Loci nominally associated with autism from genome-wide analysis show enrichment of brain expression quantitative trait loci but not lymphoblastoid cell line expression quantitative trait loci / Lea K. DAVIS in Molecular Autism, (May 2012)  

Public ISBD [article]  inMolecular Autism > (May 2012) . - 25 p. Titre : Loci nominally associated with autism from genome-wide analysis show enrichment of brain expression quantitative trait loci but not lymphoblastoid cell line expression quantitative trait loci Type de document : Texte imprimé et/ou numérique Auteurs : Lea K. DAVIS, Auteur ; Eric R. GAMAZON, Auteur ; Emily KISTNER-GRIFFIN, Auteur ; Judith A. BADNER, Auteur ; Chunyu LIU, Auteur ; Edwin H. Jr COOK, Auteur ; James S. SUTCLIFFE, Auteur ; Nancy J. COX, Auteur Année de publication : 2012 Article en page(s) : 25 p. Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Background Autism spectrum disorder is a severe early onset neurodevelopmental disorder with high heritability but significant heterogeneity. Traditional genome-wide approaches to test for an association of common variants with autism susceptibility risk have met with limited success. However, novel methods to identify moderate risk alleles in attainable sample sizes are now gaining momentum. Methods In this study, we utilized publically available genome-wide association study data from the Autism Genome Project and annotated the results (P <0.001) for expression quantitative trait loci present in the parietal lobe (GSE35977), cerebellum (GSE35974) and lymphoblastoid cell lines (GSE7761). We then performed a test of enrichment by comparing these results to simulated data conditioned on minor allele frequency to generate an empirical P-value indicating statistically significant enrichment of expression quantitative trait loci in top results from the autism genome-wide association study. Results Our findings show a global enrichment of brain expression quantitative trait loci, but not lymphoblastoid cell line expression quantitative trait loci, among top single nucleotide polymorphisms from an autism genome-wide association study. Additionally, the data implicates individual genes SLC25A12, PANX1 and PANX2 as well as pathways previously implicated in autism. Conclusions These findings provide supportive rationale for the use of annotation-based approaches to genome-wide association studies. En ligne : http://dx.doi.org/10.1186/2040-2392-3-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=178 [article] Loci nominally associated with autism from genome-wide analysis show enrichment of brain expression quantitative trait loci but not lymphoblastoid cell line expression quantitative trait loci [Texte imprimé et/ou numérique] / Lea K. DAVIS, Auteur ; Eric R. GAMAZON, Auteur ; Emily KISTNER-GRIFFIN, Auteur ; Judith A. BADNER, Auteur ; Chunyu LIU, Auteur ; Edwin H. Jr COOK, Auteur ; James S. SUTCLIFFE, Auteur ; Nancy J. COX, Auteur . - 2012 . - 25 p. Langues : Anglais (eng) in Molecular Autism > (May 2012) . - 25 p. Index. décimale : PER Périodiques Résumé : Background Autism spectrum disorder is a severe early onset neurodevelopmental disorder with high heritability but significant heterogeneity. Traditional genome-wide approaches to test for an association of common variants with autism susceptibility risk have met with limited success. However, novel methods to identify moderate risk alleles in attainable sample sizes are now gaining momentum. Methods In this study, we utilized publically available genome-wide association study data from the Autism Genome Project and annotated the results (P <0.001) for expression quantitative trait loci present in the parietal lobe (GSE35977), cerebellum (GSE35974) and lymphoblastoid cell lines (GSE7761). We then performed a test of enrichment by comparing these results to simulated data conditioned on minor allele frequency to generate an empirical P-value indicating statistically significant enrichment of expression quantitative trait loci in top results from the autism genome-wide association study. Results Our findings show a global enrichment of brain expression quantitative trait loci, but not lymphoblastoid cell line expression quantitative trait loci, among top single nucleotide polymorphisms from an autism genome-wide association study. Additionally, the data implicates individual genes SLC25A12, PANX1 and PANX2 as well as pathways previously implicated in autism. Conclusions These findings provide supportive rationale for the use of annotation-based approaches to genome-wide association studies. En ligne : http://dx.doi.org/10.1186/2040-2392-3-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=178

A practical approach to identifying autistic adults within the electronic health record / Olivia J. VEATCH ; Xinnan NIU ; Kasey A. FITZPATRICK ; Donald HUCKS ; Angie MAXWELL-HORN ; Lea K. DAVIS in Autism Research, 16-1 (January 2023)  

Public ISBD [article]  inAutism Research > 16-1 (January 2023) . - p.52-65 Titre : A practical approach to identifying autistic adults within the electronic health record Type de document : Texte imprimé et/ou numérique Auteurs : Olivia J. VEATCH, Auteur ; Xinnan NIU, Auteur ; Kasey A. FITZPATRICK, Auteur ; Donald HUCKS, Auteur ; Angie MAXWELL-HORN, Auteur ; Lea K. DAVIS, Auteur Article en page(s) : p.52-65 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Abstract The electronic health record (EHR) provides valuable data for understanding physical and mental health conditions in autism. We developed an approach to identify charts of autistic young adults, retrieved from our institution's de-identified EHR database. Clinical notes within two cohorts were identified. Cohort 1 charts had at least one International Classification of Diseases (ICD-CM) autism code. Cohort 2 charts had only autism key terms without ICD-CM codes, and at least four notes per chart. A natural language processing tool parsed medical charts to identify key terms associated with autism diagnoses and mapped them to Unified Medical Language System Concept Unique Identifiers (CUIs). Average scores were calculated for each set of charts based on captured CUIs. Chart review determined whether patients met criteria for autism using a classification rubric. In Cohort 1, of 418 patients, 361 were confirmed to have autism by chart review. Sensitivity was 0.99 and specificity was 0.68 with positive predictive value (PPV) of 0.97. Specificity improved to 0.81 (sensitivity was 0.95; PPV was 0.98) when the number of notes was limited to four or more per chart. In Cohort 2, 48 of 136 patients were confirmed to have autism by chart review. Sensitivity was 0.95, specificity was 0.73, and PPV was 0.70. Our approach, which included using key terms, identified autism charts with high sensitivity, even in the absence of ICD-CM codes. Relying on ICD-CM codes alone may result in inclusion of false positive cases and exclusion of true cases with autism. En ligne : https://doi.org/10.1002/aur.2849 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=492 [article] A practical approach to identifying autistic adults within the electronic health record [Texte imprimé et/ou numérique] / Olivia J. VEATCH, Auteur ; Xinnan NIU, Auteur ; Kasey A. FITZPATRICK, Auteur ; Donald HUCKS, Auteur ; Angie MAXWELL-HORN, Auteur ; Lea K. DAVIS, Auteur . - p.52-65. Langues : Anglais (eng) in Autism Research > 16-1 (January 2023) . - p.52-65 Index. décimale : PER Périodiques Résumé : Abstract The electronic health record (EHR) provides valuable data for understanding physical and mental health conditions in autism. We developed an approach to identify charts of autistic young adults, retrieved from our institution's de-identified EHR database. Clinical notes within two cohorts were identified. Cohort 1 charts had at least one International Classification of Diseases (ICD-CM) autism code. Cohort 2 charts had only autism key terms without ICD-CM codes, and at least four notes per chart. A natural language processing tool parsed medical charts to identify key terms associated with autism diagnoses and mapped them to Unified Medical Language System Concept Unique Identifiers (CUIs). Average scores were calculated for each set of charts based on captured CUIs. Chart review determined whether patients met criteria for autism using a classification rubric. In Cohort 1, of 418 patients, 361 were confirmed to have autism by chart review. Sensitivity was 0.99 and specificity was 0.68 with positive predictive value (PPV) of 0.97. Specificity improved to 0.81 (sensitivity was 0.95; PPV was 0.98) when the number of notes was limited to four or more per chart. In Cohort 2, 48 of 136 patients were confirmed to have autism by chart review. Sensitivity was 0.95, specificity was 0.73, and PPV was 0.70. Our approach, which included using key terms, identified autism charts with high sensitivity, even in the absence of ICD-CM codes. Relying on ICD-CM codes alone may result in inclusion of false positive cases and exclusion of true cases with autism. En ligne : https://doi.org/10.1002/aur.2849 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=492

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