A Genome-Wide Association Meta-Analysis of Attention-Deficit/Hyperactivity Disorder Symptoms in Population-Based Pediatric Cohorts.

J Am Acad Child Adolesc Psychiatry
Authors
Abstract

OBJECTIVE: The aims of this study were to elucidate the influence of common genetic variants on childhood attention-deficit/hyperactivity disorder (ADHD) symptoms, to identify genetic variants that explain its high heritability, and to investigate the genetic overlap of ADHD symptom scores with ADHD diagnosis.

METHOD: Within the EArly Genetics and Lifecourse Epidemiology (EAGLE) consortium, genome-wide single nucleotide polymorphisms (SNPs) and ADHD symptom scores were available for 17,666 children (13 years of age) from nine population-based cohorts. SNP-based heritability was estimated in data from the three largest cohorts. Meta-analysis based on genome-wide association (GWA) analyses with SNPs was followed by gene-based association tests, and the overlap in results with a meta-analysis in the Psychiatric Genomics Consortium (PGC) case-control ADHD study was investigated.

RESULTS: SNP-based heritability ranged from 5% to 34%, indicating that variation in common genetic variants influences ADHD symptom scores. The meta-analysis did not detect genome-wide significant SNPs, but three genes, lying close to each other with SNPs in high linkage disequilibrium (LD), showed a gene-wide significant association (p values between 1.46 Ã— 10(-6) and 2.66 Ã— 10(-6)). One gene, WASL, is involved in neuronal development. Both SNP- and gene-based analyses indicated overlap with the PGC meta-analysis results with the genetic correlation estimated at 0.96.

CONCLUSION: The SNP-based heritability for ADHD symptom scores indicates a polygenic architecture, and genes involved in neurite outgrowth are possibly involved. Continuous and dichotomous measures of ADHD appear to assess a genetically common phenotype. A next step is to combine data from population-based and case-control cohorts in genetic association studies to increase sample size and to improve statistical power for identifying genetic variants.

Year of Publication
2016
Journal
J Am Acad Child Adolesc Psychiatry
Volume
55
Issue
10
Pages
896-905.e6
Date Published
2016 Oct
ISSN
1527-5418
DOI
10.1016/j.jaac.2016.05.025
PubMed ID
27663945
PubMed Central ID
PMC5068552
Links
Grant list
RC2 MH089951 / MH / NIMH NIH HHS / United States
RC2 MH089995 / MH / NIMH NIH HHS / United States
MC_UU_12013/3 / Medical Research Council / United Kingdom
R01 HD059215 / HD / NICHD NIH HHS / United States
U01 NS047537 / NS / NINDS NIH HHS / United States
102215 / Wellcome Trust / United Kingdom
N01ES75558 / ES / NIEHS NIH HHS / United States
MC_PC_15018 / Medical Research Council / United Kingdom
MC_UU_12013/1 / Medical Research Council / United Kingdom
R01 HD044454 / HD / NICHD NIH HHS / United States