal logistic model through the value of RWhereb0 and bi and i.Among them, p indicates the probability of the HFMD outbreak risk level; st, 2004 and June 30th, 2007, by BBN. The study area falls between 25°N–30°N and 105°E–115°E, China. The study unit in this case was the town.In this case, a measles outbreak was defined as an outbreak on the verge of happening. The goal of this case was to assess the measles outbreak risk in regions of China between December 1Data from 438 disease cases were used in this study and were provided by the China CDC. In addition to outbreak data, the disease data included records of measles cases at the specified regions in the study area, although no outbreak had been reported simultaneously.According to the transmission characteristics of measles, the risk factors used here included residence environment factor and meteorological factors. The residence environment factor was whether there was a school or a factory in the town. The meteorological data came from the China Meteorological Data Sharing Service System and included diurnal barometric range, minimum daily temperature, daily amount of sunshine, minimum daily humidity, and maximum daily wind speed.All meteorological data were calculated by the Inverse Distance Weighting (IDW) method based on values from 756 national meteorological stations. All continuous variables were discretized before being entered into the Bayesian network.In this case, the network structure learning algorithm was applied to construct the network, and then the network structure was modified based on expert knowledge. As seen in As a comparison, we also applied the rough set method to assess measles outbreak risk in the same area during the same period. In the BBN, linear regression model, and rough set method, 75% of the sample data (328) were randomly selected as the training data, and the remaining 110 samples served as the test data. To verify the accuracy and reliability of the method, we repeated the random sampling process 30 times, producing 30 sets of experimental data. The results seen in  showed tSimilarly, we used logistic regression model to predict the measles outbreak risk level. The theory and procedure of how to use it were the same as the HFMD case before. The general form of models that be simulated from 30 sets experimental data was as follows:Whereb0 is a constant term; bi is the coefficient which corresponds to different levels of discrete various discrete risk factors xi.Among them, p represents the probability of measles outbreak risk; Because the ROC curve applies only to binary classifiers, we had to reclassify the HFMD outbreak risk level (0 or 1 or 2) into two classes. Situation 1: we defined level = 0 as the class that means there is no HFMD outbreak, and we merged level = 1 and level = 2 as the other class to represent the outbreak; Situation 2: we merged level = 0 and level = 1 to represent that the epidemic is not serious, and level = 2 as a representation of a very high HFMD outbreak risk. Meanwhile, for the measles dataset, due to the outbreak risk level (0 or 1) set as two values, reclassification was not needed. The ROC curves for each model’s average prediction of 30 replicate runs with a mean test AUC value and corresponding standard deviation are provided in In the lateral view, for the HFMD test dataset, no matter how to reclassify the risk level, the Bayesian network model achieved a very high performance evaluation (the average AUC test was 0.9628 for situation 1 and 0.9234 for situation 2), and better than the logistic regression model (average AUC test was 0.8679 for situation 1 and 0.8342 for situation 2) and rough set method (average AUC test was 0.606 for situation 1 and 0.5841 for situation 2). In the measles test dataset, the Bayesian network model (average test AUC was 0.9467) and logistic regression model (average test AUC is 0.9734) showed high prediction accuracy and were obviously superior to the rough set method (average AUC test was 0.7689). In the longitudinal view, the stability of the Bayesian network is good , regardless of the classification of the disease, and the logistic regression model  and rough set method  do not have this advantage.In recent years, early warning systems have played an important role in preventing and controlling the spread of infectious diseases. However, the recent requirement for very early detection has shifted in the direction of research. An increasing number of studies focused on improving the speed and accuracy of outbreak risk assessment have been conducted. In this article, a general approach integrating the Bayesian network concept with infectious disease risk assessment was proposed and demonstrated. We first introduced the methodology for assessing the disease outbreak risk based on a BBN, and then detailed how to assess the outbreak risks of measles and HFMD using a BBN based on disease cases and symptoms at different spatial scales. Finally, we compared the accuracy and feasibility of the BBN and rough set methods. Our results showed that the BBN performed better than the rough set method, both in terms of accuracy and stability. Moreover, once the relationship network among disease outbreak risks and factors was determined, the BBN accurately predicted the further risk of infectious disease outbreaks without several selected factors.There are many advantages to using a BBN to assess disease outbreak riskNevertheless, use of a BBN to assess infectious disease outbreak risks presents some limitations. Because a BBN is optimized to handle categorical variables, continuous data often must be discretized before modelling, which may result in information loss. Moreover, the accuracy of the method is closely related to the network structure. Although there are many network construction algorithms, it is difficult to update a proper Bayesian belief network in automatic and timely manner according to changes in the disease transmission process. This will affect the accuracy and sensitivity of disease outbreak risk assessment. In addition, the risk factors selected in this study were limited, and more related risk factors, such as symptom surveillance data and reported data from specified organizations, should be involved in future studies.How to cite this article: Liao, Y. et al. A new method for assessing the risk of infectious disease outbreak. Sci. Rep.7, 40084; doi: 10.1038/srep40084 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.