Predicting the diagnosis of autism spectrum disorder using gene pathway analysis
Autor: | Renee Testa, Christos Pantelis, Efstratios Skafidas, Ian P. Everall, Daniela Zantomio, Gursharan Chana |
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Jazyk: | angličtina |
Rok vydání: | 2012 |
Předmět: |
Male
autistic disorder/diagnosis predictive testing Large-Conductance Calcium-Activated Potassium Channel beta Subunits childhood development disorders Receptor Metabotropic Glutamate 5 Single-nucleotide polymorphism Nerve Tissue Proteins GTP-Binding Protein alpha Subunits Gi-Go Polymorphism Single Nucleotide White People Cohort Studies 03 medical and health sciences Cellular and Molecular Neuroscience 0302 clinical medicine Asian People mental disorders Medicine Humans Gene Regulatory Networks Genetic Predisposition to Disease Copy-number variation Genetic Testing Predictive testing Molecular Biology 030304 developmental biology Genetic testing Genetics 0303 health sciences medicine.diagnostic_test business.industry medicine.disease 3. Good health Psychiatry and Mental health classification Autism spectrum disorder Child Development Disorders Pervasive Cohort Autism Female Original Article business 030217 neurology & neurosurgery Cohort study |
Zdroj: | Molecular Psychiatry |
ISSN: | 1476-5578 1359-4184 |
Popis: | Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs) of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify affected cellular processes and develop a diagnostic test. This test was then applied to two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of 237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases. This classifier also predicted 84.3% of cases in an ethnically related Tuscan cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1, GRM5) had the largest effect in the classifier with some acting as vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI (ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an accurate diagnostic test for a genetically homogeneous group to aid in early detection of ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular processes common to ASD across ethnicities. Our results have wide implications for detection, intervention and prevention of ASD. |
Databáze: | OpenAIRE |
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