Symptom-based classification of 16p11.2 copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods / Hilmi BOLAT in Research in Autism, 132 (April 2026)  

Public ISBD [article]  inResearch in Autism > 132 (April 2026) . - 202865 Titre : Symptom-based classification of 16p11.2 copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods Type de document : texte imprimé Auteurs : Hilmi BOLAT, Auteur ; Gül ÃœNSEL-BOLAT, Auteur ; Edanur BULUT, Auteur ; Semiha ÖZGÃœL, Auteur ; Duygu Selin TURAN, Auteur ; Samet ÇELÄ°K, Auteur ; Özgür Ozan KOYUNCU, Auteur ; Özgür KIRBIYIK, Auteur ; Özge Özer KAYA, Auteur ; YaÅŸar Bekir KUTBAY, Auteur ; Merve Saka GÃœVENÇ, Auteur ; Kadri Murat ERDOÄžAN, Auteur ; S. ̧ener ARIKAN, Auteur ; Tuba Sözen TÃœRK, Auteur ; AltuÄŸ KOÇ, Auteur ; Taha ReÅŸid ÖZDEMÄ°R, Auteur ; Berk ÖZYILMAZ, Auteur ; Gonca ÖZYURT, Auteur ; Burak ORDÄ°N, Auteur ; Buket KOSOVA, Auteur Article en page(s) : 202865 Langues : Anglais (eng) Mots-clés : 16p11.2  CNVs  Machine Learning  Neurodevelopmental Disorders Résumé : Purpose Copy number variations (CNVs) in the 16p11.2 region are well-established contributors to neurodevelopmental disorders, yet phenotype variability across this locus remains insufficiently characterized. This study investigates clinical features and ASD-related symptoms among carriers of rare pathogenic and common CNVs, and evaluates symptom-level discriminability using machine learning (ML) methods. Methods Genetic data from 7568 individuals were retrospectively screened, identifying 147 carriers of 16p11.2 CNVs. Detailed clinical assessments were completed for 50 participants. ASD-related symptoms were evaluated using a structured 25-item instrument. Group comparisons applied nonparametric statistics with effect sizes, confidence intervals, and FDR correction. ML analyses used PCA and k-means for feature selection, oversampling methods (SMOTE, Borderline-SMOTE, ADASYN), and five classifiers, evaluated through cross-validation. Results Across pathogenic and common CNV groups, no significant differences were observed in social communication, restricted/repetitive behaviors, sensory symptoms, regression, or total autism scores (FDR-adjusted p > 0.05). Aggression was more frequently endorsed in pathogenic CNV carriers (raw p = 0.030; FDR p = 0.098). BMI was higher in pathogenic CNVs, though nonsignificant after correction (raw p = 0.027; FDR p = 0.152). ML analyses identified three recurrent discriminative symptoms across multiple datasets: delayed response to name, unusual object play, and aggression. Dataset 3 (16 symptoms) provided the most balanced classification performance but, given the very small pathogenic CNV sample, results remain exploratory. Conclusion Findings suggest that, while most autism-related symptoms do not differ between groups, aggression and increased BMI may represent preliminary phenotypic signals associated with pathogenic CNVs. Integrating clinical data from 147 CNV carriers further supports a potential widespread effect across the broader 16p11.2 locus rather than a single breakpoint-specific mechanism. However, all results should be interpreted cautiously due to limited sample size, and larger, systematically phenotyped cohorts are required to establish robust genotype–phenotype relationships. En ligne : https://doi.org/10.1016/j.reia.2026.202865 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=583 [article] Symptom-based classification of 16p11.2 copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods [texte imprimé] / Hilmi BOLAT, Auteur ; Gül ÃœNSEL-BOLAT, Auteur ; Edanur BULUT, Auteur ; Semiha ÖZGÃœL, Auteur ; Duygu Selin TURAN, Auteur ; Samet ÇELÄ°K, Auteur ; Özgür Ozan KOYUNCU, Auteur ; Özgür KIRBIYIK, Auteur ; Özge Özer KAYA, Auteur ; YaÅŸar Bekir KUTBAY, Auteur ; Merve Saka GÃœVENÇ, Auteur ; Kadri Murat ERDOÄžAN, Auteur ; S. ̧ener ARIKAN, Auteur ; Tuba Sözen TÃœRK, Auteur ; AltuÄŸ KOÇ, Auteur ; Taha ReÅŸid ÖZDEMÄ°R, Auteur ; Berk ÖZYILMAZ, Auteur ; Gonca ÖZYURT, Auteur ; Burak ORDÄ°N, Auteur ; Buket KOSOVA, Auteur . - 202865. Langues : Anglais (eng) in Research in Autism > 132 (April 2026) . - 202865 Mots-clés : 16p11.2  CNVs  Machine Learning  Neurodevelopmental Disorders Résumé : Purpose Copy number variations (CNVs) in the 16p11.2 region are well-established contributors to neurodevelopmental disorders, yet phenotype variability across this locus remains insufficiently characterized. This study investigates clinical features and ASD-related symptoms among carriers of rare pathogenic and common CNVs, and evaluates symptom-level discriminability using machine learning (ML) methods. Methods Genetic data from 7568 individuals were retrospectively screened, identifying 147 carriers of 16p11.2 CNVs. Detailed clinical assessments were completed for 50 participants. ASD-related symptoms were evaluated using a structured 25-item instrument. Group comparisons applied nonparametric statistics with effect sizes, confidence intervals, and FDR correction. ML analyses used PCA and k-means for feature selection, oversampling methods (SMOTE, Borderline-SMOTE, ADASYN), and five classifiers, evaluated through cross-validation. Results Across pathogenic and common CNV groups, no significant differences were observed in social communication, restricted/repetitive behaviors, sensory symptoms, regression, or total autism scores (FDR-adjusted p > 0.05). Aggression was more frequently endorsed in pathogenic CNV carriers (raw p = 0.030; FDR p = 0.098). BMI was higher in pathogenic CNVs, though nonsignificant after correction (raw p = 0.027; FDR p = 0.152). ML analyses identified three recurrent discriminative symptoms across multiple datasets: delayed response to name, unusual object play, and aggression. Dataset 3 (16 symptoms) provided the most balanced classification performance but, given the very small pathogenic CNV sample, results remain exploratory. Conclusion Findings suggest that, while most autism-related symptoms do not differ between groups, aggression and increased BMI may represent preliminary phenotypic signals associated with pathogenic CNVs. Integrating clinical data from 147 CNV carriers further supports a potential widespread effect across the broader 16p11.2 locus rather than a single breakpoint-specific mechanism. However, all results should be interpreted cautiously due to limited sample size, and larger, systematically phenotyped cohorts are required to establish robust genotype–phenotype relationships. En ligne : https://doi.org/10.1016/j.reia.2026.202865 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=583

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