Isaac S Kohane

Harvard Medical School, Boston, Massachusetts, United States

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Publications (169)925.28 Total impact

  • Isaac S Kohane
    Science 07/2015; 349(6243):37-8. DOI:10.1126/science.aab1328 · 31.48 Impact Factor
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    ABSTRACT: Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial. We collected narrative eligibility criteria from ClinicalTrials.gov for 55 clinical trials actively enrolling oncology patients in our institution between 12/01/2009 and 10/31/2011. In parallel, our ES algorithm extracted clinical and demographic information from the Electronic Health Record (EHR) data fields to represent profiles of all 215 oncology patients admitted to cancer treatment during the same period. The automated ES algorithm then matched the trial criteria with the patient profiles to identify potential trial-patient matches. Matching performance was validated on a reference set of 169 historical trial-patient enrollment decisions, and workload, precision, recall, negative predictive value (NPV) and specificity were calculated. Without automation, an oncologist would need to review 163 patients per trial on average to replicate the historical patient enrollment for each trial. This workload is reduced by 85% to 24 patients when using automated ES (precision/recall/NPV/specificity: 12.6%/100.0%/100.0%/89.9%). Without automation, an oncologist would need to review 42 trials per patient on average to replicate the patient-trial matches that occur in the retrospective data set. With automated ES this workload is reduced by 90% to four trials (precision/recall/NPV/specificity: 35.7%/100.0%/100.0%/95.5%). By leveraging NLP and IE technologies, automated ES could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment. The algorithm has the potential to significantly reduce the effort to execute clinical research at a point in time when new initiatives of the cancer care community intend to greatly expand both the access to trials and the number of available trials.
    BMC Medical Informatics and Decision Making 04/2015; 15(1):28. DOI:10.1186/s12911-015-0149-3 · 1.50 Impact Factor
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    ABSTRACT: Background and objective Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors. Materials and methods We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements. Results We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents. Conclusions We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years.
    Journal of the American Medical Informatics Association 06/2014; 21(6). DOI:10.1136/amiajnl-2014-002883 · 3.93 Impact Factor
  • Isaac S Kohane
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    ABSTRACT: Analysis of large-scale systems of biomedical data provides a perspective on neuropsychiatric disease that may be otherwise elusive. Described here is an analysis of three large-scale systems of data from autism spectrum disorder (ASD) and of ASD research as an exemplar of what might be achieved from study of such data. First is the biomedical literature that highlights the fact that there are two very successful but quite separate research communities and findings pertaining to genetics and the molecular biology of ASD. There are those studies positing ASD causes that are related to immunological dysregulation and those related to disorders of synaptic function and neuronal connectivity. Second is the emerging use of electronic health record systems and other large clinical databases that allow the data acquired during the course of care to be used to identify distinct subpopulations, clinical trajectories, and pathophysiological substructures of ASD. These systems reveal subsets of patients with distinct clinical trajectories, some of which are immunologically related and others which follow pathologies conventionally thought of as neurological. The third is genome-wide genomic and transcriptomic analyses which show molecular pathways that overlap neurological and immunological mechanisms. The convergence of these three large-scale data perspectives illustrates the scientific leverage that large-scale data analyses can provide in guiding researchers in an approach to the diagnosis of neuropsychiatric disease that is inclusive and comprehensive.
    Biological Psychiatry 06/2014; 77(1). DOI:10.1016/j.biopsych.2014.05.024 · 10.25 Impact Factor
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    Isaac S Kohane
    Genome Biology 05/2014; 15(5):115. DOI:10.1186/gb4175 · 10.47 Impact Factor
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    ABSTRACT: The length of the huntingtin (HTT) CAG repeat is strongly correlated with both age at onset of Huntington's disease (HD) symptoms and age at death of HD patients. Dichotomous analysis comparing HD to controls is widely used to study the effects of HTT CAG repeat expansion. However, a potentially more powerful approach is a continuous analysis strategy that takes advantage of all of the different CAG lengths, to capture effects that are expected to be critical to HD pathogenesis. We used continuous and dichotomous approaches to analyze microarray gene expression data from 107 human control and HD lymphoblastoid cell lines. Of all probes found to be significant in a continuous analysis by CAG length, only 21.4% were so identified by a dichotomous comparison of HD versus controls. Moreover, of probes significant by dichotomous analysis, only 33.2% were also significant in the continuous analysis. Simulations revealed that the dichotomous approach would require substantially more than 107 samples to either detect 80% of the CAG-length correlated changes revealed by continuous analysis or to reduce the rate of significant differences that are not CAG length-correlated to 20% (n = 133 or n = 206, respectively). Given the superior power of the continuous approach, we calculated the correlation structure between HTT CAG repeat lengths and gene expression levels and created a freely available searchable website, "HD CAGnome," that allows users to examine continuous relationships between HTT CAG and expression levels of ∼20,000 human genes. Our results reveal limitations of dichotomous approaches compared to the power of continuous analysis to study a disease where human genotype-phenotype relationships strongly support a role for a continuum of CAG length-dependent changes. The compendium of HTT CAG length-gene expression level relationships found at the HD CAGnome now provides convenient routes for discovery of candidates influenced by the HD mutation.
    PLoS ONE 04/2014; 9(4):e95556. DOI:10.1371/journal.pone.0095556 · 3.53 Impact Factor
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    ABSTRACT: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data was donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.
    Genome biology 03/2014; 15(3):R53. DOI:10.1186/gb-2014-15-3-r53 · 10.47 Impact Factor
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    ABSTRACT: Abstract Biological processes are fundamentally driven by complex interactions between biomolecules. Integrated high-throughput omics studies enable multifaceted views of cells, organisms, or their communities. With the advent of new post-genomics technologies, omics studies are becoming increasingly prevalent; yet the full impact of these studies can only be realized through data harmonization, sharing, meta-analysis, and integrated research. These essential steps require consistent generation, capture, and distribution of metadata. To ensure transparency, facilitate data harmonization, and maximize reproducibility and usability of life sciences studies, we propose a simple common omics metadata checklist. The proposed checklist is built on the rich ontologies and standards already in use by the life sciences community. The checklist will serve as a common denominator to guide experimental design, capture important parameters, and be used as a standard format for stand-alone data publications. The omics metadata checklist and data publications will create efficient linkages between omics data and knowledge-based life sciences innovation and, importantly, allow for appropriate attribution to data generators and infrastructure science builders in the post-genomics era. We ask that the life sciences community test the proposed omics metadata checklist and data publications and provide feedback for their use and improvement.
    Omics: a journal of integrative biology 01/2014; 18(1):10-4. DOI:10.1089/omi.2013.0149 · 2.73 Impact Factor
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    ABSTRACT: OBJECTIVE:The distinct trajectories of patients with autism spectrum disorders (ASDs) have not been extensively studied, particularly regarding clinical manifestations beyond the neurobehavioral criteria from the Diagnostic and Statistical Manual of Mental Disorders. The objective of this study was to investigate the patterns of co-occurrence of medical comorbidities in ASDs.METHODS:International Classification of Diseases, Ninth Revision codes from patients aged at least 15 years and a diagnosis of ASD were obtained from electronic medical records. These codes were aggregated by using phenotype-wide association studies categories and processed into 1350-dimensional vectors describing the counts of the most common categories in 6-month blocks between the ages of 0 to 15. Hierarchical clustering was used to identify subgroups with distinct courses.RESULTS:Four subgroups were identified. The first was characterized by seizures (n = 120, subgroup prevalence 77.5%). The second (n = 197) was characterized by multisystem disorders including gastrointestinal disorders (prevalence 24.3%) and auditory disorders and infections (prevalence 87.8%), and the third was characterized by psychiatric disorders (n = 212, prevalence 33.0%). The last group (n = 4316) could not be further resolved. The prevalence of psychiatric disorders was uncorrelated with seizure activity (P = .17), but a significant correlation existed between gastrointestinal disorders and seizures (P < .001). The correlation results were replicated by using a second sample of 496 individuals from a different geographic region.CONCLUSIONS:Three distinct patterns of medical trajectories were identified by unsupervised clustering of electronic health record diagnoses. These may point to distinct etiologies with different genetic and environmental contributions. Additional clinical and molecular characterizations will be required to further delineate these subgroups.
    PEDIATRICS 12/2013; 133(1). DOI:10.1542/peds.2013-0819 · 5.30 Impact Factor
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    ABSTRACT: Common variations at the loci harboring the fat mass and obesity gene (FTO), MC4R, and TMEM18 are consistently reported as being associated with obesity and body mass index (BMI) especially in adult population. In order to confirm this effect in pediatric population five European ancestry cohorts from pediatric eMERGE-II network (CCHMC-BCH) were evaluated. Method: Data on 5049 samples of European ancestry were obtained from the Electronic Medical Records (EMRs) of two large academic centers in five different genotyped cohorts. For all available samples, gender, age, height, and weight were collected and BMI was calculated. To account for age and sex differences in BMI, BMI z-scores were generated using 2000 Centers of Disease Control and Prevention (CDC) growth charts. A Genome-wide association study (GWAS) was performed with BMI z-score. After removing missing data and outliers based on principal components (PC) analyses, 2860 samples were used for the GWAS study. The association between each single nucleotide polymorphism (SNP) and BMI was tested using linear regression adjusting for age, gender, and PC by cohort. The effects of SNPs were modeled assuming additive, recessive, and dominant effects of the minor allele. Meta-analysis was conducted using a weighted z-score approach. Results: The mean age of subjects was 9.8 years (range 2–19). The proportion of male subjects was 56%. In these cohorts, 14% of samples had a BMI ≥95 and 28 ≥ 85%. Meta analyses produced a signal at 16q12 genomic region with the best result of p = 1.43 × 10-7 [p(rec) = 7.34 × 10-8) for the SNP rs8050136 at the first intron of FTO gene (z = 5.26) and with no heterogeneity between cohorts (p = 0.77). Under a recessive model, another published SNP at this locus, rs1421085, generates the best result [z = 5.782, p(rec) = 8.21 × 10-9]. Imputation in this region using dense 1000-Genome and Hapmap CEU samples revealed 71 SNPs with p < 10-6, all at the first intron of FTO locus. When hetero-geneity was permitted between cohorts, signals were also obtained in other previously identified loci, including MC4R (rs12964056, p = 6.87 × 10-7, z = -4.98), cholecystokinin CCK (rs8192472, p = 1.33 × 10-6, z = -4.85), Interleukin 15 (rs2099884, p = 1.27 × 10-5, z = 4.34), low density lipoprotein receptor-related protein 1B [LRP1B (rs7583748, p = 0.00013, z = -3.81)] and near transmembrane protein 18 (TMEM18) (rs7561317, p = 0.001, z = -3.17). We also detected a novel locus at chromosome 3 at COL6A5 [best SNP = rs1542829, minor allele frequency (MAF) of 5% p = 4.35 × 10-9, z = 5.89]. Conclusion: An EMR linked cohort study demonstrates that the BMI-Z measurements can be successfully extracted and linked to genomic data with meaningful confirmatory results. We verified the high prevalence of childhood rate of overweight and obesity in our cohort (28%). In addition, our data indicate that genetic variants in the first intron of FTO, a known adult genetic risk factor for BMI, are also robustly associated with BMI in pediatric population.
    Frontiers in Genetics 12/2013; 4:268. DOI:10.3389/fgene.2013.00268
  • Isaac S Kohane, Alal Eran
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    ABSTRACT: Newly released definitions of autism spectrum disorder demonstrate the need for precise diagnoses informed by the integration of clinical, molecular, and biochemical characteristics in a patient-information commons.
    Science translational medicine 10/2013; 5(209):209ed18. DOI:10.1126/scitranslmed.3007340 · 14.41 Impact Factor
  • Isaac S Kohane
    JAMA Internal Medicine 08/2013; 173(19). DOI:10.1001/jamainternmed.2013.8276 · 13.25 Impact Factor
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    ABSTRACT: Autism spectrum disorder (ASD) is one of the most prevalent neurodevelopmental disorders with high heritability, yet a majority of genetic contribution to pathophysiology is not known. Siblings of individuals with ASD are at increased risk for ASD and autistic traits, but the genetic contribution for simplex families is estimated to be less when compared to multiplex families. To explore the genomic (dis-) similarity between proband and unaffected sibling in simplex families, we used genome-wide gene expression profiles of blood from 20 proband-unaffected sibling pairs and 18 unrelated control individuals. The global gene expression profiles of unaffected siblings were more similar to those from probands as they shared genetic and environmental background. A total of 189 genes were significantly differentially expressed between proband-sib pairs (nominal p < 0.01) after controlling for age, sex, and family effects. Probands and siblings were distinguished into two groups by cluster analysis with these genes. Overall, unaffected siblings were equally distant from the centroid of probands and from that of unrelated controls with the differentially expressed genes. Interestingly, five of 20 siblings had gene expression profiles that were more similar to unrelated controls than to their matched probands. In summary, we found a set of genes that distinguished probands from the unaffected siblings, and a subgroup of unaffected siblings who were more similar to probands. The pathways that characterized probands compared to siblings using peripheral blood gene expression profiles were the up-regulation of ribosomal, spliceosomal, and mitochondrial pathways, and the down-regulation of neuroreceptor-ligand, immune response and calcium signaling pathways. Further integrative study with structural genetic variations such as de novo mutations, rare variants, and copy number variations would clarify whether these transcriptomic changes are structural or environmental in origin.
    Neurogenetics 04/2013; 14(2). DOI:10.1007/s10048-013-0363-z · 2.66 Impact Factor
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    ABSTRACT: In Huntington's disease (HD), the size of the expanded HTT CAG repeat mutation is the primary driver of the processes that determine age at onset of motor symptoms. However, correlation of cellular biochemical parameters also extends across the normal repeat range, supporting the view that the CAG repeat represents a functional polymorphism with dominant effects determined by the longer allele. A central challenge to defining the functional consequences of this single polymorphism is the difficulty of distinguishing its subtle effects from the multitude of other sources of biological variation. We demonstrate that an analytical approach based upon continuous correlation with CAG size was able to capture the modest (∼21%) contribution of the repeat to the variation in genome-wide gene expression in 107 lymphoblastoid cell lines, with alleles ranging from 15 to 92 CAGs. Furthermore, a mathematical model from an iterative strategy yielded predicted CAG repeat lengths that were significantly positively correlated with true CAG allele size and negatively correlated with age at onset of motor symptoms. Genes negatively correlated with repeat size were also enriched in a set of genes whose expression were CAG-correlated in human HD cerebellum. These findings both reveal the relatively small, but detectable impact of variation in the CAG allele in global data in these peripheral cells and provide a strategy for building multi-dimensional data-driven models of the biological network that drives the HD disease process by continuous analysis across allelic panels of neuronal cells vulnerable to the dominant effects of the HTT CAG repeat.
    Human Molecular Genetics 04/2013; 22(16). DOI:10.1093/hmg/ddt176 · 6.68 Impact Factor
  • Christopher G Chute, Isaac S Kohane
    JAMA The Journal of the American Medical Association 04/2013; 309(14):1467-8. DOI:10.1001/jama.2013.1414 · 30.39 Impact Factor
  • Isaac S Kohane
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    ABSTRACT: The study by Ritchie et al.,1 in this issue employs electronic health record data and DNA biobanks to identify several genomic variants previously implicated 2, 3 in the variation of ECG parameters of cardiac conduction and diseases of cardiac conduction. So why is this study worthy of note? Ever since Enthoven first named the QRS complex 4, investigators have sought to define what constitutes a normal complex and the diagnostic and prognostic significance of deviations from the norm. The growing understanding that there is no categorical set of normal values, prompted population studies of (typically white and male) subjects numbering in the 100's. 5 and eventually tens of thousands 6. These studies did generate a more robust set of reference values and did emphasize that the notion of normal vs. abnormal QRS was not appropriate and argued for "an index of the possibility of normals or abnormals occurring at various levels" and "variations in electrocardiograms ... considerably greater than the present standards would lead one to expect..." 5 Subsequent, larger population studies including clinical trial populations 7, 8 with broader age and gender distributions revealed that variation in QRS characteristics in healthy individuals was larger than suspected. In parallel, several studies analyzed the clinical correlates of ECG features, For example in 1967, Pipberger et al 9 conducted what might today be called a "phenome scan" 10, 11. For each of the identified ECG measures, they scanned multiple constitutional features (e.g. obesity) and ethnicity to assess bias and correlation. Among their findings were the significant differences in QRS measures in African Americans, even when correcting for differences in the other constitutional features.
    Circulation 03/2013; 127(13). DOI:10.1161/CIRCULATIONAHA.113.001852 · 14.95 Impact Factor
  • Science 03/2013; 339(6123):1032-3. DOI:10.1126/science.339.6123.1032-c · 31.48 Impact Factor
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    ABSTRACT: To quantify the impact of citalopram and other selective serotonin reuptake inhibitors on corrected QT interval (QTc), a marker of risk for ventricular arrhythmia, in a large and diverse clinical population. A cross sectional study using electrocardiographic, prescribing, and clinical data from electronic health records to explore the relation between antidepressant dose and QTc. Methadone, an opioid known to prolong QT, was included to demonstrate assay sensitivity. A large New England healthcare system comprising two academic medical centres and outpatient clinics. 38 397 adult patients with an electrocardiogram recorded after prescription of antidepressant or methadone between February 1990 and August 2011. Relation between antidepressant dose and QTc interval in linear regression, adjusting for potential clinical and demographic confounding variables. For a subset of patients, change in QTc after drug dose was also examined. Dose-response association with QTc prolongation was identified for citalopram (adjusted beta 0.10 (SE 0.04), P<0.01), escitalopram (adjusted beta 0.58 (0.15), P<0.001), and amitriptyline (adjusted beta 0.11 (0.03), P<0.001), but not for other antidepressants examined. An association with QTc shortening was identified for bupropion (adjusted beta 0.02 (0.01) P<0.05). Within-subject paired observations supported the QTc prolonging effect of citalopram (10 mg to 20 mg, mean QTc increase 7.8 (SE 3.6) ms, adjusted P<0.05; and 20 mg to 40 mg, mean QTc increase 10.3 (4.0) ms, adjusted P<0.01). This study confirmed a modest prolongation of QT interval with citalopram, and identified additional antidepressants with similar observed risk. Pharmacovigilance studies using electronic health record data may be a useful method of identifying potential risk associated with treatments.
    BMJ (online) 01/2013; 346:f288. DOI:10.1136/bmj.f288 · 16.38 Impact Factor
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    ABSTRACT: BACKGROUND: Psychiatric co-morbidity, in particular major depression and anxiety, is common in patients with Crohn's disease (CD) and ulcerative colitis (UC). Prior studies examining this may be confounded by the co-existence of functional bowel symptoms. Limited data exist examining an association between depression or anxiety and disease-specific endpoints such as bowel surgery. AIMS: To examine the frequency of depression and anxiety (prior to surgery or hospitalisation) in a large multi-institution electronic medical record (EMR)-based cohort of CD and UC patients; to define the independent effect of psychiatric co-morbidity on risk of subsequent surgery or hospitalisation in CD and UC, and to identify the effects of depression and anxiety on healthcare utilisation in our cohort. METHODS: Using a multi-institution cohort of patients with CD and UC, we identified those who also had co-existing psychiatric co-morbidity (major depressive disorder or generalised anxiety). After excluding those diagnosed with such co-morbidity for the first time following surgery, we used multivariate logistic regression to examine the independent effect of psychiatric co-morbidity on IBD-related surgery and hospitalisation. To account for confounding by disease severity, we adjusted for a propensity score estimating likelihood of psychiatric co-morbidity influenced by severity of disease in our models. RESULTS: A total of 5405 CD and 5429 UC patients were included in this study; one-fifth had either major depressive disorder or generalised anxiety. In multivariate analysis, adjusting for potential confounders and the propensity score, presence of mood or anxiety co-morbidity was associated with a 28% increase in risk of surgery in CD (OR: 1.28, 95% CI: 1.03-1.57), but not UC (OR: 1.01, 95% CI: 0.80-1.28). Psychiatric co-morbidity was associated with increased healthcare utilisation. CONCLUSIONS: Depressive disorder or generalised anxiety is associated with a modestly increased risk of surgery in patients with Crohn's disease. Interventions addressing this may improve patient outcomes.
    Alimentary Pharmacology & Therapeutics 01/2013; 37(4). DOI:10.1111/apt.12195 · 4.55 Impact Factor
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    ABSTRACT: Background Numerous linkage studies have been performed in pedigrees of Autism Spectrum Disorders, and these studies point to diverse loci and etiologies of autism in different pedigrees. The underlying pattern may be identified by an integrative approach, especially since ASD is a complex disorder manifested through many loci. Method Autism spectrum disorder (ASD) was studied through two different and independent genome-scale measurement modalities. We analyzed the results of copy number variation in autism and triangulated these with linkage studies. Results Consistently across both genome-scale measurements, the same two molecular themes emerged: immune/chemokine pathways and developmental pathways. Conclusion Linkage studies in aggregate do indeed share a thematic consistency, one which structural analyses recapitulate with high significance. These results also show for the first time that genomic profiling of pathways using a recombination distance metric can capture pathways that are consistent with those obtained from copy number variations (CNV).
    PLoS ONE 12/2012; 7(12):e48835. DOI:10.1371/journal.pone.0048835 · 3.53 Impact Factor

Publication Stats

6k Citations
925.28 Total Impact Points

Institutions

  • 1993–2015
    • Harvard Medical School
      • • Center for Biomedical Informatics
      • • Department of Pathology
      • • Department of Pediatrics
      • • Department of Psychiatry
      Boston, Massachusetts, United States
  • 2000–2014
    • Harvard University
      Cambridge, Massachusetts, United States
    • The Children’s Medical Group
      POU, New York, United States
  • 2011–2013
    • Brigham and Women's Hospital
      • Department of Medicine
      Boston, Massachusetts, United States
    • Idenix Pharmaceuticals, Inc.
      Cambridge, Massachusetts, United States
  • 1993–2013
    • Boston Children's Hospital
      • • Department of Pediatrics
      • • Division of Endocrinology
      • • Division of Nephrology
      • • Department of Anesthesia
      Boston, Massachusetts, United States
  • 1997–2004
    • Massachusetts Institute of Technology
      • Division of Health Sciences and Technology
      Cambridge, MA, United States