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Introduction Bile acids (BAs) arising from duodenogastric reflux are known to facilitate gastric cancer (GC) development. Although BAs traditionally contribute to carcinogenesis through direct cellular cytotoxicity, increasing evidence implicates nuclear and membrane BA receptors (BARs) as additional factors influencing cancer risk. Indeed, some BARs are already linked with GC, but conflicting evidence and lack of information regarding other endogenous BARs warrant further investigation. In this study, we meta-analyzed multiple data sets to identify clinically relevant relationships between BAR expression and prognosis, clinicopathology, and activity in GC. Methods We collected transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas to analyze associations between BAR expression and GC prognosis, subtype, and clinicopathology. We also used Ingenuity Pathway Analysis to assess and predict functions, upstream regulators, and downstream mediators of membrane and nuclear BARs in GC. Results BARs showed differential distribution in GC; membrane BARs (G protein-coupled BAR 1, sphingosine-1-phosphate receptor 2, and cholinergic receptor muscarinic 2) were enriched in diffuse-, genome-stable, and mesenchymal-type tumors, whereas nuclear BARs (pregnane-X-receptor, constitutive androstane receptor, and farnesoid-X-receptor) were enriched in chromosome instability and metabolic subtypes. High expression of all membrane but not nuclear BARs was associated with poor prognosis and unfavorable GC clinicopathologic features. Similarly, expression patterns of membrane but not nuclear BARs varied geographically, aligning with Helicobacter pylori infection and GC mortality rates. Finally, GC-related oncogenes, namely transforming growth factor β1, were associated with membrane BARs, whereas many metabolic-associated genes were associated with nuclear BARs. Discussion Through transcriptomic meta-analysis, we identified distinct expression profiles between nuclear and membrane BARs that demonstrate prognostic relevance and warrant further investigation.

Key Points Question Can machine learning be used to predict incident delirium in newly hospitalized patients using only data available in the electronic health record shortly after admission? Findings In this cohort study, classification models were trained using 5 different machine learning algorithms on 14 227 hospital stays and validated on a prospective test set of 3996 hospital stays. The gradient boosting machine model performed best, with an area under the receiver operating characteristic curve of 0.855. Meaning Machine learning can accurately predict delirium risk using electronic health record data on admission and outperforms the nurse-administered prediction rules currently used., This study describes the development and prognostic accuracy of 5 machine learning models designed to predict incident delirium in newly hospitalized patients without known cognitive impairment using clinical variables derived from the patients’ electronic health record (EHR)., Importance Current methods for identifying hospitalized patients at increased risk of delirium require nurse-administered questionnaires with moderate accuracy. Objective To develop and validate a machine learning model that predicts incident delirium risk based on electronic health data available on admission. Design, Setting, and Participants Retrospective cohort study evaluating 5 machine learning algorithms to predict delirium using 796 clinical variables identified by an expert panel as relevant to delirium prediction and consistently available in electronic health records within 24 hours of admission. The training set comprised 14 227 adult patients with non–intensive care unit hospital stays and no delirium on admission who were discharged between January 1, 2016, and August 31, 2017, from UCSF Health, a large academic health institution. The test set comprised 3996 patients with hospital stays who were discharged between August 1, 2017, and November 30, 2017. Exposures Patient demographic characteristics, diagnoses, nursing records, laboratory results, and medications available in electronic health records during hospitalization. Main Outcomes and Measures Delirium was defined as a positive Nursing Delirium Screening Scale or Confusion Assessment Method for the Intensive Care Unit score. Models were assessed using the area under the receiver operating characteristic curve (AUC) and compared against the 4-point scoring system AWOL (age >79 years, failure to spell world backward, disorientation to place, and higher nurse-rated illness severity), a validated delirium risk–assessment tool routinely administered in this cohort. Results The training set included 14 227 patients (5113 [35.9%] aged >64 years; 7335 [51.6%] female; 687 [4.8%] with delirium), and the test set included 3996 patients (1491 [37.3%] aged >64 years; 1966 [49.2%] female; 191 [4.8%] with delirium). In total, the analysis included 18 223 hospital admissions (6604 [36.2%] aged >64 years; 9301 [51.0%] female; 878 [4.8%] with delirium). The AWOL system achieved a baseline AUC of 0.678. The gradient boosting machine model performed best, with an AUC of 0.855. Setting specificity at 90%, the model had a 59.7% (95% CI, 52.4%-66.7%) sensitivity, 23.1% (95% CI, 20.5%-25.9%) positive predictive value, 97.8% (95% CI, 97.4%-98.1%) negative predictive value, and a number needed to screen of 4.8. Penalized logistic regression and random forest models also performed well, with AUCs of 0.854 and 0.848, respectively. Conclusions and Relevance Machine learning can be used to estimate hospital-acquired delirium risk using electronic health record data available within 24 hours of hospital admission. Such a model may allow more precise targeting of delirium prevention resources without increasing the burden on health care professionals.

While it has been technically feasible to create longitudinal representations of individual health at a nationwide scale, the use of these techniques to identify novel disease associations for the risk stratification of patients has had limited success. Here, we created a large-scale US longitudinal disease network of traced readmission patterns (i.e., disease trajectories), merging data from over 10.4 million inpatients from 350 California hospitals through the Healthcare Cost and Utilization Project between 1980 and 2010. We were able to create longitudinal representations of disease progression mapping over 300 common diseases, including the well-known complication of heart failure after acute myocardial infarction. Surprisingly, out of these generated disease trajectories, we discovered an unknown association between schizophrenia, a chronic mental disorder, and rhabdomyolysis, a rare disease of muscle breakdown. It was found that 92 of 3674 patients (2.5%) with schizophrenia were readmitted for rhabdomyolysis (relative risk, 2.21 [1.80–2.71, confidence interval = 0.95] P-value 9.54E-15), which has a general population incidence of 1 in 10,000. We validated this association using independent electronic health records from over 830,000 patients treated over seven years at the University of California, San Francisco (UCSF) medical center. A case review of 29 patients at UCSF who were treated for schizophrenia and who went on to develop rhabdomyolysis demonstrated that the majority of cases (62%) are idiopathic, which suggests a biological connection between these two diseases. Together, these findings demonstrate the power of using public disease registries in combination with electronic medical records to discover novel disease associations.One Sentence SummaryBased on the longitudinal health records from millions of California inpatient discharges, we created a temporal network that enabled us to understand statewide patterns of hospital readmissions, which led to the novel finding that hospitalization for schizophrenia is significantly associated with rehospitalization for rhabdomyolysis.

PURPOSE: To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 ((18)F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers. MATERIALS AND METHODS: Prospective (18)F-FDG PET brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding. RESULTS: The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P < .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain. CONCLUSION: By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Larvie in this issue.

A crucial factor in Big Data is to take advantage of available data and use that for new discovery or hypothesis generation. In this study, we analyzed Large-scale data from the literature to OMICS, such as the genome, proteome or metabolome, respectively, for skin conditions. Skin acts as a natural barrier to the world around us and protects our body from different conditions, viruses, and bacteria, and plays a big part in appearance. We have included Hyperpigmentation, Postinflammatory Hyperpigmentation, Melasma, Rosacea, Actinic keratosis, and Pigmentation in this study. These conditions have been selected based on reasoning of big scale UCSF patient data of 527,273 females from 2011 to 2017, and related publications from 2000 to 2017 regarding skin conditions. The selected conditions have been confirmed with experts in the field from different research centers and hospitals. We proposed a novel framework for large-scale available public data to find the common genotypes and phenotypes of different skin conditions. The outcome of this study based on Advance Data Analytics provides information on skin conditions and their treatments to the research community and introduces new hypotheses for possible genotype and phenotype targets. The novelty of this work is a meta-analysis of different features on different skin conditions. Instead of looking at individual conditions with one or two features, which is how most of the previous works are conducted, we looked at several conditions with different features to find the common factors between them. Our hypothesis is that by finding the overlap in genotype and phenotype between different skin conditions, we can suggest using a drug that is recommended in one condition, for treatment in the other condition which has similar genes or other common phenotypes. We identified common genes between these skin conditions and were able to find common areas for targeting between conditions, such as common drugs. Our work has implications for discovery and new hypotheses to improve health quality, and is geared towards making Big Data useful.

A central premise in systems pharmacology is that structurally similar compounds have similar cellular responses; however, this principle often does not hold. One of the most widely used measures of cellular response is gene expression. By integrating gene expression data from Library of Integrated Network-based Cellular Signatures (LINCS) with chemical structure and bioactivity data from PubChem, we performed a large-scale correlation analysis of chemical structures and gene expression profiles of over 11,000 compounds taking into account confounding factors such as biological conditions (e.g., cell line, dose) and bioactivities. We found that structurally similar compounds do indeed yield similar gene expression profiles. There is an ∼20% chance that two structurally similar compounds (Tanimoto Coefficient ≥ 0.85) share significantly similar gene expression profiles. Regardless of structural similarity, two compounds tend to share similar gene expression profiles in a cell line when they are administrated at a higher dose or when the cell line is sensitive to both compounds.

Preterm birth (PTB), or the delivery prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. Although twin studies estimate that maternal genetic contributions account for approximately 30% of the incidence of PTB, and other studies reported fetal gene polymorphism association, to date no consistent associations have been identified. In this study, we performed the largest reported genome-wide association study analysis on 1,349 cases of PTB and 12,595 ancestry-matched controls from the focusing on genomic fetal signals. We tested over 2 million single nucleotide polymorphisms (SNPs) for associations with PTB across five subpopulations: African (AFR), the Americas (AMR), European, South Asian, and East Asian. We identified only two intergenic loci associated with PTB at a genome-wide level of significance: rs17591250 (P = 4.55E-09) on chromosome 1 in the AFR population and rs1979081 (P = 3.72E-08) on chromosome 8 in the AMR group. We have queried several existing replication cohorts and found no support of these associations. We conclude that the fetal genetic contribution to PTB is unlikely due to single common genetic variant, but could be explained by interactions of multiple common variants, or of rare variants affected by environmental influences, all not detectable using a GWAS alone.

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancements in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration have ushered us into the era of radiomics, which has tremendous potential in clinical decision support as well as drug discovery. There are important issues to consider to incorporate radiomics as a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that to enterprise development (Part 2).

Enterprise imaging has channeled various technological innovations to the field of clinical radiology, ranging from advanced imaging equipment and postacquisition iterative reconstruction tools to image analysis and computer-aided detection tools. More recently, the advancement in the field of quantitative image analysis coupled with machine learning-based data analytics, classification, and integration has ushered in the era of radiomics, a paradigm shift that holds tremendous potential in clinical decision support as well as drug discovery. However, there are important issues to consider to incorporate radiomics into a clinically applicable system and a commercially viable solution. In this two-part series, we offer insights into the development of the translational pipeline for radiomics from methodology to clinical implementation (Part 1) and from that point to enterprise development (Part 2). In Part 2 of this two-part series, we study the components of the strategy pipeline, from clinical implementation to building enterprise solutions.

The Gene Expression Omnibus (GEO) contains more than two million digital samples from functional genomics experiments amassed over almost two decades. However, individual sample meta-data remains poorly described by unstructured free text attributes preventing its largescale reanalysis. We introduce the Search Tag Analyze Resource for GEO as a web application (http://STARGEO.org) to curate better annotations of sample phenotypes uniformly across different studies, and to use these sample annotations to define robust genomic signatures of disease pathology by meta-analysis. In this paper, we target a small group of biomedical graduate students to show rapid crowd-curation of precise sample annotations across all phenotypes, and we demonstrate the biological validity of these crowd-curated annotations for breast cancer. STARGEO.org makes GEO data findable, accessible, interoperable and reusable (i.e., FAIR) to ultimately facilitate knowledge discovery. Our work demonstrates the utility of crowd-curation and interpretation of open ‘big data’ under FAIR principles as a first step towards realizing an ideal paradigm of precision medicine.

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members., eLife digest Of all the data in the world today, 90% was created in the last two years. However, taking advantage of this data in order to advance our knowledge is restricted by how quickly we can access it and analyze it in a proper context. In biomedical research, data is largely fragmented and stored in databases that typically do not “talk” to each other, thus hampering progress. One particular problem in medicine today is that the process of making a new therapeutic drug from scratch is incredibly expensive and inefficient, making it a risky business. Given the low success rate in drug discovery, there is an economic incentive in trying to repurpose an existing drug that has already been shown to be safe and effective towards a new disease or condition. Himmelstein et al. used a computational approach to analyze 50,000 data points – including drugs, diseases, genes and symptoms – from 19 different public databases. This approach made it possible to create more than two million relationships among the data points, which could be used to develop models that predict which drugs currently in use by doctors might be best suited to treat any of 136 common diseases. For example, Himmelstein et al. identified specific drugs currently used to treat depression and alcoholism that could be repurposed to treat smoking addition and epilepsy. These findings provide a new and powerful way to study drug repurposing. While this work was exclusively performed with public data, an expanded and potentially stronger set of predictions could be obtained if data owned by pharmaceutical companies were incorporated. Additional studies will be needed to test the predictions made by the models.

584 Background: Cholangiocarcinoma (CCA) is a rare cancer of the bile ducts but has been increasing in incidence. The mainstay of treatment of CCA is resection or chemoradiation for more advanced disease, with immunotherapy being an evolving field in treatment. A better understanding of CCA pathogenesis will pave new avenues for treatment. Methods: We employed our STARGEO platform to conduct a meta-analysis of public data from NCBI's Gene Expression Omnibus. We performed meta-analysis with 259 CCA tumor samples against 16 normal intrahepatic duct samples as a control. We then analyzed the signature in Ingenuity Pathway Analysis. Results: Our analysis revealed FXR/RXR and LXR/RXR activation as top canonical pathways. Top upstream regulators identified included HNF1A (with predicted inhibition) and ERBB2 (with predicted activation). The most upregulated genes included several extracellular matrix proteins implicated in cancer including COL1A1, LAMC2 (correlated with poor prognosis in CCA), KRT17 (a keratin implicated in various malignancies but not well described in CCA), and LAMB3 (exerts tumorigenesis through PI3k/Akt signaling). Additionally, we found stark upregulation of the immunophillin FKPBP1A, which is involved in mTOR activation. We also noted upregulation of ubiquitin-associated gene UBASH3B, which inhibits endocytosis in EGFR and has been described in breast cancer but not CCA. From our investigation of immune checkpoint inhibitors, we found upregulation of classically described inhibitors such as CTLA4, TIGIT, and BTLA. In addition, we found upregulation of SIGLEC7, which has been recently shown to suppress immune function by binding to terminal sialic acid on glycans on the surface of immune cells. Conclusions: Our analysis highlights the possible role of ERBB2 and several extracellular genes in the pathogenesis of CCA. We also identify the role of genes not previously described in CCA such as FKBP1A and UBASH3B. Lastly, our results promote the promise of immunotherapy in CCA treatment.

Background: Monoclonal gammopathy of undetermined significance (MGUS) is characterized by plasma cell production of abnormal monoclonal protein, or M protein. While MGUS itself is asymptomatic, it generally carries a 1% per year risk to progression to multiple myeloma (MM). The etiology of MGUS, as well as why it progresses to MM in some cases, remains unclear. Moreover, it is not known why some MGUS patients, such as African Americans, have higher risk to progression to MM. Contrasting MGUS and MM can potentially highlight genes that differentiate benign gammopathies from malignant ones and may be involved in disease progression from MGUS to MM. Methods: We employed our STARGEO platform to tag samples from the NCBI Gene Expression Omnibus and performed two separate meta-analysis to compare MGUS and MM transcriptomes. For the first meta-analysis, we tagged MGUS plasma cells recovered from the bone marrow of 101 patients and tagged plasma cells from 64 healthy subjects as a control. For the second analysis. We tagged CD138+ cells from the bone marrow of 383 MM patients and used the MGUS tagged samples as a control. We then analyzed the signature in Ingenuity Pathway Analysis (IPA). Results: From our first meta-analysis of MGUS, we identified EIF2 signaling, regulation of EIF4 and p70S6K signaling, and JAK/STAT signaling as top canonical pathways. Top upstream regulators included TP53, TGFB1, and the proto-oncogene MYCN and MYC (with predicted activation). The most upregulated genes included pro-oncogenes such as KIT and MLLT3, which is well-studied in acute leukemia but not yet described in MGUS. Another top upregulated gene was NRG3, a myeloma growth factor. Additionally, our analysis highlighted key genes involved in transcription and epigenetic regulation. For example, there was upregulation of RBFOX2, which is involved in alternative splicing during oncogenesis and tumor progression, and of PARP15, a transcriptional repressor with poly(ADP-ribose) polymerase activity and candidate gene for drug targeting. Also, there was upregulation of the DNA damage-inducible gene GADD45A, found to promote global DNA methylation. Lastly, we found upregulation of COMMD3, a gene with a recently identified role in humoral activity and B cell migration. From our second meta-analysis comparing MM and MGUS directly, we identified mitochondrial dysfunction, oxidative phosphorylation, purine nucleotides de novo biosynthesis, and sirtuin signaling as top upstream regulators. Like our first analysis, TP53 (with predicted inhibition), TGFB1, and MYC (with predicted activation) were top upstream regulators. The most upregulated gene was NUP62, a nucleoporin and novel regulator of cell proliferation and inducer of MYC activity. Our analysis also illustrated pro-oncogenic signaling pathways such as the Wnt pathway through upregulation of the ubiquitin ligase RNF14 and serine/threonine kinase through upregulation of SRPK2. Moreover, we found upregulation of the super-enhancer DUSP4, a phosphatase whose over-activity may drive MM severity. Lastly, we found upregulation of lysosomal associated membrane protein LAMP5. LAMP5 was recently identified in single-cell RNA sequencing of MM patients and may play a significant role in disease. Conclusions: Our study illustrates signaling pathways in MGUS that are present in MM such as EIF2, JAK/STAT, and MYC signaling. We also illustrate gene activity in MGUS that may predispose to MM progression such as NRG3, RBFOX2, and PARP15. GADD45RA and COMMD3 may play novel roles in MGUS. Our second analysis highlighted disease activity that persist from MGUS to MM, such as MYC signaling. It is possible that the genes from this analysis that aims to distinguish MM from MGUS may be responsible for tipping the scales from benignity to malignancy. Genes such as DUSP4, RN14, LAMP5, and others could serve as novel biomarkers or targets to MM and risk of progression of MGUS to MM. Disclosures No relevant conflicts of interest to declare.

7531 Background: Chronic lymphocytic leukemia (CLL) is the most common leukemia in adults and has a heterogenous presentation. CLL is known to shape the immune response to survive. Studying these processes will help gauge the potential success of immunotherapy and point to therapeutic targets. Methods: We used our Search, Tag, Analyze, Resource platform to meta-analyze patient samples from Gene Expression Omnibus. We tagged peripheral B cells from 741 CLL patients and peripheral B cell samples from 150 healthy donors as a control. We also tagged and compared B cell samples from 84 CLL progressors to 91 patients with stable CLL. Lastly, we tagged peripheral T cells from 70 CLL patients and T cells from 35 healthy donors as a control. We then analyzed the signature in Ingenuity Pathway Analysis. Results: Analysis of CLL cell samples identified T cell exhaustion signaling as our top canonical pathway. IL2, IL5, and TGFB1 were top upstream regulators. We found upregulation of PDL1, CTLA4, and Lag3, known markers for immunosuppressive B cells. FMOD, which sequesters TGFB, was also upregulated along with molecules that modulate BCR signaling such as MIR155HG. EBF1, required for B cell differentiation, and the co-stimulatory molecule CD80 were downregulated. Analysis of progressing CLL versus stable CLL highlighted metabolic changes. S-adenosyl-L-methionine biosynthesis, methionine degradation to homocysteine, cysteine biosynthesis, and acetate conversion to acetyl-CoA were top canonical pathways. No difference was seen in PDL1, CTLA4, and Lag3 expression but EBF1 was upregulated. Lastly, our T cell analysis demonstrated NFAT in regulation of the immune response as the top canonical pathway. Conclusions: Our results reinforce the promise immunotherapy can have in treatment of CLL and suggests more aggressive cases of CLL are a function of metabolic changes as opposed to differences in immune escape. We also suggest a role of NFAT in T cell exhaustion in the context of CLL.

e13048 Background: Hurthle Cell Carcinoma (HCC), a variant of follicular thyroid carcinoma, is a rare form of thyroid cancer. Though it accounts for 3-10% of thyroid cancer cases, very little is known about its pathogenesis. While HCC holds good survival rates, it is still vital to understand the pathogenesis of HCC in order to further optimize treatment protocols. Methods: We tagged 46 HCC samples and 90 healthy thyroid samples as a control. Gene signatures were analyzed in Ingenuity Pathway Analysis, using statistical significance p < 0.05 and absolute log ratio > 0.5 between disease and controls. Methods: Search Tag Analyze Resource was employed to conduct meta-analysis using the National Center for Biotechnologys Gene Expression Omnibus to define HCC pathogenesis. Results: Hepatic fibrosis, hepatic stellate cell activation and retinoic acid receptor (RAR) activation were the top canonical activators associated with HCC. Of the molecules involved, Defensin Beta 1 (DEFB1), Lipocalin-2 (LCN2), Aldehyde Dehydrogenase 1 Family Member A3 (ALDH1A3), and CDC28 Protein Kinase Regulatory Subunit 2 (CKS2) had the highest associations with HCC. The most down-regulated molecules were Hemoglobin Subunit Beta (HBB), Hemoglobin Subunit Alpha 1 and 2 (HBA1/HBA2), Cellular Retinoic Acid Binding Protein 1 (CRABP1), and BH3-Interacting Domain-Containing Protein 3 (HRK). Conclusions: These results suggest that there are a variety of factors at play regarding the development of HCC. Defensins are peptides made by neutrophils, however DEFB1 is specifically known for its ability to resist bacterial growth on epithelial surfaces, playing a major role in innate immunity. LCN2 is also known for resisting growth on surfaces and does so by sequestering iron-containing siderophores, thus effectively stopping growth of microorganisms. Additionally, ALDH1A3 and CRABP1 are associated with RAR activation. While the exact pathogenesis of HCC is not well known yet, we demonstrate that the two most up-regulated factors in HCC are strongly involved in innate immunity. This suggests that the dysfunction of innate immunity may play a vital role in the neoplastic pathogenesis of HCC.

Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide. Previous research has yielded insights into its genetic etiology, but there remains a gap in the understanding of genetic factors that contribute to risk, and particularly in the biological mechanisms by which genetic variation modulates risk. The National Cancer Institute’s “Up for a Challenge” (U4C) competition provided an opportunity to further elucidate the genetic basis of the disease. Our group leveraged the seven datasets made available by the U4C organizers and data from the publicly available UK Biobank cohort to examine associations between imputed gene expression and breast cancer risk. In particular, we used reference datasets describing the breast tissue and whole blood transcriptomes to impute expression levels in breast cancer cases and controls. In trans-ethnic meta-analyses of U4C and UK Biobank data, we found significant associations between breast cancer risk and the expression of RCCD1 (joint p-value: 3.6x10-06) and DHODH (p-value: 7.1x10-06) in breast tissue, as well as a suggestive association for ANKLE1 (p-value: 9.3x10-05). Expression of RCCD1 in whole blood was also suggestively associated with disease risk (p-value: 1.2x10-05), as were expression of ACAP1 (p-value: 1.9x10-05) and LRRC25 (p-value: 5.2x10-05). While genome-wide association studies (GWAS) have implicated RCCD1 and ANKLE1 in breast cancer risk, they have not identified the remaining three genes. Among the genetic variants that contributed to the predicted expression of the five genes, we found 23 nominally (p-value < 0.05) associated with breast cancer risk, among which 15 are not in high linkage disequilibrium with risk variants previously identified by GWAS. In summary, we used a transcriptome-based approach to investigate the genetic underpinnings of breast carcinogenesis. This approach provided an avenue for deciphering the functional relevance of genes and genetic variants involved in breast cancer., Author summary There is a clear genetic basis of breast cancer, and previous work has identified numerous genetic variants that increase risk of this common disease. However, much of the underlying genetic variation in breast cancer remains unexplained. To address this void, as part of the National Cancer Institute’s “Up for a Challenge” (U4C) competition, we undertook a large-scale study of genetically regulated gene expression and breast cancer. Specifically, we estimated gene expression levels based on germline genetics for subjects in the seven breast cancer studies provided by U4C and for subjects in the UK Biobank. We then evaluated associations between gene expression and breast cancer and detected three novel and two known breast cancer genes. These genes exhibit potential biological mechanisms for impacting breast carcinogenesis. Our work highlights the value of leveraging different sources of data to more thoroughly study the genetic basis of complex diseases.

Drug repositioning has been based largely on genomic signatures of drugs and diseases. One challenge in these efforts lies in connecting the molecular signatures of drugs into clinical responses, including therapeutic and side effects, to the repurpose of drugs. We addressed this challenge by evaluating drug-drug relationships using a phenotypic and molecular-based approach that integrates therapeutic indications, side effects, and gene expression profiles induced by each drug. Using cosine similarity, relationships between 445 drugs were evaluated based on high-dimensional spaces consisting of phenotypic terms of drugs and genomic signatures, respectively. One hundred fifty-one of 445 drugs comprising 450 drug pairs displayed significant similarities in both phenotypic and genomic signatures (P value

During aging, intracranial volume remains unchanged and represents maximally attained brain size, while various interacting biological phenomena lead to brain volume loss. Consequently, intracranial volume and brain volume in late life reflect different genetic influences. Our genome-wide association study (GWAS) in 8,175 community-dwelling elderly persons did not reveal any associations at genome-wide significance (P < 5 × 10 -8) for brain volume. In contrast, intracranial volume was significantly associated with two loci: rs4273712 (P = 3.4 × 10 -11), a known height-associated locus on chromosome 6q22, and rs9915547 (P = 1.5 × 10 -12), localized to the inversion on chromosome 17q21. We replicated the associations of these loci with intracranial volume in a separate sample of 1,752 elderly persons (P = 1.1 × 10 -3 for 6q22 and 1.2 × 10 -3 for 17q21). Furthermore, we also found suggestive associations of the 17q21 locus with head circumference in 10,768 children (mean age of 14.5 months). Our data identify two loci associated with head size, with the inversion at 17q21 also likely to be involved in attaining maximal brain size. © 2012 Nature America, Inc. All rights reserved.

71 Background: Esophageal adenocarcinoma (EAC) is the sixth most common cause of cancer-related deaths worldwide. It commonly arises in the setting of reflux disease and Barett’s esophagus. It remains incurable and holds a poor prognosis. Dissecting the genetic signature of EAC can pave new therapeutic avenues. Methods: We employed our Search Tag Analyze Resource (STARGEO) platform to conduct meta-analysis using the National Center for Biotechnology’s (NCBI) Gene Expression Omnibus (GEO). We tagged 151 tumor samples from EAC patients and 62 normal esophageal samples as a control. We then analyzed the signature in Ingenuity Pathway Analysis, restricting genes that showed statistical significance (p < 0.05) and an absolute experimental log ratio greater than 0.15 between tumor and control. Results: Our analysis revealed granulocyte adhesion and diapedesis and FXR/RXR signaling as top canonical pathways. TGFB1, TNF, and beta-estradiol were top upstream regulators with predicted activation. TGFB1 and TNF expression have been correlated with poor prognosis in EAC. Also, beta-estradiol has tumorigenic activity in several cancers but has not been investigated in EAC. Among the top upregulated genes were oncogenic genes such as tetraspanin 8, the antiapoptotic factor OLFM4, and the protease cathepsin E (CTSE). SPINK1, a trypsin inhibitor with recently suggested role in cancer, and the choline transporter SLC44A4, a drug target for pancreatic cancer, were also upregulated. The most downregulated genes included alcohol dehydrogenase 7, associated with EAC in alcohol-drinkers, and the pro-apoptotic gene CRCT1. We also found downregulation of the serine peptidase inhibitor SPINK7. SPINK7 is involved in maintaining epithelial-barrier integrity and is implicated in eosinophilic esophagitis pathogenesis. Lastly, there was downregulation of the candidate tumor suppressor gene transglutaminase 3. Conclusions: Despite screening efforts, EAC incidence and mortality continues to increase as does the need for better treatment. This meta-analysis defines the significant gene expression changes within causal disease processes to provide markers for detection, prognostic insight, and therapeutic value for EAC.

Background: Multiple myeloma (MM) is a clonal B cell neoplasia that comes from growth of malignant B cells in the bone marrow. Stromal cells, including inflammatory cells, in the bone marrow enable MM persistence and growth. MM is characterized by an uncoordinated cytokine system with an increase in proinflammatory cytokines. While proinflammatory cytokines are essential in mounting an anti-tumor response, they can also drive cancer progression. Ultimately, it is the effects of the cytokine milieu in the immune microenvironment that help determine MM development. While the role of IFNγ in MM remains mixed and unclear, our analysis suggests IFNγ plays an oncogenic role in MM and offers other insights to MM pathology. Methods: The National Center for Biotechnology Information (NCBI) GEO is an open database of more than 2 million samples of functional genomics experiments. The Search Tag Analyze Resource for GEO (STARGEO) platform allows for meta-analysis of genomic signatures of disease and tissue. We employed the STARGEO platform to search the Gene Expression Omnibus and performed meta-analysis on 517 peripheral blood samples from multiple myeloma patients using 97 healthy peripheral blood samples as a control. We then analyzed the signature in Ingenuity Pathway Analysis (IPA) to help define the genomic signature of MM and identify disease pathways. We analyzed genes that showed statistical significance disease and control samples (p Results: We identified IFNγ signaling, primary immunodeficiency signaling, B cell development, and antigen presentation as the top canonical pathways. Top regulators included IFNγ, TNF, the oncogenic transcription regulator SMARC4, and CNOT7 (with predicted inhibition). We found inhibition of Wnt-signaling through stark upregulation of DKK1 (correlated with osteolytic lesions in MM) and the Wnt-binding protein antagonist FRZB. We also noted increased B cell survival through upregulation of the ADP-ribonyltransferase and B cell survival regulator PAPR14 and CPEB4, a gene required for cell cycle progression. Additionally, apoptotic signaling was diminished through downregulation of CNOT7 (limits B cell proliferation), genes involved in lysosomal disruption during apoptosis such as cathepsin H and lysozymes, the apoptotic Bcl-2 family protein BNIP3, and the pattern recognition receptor NOD2 (involved in autophagy). B cell development was impaired through downregulation of essential genes such as BLNK, a linker protein involved in B cell receptor signaling. Epigenetic changes were reflected by upregulation of the histone genes such as histone 1 genes HIST1H1C/1H2BD. Among histone 1's related pathways are E2F-mediated DNA replication and hypoxia-independent gene expression. Additionally, we found upregulation of tumorigenic genes such as neuron derived neurotrophic factor, NDNF, and hepatocyte growth factor, HGF, which has yet to be described in MM. Interestingly, pathway analysis demonstrated that this diverse set of disease processes are downstream of IFNγ signaling (see figure 1). Conclusion: MM pathogenesis reflects several disease processes that remain largely obscure. Our analysis clarifies the role of several mechanisms in MM such as inhibition of Wnt-signaling, defective B cell apoptosis, increased proliferation, loss of epigenetic integrity, and impaired B cell development. Activation of the antigen presentation pathway in our analysis may seem paradoxical to MM progression but recent evidence illustrates increased antigen presentation to T-regulatory cells, leading to immune tolerance. Most importantly, we are beginning to clarify the multifactorial, oncogenic qualities of IFNγ signaling in MM and are working to validate these results with analysis of our patient samples. Disclosures No relevant conflicts of interest to declare.

e15601Background: Colorectal cancer (CRC) is the third most common cancer globally. Most cases are not linked with known genetic risk factors but rather with old age and lifestyle. While cancers th...

e16239Background: Pancreatic adenocarcinoma (PAC) is the fourth leading cause of cancer-related deaths in the US and is particularly known for its aggressiveness and lack of effective therapeutic o...

Background Ehlers Danlos Syndrome is a rare form of inherited connective tissue disorder, which primarily affects skin, joints, muscle, and blood cells. The current study aimed at finding the mutation that causing EDS type VII C also known as “Dermatosparaxis” in this family. Methods Through systematic data querying of the electronic medical records (EMRs) of over 80,000 individuals, we recently identified an EDS family that indicate an autosomal dominant inheritance. The family was consented for genomic analysis of their de-identified data. After a negative screen for known mutations, we performed whole genome sequencing on the male proband, his affected father, and unaffected mother. We filtered the list of non-synonymous variants that are common between the affected individuals. Results The analysis of non-synonymous variants lead to identifying a novel mutation in the ADAMTSL2 (p. Gly421Ser) gene in the affected individuals. Sanger sequencing confirmed the mutation. Conclusion Our work is significant not only because it sheds new light on the pathophysiology of EDS for the affected family and the field at large, but also because it demonstrates the utility of unbiased large-scale clinical recruitment in deciphering the genetic etiology of rare mendelian diseases. With unbiased large-scale clinical recruitment we strive to sequence as many rare mendelian diseases as possible, and this work in EDS serves as a successful proof of concept to that effect.

Comprehensive identification and cataloging of copy number variations (CNVs) is required to provide a complete view of human genetic variation. The resolution of CNV detection in previous experimental designs has been limited to tens or hundreds of kilobases. Here we present PennCNV, a hidden Markov model (HMM) based approach, for kilobase-resolution detection of CNVs from Illumina high-density SNP genotyping data. This algorithm incorporates multiple sources of information, including total signal intensity and allelic intensity ratio at each SNP marker, the distance between neighboring SNPs, the allele frequency of SNPs, and the pedigree information where available. We applied PennCNV to genotyping data generated for 112 HapMap individuals; on average, we detected approximately 27 CNVs for each individual with a median size of approximately 12 kb. Excluding common rearrangements in lymphoblastoid cell lines, the fraction of CNVs in offspring not detected in parents (CNV-NDPs) was 3.3%. Our results demonstrate the feasibility of whole-genome fine-mapping of CNVs via high-density SNP genotyping.

Background: Malaria accounts for a considerable amount of mortality around the world, especially among young children. Artemisinin Combination Therapies (ACTs) remain the most potent treatment for uncomplicated cases of malaria. Early signs of drug resistance have emerged in the form of delayed clearance of the parasite in Southeast Asia. There is a high demand for newer drugs and vaccines to help control the worldwide malaria burden pending complete drug resistance to ACTs. The aim of this study is to characterize the disease signature of a malaria infection using meta-analysis of microarray data, and use this data to facilitate the pursuit of newer drugs and vaccines to fight malaria worldwide. Methods: Through a unique collaboration between medical students and computer scientists in systems biology, Search Tag & Analyze Resource (STAR) is a platform that was created that allows us to easily analyze gene signatures through meta-analyses of series in the Gene Expression Omnibus (GEO). Experiment samples within the database are annotated with tags and run through meta-analysis. Results: Using STAR, we generated one preliminary gene signature using samples on GEO: whole blood samples from malaria infections versus blood samples from health controls. We have found LGR4 and p70-S6k to be the top upstream regulators (p=0.002 for both). GBA and C15orf48 were the top significantly up-regulated genes. LEF1, PDCD4, and CNST were the top significantly down-regulated genes. GBA, LEF1, and the p70-S6k pathway have all been reported before as associated with malaria disease expression, the other genes have not. Conclusion: These preliminary results are proof-of-concept to the potential of STAR. With the collaboration fully functional, thousands of samples will be added to improve the gene signature of malaria infection. The study will also include parasite gene expression run with the same meta-analysis. We report here a first step towards novel transitional opportunities for better biomarkers and drugs for infectious diseases like malaria and beyond.

Importance Autism spectrum disorders (ASD) are characterized by impairments in social interaction, communication, and repetitive or restrictive behavior. Although multiple physiologic and biochemical studies have reported defects in mitochondrial oxidative phosphorylation in patients with ASD, the role of mitochondrial DNA (mtDNA) variation has remained relatively unexplored. Objective To assess what impact mitochondrial lineages encompassing ancient mtDNA functional polymorphisms, termed haplogroups, have on ASD risk. Design, Setting, and Participants In this cohort study, individuals with autism and their families were studied using the Autism Genetic Resource Exchange cohort genome-wide association studies data previously generated at the Children’s Hospital of Philadelphia. From October 2010 to January 2017, we analyzed the data and used the mtDNA single-nucleotide polymorphisms interrogated by the Illumina HumanHap 550 chip to determine the mtDNA haplogroups of the individuals. Taking into account the familial structure of the Autism Genetic Resource Exchange data, we then determined whether the mtDNA haplogroups correlate with ASD risk. Main Outcomes and Measures Odds ratios of mitochondrial haplogroup as predictors of ASD risk. Results Of 1624 patients with autism included in this study, 1299 were boys (80%) and 325 were girls (20%). Families in the Autism Genetic Resource Exchange collection (933 families, encompassing 4041 individuals: 1624 patients with ASD and 2417 healthy parents and siblings) had been previously recruited in the United States with no restrictions on age, sex, race/ethnicity, or socioeconomic status. Relative to the most common European haplogroup HHV, European haplogroups I, J, K, O-X, T, and U were associated with increased risk of ASD, as were Asian and Native American haplogroups A and M, with odds ratios ranging from 1.55 (95% CI, 1.16-2.06) to 2.18 (95% CI, 1.59-3) (adjusted P Conclusions and Relevance Because haplogroups I, J, K, O-X, T, and U encompass 55% of the European population, mtDNA lineages must make a significant contribution to overall ASD risk.

Background: Genetics clearly plays a major role in the etiology of autism spectrum disorders (ASDs), but studies to date are only beginning to characterize the causal genetic variants responsible. Until recently, studies using multiple extended multi-generation families to identify ASD risk genes had not been undertaken. Methods: We identified haplotypes shared among individuals with ASDs in large multiplex families, followed by targeted DNA capture and sequencing to identify potential causal variants. We also assayed the prevalence of the identified variants in a large ASD case/control population. Results: We identified 584 non-conservative missense, nonsense, frameshift and splice site variants that might predispose to autism in our high-risk families. Eleven of these variants were observed to have odds ratios greater than 1.5 in a set of 1,541 unrelated children with autism and 5,785 controls. Three variants, in the RAB11FIP5, ABP1, and JMJD7-PLA2G4B genes, each were observed in a single case and not in any controls. These variants also were not seen in public sequence databases, suggesting that they may be rare causal ASD variants. Twenty-eight additional rare variants were observed only in high-risk ASD families. Collectively, these 39 variants identify 36 genes as ASD risk genes. Segregation of sequence variants and of copy number variants previously detected in these families reveals a complex pattern, with only a RAB11FIP5 variant segregating to all affected individuals in one two-generation pedigree. Some affected individuals were found to have multiple potential risk alleles, including sequence variants and copy number variants (CNVs), suggesting that the high incidence of autism in these families could be best explained by variants at multiple loci. Conclusions: Our study is the first to use haplotype sharing to identify familial ASD risk loci. In total, we identified 39 variants in 36 genes that may confer a genetic risk of developing autism. The observation of 11 of these variants in unrelated ASD cases further supports their role as ASD risk variants.

We carried out a GWAS meta-analysis of combined mixed-ancestry schizophrenia, schizoaffective, and bipolar cohorts that resulted in the identification of six genome-wide significant loci, including one novel locus at chr8q24.3, encompassing TSNARE1 (P = 1.28 × 10(-9)). The analysis included a total of 13,394 cases and 34,676 controls. While the function of TSNARE1 remains unknown, bioinformatic predictions based on phylogenetic ancestry indicate it may have a vertebrate-specific function in intracellular protein transport and synaptic vesicle exocytosis.

Structural variation is thought to play a major etiological role in the development of autism spectrum disorders (ASDs), and numerous studies documenting the relevance of copy number variants (CNVs) in ASD have been published since 2006. To determine if large ASD families harbor high-impact CNVs that may have broader impact in the general ASD population, we used the Affymetrix genome-wide human SNP array 6.0 to identify 153 putative autism-specific CNVs present in 55 individuals with ASD from 9 multiplex ASD pedigrees. To evaluate the actual prevalence of these CNVs as well as 185 CNVs reportedly associated with ASD from published studies many of which are insufficiently powered, we designed a custom Illumina array and used it to interrogate these CNVs in 3,000 ASD cases and 6,000 controls. Additional single nucleotide variants (SNVs) on the array identified 25 CNVs that we did not detect in our family studies at the standard SNP array resolution. After molecular validation, our results demonstrated that 15 CNVs identified in high-risk ASD families also were found in two or more ASD cases with odds ratios greater than 2.0, strengthening their support as ASD risk variants. In addition, of the 25 CNVs identified using SNV probes on our custom array, 9 also had odds ratios greater than 2.0, suggesting that these CNVs also are ASD risk variants. Eighteen of the validated CNVs have not been reported previously in individuals with ASD and three have only been observed once. Finally, we confirmed the association of 31 of 185 published ASD-associated CNVs in our dataset with odds ratios greater than 2.0, suggesting they may be of clinical relevance in the evaluation of children with ASDs. Taken together, these data provide strong support for the existence and application of high-impact CNVs in the clinical genetic evaluation of children with ASD.

PURPOSE: To investigate whether there is an association between known age-related macular degeneration genetic risk variants in the CFH, ARMS2, and HTRA1 genes and response to anti-vascular endothelial growth factor (VEGF) (ranibizumab or bevacizumab) treatment for wet age-related macular degeneration. METHODS: A retrospective review of 150 patients with documented wet age-related macular degeneration based on clinical examination and fluorescein angiogram was performed. Patients received anti-VEGF therapy with ranibizumab and/or bevacizumab. Patients were genotyped for the single-nucleotide polymorphism rs1061170, rs10490924, rs3750848, rs3793917, rs11200638, and rs932275 and for the indel del443ins54 spanning the CFH, ARMS2, and HTRA1 genes. RESULTS: There were 57 patients who were characterized as negative responders to anti-VEGF therapy, and 93 patients who were characterized as positive responders. There was no significant difference in mean baseline visual acuity between the groups. Negative responders were followed for a mean duration of 24.0 months, while positive responders were followed for a mean duration of 22.0 months. Although the frequency of the at-risk alleles was higher in the positive responders when compared with the negative responder, this did not reach statistical significance. Additionally, there was no significant association between genotype and the number of injections or absolute change in visual acuity in both groups of responders. CONCLUSION: In our patient cohort, there was no statistically significant association between response to anti-VEGF therapy and the genotype in both positive-responder and negative-responder groups. Larger studies with more power are necessary to further determine whether a pharmacogenetic association exists between wet age-related macular degeneration and anti-VEGF therapy.

Item does not contain fulltext Attention deficit hyperactivity disorder (ADHD) is a common, heritable neuropsychiatric disorder of unknown etiology. We performed a whole-genome copy number variation (CNV) study on 1,013 cases with ADHD and 4,105 healthy children of European ancestry using 550,000 SNPs. We evaluated statistically significant findings in multiple independent cohorts, with a total of 2,493 cases with ADHD and 9,222 controls of European ancestry, using matched platforms. CNVs affecting metabotropic glutamate receptor genes were enriched across all cohorts (P = 2.1 x 10(-9)). We saw GRM5 (encoding glutamate receptor, metabotropic 5) deletions in ten cases and one control (P = 1.36 x 10(-6)). We saw GRM7 deletions in six cases, and we saw GRM8 deletions in eight cases and no controls. GRM1 was duplicated in eight cases. We experimentally validated the observed variants using quantitative RT-PCR. A gene network analysis showed that genes interacting with the genes in the GRM family are enriched for CNVs in approximately 10% of the cases (P = 4.38 x 10(-10)) after correction for occurrence in the controls. We identified rare recurrent CNVs affecting glutamatergic neurotransmission genes that were overrepresented in multiple ADHD cohorts.

Purpose. To determine the relationship of six genetic variants (rs10490924, rs3750848, del443ins54, rs3793917, rs11200638, and rs932275) localized to the ARMS2-HTRA1 region of chromosome 10, region q26, as risk factors for age-related macular degeneration (AMD), to define the haplotype structure of these six loci, and to confirm their genetic association with the disease. Methods. Caucasian patients (n = 482) were stratified into categories based on AREDS (Age-Related Eye Disease Study) grading criteria (groups 0 and 1 served as the control, groups 3 and 4 contained subjects with AMD, and group 2 was excluded from the analysis). The six genetic variants in the ARMS2-HTRA1 region were genotyped and analyzed both independently and as a joint haplotype for association in subjects with disease (n = 291) compared with the control (n = 191). Results. The six high-risk alleles all showed a statistically significant association with AMD (the most significant SNP was rs10490924 [Por = 3.31 x 10(-5), OR = 1.86]; the least significant SNP was rs932275 [Por = 9.15 x 10(-5), OR = 1.78]). Multimarker analysis revealed that all six markers were in strong linkage disequilibrium with each other, and the two major haplotypes that captured98% of the genetic variation in the region were both significantly associated with the disease: One increased the risk of AMD and contained only risk alleles (Por = 2.20 x 10(-5)), and the other haplotype decreased the risk of AMD and contained only wild-type alleles (Por = 6.81 x 10(-5)). Furthermore, 36 individuals comprising both cases and controls were identified outside of these two major haplotypes, with at least one discordant marker. Conclusions. The results replicate the previously reported association between the high-risk alleles and AMD and independently confirm, for the first time, an association with AMD and the indel (del443ins54) polymorphism in a Caucasian population. Two major haplotypes that are associated with AMD and many minor novel haplotypes were identified. The novel haplotypes, identified from 36 cases and controls with discordant alleles spanning the ARMS2-HTRA1 region provide unique opportunities to gauge the relative phenotypic contributions of each of these genetic risk factors. With the identification of more discordant patients in the future, it may be possible to resolve the ongoing controversy as to which of the risk alleles and genes (ARMS2 vs. HTRA1) has the greatest impact on disease susceptibility. Future work should include the analysis of larger and more diverse populations, to further define the linkage structure of the region with a focus on phenotypic effects on AMD of the various haplotypes involving 10q26, as well as a functional analysis of the normal ARMS2 protein.

The genetics underlying the autism spectrum disorders (ASDs) is complex and remains poorly understood. Previous work has demonstrated an important role for structural variation in a subset of cases, but has lacked the resolution necessary to move beyond detection of large regions of potential interest to identification of individual genes. To pinpoint genes likely to contribute to ASD etiology, we performed high density genotyping in 912 multiplex families from the Autism Genetics Resource Exchange (AGRE) collection and contrasted results to those obtained for 1,488 healthy controls. Through prioritization of exonic deletions (eDels), exonic duplications (eDups), and whole gene duplication events (gDups), we identified more than 150 loci harboring rare variants in multiple unrelated probands, but no controls. Importantly, 27 of these were confirmed on examination of an independent replication cohort comprised of 859 cases and an additional 1,051 controls. Rare variants at known loci, including exonic deletions at NRXN1 and whole gene duplications encompassing UBE3A and several other genes in the 15q11–q13 region, were observed in the course of these analyses. Strong support was likewise observed for previously unreported genes such as BZRAP1, an adaptor molecule known to regulate synaptic transmission, with eDels or eDups observed in twelve unrelated cases but no controls (p = 2.3×10−5). Less is known about MDGA2, likewise observed to be case-specific (p = 1.3×10−4). But, it is notable that the encoded protein shows an unexpectedly high similarity to Contactin 4 (BLAST E-value = 3×10−39), which has also been linked to disease. That hundreds of distinct rare variants were each seen only once further highlights complexity in the ASDs and points to the continued need for larger cohorts., Author Summary Autism spectrum disorders (ASDs) are common neurodevelopmental syndromes with a strong genetic component. ASDs are characterized by disturbances in social behavior, impaired verbal and nonverbal communication, as well as repetitive behaviors and/or a restricted range of interests. To identify genes likely to contribute to ASD etiology, we performed high density genotyping in 912 multiplex families from the Autism Genetics Resource Exchange (AGRE) collection and contrasted results to those obtained for 1,488 healthy controls. To enrich for variants most likely to interfere with gene function, we restricted our analyses to deletions and gains encompassing exons. Of the many genomic regions highlighted, 27 were seen to harbor rare variants in cases and not controls, both in the first phase of our analysis, and also in an independent replication cohort comprised of 859 cases and 1,051 controls. More work in a larger number of individuals will be required to determine which of the rare alleles highlighted here are indeed related to the ASDs and how they act to shape risk.

The neuronal synapse is a fundamental functional unit in the central nervous system of animals. Because synaptic function is evolutionarily conserved, we reasoned that functional sequences of genes and related genomic elements known to play important roles in neurotransmitter release would also be conserved. Evolutionary rate analysis revealed that presynaptic proteins evolve slowly, although some members of large gene families exhibit accelerated evolutionary rates relative to other family members. Comparative sequence analysis of 46 megabases spanning 150 presynaptic genes identified more than 26,000 elements that are highly conserved in eight vertebrate species, as well as a small subset of sequences (6%) that are shared among unrelated presynaptic genes. Analysis of large gene families revealed that upstream and intronic regions of closely related family members are extremely divergent. We also identified 504 exceptionally long conserved elements (≥360 base pairs, ≥80% pair-wise identity between human and other mammals) in intergenic and intronic regions of presynaptic genes. Many of these elements form a highly stable stem-loop RNA structure and consequently are candidates for novel regulatory elements, whereas some conserved noncoding elements are shown to correlate with specific gene expression profiles. The SynapseDB online database integrates these findings and other functional genomic resources for synaptic genes. Highly conserved elements in nonprotein coding regions of 150 presynaptic genes represent sequences that may be involved in the transcriptional or post-transcriptional regulation of these genes. Furthermore, comparative sequence analysis will facilitate selection of genes and noncoding sequences for future functional studies and analysis of variation studies in neurodevelopmental and psychiatric disorders.

We examined the burden of large, rare, copy-number variants (CNVs) in 192 individuals with renal hypodysplasia (RHD) and replicated findings in 330 RHD cases from two independent cohorts. CNV distribution was significantly skewed toward larger gene-disrupting events in RHD cases compared to 4,733 ethnicity-matched controls (p = 4.8 × 10(-11)). This excess was attributable to known and novel (i.e., not present in any database or in the literature) genomic disorders. All together, 55/522 (10.5%) RHD cases harbored 34 distinct known genomic disorders, which were detected in only 0.2% of 13,839 population controls (p = 1.2 × 10(-58)). Another 32 (6.1%) RHD cases harbored large gene-disrupting CNVs that were absent from or extremely rare in the 13,839 population controls, identifying 38 potential novel or rare genomic disorders for this trait. Deletions at the HNF1B locus and the DiGeorge/velocardiofacial locus were most frequent. However, the majority of disorders were detected in a single individual. Genomic disorders were detected in 22.5% of individuals with multiple malformations and 14.5% of individuals with isolated urinary-tract defects; 14 individuals harbored two or more diagnostic or rare CNVs. Strikingly, the majority of the known CNV disorders detected in the RHD cohort have previous associations with developmental delay or neuropsychiatric diseases. Up to 16.6% of individuals with kidney malformations had a molecular diagnosis attributable to a copy-number disorder, suggesting kidney malformations as a sentinel manifestation of pathogenic genomic imbalances. A search for pathogenic CNVs should be considered in this population for the diagnosis of their specific genomic disorders and for the evaluation of the potential for developmental delay.