Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States.

BACKGROUND: Early diagnosis of Gaucher disease (GD) allows for disease-specific treatment before significant symptoms arise, preventing/delaying onset of complications. Yet, many endure years-long diagnostic odysseys. We report the development of a machine learning algorithm to identify patients with GD from electronic health records. METHODS: We utilized Optum's de-identified Integrated Claims-Clinical dataset (2007-2019) for feature engineering and algorithm training/testing, based on clinical characteristics of GD. Two algorithms were selected: one based on age of feature occurrence (age-based), and one based on occurrence of features (prevalence-based). Performance was compared with an adaptation of the available clinical diagnostic algorithm for identifying patients with diagnosed GD. Undiagnosed patients highly-ranked by the algorithms were compared with diagnosed GD patients. RESULTS: Splenomegaly was the most important predictor for diagnosed GD with both algorithms, followed by geographical location (northeast USA), thrombocytopenia, osteonecrosis, bone density disorders, and bone pain. Overall, 1204 and 2862 patients, respectively, would need to be assessed with the age- and prevalence-based algorithms, compared with 20,743 with the clinical diagnostic algorithm, to identify 28 patients with diagnosed GD in the integrated dataset. Undiagnosed patients highly-ranked by the algorithms had similar clinical manifestations as diagnosed GD patients. CONCLUSIONS: The age-based algorithm identified younger patients, while the prevalence-based identified patients with advanced clinical manifestations. Their combined use better captures GD heterogeneity. The two algorithms were about 10-20-fold more efficient at identifying GD patients than the clinical diagnostic algorithm. Application of these algorithms could shorten diagnostic delay by identifying undiagnosed GD patients.

Racial and Ethnic Minorities at the Highest Risk of Uncontrolled Moderate-to-Severe Asthma: A United States Electronic Health Record Analysis.

Purpose: The identification of risk factors associated with uncontrolled moderate-to-severe asthma is important to improve asthma outcomes. Aim of this study was to identify risk factors for uncontrolled asthma in United States cohort using electronic health record (EHR)-derived data. Patients and Methods: In this retrospective real-world study, de-identified data of adolescent and adult patients (≥12 years old) with moderate-to-severe asthma, based on asthma medications within 12 months prior to asthma-related visit (index date), were extracted from the Optum® Humedica EHR. The baseline period was 12 months prior to the index date. Uncontrolled asthma was defined as ≥2 outpatient oral corticosteroid bursts for asthma or ≥2 emergency department visits or ≥1 inpatient visit for asthma. A Cox proportional hazard model was applied. Results: There were 402,403 patients in the EHR between January 1, 2012, and December 31, 2018, who met the inclusion criteria and were analyzed. African American (AA) race (hazard ratio [HR]: 2.08), Medicaid insurance (HR: 1.71), Hispanic ethnicity (HR: 1.34), age of 12 to <18 years (HR 1.20), body mass index of ≥35 kg/m2 (HR: 1.20), and female sex (HR 1.19) were identified as risk factors associated with uncontrolled asthma (P < 0.001). Comorbidities characterized by type 2 inflammation, including a blood eosinophil count of ≥300 cells/µL (as compared with eosinophil <150 cells/µL; HR: 1.40, P < 0.001) and food allergy (HR: 1.31), were associated with a significantly higher risk of uncontrolled asthma; pneumonia was also a comorbidity associated with an increased risk (HR: 1.35) of uncontrolled asthma. Conversely, allergic rhinitis (HR: 0.84) was associated with a significantly lower risk of uncontrolled asthma. Conclusion: This large study demonstrates multiple risk factors for uncontrolled asthma. Of note, AA and Hispanic individuals with Medicaid insurance are at a significantly higher risk of uncontrolled asthma versus their White, non-Hispanic counterparts with commercial insurance.

Immunolab: Combining targeted real-world data with advanced analytics to generate evidence at scale in immunology.

Real-world evidence (RWE) has traditionally been used by regulatory or payer authorities to inform disease burden, background risk, or conduct post-launch pharmacovigilance, but in recent years RWE has been increasingly used to inform regulatory decision-making. However, RWE data sources remain fragmented, and datasets are disparate and often collected inconsistently. To this end, we have constructed an RWE-generation platform, Immunolab, to facilitate data-driven insights, hypothesis generation and research in immunological diseases driven by type 2 inflammation. Immunolab leverages a large, anonymized patient cohort from the Optum electronic health record and claims dataset containing over 17 million patient lives. Immunolab is an interactive platform that hosts three analytical modules: the Patient Journey Mapper, to describe the drug treatment patterns over time in patient cohorts; the Switch Modeler, to model treatment switching patterns and identify its drivers; and the Head-to-Head Simulator, to model the comparative effectiveness of treatments based on relevant clinical outcomes. The Immunolab modules utilize various analytic methodologies including machine learning algorithms for result generation which can then be presented in various formats for further analysis and interpretation.

Using social media listening and data mining to understand travellers' perspectives on travel disease risks and vaccine-related attitudes and behaviours.

BACKGROUND: Travellers can access online information to research and plan their expeditions/excursions, and seek travel-related health information. We explored German travellers' attitude and behaviour toward vaccination, and their travel-related health information seeking activities. METHODS: We used two approaches: web 'scraping' of comments on German travel-related sites and an online survey. 'Scraping' of travel-related sites was undertaken using keywords/synonyms to identify vaccine- and disease-related posts. The raw unstructured text extracted from online comments was converted to a structured dataset using Natural Language Processing Techniques. Traveller personas were defined using K-means based on the online survey results, with cluster (i.e. persona) descriptions made from the most discriminant features in a distinguished set of observations. The web-scraped profiles were mapped to the personas identified. Travel and vaccine-related behaviours were described for each persona. RESULTS: We identified ~2.6 million comments; ~880 k were unique and mentioned ~280 k unique trips by ~65 k unique profiles. Most comments were on destinations in Europe (37%), Africa (21%), Southeast Asia (12%) and the Middle East (11%). Eight personas were identified: 'middle-class family woman', 'young woman travelling with partner', 'female globe-trotter', 'upper-class active man', 'single male traveller', 'retired traveller', 'young backpacker', and 'visiting friends and relatives'. Purpose of travel was leisure in 82-94% of profiles, except the 'visiting friends and relatives' persona. Malaria and rabies were the most commented diseases with 12.7 k and 6.6 k comments, respectively. The 'middle-class family woman' and the 'upper-class active man' personas were the most active in online conversations regarding endemic disease and vaccine-related topics, representing 40% and 19% of comments, respectively. Vaccination rates were 54%-71% across the traveller personas in the online survey. Reasons for vaccination reluctance included perception of low risk to disease exposure (21%), price (14%), fear of side effects (12%) and number of vaccines (11%). CONCLUSIONS: The information collated on German traveller personas and behaviours toward vaccinations should help guide counselling by healthcare professionals.

Performance assessment of different machine learning approaches in predicting diabetic ketoacidosis in adults with type 1 diabetes using electronic health records data.

PURPOSE: To assess the performance of different machine learning (ML) approaches in identifying risk factors for diabetic ketoacidosis (DKA) and predicting DKA. METHODS: This study applied flexible ML (XGBoost, distributed random forest [DRF] and feedforward network) and conventional ML approaches (logistic regression and least absolute shrinkage and selection operator [LASSO]) to 3400 DKA cases and 11 780 controls nested in adults with type 1 diabetes identified from Optum® de-identified Electronic Health Record dataset (2007-2018). Area under the curve (AUC), accuracy, sensitivity and specificity were computed using fivefold cross validation, and their 95% confidence intervals (CI) were established using 1000 bootstrap samples. The importance of predictors was compared across these models. RESULTS: In the training set, XGBoost and feedforward network yielded higher AUC values (0.89 and 0.86, respectively) than logistic regression (0.83), LASSO (0.83) and DRF (0.81). However, the AUC values were similar (0.82) among these approaches in the test set (95% CI range, 0.80-0.84). While the accuracy values >0.8 and the specificity values >0.9 for all models, the sensitivity values were only 0.4. The differences in these metrics across these models were minimal in the test set. All approaches selected some known risk factors for DKA as the top 10 features. XGBoost and DRF included more laboratory measurements or vital signs compared with conventional ML approaches, while feedforward network included more social demographics. CONCLUSIONS: In our empirical study, all ML approaches demonstrated similar performance, and identified overlapping, but different, top 10 predictors. The difference in selected top predictors needs further research.

FAM13A affects body fat distribution and adipocyte function.

Genetic variation in the FAM13A (Family with Sequence Similarity 13 Member A) locus has been associated with several glycemic and metabolic traits in genome-wide association studies (GWAS). Here, we demonstrate that in humans, FAM13A alleles are associated with increased FAM13A expression in subcutaneous adipose tissue (SAT) and an insulin resistance-related phenotype (e.g. higher waist-to-hip ratio and fasting insulin levels, but lower body fat). In human adipocyte models, knockdown of FAM13A in preadipocytes accelerates adipocyte differentiation. In mice, Fam13a knockout (KO) have a lower visceral to subcutaneous fat (VAT/SAT) ratio after high-fat diet challenge, in comparison to their wild-type counterparts. Subcutaneous adipocytes in KO mice show a size distribution shift toward an increased number of smaller adipocytes, along with an improved adipogenic potential. Our results indicate that GWAS-associated variants within the FAM13A locus alter adipose FAM13A expression, which in turn, regulates adipocyte differentiation and contribute to changes in body fat distribution.

A New Liver Expression Quantitative Trait Locus Map From 1,183 Individuals Provides Evidence for Novel Expression Quantitative Trait Loci of Drug Response, Metabolic, and Sex-Biased Phenotypes.

Expression quantitative trait locus (eQTL) studies in human liver are crucial for elucidating how genetic variation influences variability in disease risk and therapeutic outcomes and may help guide strategies to obtain maximal efficacy and safety of clinical interventions. Associations between expression microarray and genome-wide genotype data from four human liver eQTL studies (n = 1,183) were analyzed. More than 2.3 million cis-eQTLs for 15,668 genes were identified. When eQTLs were filtered against a list of 1,496 drug response genes, 187,829 cis-eQTLs for 1,191 genes were identified. Additionally, 1,683 sex-biased cis-eQTLs were identified, as well as 49 and 73 cis-eQTLs that colocalized with genome-wide association study signals for blood metabolite or lipid levels, respectively. Translational relevance of these results is evidenced by linking DPYD eQTLs to differences in safety of chemotherapy, linking the sex-biased regulation of PCSK9 expression to anti-lipid therapy, and identifying the G-protein coupled receptor GPR180 as a novel drug target for hypertriglyceridemia.

Genomic Characterization of Metformin Hepatic Response.

Metformin is used as a first-line therapy for type 2 diabetes (T2D) and prescribed for numerous other diseases. However, its mechanism of action in the liver has yet to be characterized in a systematic manner. To comprehensively identify genes and regulatory elements associated with metformin treatment, we carried out RNA-seq and ChIP-seq (H3K27ac, H3K27me3) on primary human hepatocytes from the same donor treated with vehicle control, metformin or metformin and compound C, an AMP-activated protein kinase (AMPK) inhibitor (allowing to identify AMPK-independent pathways). We identified thousands of metformin responsive AMPK-dependent and AMPK-independent differentially expressed genes and regulatory elements. We functionally validated several elements for metformin-induced promoter and enhancer activity. These include an enhancer in an ataxia telangiectasia mutated (ATM) intron that has SNPs in linkage disequilibrium with a metformin treatment response GWAS lead SNP (rs11212617) that showed increased enhancer activity for the associated haplotype. Expression quantitative trait locus (eQTL) liver analysis and CRISPR activation suggest that this enhancer could be regulating ATM, which has a known role in AMPK activation, and potentially also EXPH5 and DDX10, its neighboring genes. Using ChIP-seq and siRNA knockdown, we further show that activating transcription factor 3 (ATF3), our top metformin upregulated AMPK-dependent gene, could have an important role in gluconeogenesis repression. Our findings provide a genome-wide representation of metformin hepatic response, highlight important sequences that could be associated with interindividual variability in glycemic response to metformin and identify novel T2D treatment candidates.

Species specific exome probes reveal new insights in positively selected genes in nonhuman primates.

Nonhuman primates (NHP) are important biomedical animal models for the study of human disease. Of these, the most widely used models in biomedical research currently are from the genus Macaca. However, evolutionary genetic divergence between human and NHP species makes human-based probes inefficient for the capture of genomic regions of NHP for sequencing and study. Here we introduce a new method to resequence the exome of NHP species by a designed capture approach specifically targeted to the NHP, and demonstrate its superior performance on four NHP species or subspecies. Detailed investigation on biomedically relevant genes demonstrated superior capture by the new approach. We identified 28 genes that appeared to be pseudogenized and inactivated in macaque. Finally, we identified 187 genes showing strong evidence for positive selection across all branches of the primate phylogeny including many novel findings.

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study.

BACKGROUND: Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. METHODS: We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). FINDINGS: In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003). INTERPRETATION: Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. FUNDING: National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

Identification and validation of N-acetyltransferase 2 as an insulin sensitivity gene.

Decreased insulin sensitivity, also referred to as insulin resistance (IR), is a fundamental abnormality in patients with type 2 diabetes and a risk factor for cardiovascular disease. While IR predisposition is heritable, the genetic basis remains largely unknown. The GENEticS of Insulin Sensitivity consortium conducted a genome-wide association study (GWAS) for direct measures of insulin sensitivity, such as euglycemic clamp or insulin suppression test, in 2,764 European individuals, with replication in an additional 2,860 individuals. The presence of a nonsynonymous variant of N-acetyltransferase 2 (NAT2) [rs1208 (803A>G, K268R)] was strongly associated with decreased insulin sensitivity that was independent of BMI. The rs1208 "A" allele was nominally associated with IR-related traits, including increased fasting glucose, hemoglobin A1C, total and LDL cholesterol, triglycerides, and coronary artery disease. NAT2 acetylates arylamine and hydrazine drugs and carcinogens, but predicted acetylator NAT2 phenotypes were not associated with insulin sensitivity. In a murine adipocyte cell line, silencing of NAT2 ortholog Nat1 decreased insulin-mediated glucose uptake, increased basal and isoproterenol-stimulated lipolysis, and decreased adipocyte differentiation, while Nat1 overexpression produced opposite effects. Nat1-deficient mice had elevations in fasting blood glucose, insulin, and triglycerides and decreased insulin sensitivity, as measured by glucose and insulin tolerance tests, with intermediate effects in Nat1 heterozygote mice. Our results support a role for NAT2 in insulin sensitivity.

Genome-wide epistatic expression quantitative trait loci discovery in four human tissues reveals the importance of local chromosomal interactions governing gene expression.

BACKGROUND: Epistasis (synergistic interaction) among SNPs governing gene expression is likely to arise within transcriptional networks. However, the power to detect it is limited by the large number of combinations to be tested and the modest sample sizes of most datasets. By limiting the interaction search space firstly to cis-trans and then cis-cis SNP pairs where both SNPs had an independent effect on the expression of the most variable transcripts in the liver and brain, we greatly reduced the size of the search space. RESULTS: Within the cis-trans search space we discovered three transcripts with significant epistasis. Surprisingly, all interacting SNP pairs were located nearby each other on the chromosome (within 290 kb-2.16 Mb). Despite their proximity, the interacting SNPs were outside the range of linkage disequilibrium (LD), which was absent between the pairs (r(2) < 0.01). Accordingly, we redefined the search space to detect cis-cis interactions, where a cis-SNP was located within 10 Mb of the target transcript. The results of this show evidence for the epistatic regulation of 50 transcripts across the tissues studied. Three transcripts, namely, HLA-G, PSORS1C1 and HLA-DRB5 share common regulatory SNPs in the pre-frontal cortex and their expression is significantly correlated. This pattern of epistasis is consistent with mediation via long-range chromatin structures rather than the binding of transcription factors in trans. Accordingly, some of the interactions map to regions of the genome known to physically interact in lymphoblastoid cell lines while others map to known promoter and enhancer elements. SNPs involved in interactions appear to be enriched for promoter markers. CONCLUSIONS: In the context of gene expression and its regulation, our analysis indicates that the study of cis-cis or local epistatic interactions may have a more important role than interchromosomal interactions.

Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases.

Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 × 10(-12)), while Dnmt3a KO signature does not (P = 0.017).

Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer's disease.

The genetics of complex disease produce alterations in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of molecular networks. To characterize molecular systems associated with late-onset Alzheimer's disease (LOAD), we constructed gene-regulatory networks in 1,647 postmortem brain tissues from LOAD patients and nondemented subjects, and we demonstrate that LOAD reconfigures specific portions of the molecular interaction structure. Through an integrative network-based approach, we rank-ordered these network structures for relevance to LOAD pathology, highlighting an immune- and microglia-specific module that is dominated by genes involved in pathogen phagocytosis, contains TYROBP as a key regulator, and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated TYROBP revealed expression changes that significantly overlapped the human brain TYROBP network. Thus the causal network structure is a useful predictor of response to gene perturbations and presents a framework to test models of disease mechanisms underlying LOAD.

Loci influencing blood pressure identified using a cardiovascular gene-centric array.

Blood pressure (BP) is a heritable determinant of risk for cardiovascular disease (CVD). To investigate genetic associations with systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) and pulse pressure (PP), we genotyped ∼50 000 single-nucleotide polymorphisms (SNPs) that capture variation in ∼2100 candidate genes for cardiovascular phenotypes in 61 619 individuals of European ancestry from cohort studies in the USA and Europe. We identified novel associations between rs347591 and SBP (chromosome 3p25.3, in an intron of HRH1) and between rs2169137 and DBP (chromosome1q32.1 in an intron of MDM4) and between rs2014408 and SBP (chromosome 11p15 in an intron of SOX6), previously reported to be associated with MAP. We also confirmed 10 previously known loci associated with SBP, DBP, MAP or PP (ADRB1, ATP2B1, SH2B3/ATXN2, CSK, CYP17A1, FURIN, HFE, LSP1, MTHFR, SOX6) at array-wide significance (P < 2.4 × 10(-6)). We then replicated these associations in an independent set of 65 886 individuals of European ancestry. The findings from expression QTL (eQTL) analysis showed associations of SNPs in the MDM4 region with MDM4 expression. We did not find any evidence of association of the two novel SNPs in MDM4 and HRH1 with sequelae of high BP including coronary artery disease (CAD), left ventricular hypertrophy (LVH) or stroke. In summary, we identified two novel loci associated with BP and confirmed multiple previously reported associations. Our findings extend our understanding of genes involved in BP regulation, some of which may eventually provide new targets for therapeutic intervention.

Factors that predict response of patients with hepatitis C virus infection to boceprevir.

BACKGROUND & AIMS: Little is known about factors associated with a sustained virologic response (SVR) among patients with hepatitis C virus (HCV) infection to treatment with protease inhibitors. METHODS: Previously untreated patients (from the Serine Protease Inhibitor Therapy 2 [SPRINT-2] trial) and those who did not respond to prior therapy (from the Retreatment with HCV Serine Protease Inhibitor Boceprevir and PegIntron/Rebetol 2 [RESPOND-2] trial) received either a combination of peginterferon and ribavirin for 48 weeks or boceprevir, peginterferon, and ribavirin (triple therapy) after 4 weeks of peginterferon and ribavirin (total treatment duration, 28-48 wk). A good response to interferon was defined as a ≥ 1 log(10) decrease in HCV RNA at week 4; a poor response was defined as a <1 log(10) decrease. We used multivariate regression analyses to identify baseline factors of the host (including the polymorphism interleukin [IL]-28B rs12979860) associated with response. The polymorphism IL-28B rs8099917 also was assessed. RESULTS: In the SPRINT-2 trial, factors that predicted a SVR to triple therapy included low viral load (odds ratio [OR], 11.6), IL-28B genotype (rs 12979860 CC vs TT and CT; ORs, 2.6 and 2.1, respectively), absence of cirrhosis (OR, 4.3), HCV subtype 1b (OR, 2.0), and non-black race (OR, 2.0). In the RESPOND-2 trial, the only factor significantly associated with a SVR was previous relapse, compared with previous nonresponse (OR, 2.6). Most patients with rs12979860 CC who received triple therapy had undetectable levels of HCV RNA by week 8 (76%-89%), and were eligible for shortened therapy. In both studies, IL-28B rs12979860 CC was associated more strongly with a good response to interferon than other baseline factors; however, a ≥ 1 log(10) decrease in HCV-RNA level at week 4 was associated more strongly with SVR than IL-28B rs12979860. Combining the rs8099917 and rs12979860 genotypes does not increase the association with SVR. CONCLUSIONS: The CC polymorphism at IL-28B rs12979860 is associated with response to triple therapy and can identify candidates for shorter treatment durations. A ≥ 1 log(10) decrease in HCV RNA at week 4 of therapy is the strongest predictor of a SVR, regardless of polymorphisms in IL-28B.

Inference of modules associated to eQTLs.

Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this 'main' SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.

Clinicopathologic and gene expression parameters predict liver cancer prognosis.

BACKGROUND: The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. METHODS: Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. RESULTS: HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. CONCLUSION: When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome.

Predictive genes in adjacent normal tissue are preferentially altered by sCNV during tumorigenesis in liver cancer and may rate limiting.

BACKGROUND: In hepatocellular carcinoma (HCC) genes predictive of survival have been found in both adjacent normal (AN) and tumor (TU) tissues. The relationships between these two sets of predictive genes and the general process of tumorigenesis and disease progression remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: Here we have investigated HCC tumorigenesis by comparing gene expression, DNA copy number variation and survival using ∼250 AN and TU samples representing, respectively, the pre-cancer state, and the result of tumorigenesis. Genes that participate in tumorigenesis were defined using a gene-gene correlation meta-analysis procedure that compared AN versus TU tissues. Genes predictive of survival in AN (AN-survival genes) were found to be enriched in the differential gene-gene correlation gene set indicating that they directly participate in the process of tumorigenesis. Additionally the AN-survival genes were mostly not predictive after tumorigenesis in TU tissue and this transition was associated with and could largely be explained by the effect of somatic DNA copy number variation (sCNV) in cis and in trans. The data was consistent with the variance of AN-survival genes being rate-limiting steps in tumorigenesis and this was confirmed using a treatment that promotes HCC tumorigenesis that selectively altered AN-survival genes and genes differentially correlated between AN and TU. CONCLUSIONS/SIGNIFICANCE: This suggests that the process of tumor evolution involves rate-limiting steps related to the background from which the tumor evolved where these were frequently predictive of clinical outcome. Additionally treatments that alter the likelihood of tumorigenesis occurring may act by altering AN-survival genes, suggesting that the process can be manipulated. Further sCNV explains a substantial fraction of tumor specific expression and may therefore be a causal driver of tumor evolution in HCC and perhaps many solid tumor types.