Publications

Abstract

Background: Several nonpsychotic psychiatric disorders in childhood and adolescence can precede the onset of schizophrenia, but the etiology of this relationship remains unclear. We investigated to what extent the association between schizophrenia and psychiatric disorders in childhood is explained by correlated genetic risk factors. Methods: Polygenic risk scores (PRS), reflecting an individual’s genetic risk for schizophrenia, were constructed for 2588 children from the Netherlands Twin Register (NTR) and 6127 from the Avon Longitudinal Study of Parents And Children (ALSPAC). The associations between schizophrenia PRS and measures of anxiety, depression, attention deficit hyperactivity disorder (ADHD), and oppositional defiant disorder/conduct disorder (ODD/CD) were estimated at age 7, 10, 12/13, and 15 years in the 2 cohorts. Results were then meta-analyzed, and a meta-regression analysis was performed to test differences in effects sizes over, age and disorders. Results: Schizophrenia PRS were associated with childhood and adolescent psychopathology. Meta-regression analysis showed differences in the associations over disorders, with the strongest association with childhood and adolescent depression and a weaker association for ODD/CD at age 7. The associations increased with age and this increase was steepest for ADHD and ODD/CD. Genetic correlations varied between 0.10 and 0.25. Conclusion: By optimally using longitudinal data across diagnoses in a multivariate meta-analysis this study sheds light on the development of childhood disorders into severe adult psychiatric disorders. The results are consistent with a common genetic etiology of schizophrenia and developmental psychopathology as well as with a stronger shared genetic etiology between schizophrenia and adolescent onset psychopathology.

Abstract

This study sought to identify trajectories of DSM-IV based internalizing (INT) and externalizing (EXT) problem scores across childhood and adolescence and to provide insight into the comorbidity by modeling the co-occurrence of INT and EXT trajectories. INT and EXT were measured repeatedly between age 7 and age 15 years in over 7,000 children and analyzed using growth mixture models. Five trajectories were identified for both INT and EXT, including very low, low, decreasing, and increasing trajectories. In addition, an adolescent onset trajectory was identified for INT and a stable high trajectory was identified for EXT. Multinomial regression showed that similar EXT and INT trajectories were associated. However, the adolescent onset INT trajectory was independent of high EXT trajectories, and persisting EXT was mainly associated with decreasing INT. Sex and early life environmental risk factors predicted EXT and, to a lesser extent, INT trajectories. The association between trajectories indicates the need to consider comorbidity when a child presents with INT or EXT disorders, particularly when symptoms start early. This is less necessary when INT symptoms start at adolescence. Future studies should investigate the etiology of co-occurring INT and EXT and the specific treatment needs of these severely affected children.

Abstract

Background: Children with a diagnosis of attention-deficit hyperactivity disorder (ADHD) have lower cognitive ability and are at risk of adverse educational outcomes; ADHD genetic risks have been found to predict childhood cognitive ability and other neurodevelopmental traits in the general population; thus genetic risks might plausibly also contribute to cognitive ability later in development and to educational underachievement.

Methods: We generated ADHD polygenic risk scores in the Avon Longitudinal Study of Parents and Children participants (maximum N: 6928 children and 7280 mothers) based on the results of a discovery clinical sample, a genome-wide association study of 727 cases with ADHD diagnosis and 5081 controls. We tested if ADHD polygenic risk scores were associated with educational outcomes and IQ in adolescents and their mothers.

Results: High ADHD polygenic scores in adolescents were associated with worse educational outcomes at Key Stage 3 [national tests conducted at age 13–14 years; β = −1.4 (−2.0 to −0.8), P = 2.3 × 10−6), at General Certificate of Secondary Education exams at age 15–16 years (β = −4.0 (−6.1 to −1.9), P = 1.8 × 10−4], reduced odds of sitting Key Stage 5 examinations at age 16–18 years [odds ratio (OR) = 0.90 (0.88 to 0.97), P = 0.001] and lower IQ scores at age 15.5 [β = −0.8 (−1.2 to −0.4), P = 2.4 × 10−4]. Moreover, maternal ADHD polygenic scores were associated with lower maternal educational achievement [β = −0.09 (−0.10 to −0.06), P = 0.005] and lower maternal IQ [β = −0.6 (−1.2 to −0.1), P = 0.03].

Conclusions: ADHD diagnosis risk alleles impact on functional outcomes in two generations (mother and child) and likely have intergenerational environmental effects.

Abstract

Background: Shared genetic influences between attention-deficit/hyperactivity disorder (ADHD) symptoms and
autism spectrum disorder (ASD) symptoms have been reported. Cross-trait genetic relationships are, however,
subject to dynamic changes during development. We investigated the continuity of genetic overlap between ASD
and ADHD symptoms in a general population sample during childhood and adolescence. We also studied uni- and
cross-dimensional trait-disorder links with respect to genetic ADHD and ASD risk.
Methods: Social-communication difficulties (N ≤ 5551, Social and Communication Disorders Checklist, SCDC) and
combined hyperactive-impulsive/inattentive ADHD symptoms (N ≤ 5678, Strengths and Difficulties Questionnaire,
SDQ-ADHD) were repeatedly measured in a UK birth cohort (ALSPAC, age 7 to 17 years). Genome-wide summary
statistics on clinical ASD (5305 cases; 5305 pseudo-controls) and ADHD (4163 cases; 12,040 controls/pseudo-controls)
were available from the Psychiatric Genomics Consortium. Genetic trait variances and genetic overlap between
phenotypes were estimated using genome-wide data.
Results: In the general population, genetic influences for SCDC and SDQ-ADHD scores were shared throughout
development. Genetic correlations across traits reached a similar strength and magnitude (cross-trait rg ≤ 1,
pmin = 3 × 10−4) as those between repeated measures of the same trait (within-trait rg ≤ 0.94, pmin = 7 × 10−4).
Shared genetic influences between traits, especially during later adolescence, may implicate variants in K-RAS signalling
upregulated genes (p-meta = 6.4 × 10−4).
Uni-dimensionally, each population-based trait mapped to the expected behavioural continuum: risk-increasing alleles
for clinical ADHD were persistently associated with SDQ-ADHD scores throughout development (marginal regression
R2 = 0.084%). An age-specific genetic overlap between clinical ASD and social-communication difficulties during
childhood was also shown, as per previous reports. Cross-dimensionally, however, neither SCDC nor SDQ-ADHD scores
were linked to genetic risk for disorder.
Conclusions: In the general population, genetic aetiologies between social-communication difficulties and ADHD
symptoms are shared throughout child and adolescent development and may implicate similar biological pathways
that co-vary during development. Within both the ASD and the ADHD dimension, population-based traits are also linked
to clinical disorder, although much larger clinical discovery samples are required to reliably detect cross-dimensional
trait-disorder relationships.

Abstract

Deep sequence-based imputation can enhance the discovery power of genome-wide association studies by assessing previously unexplored variation across the common- and low-frequency spectra. We applied a hybrid whole-genome sequencing (WGS) and deep imputation approach to examine the broader allelic architecture of 12 anthropometric traits associated with height, body mass, and fat distribution in up to 267,616 individuals. We report 106 genome-wide significant signals that have not been previously identified, including 9 low-frequency variants pointing to functional candidates. Of the 106 signals, 6 are in genomic regions that have not been implicated with related traits before, 28 are independent signals at previously reported regions, and 72 represent previously reported signals for a different anthropometric trait. 71% of signals reside within genes and fine mapping resolves 23 signals to one or two likely causal variants. We confirm genetic overlap between human monogenic and polygenic anthropometric traits and find signal enrichment in cis expression QTLs in relevant tissues. Our results highlight the potential of WGS strategies to enhance biologically relevant discoveries across the frequency spectrum.

Abstract

Using parametric and nonparametric techniques, our study investigated the presence of single locus and pairwise effects between 20 markers of the Genetic Analysis Workshop 15 (GAW15) North American Rheumatoid Arthritis Consortium (NARAC) candidate gene data set (Problem 2), analyzing 463 independent patients and 855 controls. Specifically, our work examined the correspondence between logistic regression (LR) analysis of single-locus and pairwise interaction effects, and random forest (RF) single and joint importance measures. For this comparison, we selected small but stable RFs (500 trees), which showed strong correlations (r~0.98) between their importance measures and those by RFs grown on 5000 trees. Both RF importance measures captured most of the LR single-locus and pairwise interaction effects, while joint importance measures also corresponded to full LR models containing main and interaction effects. We furthermore showed that RF measures were particularly sensitive to data imputation. The most consistent pairwise effect on rheumatoid arthritis was found between two markers within MAP3K7IP2/SUMO4 on 6q25.1, although LR and RFs assigned different significance levels. Within a hypothetical two-stage design, pairwise LR analysis of all markers with significant RF single importance would have reduced the number of possible combinations in our small data set by 61%, whereas joint importance measures would have been less efficient for marker pair reduction. This suggests that RF single importance measures, which are able to detect a wide range of interaction effects and are computationally very efficient, might be exploited as pre-screening tool for larger association studies. Follow-up analysis, such as by LR, is required since RFs do not indicate highrisk genotype combinations.

Abstract

Rheumatoid arthritis is the most common systematic autoimmune disease and its etiology is believed to have both strong genetic and environmental components. We demonstrate the utility of including genetic and clinical phenotypes as covariates within a linkage analysis framework to search for rheumatoid arthritis susceptibility loci. The raw genotypes of 1302 affected relative pairs were combined from four large family-based samples (North American Rheumatoid Arthritis Consortium, United Kingdom, European Consortium on Rheumatoid Arthritis Families, and Canada). The familiality of the clinical phenotypes was assessed. The affected relative pairs were subjected to autosomal multipoint affected relative-pair linkage analysis. Covariates were included in the linkage analysis to take account of heterogeneity within the sample. Evidence of familiality was observed with age at onset (p <} 0.001) and rheumatoid factor (RF) IgM (p {< 0.001), but not definite erosions (p = 0.21). Genome-wide significant evidence for linkage was observed on chromosome 6. Genome-wide suggestive evidence for linkage was observed on chromosomes 13 and 20 when conditioning on age at onset, chromosome 15 conditional on gender, and chromosome 19 conditional on RF IgM after allowing for multiple testing of covariates.

Abstract

We have used the genome-wide marker genotypes from Genetic Analysis Workshop 15 Problem 2 to explore joint evidence for genetic linkage to rheumatoid arthritis across several samples. The data consisted of four high-density genome scans on samples selected for rheumatoid arthritis. We cleaned the data, removed intermarker linkage disequilibrium, and assembled the samples onto a common genetic map using genome sequence positions as a reference for map interpolation. The individual studies were combined first at the genotype level (mega-analysis) prior to a multipoint linkage analysis on the combined sample, and second using the genome scan meta-analysis method after linkage analysis of each sample. The two approaches were compared, and give strong support to the HLA locus on chromosome 6 as a susceptibility locus. Other regions of interest include loci on chromosomes 11, 2, and 12.

Abstract

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.