Genome-Wide Polygenic Scores for Common Traits and Psychiatric Disorders Identify Young Children with Risk for Suicides

AbstractBackgroundSuicide is the leading cause of death in youth worldwide.1 Identifying children with high risk for suicide remains challenging.2 Here we test the extents to which genome-wide polygenic scores (GPS) for common traits and psychiatric disorders are linked to the risk for suicide in young children.MethodsWe constructed GPSs of 24 traits and psychiatric disorders broadly related to suicidality from 8,212 US children with ages of 9 to 10 from the Adolescent Brain Cognitive Development study. We performed multiple logistic regression to test the association between childhood suicidality, defined as suicidal ideation or suicidal attempt, and the GPSs. Machine learning techniques were used to test the predictive utility of the GPSs and other phenotypic outcomes on suicide and suicidal behaviors in the youth.OutcomesWe identified three GPSs significantly associated with childhood suicidality: Attention deficit hyperactivity disorder (ADHD) (P = 2.83×10−4; odds ratio (OR) = 1.12, FDR correction), general happiness with belief that own life is meaningful (P = 1.30×10−3; OR = 0.89) and autism spectrum disorder (ASD) (P = 1.81×10−3; OR = 1.14). Furthermore, the ASD GPS showed significant interaction with ELS such that a greater polygenic score in the presence of a greater ELS has even greater likelihood of suicidality (with active suicidal ideation, P = 1.39×10−2, OR = 1.11). In machine learning predictions, the cross validated and optimized model showed an ROC-AUC of 0.72 and accuracy of 0.756 for the hold-out set of overall suicidal ideation prediction, and showed an ROC-AUC of 0.765 and accuracy of 0.750 for the hold-out set of suicidal attempts.InterpretationOur results show that childhood suicidality is linked to the GPSs for psychiatric disorders, ADHD and ASD, and for a common trait, general happiness, respectively; and that GPSs for ASD and insomnia, respectively, have synergistic effects on suicidality via an interaction with early life stress. By providing the quantitative account of the polygenic and environmental factors of childhood suicidality in a large, representative population, this study shows the potential utility of the GPS in investigation of childhood suicidality for early screening, intervention, and prevention.

Download Full-text

Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide

10.21203/rs.3.rs-136949/v2 ◽

2021 ◽

Author(s):

Yoonjung Yoonie Joo ◽

Seo-Yoon Moon ◽

Hee-Hwan Wang ◽

Hyeonjin Kim ◽

Eun-Ji Lee ◽

...

Keyword(s):

Machine Learning ◽

Suicidal Ideation ◽

Family Environment ◽

Autism Spectrum ◽

Cognitive Capacity ◽

European Ancestry ◽

Environment Interaction ◽

Genome Wide ◽

Polygenic Scores ◽

Preadolescent Youth

Abstract Importance. Suicide is the second leading cause of death in children worldwide but no available means exist to identify the risk in youth. Objective. To predict the risk of suicide in children and to investigate whether and to what extents genetic factors and a major environmental risk factor, early life stress(ELS), influence youth suicide. Design, Setting and Participants. We analyzed the genotype-phenotype data of 11,869 preadolescent children ages 9- to 10-year-old from the Adolescent Brain and Cognitive Development (ABCD) study. We estimated genome-wide polygenic scores (GPSs) of 25 complex traits to investigate their phenome-wide associations and predictive utility with suicidality (suicidal ideation and attempt) with machine learning approaches. Predictors. GPSs of 25 traits including psychiatric disorders, personality, cognitive capacity, and psychological traits. Parent Child Behavior Checklist to measure ELS in youth and Youth Family Environment Scale to assess family environment. Main outcomes and Measures. Records of suicidal ideation and attempt of the participants were derived from the computerized version of Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS). Results. We identified three GPSs associated with youth suicidality in multiethnic (n = 7,206) and European-ancestry (n = 5,749) participants: ADHD (P = 3.48x10− 4; odds ratio = 1.13 in multiethnic participants, P = 5.60x10− 5, OR = 1.25 in European-ancestry participants), general happiness (P = 1.43x10− 3; OR = 0.89 in multiethnic, P = 8.61x10− 4, OR = 0.89 in European) and autism spectrum disorder(ASD) (P = 1.81x10− 3; OR = 1.15 in multiethnic, P = 1.26x10− 3, OR = 1.18 in European). We also found a significant GPS-by-environment interaction between the effects of genetic risk factors for ASD and the level of ELS in increasing the risk for suicidal ideation (P = 1.36x10− 2, OR = 1.12 in multiethnic, P = 1.39x10− 3, OR = 1.19 in European). A machine learning model trained on the same data showed moderately accurate prediction of children with overall suicidal ideation with a test ROC-AUC of 0.727 (0.746 in European), and with suicidal attempts with a test ROC-AUC of 0.641 (0.975 in European) in held-out samples. Conclusions and Relevance. This study provides the first quantitative account of polygenic and environmental factors of suicidality in a large, representative population of preadolescent youth. It thus shows the potential utility of the GPSs in identifying a child with high risk for suicidality for early screening, intervention, and prevention.

Download Full-text

Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide

10.21203/rs.3.rs-136949/v1 ◽

2020 ◽

Author(s):

Yoonjung Yoonie Joo ◽

Seo-Yoon Moon ◽

Hee-Hwan Wang ◽

Hyeonjin Kim ◽

Eun-Ji Lee ◽

...

Keyword(s):

Suicidal Ideation ◽

Life Stress ◽

Odds Ratio ◽

Area Under The Curve ◽

Autism Spectrum ◽

Environment Interaction ◽

Early Screening ◽

Genome Wide ◽

Polygenic Scores ◽

Preadolescent Youth

Abstract Suicide is a leading cause of death in youth worldwide, but identifying which youth are at high risk for suicide remains challenging. We constructed genome-wide polygenic scores (GPSs) from 24 psychiatric disorders and common traits from 8,212 US preadolescent children ages 9 to 10 and investigated their associations and predictive utility with suicidality (suicidal ideation and attempt). We identified three GPSs significantly associated with youth suicidality: ADHD (P=2.83x10-4; odds ratio=1.12), general happiness with a belief that life is meaningful (P=1.30x10-3; odds ratio=0.89) and autism spectrum disorder (ASD) (P=1.81x10-3; odds ratio=1.14). We also found a significant gene-by-environment interaction such that the GPS of ASD in the context of early life stress substantially increased suicidal ideation (P=1.39x10-2, odds ratio=1.11). Machine learning models showed, in predicting suicidal ideation, a receiver operators characteristics-area under the curve (ROC-AUC) of 0.72, and, in suicidal attempts, a ROC-AUC of 0.765. By providing the first quantitative account of the polygenic and environmental factors of suicidality in a large, representative population of preadolescent youth, this study shows the potential utility of the GPSs in investigating youth suicidality for early screening, intervention, and prevention.

Download Full-text

Association between polygenic propensity for psychiatric disorders and nutrient intake

Communications Biology ◽

10.1038/s42003-021-02469-4 ◽

2021 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Avina K. Hunjan ◽

Christopher Hübel ◽

Yuhao Lin ◽

Thalia C. Eley ◽

Gerome Breen

Keyword(s):

Psychiatric Disorders ◽

Nutrient Intake ◽

Autism Spectrum ◽

Dietary Behaviour ◽

Obsessive Compulsive ◽

Higher Alcohol ◽

Polygenic Score ◽

Compulsive Disorder ◽

Genome Wide ◽

Polygenic Scores

AbstractDespite the observed associations between psychiatric disorders and nutrient intake, genetic studies are limited. We examined whether polygenic scores for psychiatric disorders are associated with nutrient intake in UK Biobank (N = 163,619) using linear mixed models. We found polygenic scores for attention-deficit/hyperactivity disorder, bipolar disorder, and schizophrenia showed the highest number of associations, while a polygenic score for autism spectrum disorder showed no association. The relatively weaker obsessive-compulsive disorder polygenic score showed the greatest effect sizes suggesting its association with diet traits may become more apparent with larger genome-wide analyses. A higher alcohol dependence polygenic score was associated with higher alcohol intake and individuals with higher persistent thinness polygenic scores reported their food to weigh less, both independent of socioeconomic status. Our findings suggest that polygenic propensity for a psychiatric disorder is associated with dietary behaviour. Note, nutrient intake was self-reported and findings must therefore be interpreted mindfully.

Download Full-text

A Review on Machine Learning Techniques in the Diagnosis of Psychiatric Disorders

Indian Journal of Public Health Research & Development ◽

10.37506/ijphrd.v11i7.10133 ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Psychiatric Disorders ◽

Machine Learning Techniques ◽

Learning Techniques

Download Full-text

Traditional versus Neural Network Classification Methods for Facial Emotion Recognition

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2021-2052 ◽

2021 ◽

Vol 7 (2) ◽

pp. 203-206

Author(s):

Herag Arabian ◽

Verena Wagner-Hartl ◽

Knut Moeller

Keyword(s):

Neural Network ◽

Machine Learning ◽

Emotion Recognition ◽

Network Architecture ◽

Autism Spectrum ◽

Facial Emotion Recognition ◽

Facial Emotion ◽

Machine Learning Techniques ◽

Support Vector ◽

Machine Model

Abstract Facial emotion recognition (FER) is a topic that has gained interest over the years for its role in bridging the gap between Human and Machine interactions. This study explores the potential of real time FER modelling, to be integrated in a closed loop system, to help in treatment of children suffering from Autism Spectrum Disorder (ASD). The aim of this study is to show the differences between implementing Traditional machine learning and Deep learning approaches for FER modelling. Two classification approaches were taken, the first approach was based on classic machine learning techniques using Histogram of Oriented Gradients (HOG) for feature extraction, with a k-Nearest Neighbor and a Support Vector Machine model as classifiers. The second approach uses Transfer Learning based on the popular “Alex Net” Neural Network architecture. The performance of the approaches was based on the accuracy of randomly selected validation sets after training on random training sets of the Oulu-CASIA database. The data analyzed shows that traditional machine learning methods are as effective as deep neural net models and are a good compromise between accuracy, extracted features, computational speed and costs.

Download Full-text

An atlas of genetic correlations between psychiatric disorders and human blood plasma proteome

European Psychiatry ◽

10.1192/j.eurpsy.2019.6 ◽

2020 ◽

Vol 63 (1) ◽

Author(s):

Shiqiang Cheng ◽

Fanglin Guan ◽

Mei Ma ◽

Lu Zhang ◽

Bolun Cheng ◽

...

Keyword(s):

Psychiatric Disorders ◽

Plasma Proteins ◽

Genome Wide Association Study ◽

Genetic Relationships ◽

Genetic Correlations ◽

Tumor Necrosis Factor Receptor ◽

Autism Spectrum ◽

Human Blood Plasma ◽

Plasma Proteome ◽

Genome Wide

Abstract Background. Psychiatric disorders are a group of complex psychological syndromes with high prevalence. Recent studies observed associations between altered plasma proteins and psychiatric disorders. This study aims to systematically explore the potential genetic relationships between five major psychiatric disorders and more than 3,000 plasma proteins. Methods. The genome-wide association study (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) were driven from the Psychiatric GWAS Consortium. The GWAS datasets of 3,283 human plasma proteins were derived from recently published study, including 3,301 study subjects. Linkage disequilibrium score (LDSC) regression analysis were conducted to evaluate the genetic correlations between psychiatric disorders and each of the 3,283 plasma proteins. Results. LDSC observed several genetic correlations between plasma proteins and psychiatric disorders, such as ADHD and lysosomal Pro-X carboxypeptidase (p value = 0.015), ASD and extracellular superoxide dismutase (Cu-Zn; p value = 0.023), BD and alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 6 (p value = 0.007), MDD and trefoil factor 1 (p value = 0.011), and SCZ and insulin-like growth factor-binding protein 6 (p value = 0.011). Additionally, we detected four common plasma proteins showing correlation evidence with both BD and SCZ, such as tumor necrosis factor receptor superfamily member 1B (p value = 0.012 for BD, p value = 0.011 for SCZ). Conclusions. This study provided an atlas of genetic correlations between psychiatric disorders and plasma proteome, providing novel clues for pathogenetic and biomarkers, therapeutic studies of psychiatric disorders.

Download Full-text

Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques

Methods in Molecular Biology - Oral Biology ◽

10.1007/978-1-4939-6685-1_20 ◽

2016 ◽

pp. 347-364 ◽

Cited By ~ 2

Author(s):

Moritz Kebschull ◽

Panos N. Papapanou

Keyword(s):

Machine Learning ◽

Expression Profiles ◽

Machine Learning Techniques ◽

Genome Wide ◽

Learning Techniques ◽

Genome Wide Expression

Download Full-text

Development of an Autism Screening Classification Model for Toddlers

10.5121/csit.2021.111508 ◽

2021 ◽

Author(s):

Afef Saihi ◽

Hussam Alshraideh

Keyword(s):

Neural Network ◽

Machine Learning ◽

Early Stage ◽

Neurodevelopmental Disorder ◽

Autism Spectrum ◽

Repetitive Behaviors ◽

Therapeutic Interventions ◽

Classification Model ◽

Machine Learning Techniques ◽

Early Screening

Autism spectrum disorder ASD is a neurodevelopmental disorder associated with challenges in communication, social interaction, and repetitive behaviors. Getting a clear diagnosis for a child is necessary for starting early intervention and having access to therapy services. However, there are many barriers that hinder the screening of these kids for autism at an early stage which might delay further the access to therapeutic interventions. One promising direction for improving the efficiency and accuracy of ASD detection in toddlers is the use of machine learning techniques to build classifiers that serve the purpose. This paper contributes to this area and uses the data developed by Dr. Fadi Fayez Thabtah to train and test various machine learning classifiers for the early ASD screening. Based on various attributes, three models have been trained and compared which are Decision tree C4.5, Random Forest, and Neural Network. The three models provided very good accuracies based on testing data, however, it is the Neural Network that outperformed the other two models. This work contributes to the early screening of toddlers by helping identify those who have ASD traits and should pursue formal clinical diagnosis.

Download Full-text