Nancy J Cox

Vanderbilt University, Нашвилл, Michigan, United States

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Publications (489)4605.37 Total impact

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    ABSTRACT: Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood. We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual's genetic profile and correlates 'imputed' gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype. Genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome data sets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple-testing burden and a principled approach to the design of follow-up experiments. Our results demonstrate that PrediXcan can detect known and new genes associated with disease traits and provide insights into the mechanism of these associations.
    Nature Genetics 08/2015; 47(9). DOI:10.1038/ng.3367 · 29.35 Impact Factor
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    Susan C Trock · Stephen A Burke · Nancy J Cox
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    ABSTRACT: Although predicting which influenza virus subtype will cause the next pandemic is not yet possible, public health authorities must continually assess the pandemic risk associated with animal influenza viruses, particularly those that have caused infections in humans, and determine what resources should be dedicated to mitigating that risk. To accomplish this goal, a risk assessment framework was created in collaboration with an international group of influenza experts. Compared with the previously used approach, this framework, named the Influenza Risk Assessment Tool, provides a systematic and transparent approach for assessing and comparing threats posed primarily by avian and swine influenza viruses. This tool will be useful to the international influenza community and will remain flexible and responsive to changing information.
    Emerging Infectious Diseases 08/2015; 21(8):1372-1378. DOI:10.3201/eid2108.141086 · 6.75 Impact Factor
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    ABSTRACT: Despite long-recognized challenges and constraints associated with their updating and manufacture, influenza vaccines remain at the heart of public health preparedness and response efforts against both seasonal and potentially pandemic influenza viruses. Globally coordinated virological and epidemiological surveillance is the foundation of the influenza vaccine virus selection and development process. Although national influenza surveillance and reporting capabilities are being strengthened and expanded, sustaining and building upon recent gains has become a major challenge. Strengthening the vaccine virus selection process additionally requires the continuation of initiatives to improve the timeliness and representativeness of influenza viruses shared by countries for detailed analysis by the WHO Global Influenza Surveillance and Response System (GISRS). Efforts are also continuing at the national, regional, and global levels to better understand the dynamics of influenza transmission in both temperate and tropical regions. Improved understanding of the degree of influenza seasonality in tropical countries of the world should allow for the strengthening of national vaccination policies and use of the most appropriate available vaccines. There remain a number of limitations and difficulties associated with the use of HAI assays for the antigenic characterization and selection of influenza vaccine viruses by WHOCCs. Current approaches to improving the situation include the more-optimal use of HAI and other assays; improved understanding of the data produced by neutralization assays; and increased standardization of serological testing methods. A number of new technologies and associated tools have the potential to revolutionize influenza surveillance and response activities. These include the increasingly routine use of whole genome next-generation sequencing and other high-throughput approaches. Such approaches could not only become key elements in outbreak investigations but could drive a new surveillance paradigm. However, despite the advances made, significant challenges will need to be addressed before next-generation technologies become routine, particularly in low-resource settings. Emerging approaches and techniques such as synthetic genomics, systems genetics, systems biology and mathematical modelling are capable of generating potentially huge volumes of highly complex and diverse datasets. Harnessing the currently theoretical benefits of such bioinformatics ("big data") concepts for the influenza vaccine virus selection and development process will depend upon further advances in data generation, integration, analysis and dissemination. Over the last decade, growing awareness of influenza as an important global public health issue has been coupled to ever-increasing demands from the global community for more-equitable access to effective and affordable influenza vaccines. The current influenza vaccine landscape continues to be dominated by egg-based inactivated and live attenuated vaccines, with a small number of cell-based and recombinant vaccines. Successfully completing each step in the annual influenza vaccine manufacturing cycle will continue to rely upon timely and regular communication between the WHO GISRS, manufacturers and regulatory authorities. While the pipeline of influenza vaccines appears to be moving towards a variety of niche products in the near term, it is apparent that the ultimate aim remains the development of effective "universal" influenza vaccines that offer longer-lasting immunity against a broad range of influenza A subtypes. Copyright © 2015. Published by Elsevier Ltd.
    Vaccine 07/2015; 33(36). DOI:10.1016/j.vaccine.2015.06.090 · 3.62 Impact Factor
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    ABSTRACT: One of the important lessons learned from the 2009 H1N1 pandemic is that a high yield influenza vaccine virus is essential for efficient and timely production of pandemic vaccines in eggs. The current seasonal and pre-pandemic vaccine viruses are generated either by classical reassortment or reverse genetics. Both approaches utilize a high growth virus, generally A/Puerto Rico/8/1934 (PR8), as the donor of all or most of the internal genes, and the wild type virus recommended for inclusion in the vaccine to contribute the hemagglutinin (HA) and neuraminidase (NA) genes encoding the surface glycoproteins. As a result of extensive adaptation through sequential egg passaging, PR8 viruses with different gene sequences and high growth properties have been selected at different laboratories in past decades. The effect of these related but distinct internal PR8 genes on the growth of vaccine viruses in eggs has not been examined previously. Here, we use reverse genetics to analyze systematically the growth and HA antigen yield of reassortant viruses with 3 different PR8 backbones. A panel of 9 different HA/NA gene pairs in combination with each of the 3 different lineages of PR8 internal genes (27 reassortant viruses) was generated to evaluate their performance. Virus and HA yield assays showed that the PR8 internal genes influence HA yields in most subtypes. Although no single PR8 internal gene set outperformed the others in all candidate vaccine viruses, a combination of specific PR8 backbone with individual HA/NA pairs demonstrated improved HA yield and consequently the speed of vaccine production. These findings may be important both for production of seasonal vaccines and for a rapid global vaccine response during a pandemic.
    PLoS ONE 06/2015; 10(6):e0128982. DOI:10.1371/journal.pone.0128982 · 3.23 Impact Factor
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    ABSTRACT: Although there is considerable evidence that individual differences in language development are highly heritable, there have been few genome-wide scans to locate genes associated with the trait. Previous analyses of language impairment have yielded replicable evidence for linkage to regions on chromosomes 16q, 19q, 13q (within lab), and at 13q (between labs). Here we report the first linkage study to screen the continuum of language ability, from normal to disordered, as found in the general population. 383 children from 147 sib-ships (214 sib-pairs) were genotyped on the Illumina® Linkage IVb Marker Panel using three composite language-related phenotypes and a measure of phonological memory (PM). Two regions (10q23.33; 13q33.3) yielded genome-wide significant peaks for linkage with PM. A peak suggestive of linkage was also found at 17q12 for the overall language composite. This study presents two novel genetic loci for the study of language development and disorders, but fails to replicate findings by previous groups. Possible reasons for this are discussed. This article is protected by copyright. All rights reserved.
    Genes Brain and Behavior 05/2015; 14(5). DOI:10.1111/gbb.12223 · 3.66 Impact Factor
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    ABSTRACT: The emergence of avian influenza A(H7N9) virus in poultry causing zoonotic human infections was reported on March 31, 2013. Development of A(H7N9) candidate vaccine viruses (CVV) for pandemic preparedness purposes was initiated without delay. Candidate vaccine viruses were derived by reverse genetics using the internal genes of A/Puerto/Rico/8/34 (PR8). The resulting A(H7N9) CVVs needed improvement because they had titers and antigen yields that were suboptimal for vaccine manufacturing in eggs, especially in a pandemic situation. Two CVVs derived by reverse genetics were serially passaged in embryonated eggs to improve the hemagglutinin (HA) antigen yield. The total viral protein and HA antigen yields of six egg-passaged CVVs were determined by the BCA assay and isotope dilution mass spectrometry (IDMS) analysis, respectively. CVVs were antigenically characterized by hemagglutination inhibition (HI) assays with ferret antisera. Improvement of total viral protein yield was observed for the six egg-passaged CVVs; HA quantification by IDMS indicated approximately a two-fold increase in yield of several egg-passaged viruses as compared to that of the parental CVV. Several different amino acid substitutions were identified in the HA of all viruses after serial passage; however HI tests indicated that the antigenic properties of two CVVs remained unchanged. If influenza A(H7N9) viruses were to acquire sustained human to human transmissibility, the improved HA yield of the egg-passaged CVVs generated in this study could expedite vaccine manufacturing for pandemic mitigation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
    Influenza and Other Respiratory Viruses 05/2015; 9(5). DOI:10.1111/irv.12322 · 2.20 Impact Factor
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    ABSTRACT: Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.
    Science 05/2015; 348(6235):648-660. · 33.61 Impact Factor
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    ABSTRACT: Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5x10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
    PLoS Genetics 01/2015; 11(1). DOI:10.1371/journal.pgen.1004876 · 7.53 Impact Factor
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    ABSTRACT: Functional annotation of genetic variants including single nucleotide polymorphisms (SNPs) and copy number variations (CNV) promises to greatly improve our understanding of human complex traits. Previous transcriptomic studies involving individuals from different global populations have investigated the genetic architecture of gene expression variation by mapping expression quantitative trait loci (eQTL). Functional interpretation of genome-wide association studies (GWAS) has identified enrichment of eQTL in top signals from GWAS of human complex traits. The SCAN (SNP and CNV Annotation) database was developed as a web-based resource of genetical genomic studies including eQTL detected in the HapMap lymphoblastoid cell line samples derived from apparently healthy individuals of European and African ancestry. Considering the critical roles of epigenetic gene regulation, cytosine modification quantitative trait loci (mQTL) are expected to add a crucial layer of annotation to existing functional genomic information. Here, we describe the new features of the SCAN database that integrate comprehensive mQTL mapping results generated in the HapMap CEU (Caucasian residents from Utah, USA) and YRI (Yoruba people from Ibadan, Nigeria) LCL samples and demonstrate the utility of the enhanced functional annotation system. Database URL: © The Author(s) 2015. Published by Oxford University Press.
    Database The Journal of Biological Databases and Curation 01/2015; 2015. DOI:10.1093/database/bav025 · 3.37 Impact Factor
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    ABSTRACT: A major focus of current sequencing studies for human genetics is to identify rare variants associated with complex diseases. Aside from reduced power of detecting associated rare variants, controlling for population stratification is particularly challenging for rare variants. Transmission/disequilibrium tests (TDT) based on family designs are robust to population stratification and admixture, and therefore provide an effective approach to rare variant association studies to eliminate spurious associations. To increase power of rare variant association analysis, gene-based collapsing methods become standard approaches for analyzing rare variants. Existing methods that extend this strategy to rare variants in families usually combine TDT statistics at individual variants and therefore lack the flexibility of incorporating other genetic models. In this study we describe a haplotype-based framework for group-wise TDT (gTDT) that is flexible to encompass a variety of genetic models such as additive, dominant and compound heterozygous (i.e. recessive) models as well as other complex interactions. Unlike existing methods, gTDT constructs haplotypes by transmission when possible and inherently takes into account the linkage disequilibrium among variants. Through extensive simulations we showed that type I error was correctly controlled for rare variants under all models investigated, and this remained true in the presence of population stratification. Under a variety of genetic models, gTDT showed increased power compared to the single marker TDT. Application of gTDT to an autism exome sequencing data of 118 trios identified potentially interesting candidate genes with compound heterozygous rare variants. Availability and Implementation: We implemented gTDT in C++ and the source code and the detailed usage are available on the authors' website ( Bingshan Li ( or Wei Chen ( SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author (2015). Published by Oxford University Press. All rights reserved. For Permissions, please email:
    Bioinformatics 01/2015; 31(9). DOI:10.1093/bioinformatics/btu860 · 4.98 Impact Factor
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    ABSTRACT: Insulin sensitivity, insulin secretion, insulin clearance, and glucose effectiveness exhibit strong genetic components although few studies have examined their genetic architecture or their influence on type 2 diabetes (T2D) risk. We hypothesized that loci affecting variation in these quantitative traits influence T2D. We completed a multi-cohort genome-wide association study to search for loci influencing T2D-related quantitative traits in 4,176 Mexican Americans. Quantitative traits were measured by the frequently-sampled intravenous glucose tolerance test (4 cohorts) or euglycemic clamp (3 cohorts) and random effects models were used to test association between loci and quantitative traits, adjusting for age, gender, and admixture proportions (Discovery). Analysis revealed significant (P<5.00x10(-8)) association at 11q14.3 (MTNR1B) with acute insulin response. Loci with P<0.0001 among the quantitative traits were examined for translation to T2D risk in 6,463 T2D cases and 9,232 controls of Mexican ancestry (Translation). Non-parametric meta-analysis of the Discovery and Translation cohorts identified significant associations at 6p24 (SLC35B3/TFAP2A) with glucose effectiveness/T2D, 11p15 (KCNQ1) with disposition index/T2D, and 6p22 (CDKAL1) and 11q14 (MTNR1B) with acute insulin response/T2D. These results suggest that T2D and insulin secretion and sensitivity have both shared and distinct genetic factors, potentially delineating genomic components of these quantitative traits that drive risk for T2D. © 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
    Diabetes 12/2014; 64(5). DOI:10.2337/db14-0732 · 8.10 Impact Factor
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    mBio 12/2014; 5(6). DOI:10.1128/mBio.02431-14 · 6.79 Impact Factor
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    ABSTRACT: Controversy exists regarding the impact of CYP2D6 genotype on tamoxifen responsiveness. We examined loss of heterozygosity (LOH) at the CYP2D6 locus and determined its impact on genotyping error when tumor tissue is used as a DNA source. Genomic tumor data from the adjuvant and metastatic settings (The Cancer Genome Atlas [TCGA] and Foundation Medicine [FM]) were analyzed to characterize the impact of CYP2D6 copy number alterations (CNAs) and LOH on Hardy Weinberg equilibrium (HWE). Additionally, we analyzed CYP2D6 *4 genotype from formalin-fixed paraffin-embedded (FFPE) tumor blocks containing nonmalignant tissue and buccal (germline) samples from patients on the North Central Cancer Treatment Group (NCCTG) 89-30-52 tamoxifen trial. All statistical tests were two-sided. In TCGA samples (n =627), the CYP2D6 LOH rate was similar in estrogen receptor (ER)-positive (41.2%) and ER-negative (35.2%) but lower in HER2-positive tumors (15.1%) (P < .001). In FM ER+ samples (n = 290), similar LOH rates were observed (40.8%). In 190 NCCTG samples, the agreement between CYP2D6 genotypes derived from FFPE tumors and FFPE tumors containing nonmalignant tissue was moderate (weighted Kappa = 0.74; 95% CI = 0.63 to 0.84). Comparing CYP2D6 genotypes derived from buccal cells to FFPE tumor DNA, CYP2D6*4 genotype was discordant in six of 31(19.4%). In contrast, there was no disagreement between CYP2D6 genotypes derived from buccal cells with FFPE tumors containing nonmalignant tissue. LOH at the CYP2D6 locus is common in breast cancer, resulting in potential misclassification of germline CYP2D6 genotypes. Tumor DNA should not be used to determine germline CYP2D6 genotype without sensitive techniques to detect low frequency alleles and quality control procedures appropriate for somatic DNA. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:
    JNCI Journal of the National Cancer Institute 12/2014; 107(2). DOI:10.1093/jnci/dju401 · 12.58 Impact Factor
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    ABSTRACT: Top signals from genome-wide association studies (GWASs) of type 2 diabetes (T2D) are enriched with expression quantitative trait loci (eQTLs) identified in skeletal muscle and adipose tissue. We therefore hypothesized that such eQTLs might account for a dispropor-tionate share of the heritability estimated from all SNPs interrogated through GWASs. To test this hypothesis, we applied linear mixed models to the Wellcome Trust Case Control Consortium (WTCCC) T2D data set and to data sets representing Mexican Americans from Starr County, TX, and Mexicans from Mexico City. We estimated the proportion of phenotypic variance attributable to the additive effect of all variants interrogated in these GWASs, as well as a much smaller set of variants identified as eQTLs in human adipose tissue, skeletal muscle, and lymphoblastoid cell lines. The narrow-sense heritability explained by all interrogated SNPs in each of these data sets was substantially greater than the heritability accounted for by genome-wide-significant SNPs (~10%); GWAS SNPs explained over 50% of phenotypic variance in the WTCCC, Starr County, and Mexico City data sets. The estimate of heritability attributable to cross-tissue eQTLs was greater in the WTCCC data set and among lean Hispanics, whereas adipose eQTLs significantly explained heritability among Hispanics with a body mass index R 30. These results support an important role for regulatory variants in the genetic component of T2D susceptibility, particularly for eQTLs that elicit effects across insulin-responsive peripheral tissues.
    The American Journal of Human Genetics 10/2014; DOI:10.1016/j.ajhg.2014.10.001 · 10.93 Impact Factor
  • Daniel B Jernigan · Nancy J Cox
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    ABSTRACT: In the years prior to 2013, avian influenza A H7 viruses were a cause of significant poultry mortality; however, human illness was generally mild. In March 2013, a novel influenza A(H7N9) virus emerged in China as an unexpected cause of severe human illness with 36% mortality. Chinese and other public health officials responded quickly, characterizing the virus and identifying more than 400 cases through use of new technologies and surveillance tools made possible by past preparedness and response efforts. Genetic sequencing, glycan-array receptor-binding assays, and ferret studies reveal theH7N9 virus to have increased binding to mammalian respiratory cells and to have mutations associated with higher virus replication rates and illness severity. New risk-assessment tools indicate H7N9 has the potential for further mammalian adaptation with possible human-to-human transmission. Vigilant virologic and epidemiologic surveillance are needed to monitor H7N9 and detect other unexpected novel influenza viruses that may emerge. Expected final online publication date for the Annual Review of Medicine Volume 66 is January 14, 2015. Please see for revised estimates.
    Annual Review of Medicine 10/2014; 66(1). DOI:10.1146/annurev-med-010714-112311 · 12.93 Impact Factor
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    Eric R Gamazon · Nancy J Cox · Lea K Davis
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    ABSTRACT: Despite the discovery of copy-number variation (CNV) across the genome nearly 10 years ago, current SNP-based analysis methodologies continue to collapse the homozygous (i.e., A/A), hemizygous (i.e., A/0), and duplicative (i.e., A/A/A) genotype states, treating the genotype variable as irreducible or unaltered by other colocalizing forms of genetic (e.g., structural) variation. Our understanding of common, genome-wide CNVs suggests that the canonical genotype construct might belie the enormous complexity of the genome. Here we present multiple analyses of several phenotypes and provide methods supporting a conceptual shift that embraces the structural dimension of genotype. We comprehensively investigate the impact of the structural dimension of genotype on (1) GWAS methods, (2) interpretation of rare LOF variants, (3) characterization of genomic architecture, and (4) implications for mapping loci involved in complex disease. Taken together, these results argue for the inclusion of a structural dimension and suggest that some portion of the "missing" heritability might be recovered through integration of the structural dimension of SNP effects on complex traits.
    The American Journal of Human Genetics 10/2014; 95(5). DOI:10.1016/j.ajhg.2014.09.009 · 10.93 Impact Factor
  • Cancer Research 10/2014; 74(19 Supplement):3276-3276. DOI:10.1158/1538-7445.AM2014-3276 · 9.33 Impact Factor
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    ABSTRACT: Motivation: The development of cost-effective next-generation sequencing methods has spurred the development of high-throughput bioinformatics tools for detection of sequence variation. With many disparate variant-calling algorithms available, investigators must ask, 'Which method is best for my data?' Machine learning research has shown that so-called ensemble methods that combine the output of multiple models can dramatically improve classifier performance. Here we describe a novel variant-calling approach based on an ensemble of variant-calling algorithms, which we term the Consensus Genotyper for Exome Sequencing (CGES). CGES uses a two-stage voting scheme among four algorithm implementations. While our ensemble method can accept variants generated by any variant-calling algorithm, we used GATK2.8, SAMtools, FreeBayes and Atlas-SNP2 in building CGES because of their performance, widespread adoption and diverse but complementary algorithms. Results: We apply CGES to 132 samples sequenced at the Hudson Alpha Institute for Biotechnology (HAIB, Huntsville, AL) using the Nimblegen Exome Capture and Illumina sequencing technology. Our sample set consisted of 40 complete trios, two families of four, one parent-child duo and two unrelated individuals. CGES yielded the fewest total variant calls (N(CGES) = 139° 897), the highest Ts/Tv ratio (3.02), the lowest Mendelian error rate across all genotypes (0.028%), the highest rediscovery rate from the Exome Variant Server (EVS; 89.3%) and 1000 Genomes (1KG; 84.1%) and the highest positive predictive value (PPV; 96.1%) for a random sample of previously validated de novo variants. We describe these and other quality control (QC) metrics from consensus data and explain how the CGES pipeline can be used to generate call sets of varying quality stringency, including consensus calls present across all four algorithms, calls that are consistent across any three out of four algorithms, calls that are consistent across any two out of four algorithms or a more liberal set of all calls made by any algorithm. Availability and implementation: To enable accessible, efficient and reproducible analysis, we implement CGES both as a stand-alone command line tool available for download in GitHub and as a set of Galaxy tools and workflows configured to execute on parallel computers. Supplementary information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 09/2014; 31(2). DOI:10.1093/bioinformatics/btu591 · 4.98 Impact Factor
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    ABSTRACT: The complexities of planning for and responding to the emergence of novel influenza viruses emphasize the need for systematic frameworks to describe the progression of the event; weigh the risk of emergence and potential public health impact; evaluate transmissibility, antiviral resistance, and severity; and make decisions about interventions. On the basis of experience from recent influenza responses, CDC has updated its framework to describe influenza pandemic progression using six intervals (two prepandemic and four pandemic intervals) and eight domains. This updated framework can be used for influenza pandemic planning and serves as recommendations for risk assessment, decision-making, and action in the United States. The updated framework replaces the U.S. federal government stages from the 2006 implementation plan for the National Strategy for Pandemic Influenza (US Homeland Security Council. National strategy for pandemic influenza: implementation plan. Washington, DC: US Homeland Security Council; 2006. Available at The six intervals of the updated framework are as follows: 1) investigation of cases of novel influenza, 2) recognition of increased potential for ongoing transmission, 3) initiation of a pandemic wave, 4) acceleration of a pandemic wave, 5) deceleration of a pandemic wave, and 6) preparation for future pandemic waves. The following eight domains are used to organize response efforts within each interval: incident management, surveillance and epidemiology, laboratory, community mitigation, medical care and countermeasures, vaccine, risk communications, and state/local coordination. Compared with the previous U.S. government stages, this updated framework provides greater detail and clarity regarding the potential timing of key decisions and actions aimed at slowing the spread and mitigating the impact of an emerging pandemic. Use of this updated framework is anticipated to improve pandemic preparedness and response in the United States. Activities and decisions during a response are event-specific. These intervals serve as a reference for public health decision-making by federal, state, and local health authorities in the United States during an influenza pandemic and are not meant to be prescriptive or comprehensive. This framework incorporates information from newly developed tools for pandemic planning and response, including the Influenza Risk Assessment Tool and the Pandemic Severity Assessment Framework, and has been aligned with the pandemic phases restructured in 2013 by the World Health Organization.
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    ABSTRACT: We recently described a methodology that reliably predicted chemotherapeutic response in multiple independent clinical trials. The method worked by building statistical models from gene expression and drug sensitivity data in a very large panel of cancer cell lines, then applying these models to gene expression data from primary tumor biopsies. Here, to facilitate the development and adoption of this methodology we have created an R package called pRRophetic. This also extends the previously described pipeline, allowing prediction of clinical drug response for many cancer drugs in a user-friendly R environment. We have developed several other important use cases; as an example, we have shown that prediction of bortezomib sensitivity in multiple myeloma may be improved by training models on a large set of neoplastic hematological cell lines. We have also shown that the package facilitates model development and prediction using several different classes of data.
    PLoS ONE 09/2014; 9(9):e107468. DOI:10.1371/journal.pone.0107468 · 3.23 Impact Factor

Publication Stats

44k Citations
4,605.37 Total Impact Points


  • 2015
    • Vanderbilt University
      • Department of Medicine
      Нашвилл, Michigan, United States
  • 1993–2015
    • University of Chicago
      • • Department of Medicine
      • • Section of Genetic Medicine
      • • Department of Human Genetics
      Chicago, Illinois, United States
    • University of Rochester
      Rochester, New York, United States
  • 1988–2015
    • Centers for Disease Control and Prevention
      • • Influenza Division
      • • Division of HIV/AIDS Prevention, Surveillance and Epidemiology
      • • Division of Viral Diseases
      Атланта, Michigan, United States
  • 2013
    • Northwestern University
      Evanston, Illinois, United States
  • 2012
    • Erasmus MC
      • Department of Virology
      Rotterdam, South Holland, Netherlands
    • International Centre for Genetic Engineering and Biotechnology
      Trst, Friuli Venezia Giulia, Italy
  • 2011–2012
    • University of Illinois at Chicago
      • Section of General Internal Medicine
      Chicago, Illinois, United States
  • 2010
    • University of Wisconsin, Madison
      • Department of Pathobiological Sciences
      Madison, MS, United States
    • New York Medical College
      • Department of Microbiology and Immunology
      New York City, New York, United States
  • 1995–2009
    • National Institute of Allergy and Infectious Diseases
      베서스다, Maryland, United States
    • University of Turku
      • Department of Virology
      Turku, Province of Western Finland, Finland
  • 2008
    • Medical College of Wisconsin
      • Department of Psychiatry and Behavioral Medicine
      Milwaukee, WI, United States
  • 2006–2007
    • University of Colorado at Boulder
      • Department of Chemistry and Biochemistry
      Boulder, Colorado, United States
  • 2003
    • National Institutes of Health
      • Laboratory of Infectious Diseases
      베서스다, Maryland, United States
    • Johns Hopkins University
      Baltimore, Maryland, United States
  • 1993–2003
    • Howard Hughes Medical Institute
      • Molecular Biology Facility
      Ашбърн, Virginia, United States
  • 2002
    • World Health Organization WHO
      Genève, Geneva, Switzerland
    • Queen Mary Hospital
      Hong Kong, Hong Kong
  • 1997–2001
    • University of Exeter
      • Peninsula College of Medicine and Dentistry
      Exeter, England, United Kingdom
    • The Rockefeller University
      New York, New York, United States
  • 2000
    • San Diego State University
      San Diego, California, United States
    • The Chinese University of Hong Kong
      Hong Kong, Hong Kong
    • University of California, Irvine
      • Department of Ecology and Evolutionary Biology
      Irvine, CA, United States
  • 1990
    • The Scripps Research Institute
      لا هویا, California, United States
  • 1983–1990
    • U.S. Department of Health and Human Services
      Washington, Washington, D.C., United States
  • 1989
    • Yamagata University
      • School of Medicine
      Ямагата, Yamagata, Japan