[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.
Probability of Being in the High-Scoring Range for Attention-Deficit/Hyperactivity Disorder Symptoms by Latent Class
Probability of Being in the High-Scoring Range for Attention-Deficit/Hyperactivity Disorder Symptoms by Latent Class

Trajectories of attention-deficit/hyperactivity disorder symptoms identified by latent class growth analysis.

Figure 2.
Mean Polygenic Risk Score for Attention-Deficit/Hyperactivity Disorder (ADHD) by Subgroup
Mean Polygenic Risk Score for Attention-Deficit/Hyperactivity Disorder (ADHD) by Subgroup

Subgroups based on 2 time points (ages 7 and 17 years). Error bars indicate 95% CI.

Table 1.  
Associations Between Psychiatric Polygenic Risk Scores and All ADHD Latent Trajectory Classes
Associations Between Psychiatric Polygenic Risk Scores and All ADHD Latent Trajectory Classes
Table 2.  
Associations Between Co-occurring Childhood Characteristics at Ages 7 to 9 Years and ADHD Trajectory Class
Associations Between Co-occurring Childhood Characteristics at Ages 7 to 9 Years and ADHD Trajectory Class
Table 3.  
Associations Between Psychiatric Polygenic Risk Scores and ADHD Symptoms at Ages 7 and 17 Years
Associations Between Psychiatric Polygenic Risk Scores and ADHD Symptoms at Ages 7 and 17 Years
1.
Thapar  A, Rutter  M. Neurodevelopmental disorders. In: Thapar  A, Pine  DS, Leckman  JF, Scott  S, Snowling  MJ, Taylor  E, eds.  Rutter’s Child and Adolescent Psychiatry. 6th ed. Oxford: Wiley Press; 2015.
2.
Thapar  A, Cooper  M.  Attention deficit hyperactivity disorder.  Lancet. 2016;387(10024):1240-1250.PubMedGoogle ScholarCrossref
3.
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Washington, DC: American Psychiatric Association; 2013.
4.
Faraone  SV, Biederman  J, Mick  E.  The age-dependent decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies.  Psychol Med. 2006;36(2):159-165.PubMedGoogle ScholarCrossref
5.
Moffitt  TE, Houts  R, Asherson  P,  et al.  Is adult ADHD a childhood-onset neurodevelopmental disorder? evidence from a four-decade longitudinal cohort study.  Am J Psychiatry. 2015;172(10):967-977.PubMedGoogle ScholarCrossref
6.
Agnew-Blais  JC, Polanczyk  GV, Danese  A, Wertz  J, Moffitt  TE, Arseneault  L.  Evaluation of the persistence, remission, and emergence of attention-deficit/hyperactivity disorder in young adulthood.  JAMA Psychiatry. 2016;73(7):713-720.PubMedGoogle ScholarCrossref
7.
Caye  A, Rocha  TB, Anselmi  L,  et al.  Attention-deficit/hyperactivity disorder trajectories from childhood to young adulthood: evidence from a birth cohort supporting a late-onset syndrome.  JAMA Psychiatry. 2016;73(7):705-712.PubMedGoogle ScholarCrossref
8.
Pingault  JB, Viding  E, Galéra  C,  et al.  Genetic and environmental influences on the developmental course of attention-deficit/hyperactivity disorder symptoms from childhood to adolescence.  JAMA Psychiatry. 2015;72(7):651-658.PubMedGoogle ScholarCrossref
9.
Biederman  J, Petty  CR, Clarke  A, Lomedico  A, Faraone  SV.  Predictors of persistent ADHD: an 11-year follow-up study.  J Psychiatr Res. 2011;45(2):150-155.PubMedGoogle ScholarCrossref
10.
Kessler  RC, Adler  LA, Barkley  R,  et al.  Patterns and predictors of attention-deficit/hyperactivity disorder persistence into adulthood: results from the national comorbidity survey replication.  Biol Psychiatry. 2005;57(11):1442-1451.PubMedGoogle ScholarCrossref
11.
Lara  C, Fayyad  J, de Graaf  R,  et al.  Childhood predictors of adult attention-deficit/hyperactivity disorder: results from the World Health Organization World Mental Health Survey Initiative.  Biol Psychiatry. 2009;65(1):46-54.PubMedGoogle ScholarCrossref
12.
Shaw  P, Malek  M, Watson  B, Greenstein  D, de Rossi  P, Sharp  W.  Trajectories of cerebral cortical development in childhood and adolescence and adult attention-deficit/hyperactivity disorder.  Biol Psychiatry. 2013;74(8):599-606.PubMedGoogle ScholarCrossref
13.
Larsson  H, Dilshad  R, Lichtenstein  P, Barker  ED.  Developmental trajectories of DSM-IV symptoms of attention-deficit/hyperactivity disorder: genetic effects, family risk and associated psychopathology.  J Child Psychol Psychiatry. 2011;52(9):954-963.PubMedGoogle ScholarCrossref
14.
Caye  A, Spadini  AV, Karam  RG,  et al.  Predictors of persistence of ADHD into adulthood: a systematic review of the literature and meta-analysis [published online March 28, 2016].  Eur Child Adolesc Psychiatry.PubMedGoogle Scholar
15.
Langley  K, Fowler  T, Ford  T,  et al.  Adolescent clinical outcomes for young people with attention-deficit hyperactivity disorder.  Br J Psychiatry. 2010;196(3):235-240.PubMedGoogle ScholarCrossref
16.
Thapar  A, Cooper  M, Eyre  O, Langley  K.  What have we learnt about the causes of ADHD?  J Child Psychol Psychiatry. 2013;54(1):3-16.PubMedGoogle ScholarCrossref
17.
Chang  Z, Lichtenstein  P, Asherson  PJ, Larsson  H.  Developmental twin study of attention problems: high heritabilities throughout development.  JAMA Psychiatry. 2013;70(3):311-318.PubMedGoogle ScholarCrossref
18.
Franke  B, Faraone  SV, Asherson  P,  et al; International Multicentre Persistent ADHD Collaboration.  The genetics of attention deficit/hyperactivity disorder in adults, a review.  Mol Psychiatry. 2012;17(10):960-987.PubMedGoogle ScholarCrossref
19.
Larsson  H, Asherson  P, Chang  Z,  et al.  Genetic and environmental influences on adult attention deficit hyperactivity disorder symptoms: a large Swedish population-based study of twins.  Psychol Med. 2013;43(1):197-207.PubMedGoogle ScholarCrossref
20.
Hamshere  ML, Langley  K, Martin  J,  et al.  High loading of polygenic risk for ADHD in children with comorbid aggression.  Am J Psychiatry. 2013;170(8):909-916.PubMedGoogle ScholarCrossref
21.
Martin  J, Hamshere  ML, Stergiakouli  E, O’Donovan  MC, Thapar  A.  Genetic risk for attention-deficit/hyperactivity disorder contributes to neurodevelopmental traits in the general population.  Biol Psychiatry. 2014;76(8):664-671.PubMedGoogle ScholarCrossref
22.
Groen-Blokhuis  MM, Middeldorp  CM, Kan  KJ,  et al.  Attention-deficit/hyperactivity disorder polygenic risk scores predict attention problems in a population-based sample of children.  J Am Acad Child Adolesc Psychiatry. 2014;53(10):1123-1129.e6. PubMedGoogle Scholar
23.
Martin  J, Hamshere  ML, Stergiakouli  E, O’Donovan  MC, Thapar  A.  Neurocognitive abilities in the general population and composite genetic risk scores for attention-deficit hyperactivity disorder.  J Child Psychol Psychiatry. 2015;56(6):648-656.PubMedGoogle ScholarCrossref
24.
Boyd  A, Golding  J, Macleod  J,  et al.  Cohort profile: the ‘children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children.  Int J Epidemiol. 2013;42(1):111-127.PubMedGoogle ScholarCrossref
25.
Fraser  A, Macdonald-Wallis  C, Tilling  K,  et al.  Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort.  Int J Epidemiol. 2013;42(1):97-110.PubMedGoogle ScholarCrossref
26.
Goodman  R.  The Strengths and Difficulties Questionnaire: a research note.  J Child Psychol Psychiatry. 1997;38(5):581-586.PubMedGoogle ScholarCrossref
27.
St Pourcain  B, Mandy  WP, Heron  J, Golding  J, Davey Smith  G, Skuse  DH.  Links between co-occurring social-communication and hyperactive-inattentive trait trajectories.  J Am Acad Child Adolesc Psychiatry. 2011;50(9):892-902.e5. PubMedGoogle Scholar
28.
Cross-Disorder Group of the Psychiatric Genomics Consortium.  Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis.  Lancet. 2013;381(9875):1371-1379.PubMedGoogle ScholarCrossref
29.
Cross-Disorder Group of the Psychiatric Genomics Consortium.  Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.  Nat Genet. 2013;45(9):984-994. PubMedGoogle ScholarCrossref
30.
Neale  BM, Medland  SE, Ripke  S,  et al; Psychiatric GWAS Consortium: ADHD Subgroup.  Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder.  J Am Acad Child Adolesc Psychiatry. 2010;49(9):884-897.PubMedGoogle ScholarCrossref
31.
Yang  L, Neale  BM, Liu  L,  et al; Psychiatric GWAS Consortium: ADHD Subgroup.  Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants.  Am J Med Genet B Neuropsychiatr Genet. 2013;162B(5):419-430.PubMedGoogle ScholarCrossref
32.
Schizophrenia Working Group of the Psychiatric Genomics Consortium.  Biological insights from 108 schizophrenia-associated genetic loci.  Nature. 2014;511(7510):421-427.PubMedGoogle ScholarCrossref
33.
Sklar  P, Ripke  S, Scott  LJ,  et al; Psychiatric GWAS Consortium Bipolar Disorder Working Group.  Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4 [published correction appears in Nat Genet. 2012 Sep;44(9):1072].  Nat Genet. 2011;43(10):977-983.PubMedGoogle ScholarCrossref
34.
Ripke  S, Wray  NR, Lewis  CM,  et al; Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium.  A mega-analysis of genome-wide association studies for major depressive disorder.  Mol Psychiatry. 2013;18(4):497-511.PubMedGoogle ScholarCrossref
35.
Dudbridge  F.  Power and predictive accuracy of polygenic risk scores.  PLoS Genet. 2013;9(3):e1003348.PubMedGoogle ScholarCrossref
36.
Wechsler  D, Golombok  S, Rust  J.  WISC-III UK Wechsler Intelligence Scale for Children: UK Manual. Sidcup, UK: The Psychological Corporation; 1992.
37.
Skuse  DH, Mandy  WP, Scourfield  J.  Measuring autistic traits: heritability, reliability and validity of the Social and Communication Disorders Checklist.  Br J Psychiatry. 2005;187(6):568-572.PubMedGoogle ScholarCrossref
38.
Bishop  DV.  Development of the Children’s Communication Checklist (CCC): a method for assessing qualitative aspects of communicative impairment in children.  J Child Psychol Psychiatry. 1998;39(6):879-891.PubMedGoogle ScholarCrossref
39.
Muthén  LK, Muthén  BO.  Mplus User’s Guide. 7th ed. Los Angeles, CA: Muthén & Muthén; 2012.
40.
Muthén  B, Muthén  LK.  Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes.  Alcohol Clin Exp Res. 2000;24(6):882-891.PubMedGoogle ScholarCrossref
41.
Asparouhouv  T.  Wald Test of Mean Equality for Potential Latent Class Predictors in Mixture Modeling: Technical Appendix. Los Angeles, CA: Muthén & Muthén; 2007.
42.
Asparouhov  T, Muthén  B.  Auxiliary variables in mixture modeling: three-step approaches using Mplus.  Struct Equ Modeling. 2014;21(3):329-341. doi:10.1080/10705511.2014.915181Google ScholarCrossref
43.
Heron  JE, Croudace  TJ, Barker  ED, Tilling  K.  A comparison of approaches for assessing covariate effects in latent class analysis.  Longit Life Course Stud. 2015;6(4):420-434. doi:10.14301/llcs.v6i4.322Google Scholar
44.
Faraone  SV, Biederman  J, Monuteaux  MC.  Toward guidelines for pedigree selection in genetic studies of attention deficit hyperactivity disorder.  Genet Epidemiol. 2000;18(1):1-16.PubMedGoogle ScholarCrossref
45.
Larsson  J-O, Larsson  H, Lichtenstein  P.  Genetic and environmental contributions to stability and change of ADHD symptoms between 8 and 13 years of age: a longitudinal twin study.  J Am Acad Child Adolesc Psychiatry. 2004;43(10):1267-1275.PubMedGoogle ScholarCrossref
46.
Barnett  K, Mercer  SW, Norbury  M, Watt  G, Wyke  S, Guthrie  B.  Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.  Lancet. 2012;380(9836):37-43.PubMedGoogle ScholarCrossref
47.
National Institute for Health and Care Excellence. Multimorbidity: clinical assessment and management. https://www.nice.org.uk/guidance/GID-CGWAVE0704/documents/draft-guideline. Accessed May 5, 2016.
48.
Wolke  D, Waylen  A, Samara  M,  et al.  Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders.  Br J Psychiatry. 2009;195(3):249-256.PubMedGoogle ScholarCrossref
49.
Widaman  KF.  Missing data: what to do with or without them.  Monogr Soc Res Child Dev. 2006;71(3):42-64. doi:10.1111/j.1540-5834.2006.00404.xGoogle Scholar
Original Investigation
December 2016

Association of Genetic Risk Variants With Attention-Deficit/Hyperactivity Disorder Trajectories in the General Population

Author Affiliations
  • 1Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
  • 2National Centre for Register-Based Research, School of Business and Social Sciences, Aarhus University, Aarhus, Denmark
  • 3The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
  • 4School of Psychology, Cardiff University, Cardiff, United Kingdom
  • 5MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
  • 6School of Oral and Dental Sciences, University of Bristol, Bristol, United Kingdom
  • 7MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Kings College London, London, United Kingdom
 

Copyright 2016 American Medical Association. All Rights Reserved.

JAMA Psychiatry. 2016;73(12):1285-1292. doi:10.1001/jamapsychiatry.2016.2817
Key Points

Question  Are symptom trajectories of attention-deficit/hyperactivity disorder (ADHD) across childhood and adolescence associated with an individual’s genetic risk variant load for ADHD, as indexed by polygenic risk scores, and childhood multimorbidity, measured as the number of neurodevelopmental disorders or conduct problems?

Findings  This cohort study found that polygenic risk scores for ADHD and multimorbidity were significantly higher in individuals with persistent ADHD. Polygenic risk scores for ADHD were also significantly associated with multimorbidity.

Meaning  The course of ADHD symptoms across childhood and adolescence in the general population is associated with polygenic risk scores for ADHD; childhood multimorbidity may help clinicians identify children most likely to show ADHD persistence.

Abstract

Importance  Attention-deficit/hyperactivity disorder (ADHD) is a heritable neurodevelopmental disorder that shows clinical and genetic overlap with other childhood neurodevelopmental disorders. Levels of ADHD symptoms typically decline across childhood and adolescence, although they remain elevated for some individuals. The determinants of symptom persistence and decline are not yet fully understood.

Objectives  To test the hypothesis that genetic risk variant load for ADHD (indexed by polygenic risk scores [PRS]), but not for other psychiatric disorders, is associated with population-based ADHD symptom trajectories across childhood and adolescence, and to examine whether higher genetic liability for ADHD is correlated with total number of additional neurodevelopmental disorders (multimorbidity) in childhood.

Design, Setting, and Participants  The Avon Longitudinal Study of Parents and Children, an ongoing prospective population-based cohort study, has been collecting data on 14 701 children, including 9757 with data on symptoms of ADHD at multiple time points, since September 6, 1990. The primary exposure variables, PRS, were generated using results of a genome-wide association study from the Psychiatric Genomics Consortium. Childhood multimorbidity scores (ages 7-9 years) were measured by total impairments in 4 domains known to share genetic liability with ADHD: IQ, social communication, pragmatic language, and conduct. Data analysis was conducted from March 1 to September 8, 2016.

Main Outcomes and Measures  Attention-deficit/hyperactivity disorder symptom trajectories from ages 4 to 17 years (7 time points).

Results  Among 9757 children with data on symptoms of ADHD at multiple time points (age range, 4-17 years; 4968 boys and 4789 girls), 4 ADHD symptom trajectories were identified: low (82.6%), intermediate (7.7%), childhood-limited (5.8%), and persistent (3.9%). Mean (SE) PRS for ADHD were higher in children in the persistent trajectory (0.254 [0.069]) compared with each of the other 3 trajectories (low, –0.018 [0.014], χ21 = 14.67, P < .001, odds ratio, 1.31; intermediate, 0.054 [0.055], χ21 = 4.70, P = .03, odds ratio, 1.22; and childhood-limited, 0.017 [0.060], χ21 = 6.50, P = .01, odds ratio, 1.27). Findings were specific to PRS for ADHD; PRS for other psychiatric conditions did not differ across trajectories. The proportion of children with multimorbidity was also highest in those in the persistent trajectory (42.5%; 95% CI, 33.9%-51.1%; P < .001) and was associated with persistence of ADHD symptoms independent of PRS.

Conclusions and Relevance  Persistence of ADHD symptoms across childhood and adolescence in the general population is associated with higher PRS for ADHD. Childhood multimorbidity was also associated with persistence of ADHD symptoms and may help to identify children with ADHD whose symptoms are most likely to continue into adolescence.

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with an onset in childhood.1-3 Although it is considered to manifest most commonly in children, approximately 15% of people with a childhood diagnosis continue to meet clinical criteria for ADHD in adulthood, with up to 65% showing symptoms of ADHD that do not meet the criteria for diagnosis.4 Furthermore, recent work suggests that for some individuals, ADHD first emerges in adulthood,5-7 emphasizing the need to investigate the natural history of ADHD in general population samples. Although ADHD is relevant across the lifespan, most children show a decline in symptom levels across childhood and adolescence, which also occurs in other childhood-onset neurodevelopmental disorders, such as autism spectrum disorder, communication disorders, and specific learning disorders.1 The determinants of the persistence of a neurodevelopmental disorder are not fully understood, although for ADHD, severity of initial symptoms, comorbidities, cortical maturation, and family history of ADHD, among other factors, have been considered as contributors.8-15

Attention-deficit/hyperactivity disorder has a heritability estimate of 71% to 90%.16 Twin studies suggest that persistence of ADHD symptoms is also heritable, but it has only recently become possible to directly assess genetic contributions.8,17-19 Genomic studies of ADHD have revealed a genetic architecture of multiple common risk alleles as well as rare mutations.16 Although individual common risk alleles typically have small effect sizes for multifactorial disorders, such as ADHD, composite measures—polygenic risk scores (PRS)—representing an individual’s estimated total burden of common risk alleles (where risk alleles are defined by their association statistics and effect sizes in a discovery genome-wide association study) are useful biological indicators of disease risk.19 Polygenic risk scores for ADHD are higher in patients with disorder than in controls20 and are associated with ADHD symptom levels in the general population.21,22 Attention-deficit/hyperactivity disorder also shares genetic liability with other neurodevelopmental traits and conduct problems,1,20,23 suggesting that those with higher genetic loading for ADHD are likely to manifest elevated levels of problems in these domains.

We examine the associations between psychiatric PRS and population-based developmental trajectories of ADHD symptoms from early childhood to adolescence. We hypothesized that PRS for ADHD, but not for other psychiatric disorders (eg, schizophrenia, bipolar disorder, and depression) would be associated with persistence of ADHD symptoms from ages 4 to 17 years. We also postulated that a trajectory of persistent ADHD symptoms would be associated with a higher burden of childhood neurodevelopmental impairments and conduct problems, as this multimorbidity would index underlying ADHD genetic liability.

Methods
Sample

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a well-established ongoing prospective longitudinal birth cohort study that has been collecting data since September 6, 1990. The enrolled core sample consisted of 14 541 mothers living in Avon, England, who had expected delivery dates between April 1, 1991, and December 31, 1992. Of these pregnancies, 13 988 children were alive at 1 year. When the oldest children were approximately 7 years, the initial sample was increased by recruiting eligible families who did not originally join the study, resulting in an additional 713 children being enrolled. The resulting total sample size of children who were alive at 1 year was 14 701. Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. All participants provided written informed consent. Full details of the study, measures, and sample can be found elsewhere24,25 (see http://www.bristol.ac.uk/alspac/researchers/access/). For families with multiple births, we included the oldest sibling. Individuals were included in our analyses when primary data on ADHD symptoms were available for at least 2 time points (n = 9757). The numbers of individuals with data available at different time points are in eFigure 1 in the Supplement.

Symptoms of ADHD

The primary outcome was ADHD symptoms assessed repeatedly across time using the parent-rated 5-item Strengths and Difficulties Questionnaire (SDQ)26 subscale designed to measure hyperactive and inattentive symptoms (score range, 0-10). In line with recommendations and to maintain consistency with previous work in ALSPAC,26,27 abnormal scores were defined as those 7 or higher, while 6 was considered a borderline score. The SDQ showed high sensitivity and specificity for detecting a DSM-IV diagnosis of ADHD assessed using a diagnostic interview at age 7 years (eAppendix in the Supplement). Data were available from parent reports at ages 47, 81, 97, 115, 140, 157, and 198 months (approximately aged 4-17 years).

Polygenic Risk Scores

Polygenic risk scores were generated as the standardized mean number of disorder risk alleles in approximate linkage equilibrium (R2<0.25), weighted by genome-wide association study allele effect size, derived from dosage data of imputed autosomal single-nucleotide polymorphisms using standard procedures.28 Risk alleles were defined as those associated with case status in the Psychiatric Genomics Consortium analyses of several phenotypes at a threshold of P < .50 for ADHD, bipolar disorder, and depression and P < .05 for schizophrenia. These thresholds maximally capture phenotypic variance.29-34 Genome-wide association study case and control sample sizes were as follows: ADHD, 5621 cases and 13 589 controls; schizophrenia, 35 476 cases and 46 839 controls; bipolar disorder, 7481 cases and 9250 controls; and depression, 9240 cases and 9519 controls). Genotyping and full PRS details are in the eAppendix in the Supplement. Power was estimated using the Dudbridge calculator with approximations for some of the required parameters.35

Other Characteristics

We investigated whether co-occurring neurodevelopmental traits and conduct problems in childhood (aged 7-9 years) are associated with ADHD genetic liability (defined by PRS). Problems (defined categorically using established cut-points to enable computation of multimorbidity) included the following: low IQ (defined as a score of <80 on the Wechsler Intelligence Scale for Children36); social communication problems (defined as a score of ≥9 on the parent-rated Social and Communication Disorders Checklist37); impairment of pragmatic language (defined as a score of ≤132 on the parent-rated Children’s Communication Checklist subscale38); and conduct problems, measured at age 81 months (defined as a score ≥4 on the parent-rated SDQ subscale26). Multimorbidity was defined as the sum of the number of impairments in domains i-iv (range, 0-4).

Statistical Analysis

Data analysis was conducted from March 1 to September 8, 2016. Latent class growth analysis was conducted in Mplus (Muthén and Muthén)39 to identify ADHD developmental trajectories across all 7 time points using binary data on ADHD symptoms, in line with previous work in ALSPAC.27 Latent class growth analysis aims to group individuals into categories (classes) based on different patterns of change (growth curves) across multiple time points, with within-class covariance matrices fixed to zero (ie, individuals within the same class are specified to have the same growth curve).40 Starting with a single k-class solution, k+1 solutions are fitted until the optimum solution is reached. Models were run using a robust maximum likelihood parameter estimator and full information maximum likelihood estimation.39 The optimal number of categories was determined using adjusted Bayesian information criterion to assess model fit and entropy to assess classification accuracy. Differences in PRS and multimorbidity were assessed by a Wald test of equality of means using posterior probability–based multiple imputations (odds ratios [ORs] were generated using multinomial logistic regressions),41 which takes profile measurement error into account (PRS and multimorbidity were not used to generate the trajectories). Finally, we investigated the independent associations of PRS for ADHD and multimorbidity with class membership using a bias-free 3-step approach (R3STEP)42 that performs better than conventional 3-step methods.43

Results
Symptom Trajectories of ADHD

Latent class growth analysis indicated that the 4-class solution of ADHD trajectories had the best model fit (adjusted Bayesian information criterion, 25574.45; Vuong-Lo-Mendell-Rubin likelihood ratio test, P < .001 vs a 3-class solution) and classification accuracy (entropy, 0.82), consistent with previous work in ALPSAC.27 As shown in Figure 1, this solution included the 4 classes: low (82.6%), intermediate (7.7%), childhood-limited (5.8%), and persistent (3.9%). The solution did not include an adolescent-onset group. The proportion of boys differed across the trajectories, with the largest proportion in the persistent (72.9%), smallest in the low (48%), and intermediate levels in the child-limited (62.3%) and intermediate (63%) trajectories (overall χ23 = 45.22; P < .001).

Genetic Variables: Psychiatric PRS

Polygenic risk scores for ADHD differed across the 4 trajectories; mean (SE) scores were highest in the persistent trajectory (0.254 [0.069]), lowest for the low symptom group (–0.018 [0.014]), and intermediate for the childhood-limited (0.017 [0.060]) and intermediate (0.054 [0.055]) trajectories (Table 1). Differences were observed for the persistent trajectory when compared separately with the childhood-limited (OR, 1.27; χ2 = 6.50; P = .01), intermediate (OR, 1.22; χ2 = 4.70; P = .03), and low (OR, 1.31; χ2 = 14.67; P < .001) trajectories. The association with trajectory was specific to the PRS for ADHD: PRS for schizophrenia, bipolar disorder, and depression were not associated with ADHD symptom trajectories (Table 1).

Childhood Multimorbidity

The proportion of individuals with neurodevelopmental traits and conduct problems across the 4 trajectories are shown in Table 2. Low IQ, social communication problems, impairment of pragmatic language, and conduct problems at ages 7 to 9 years differed by trajectory, with the highest levels in the persistent trajectory compared with all other trajectories. The childhood-limited and intermediate trajectories also showed elevated levels compared with the low trajectory. Low IQ was seen in 5.4% (95% CI, 4.8%-6%) of those in the low trajectory, 11.4% (95% CI, 8.1%-14.7%) of those in the intermediate trajectory, 11.7% (95% CI, 7.8%-15.6%) of those in the childhood-limited trajectory, and 21.4% (95% CI, 15.5%-27.3%) of those in the persistent trajectory. Social communication problems were seen in 3.4% (95% CI, 3%-3.8%) of those in the low trajectory, 19.9% (95% CI, 16%-23.8%) of those in the intermediate trajectory, 22.9% (95% CI, 18.2%-27.6%) of those in the childhood-limited trajectory, and 53.1% (95% CI, 46.6%-59.6%) of those in the persistent trajectory. Impairment of pragmatic language was seen in 1.2% (95% CI, 1%-1.4%) of those in the low trajectory, 7.7% (95% CI, 5%-10.4%) of those in the intermediate trajectory, 10.3% (95% CI, 7%-13.6%) of those in the childhood-limited trajectory, and 27.8% (95% CI, 22.1%-33.5%) of those in the persistent trajectory. Conduct problems were seen in 6.7% (95% CI, 6.1%-7.3%) of those in the low trajectory, 21.1% (95% CI, 17%-25.2%) of those in the intermediate trajectory, 27.5% (95% CI, 22.6%-32.4%) of those in the childhood-limited trajectory, and 42% (95% CI, 35.7%-48.3%) of those in the persistent trajectory.

The proportions of children with multimorbidities across the 4 trajectories are shown in Table 2. Multimorbidity varied by latent trajectory, with a higher burden of childhood neurodevelopmental impairments or conduct problems in those in the persistent trajectory compared with all other trajectories and elevated levels in the childhood-limited and intermediate trajectories compared with the low trajectory (>1 neurodevelopmental or conduct domain affected: low trajectory, 1.7% [95% CI, 1.3%-2.1%]; intermediate trajectory, 13.1% [95% CI, 8.6%-17.6%]; childhood-limited trajectory, 16.1% [95% CI, 10.8%-21.4%]; and persistent trajectory, 42.5% [95% CI, 33.9%-51.1%]; χ23 = 38.93; P < .001; >2 neurodevelopmental or conduct domains affected: low trajectory, 0.2% [95% CI, 0%-0.4%]; intermediate trajectory, 2.8% [95% CI, 0.6%-5%]; childhood-limited trajectory, 3.4% [95% CI, 0.7%-6.1%]; and persistent trajectory, 17.8% [95% CI, 11.3%-24.3%]; χ23 = 17.31, P = .001).

Independent Contributions of PRS for ADHD and Childhood Multimorbidity to Persistence of ADHD

Multimorbidity at ages 7 to 9 years was associated with PRS for ADHD (OR, 1.16; P < .001) but not with PRS for schizophrenia (OR, 1.02; P = .61), bipolar disorder (OR, 1.09; P = .06), or depression (OR, 1.06; P = .18).

When entered simultaneously (n = 3870), both multimorbidity and PRS for ADHD were independently associated with the persistent trajectory compared with the low trajectory (OR per additional neurodevelopmental or conduct domain, 9.21; multimorbidity β [SE], 2.22 [0.15]; P < .001; OR per SD increase in PRS for ADHD, 1.42; PRS β [SE], 0.35 [0.13]; P = .01).

There was also evidence that multimorbidity was independently associated with persistence of ADHD relative to the childhood-limited trajectory (OR per additional neurodevelopmental or conduct domain, 1.46; β [SE], 0.38 [0.14]; P = .01), whereas evidence for the PRS for ADHD was weak (OR per SD increase in PRS for ADHD, 1.30; β [SE], 0.26 [0.18]; P = .14).

Grouping Individuals Using ADHD Cut-Points at 2 Time Points

Given recent findings on adolescent-onset ADHD,5-7 in a post-hoc investigation, we categorized individuals as showing ADHD symptom persistence if they scored above the SDQ ADHD subscale cut-point at 2 time points: ages 7 and 17 years (n = 4824; individuals with data on ADHD symptoms at both time points).

Most individuals did not meet the threshold for high levels of ADHD symptoms at either age and were categorized in the low trajectory (4193 [86.9%]). Ten percent of individuals (n = 476) met the cut-point at age 7 years; most no longer met the cut-point at age 17 years and were categorized in the child-limited trajectory (370 [7.7%]), with those meeting the cut-point at both ages categorized in the persistent trajectory (106 [2.2%]). One hundred fifty-five children (3.2%) did not meet the threshold for high levels of ADHD symptoms at age 7 years, but did at age 17 years (33 [0.7%] had borderline levels of ADHD traits at age 7 years).26 We categorized an adolescent-onset subgroup as those who met the cut-point at age 17 years but did not have borderline or abnormal symptoms at age 7 years (122 [2.5%]).

As shown in Table 3, mean (SE) PRS for ADHD differed across the 4 ADHD subgroups (low, –0.035 [0.018]; childhood-limited, 0.043 [0.061]; persistent, 0.252 [0.102]; and adolescent-onset, 0.101 [0.104]; P = .03) but PRS for schizophrenia, bipolar disorder, and depression were not associated with ADHD symptom subgroups. Specifically, there was evidence of higher PRS for ADHD in the persistent compared with the low subgroup (OR per SD increase in PRS for ADHD, 1.33; B [SE], 0.29 [0.11]; P = .01) (Figure 2). Follow-up analyses categorizing individuals using symptoms at ages 4 and 17 years, symptoms at ages 12 and 17 years, and inattentive and hyperactive or impulsive traits separately revealed the same pattern of results (eAppendix and eFigure 2 in the Supplement).

Discussion

Our study aimed to test the hypothesis that ADHD common risk allele burden as indexed by PRS contributes to population-based ADHD developmental trajectories from early childhood to adolescence. Defining susceptibility alleles for a range of psychiatric disorders from large patient case-control discovery samples,29-34 we found that in a population cohort, higher PRS for ADHD were associated with persistence of ADHD symptoms but that PRS for other disorders were not. The persistent trajectory also had the highest burden of multimorbidity for neurodevelopmental traits and conduct problems in childhood.

Although ADHD typically has an onset—and is thought to be most common—in childhood, approximately 15% of children with a childhood diagnosis show persistence across childhood and adolescence and still meet diagnostic criteria for ADHD in adulthood4 while only approximately 35% achieve full remission.2,4 In line with this finding and previous work in the ALSPAC population cohort,27 we identified 2 trajectory groups of children with a high probability of having ADHD symptoms in childhood that were initially elevated (total of 9.7%). Of these children, approximately 40% were estimated to be in the persistent trajectory, with a high probability of elevated ADHD traits at age 17 years. The other 60% were estimated to be in a childhood-limited trajectory, with a low probability of high levels of ADHD symptoms after approximately age 10 years.

We found that ADHD genetic risk scores were higher specifically in the trajectory with persistent symptoms compared with individuals with consistently low symptoms and childhood-limited symptoms. Twin studies that indirectly infer genetic contributions have suggested that most of the persistence in ADHD symptoms is explained by additive genetic variance and is heritable.8,17-19 Those with persistent ADHD symptoms also have a higher familial load, with almost a 4-fold higher risk of ADHD in their families than among individuals with childhood ADHD.18,44 Some twin studies suggest that different genetic risk factors are associated with persistence of ADHD symptoms compared with baseline levels of symptoms.45 Our work suggests that common genetic variants associated with ADHD diagnosis contribute to the persistence of ADHD symptoms in the general population, as well as initial childhood levels, found in previous work.21,23 The finding that PRS for ADHD were higher in those with persistent vs childhood-limited symptoms is novel and potentially clinically important given that these trajectories would be indistinguishable on the basis of their ADHD symptoms in early childhood, although this finding would need to be replicated in a clinical sample.

As well as being associated with genetic risk of ADHD, we also found the persistent ADHD trajectory class to be strongly associated with multimorbidity: low IQ, social communication problems, impairment of pragmatic language, and conduct problems in childhood. Individual childhood comorbidities have been implicated as possible predictors of the persistence of ADHD14 but a global burden of multimorbidity has not previously been assessed, to our knowledge. Although multimorbidity was highest among children in the persistent trajectory, it was also elevated among those in the childhood-limited trajectory. Attention-deficit/hyperactivity disorder shares genetic liability with childhood neurodevelopmental traits and conduct problems in the general population20,21,23 and our study shows PRS for ADHD to be associated with multimorbidity in these domains. Thus, it is plausible that multimorbidity might be an observable early phenotype marker of this loading and be associated with ADHD developmental trajectories. When controlling for multimorbidity, PRS for ADHD were no longer associated with persistence compared with childhood-limited symptoms, suggesting that the overall childhood burden of neurodevelopmental morbidity may be a phenotypic correlate, and perhaps for now a better index, of a higher genetic loading. Further work will be needed to assess the predictive value of multimorbidity. Although this is a population-based sample, the findings highlight the likely developmental, biological, and clinical importance of multimorbidity, an issue that until now has been considered a health care problem in old age.46,47 In clinical settings, a hierarchical approach is typically used to simplify and reduce the number of diagnoses. Assessing and describing multimorbidity are therefore not easily achieved with current approaches, yet might be very important for clinical reasons and scientific research.

Recent work has suggested that some forms of ADHD first emerge in adulthood.5-7 Although our analyses did not identify an adolescent-onset trajectory, using an alternative method for defining the course of ADHD, we identified a subgroup of approximately 2.5% of individuals who had elevated levels of ADHD symptoms at age 17 but not age 7 years. Although our study focused on an earlier age, in line with the observation made by Moffitt and colleagues,5 this adolescent-onset subgroup did not show elevated genetic risk scores for ADHD. Although it is possible this finding is owing to low PRS power, we did find an association for the persistent subgroup, despite the subgroup being smaller. This study is now the fourth population study that suggests that a substantial proportion of adolescents and young adults with ADHD have onset at later ages and that finds a very low rate of persistence of ADHD symptoms; moreover, our study finds this pattern when using the same informant (parent) at both time points.

Our findings should be considered in light of some limitations. The Avon Longitudinal Study of Parents and Children is a longitudinal birth cohort study with nonrandom attrition, and more complete data are likely to have been available for individuals with lower levels of psychopathology as well as PRS.48 However, we used full information maximum likelihood estimation, which fits the model to the nonmissing values for each observation, allowing the use of all individuals, including those with missing data.49 Results using an alternative method examining 2 time points (ages 7 and 17 years) in individuals with complete data revealed the same pattern of higher PRS for ADHD in children with persistent ADHD traits. In addition, we used a questionnaire to investigate trajectories of ADHD symptoms, which may not generalize to ADHD diagnosis although the SDQ cut-point is well validated against diagnosis. Furthermore, owing to current discovery sample sizes, psychiatric PRS currently explain only a small proportion of the heritability and of phenotypic variance and are therefore underpowered (approximately 0.60 for the analysis using 2 time points).35 However, our intention was not to explain substantial proportions of phenotype variance but to use PRS as a molecular index of common genetic loading. Finally, ADHD data in ALSPAC were only available up to age 17 years. Future work is needed on environmental factors that may also contribute to the developmental course of ADHD.

Conclusions

We found genetic risk of ADHD to be associated with the developmental course of ADHD traits from early childhood to adolescence in the general population: specifically, ADHD genetic loading was highest in children with persistent symptoms. Genome-wide association studies may benefit from deeper phenotyping of cases to characterize the developmental course of psychiatric disorders. Persistence of ADHD was also associated with greater multimorbidity of childhood neurodevelopmental impairments and conduct problems, which may be a phenotypic correlate of genetic loading and help to identify children with ADHD who are most likely to show persistence of symptoms into adolescence.

Back to top
Article Information

Accepted for Publication: September 9, 2016.

Corresponding Author: Anita Thapar, PhD, FRCPsych, Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Hadyn Ellis Bldg, Maindy Road, Cardiff CF24 4HQ, United Kingdom (thapar@cardiff.ac.uk).

Published Online: November 2, 2016. doi:10.1001/jamapsychiatry.2016.2817

Author Contributions: Drs Riglin and A. Thapar had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: All authors.

Acquisition, analysis, or interpretation of data: Riglin, Collishaw, A. K. Thapar, Maughan, O’Donovan, A. Thapar.

Drafting of the manuscript: Riglin, A. Thapar.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Riglin.

Obtained funding: Collishaw, Maughan, O’Donovan, A. Thapar.

Administrative, technical, or material support: A. K. Thapar, O’Donovan.

Study supervision: O’Donovan, A. Thapar.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by grant MR/M012964/1 from the Medical Research Council. Drs Smith and Stergiakouli work in a unit supported by grant MC_UU_12013/1 from the Medical Research Council.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: We acknowledge the members of the Psychiatric Genomics Consortium for the publicly available data used as the discovery samples in this article. We thank all the families who took part in this study, the midwives for their help in recruiting them, and the entire Avon Longitudinal Study of Parents and Children team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses. The UK Medical Research Council, grant 102215/2/13/2 from the Wellcome Trust, and the University of Bristol provide core support for the Avon Longitudinal Study of Parents and Children. Genome-wide association study data were generated by Sample Logistics and Genotyping Facilities at the Wellcome Trust Sanger Institute and Laboratory Corporation of America using support from 23andMe. Valentina Escott-Price, PhD, Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, provided statistical advice on using the Dudbridge calculator. No contributors were compensated for their contribution.

References
1.
Thapar  A, Rutter  M. Neurodevelopmental disorders. In: Thapar  A, Pine  DS, Leckman  JF, Scott  S, Snowling  MJ, Taylor  E, eds.  Rutter’s Child and Adolescent Psychiatry. 6th ed. Oxford: Wiley Press; 2015.
2.
Thapar  A, Cooper  M.  Attention deficit hyperactivity disorder.  Lancet. 2016;387(10024):1240-1250.PubMedGoogle ScholarCrossref
3.
American Psychiatric Association.  Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Washington, DC: American Psychiatric Association; 2013.
4.
Faraone  SV, Biederman  J, Mick  E.  The age-dependent decline of attention deficit hyperactivity disorder: a meta-analysis of follow-up studies.  Psychol Med. 2006;36(2):159-165.PubMedGoogle ScholarCrossref
5.
Moffitt  TE, Houts  R, Asherson  P,  et al.  Is adult ADHD a childhood-onset neurodevelopmental disorder? evidence from a four-decade longitudinal cohort study.  Am J Psychiatry. 2015;172(10):967-977.PubMedGoogle ScholarCrossref
6.
Agnew-Blais  JC, Polanczyk  GV, Danese  A, Wertz  J, Moffitt  TE, Arseneault  L.  Evaluation of the persistence, remission, and emergence of attention-deficit/hyperactivity disorder in young adulthood.  JAMA Psychiatry. 2016;73(7):713-720.PubMedGoogle ScholarCrossref
7.
Caye  A, Rocha  TB, Anselmi  L,  et al.  Attention-deficit/hyperactivity disorder trajectories from childhood to young adulthood: evidence from a birth cohort supporting a late-onset syndrome.  JAMA Psychiatry. 2016;73(7):705-712.PubMedGoogle ScholarCrossref
8.
Pingault  JB, Viding  E, Galéra  C,  et al.  Genetic and environmental influences on the developmental course of attention-deficit/hyperactivity disorder symptoms from childhood to adolescence.  JAMA Psychiatry. 2015;72(7):651-658.PubMedGoogle ScholarCrossref
9.
Biederman  J, Petty  CR, Clarke  A, Lomedico  A, Faraone  SV.  Predictors of persistent ADHD: an 11-year follow-up study.  J Psychiatr Res. 2011;45(2):150-155.PubMedGoogle ScholarCrossref
10.
Kessler  RC, Adler  LA, Barkley  R,  et al.  Patterns and predictors of attention-deficit/hyperactivity disorder persistence into adulthood: results from the national comorbidity survey replication.  Biol Psychiatry. 2005;57(11):1442-1451.PubMedGoogle ScholarCrossref
11.
Lara  C, Fayyad  J, de Graaf  R,  et al.  Childhood predictors of adult attention-deficit/hyperactivity disorder: results from the World Health Organization World Mental Health Survey Initiative.  Biol Psychiatry. 2009;65(1):46-54.PubMedGoogle ScholarCrossref
12.
Shaw  P, Malek  M, Watson  B, Greenstein  D, de Rossi  P, Sharp  W.  Trajectories of cerebral cortical development in childhood and adolescence and adult attention-deficit/hyperactivity disorder.  Biol Psychiatry. 2013;74(8):599-606.PubMedGoogle ScholarCrossref
13.
Larsson  H, Dilshad  R, Lichtenstein  P, Barker  ED.  Developmental trajectories of DSM-IV symptoms of attention-deficit/hyperactivity disorder: genetic effects, family risk and associated psychopathology.  J Child Psychol Psychiatry. 2011;52(9):954-963.PubMedGoogle ScholarCrossref
14.
Caye  A, Spadini  AV, Karam  RG,  et al.  Predictors of persistence of ADHD into adulthood: a systematic review of the literature and meta-analysis [published online March 28, 2016].  Eur Child Adolesc Psychiatry.PubMedGoogle Scholar
15.
Langley  K, Fowler  T, Ford  T,  et al.  Adolescent clinical outcomes for young people with attention-deficit hyperactivity disorder.  Br J Psychiatry. 2010;196(3):235-240.PubMedGoogle ScholarCrossref
16.
Thapar  A, Cooper  M, Eyre  O, Langley  K.  What have we learnt about the causes of ADHD?  J Child Psychol Psychiatry. 2013;54(1):3-16.PubMedGoogle ScholarCrossref
17.
Chang  Z, Lichtenstein  P, Asherson  PJ, Larsson  H.  Developmental twin study of attention problems: high heritabilities throughout development.  JAMA Psychiatry. 2013;70(3):311-318.PubMedGoogle ScholarCrossref
18.
Franke  B, Faraone  SV, Asherson  P,  et al; International Multicentre Persistent ADHD Collaboration.  The genetics of attention deficit/hyperactivity disorder in adults, a review.  Mol Psychiatry. 2012;17(10):960-987.PubMedGoogle ScholarCrossref
19.
Larsson  H, Asherson  P, Chang  Z,  et al.  Genetic and environmental influences on adult attention deficit hyperactivity disorder symptoms: a large Swedish population-based study of twins.  Psychol Med. 2013;43(1):197-207.PubMedGoogle ScholarCrossref
20.
Hamshere  ML, Langley  K, Martin  J,  et al.  High loading of polygenic risk for ADHD in children with comorbid aggression.  Am J Psychiatry. 2013;170(8):909-916.PubMedGoogle ScholarCrossref
21.
Martin  J, Hamshere  ML, Stergiakouli  E, O’Donovan  MC, Thapar  A.  Genetic risk for attention-deficit/hyperactivity disorder contributes to neurodevelopmental traits in the general population.  Biol Psychiatry. 2014;76(8):664-671.PubMedGoogle ScholarCrossref
22.
Groen-Blokhuis  MM, Middeldorp  CM, Kan  KJ,  et al.  Attention-deficit/hyperactivity disorder polygenic risk scores predict attention problems in a population-based sample of children.  J Am Acad Child Adolesc Psychiatry. 2014;53(10):1123-1129.e6. PubMedGoogle Scholar
23.
Martin  J, Hamshere  ML, Stergiakouli  E, O’Donovan  MC, Thapar  A.  Neurocognitive abilities in the general population and composite genetic risk scores for attention-deficit hyperactivity disorder.  J Child Psychol Psychiatry. 2015;56(6):648-656.PubMedGoogle ScholarCrossref
24.
Boyd  A, Golding  J, Macleod  J,  et al.  Cohort profile: the ‘children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children.  Int J Epidemiol. 2013;42(1):111-127.PubMedGoogle ScholarCrossref
25.
Fraser  A, Macdonald-Wallis  C, Tilling  K,  et al.  Cohort profile: the Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort.  Int J Epidemiol. 2013;42(1):97-110.PubMedGoogle ScholarCrossref
26.
Goodman  R.  The Strengths and Difficulties Questionnaire: a research note.  J Child Psychol Psychiatry. 1997;38(5):581-586.PubMedGoogle ScholarCrossref
27.
St Pourcain  B, Mandy  WP, Heron  J, Golding  J, Davey Smith  G, Skuse  DH.  Links between co-occurring social-communication and hyperactive-inattentive trait trajectories.  J Am Acad Child Adolesc Psychiatry. 2011;50(9):892-902.e5. PubMedGoogle Scholar
28.
Cross-Disorder Group of the Psychiatric Genomics Consortium.  Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis.  Lancet. 2013;381(9875):1371-1379.PubMedGoogle ScholarCrossref
29.
Cross-Disorder Group of the Psychiatric Genomics Consortium.  Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs.  Nat Genet. 2013;45(9):984-994. PubMedGoogle ScholarCrossref
30.
Neale  BM, Medland  SE, Ripke  S,  et al; Psychiatric GWAS Consortium: ADHD Subgroup.  Meta-analysis of genome-wide association studies of attention-deficit/hyperactivity disorder.  J Am Acad Child Adolesc Psychiatry. 2010;49(9):884-897.PubMedGoogle ScholarCrossref
31.
Yang  L, Neale  BM, Liu  L,  et al; Psychiatric GWAS Consortium: ADHD Subgroup.  Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: genome-wide association study of both common and rare variants.  Am J Med Genet B Neuropsychiatr Genet. 2013;162B(5):419-430.PubMedGoogle ScholarCrossref
32.
Schizophrenia Working Group of the Psychiatric Genomics Consortium.  Biological insights from 108 schizophrenia-associated genetic loci.  Nature. 2014;511(7510):421-427.PubMedGoogle ScholarCrossref
33.
Sklar  P, Ripke  S, Scott  LJ,  et al; Psychiatric GWAS Consortium Bipolar Disorder Working Group.  Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4 [published correction appears in Nat Genet. 2012 Sep;44(9):1072].  Nat Genet. 2011;43(10):977-983.PubMedGoogle ScholarCrossref
34.
Ripke  S, Wray  NR, Lewis  CM,  et al; Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium.  A mega-analysis of genome-wide association studies for major depressive disorder.  Mol Psychiatry. 2013;18(4):497-511.PubMedGoogle ScholarCrossref
35.
Dudbridge  F.  Power and predictive accuracy of polygenic risk scores.  PLoS Genet. 2013;9(3):e1003348.PubMedGoogle ScholarCrossref
36.
Wechsler  D, Golombok  S, Rust  J.  WISC-III UK Wechsler Intelligence Scale for Children: UK Manual. Sidcup, UK: The Psychological Corporation; 1992.
37.
Skuse  DH, Mandy  WP, Scourfield  J.  Measuring autistic traits: heritability, reliability and validity of the Social and Communication Disorders Checklist.  Br J Psychiatry. 2005;187(6):568-572.PubMedGoogle ScholarCrossref
38.
Bishop  DV.  Development of the Children’s Communication Checklist (CCC): a method for assessing qualitative aspects of communicative impairment in children.  J Child Psychol Psychiatry. 1998;39(6):879-891.PubMedGoogle ScholarCrossref
39.
Muthén  LK, Muthén  BO.  Mplus User’s Guide. 7th ed. Los Angeles, CA: Muthén & Muthén; 2012.
40.
Muthén  B, Muthén  LK.  Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes.  Alcohol Clin Exp Res. 2000;24(6):882-891.PubMedGoogle ScholarCrossref
41.
Asparouhouv  T.  Wald Test of Mean Equality for Potential Latent Class Predictors in Mixture Modeling: Technical Appendix. Los Angeles, CA: Muthén & Muthén; 2007.
42.
Asparouhov  T, Muthén  B.  Auxiliary variables in mixture modeling: three-step approaches using Mplus.  Struct Equ Modeling. 2014;21(3):329-341. doi:10.1080/10705511.2014.915181Google ScholarCrossref
43.
Heron  JE, Croudace  TJ, Barker  ED, Tilling  K.  A comparison of approaches for assessing covariate effects in latent class analysis.  Longit Life Course Stud. 2015;6(4):420-434. doi:10.14301/llcs.v6i4.322Google Scholar
44.
Faraone  SV, Biederman  J, Monuteaux  MC.  Toward guidelines for pedigree selection in genetic studies of attention deficit hyperactivity disorder.  Genet Epidemiol. 2000;18(1):1-16.PubMedGoogle ScholarCrossref
45.
Larsson  J-O, Larsson  H, Lichtenstein  P.  Genetic and environmental contributions to stability and change of ADHD symptoms between 8 and 13 years of age: a longitudinal twin study.  J Am Acad Child Adolesc Psychiatry. 2004;43(10):1267-1275.PubMedGoogle ScholarCrossref
46.
Barnett  K, Mercer  SW, Norbury  M, Watt  G, Wyke  S, Guthrie  B.  Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study.  Lancet. 2012;380(9836):37-43.PubMedGoogle ScholarCrossref
47.
National Institute for Health and Care Excellence. Multimorbidity: clinical assessment and management. https://www.nice.org.uk/guidance/GID-CGWAVE0704/documents/draft-guideline. Accessed May 5, 2016.
48.
Wolke  D, Waylen  A, Samara  M,  et al.  Selective drop-out in longitudinal studies and non-biased prediction of behaviour disorders.  Br J Psychiatry. 2009;195(3):249-256.PubMedGoogle ScholarCrossref
49.
Widaman  KF.  Missing data: what to do with or without them.  Monogr Soc Res Child Dev. 2006;71(3):42-64. doi:10.1111/j.1540-5834.2006.00404.xGoogle Scholar
×