Publications

Abstract

Attention deficit/hyperactivity disorder (ADHD) is a highly heritable childhood behavioral disorder affecting 5% of children and 2.5% of adults. Common genetic variants contribute substantially to ADHD susceptibility, but no variants have been robustly associated with ADHD. We report a genome-wide association meta-analysis of 20,183 individuals diagnosed with ADHD and 35,191 controls that identifies variants surpassing genome-wide significance in 12 independent loci, finding important new information about the underlying biology of ADHD. Associations are enriched in evolutionarily constrained genomic regions and loss-of-function intolerant genes and around brain-expressed regulatory marks. Analyses of three replication studies: a cohort of individuals diagnosed with ADHD, a self-reported ADHD sample and a meta-analysis of quantitative measures of ADHD symptoms in the population, support these findings while highlighting study-specific differences on genetic overlap with educational attainment. Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits.

Abstract

Developmental dyslexia (DD) is one of the most prevalent learning disorders, with high impact on school and psychosocial development and high comorbidity with conditions like attention-deficit hyperactivity disorder (ADHD), depression, and anxiety. DD is characterized by deficits in different cognitive skills, including word reading, spelling, rapid naming, and phonology. To investigate the genetic basis of DD, we conducted a genome-wide association study (GWAS) of these skills within one of the largest studies available, including nine cohorts of reading-impaired and typically developing children of European ancestry (N = 2562–3468). We observed a genome-wide significant effect (p < 1 × 10−8) on rapid automatized naming of letters (RANlet) for variants on 18q12.2, within MIR924HG (micro-RNA 924 host gene; rs17663182 p = 4.73 × 10−9), and a suggestive association on 8q12.3 within NKAIN3 (encoding a cation transporter; rs16928927, p = 2.25 × 10−8). rs17663182 (18q12.2) also showed genome-wide significant multivariate associations with RAN measures (p = 1.15 × 10−8) and with all the cognitive traits tested (p = 3.07 × 10−8), suggesting (relational) pleiotropic effects of this variant. A polygenic risk score (PRS) analysis revealed significant genetic overlaps of some of the DD-related traits with educational attainment (EDUyears) and ADHD. Reading and spelling abilities were positively associated with EDUyears (p ~ [10−5–10−7]) and negatively associated with ADHD PRS (p ~ [10−8−10−17]). This corroborates a long-standing hypothesis on the partly shared genetic etiology of DD and ADHD, at the genome-wide level. Our findings suggest new candidate DD susceptibility genes and provide new insights into the genetics of dyslexia and its comorbities.

Abstract

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD. With a marked sample-size increase from a unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 individuals with ASD and 27,969 controls that identified five genome-wide-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), we identified seven additional loci shared with other traits at equally strict significance levels. Dissecting the polygenic architecture, we found both quantitative and qualitative polygenic heterogeneity across ASD subtypes. These results highlight biological insights, particularly relating to neuronal function and corticogenesis, and establish that GWAS performed at scale will be much more productive in the near term in ASD.

Abstract

One of the features that distinguishes modern humans from our extinct relatives
and ancestors is a globular shape of the braincase [1-4]. As the endocranium
closely mirrors the outer shape of the brain, these differences might reflect
altered neural architecture [4,5]. However, in the absence of fossil brain tissue the
underlying neuroanatomical changes as well as their genetic bases remain
elusive. To better understand the biological foundations of modern human
endocranial shape, we turn to our closest extinct relatives, the Neandertals.
Interbreeding between modern humans and Neandertals has resulted in
introgressed fragments of Neandertal DNA in the genomes of present-day non-
Africans [6,7]. Based on shape analyses of fossil skull endocasts, we derive a
measure of endocranial globularity from structural magnetic resonance imaging
(MRI) scans of thousands of modern humans, and study the effects of
introgressed fragments of Neandertal DNA on this phenotype. We find that
Neandertal alleles on chromosomes 1 and 18 are associated with reduced
endocranial globularity. These alleles influence expression of two nearby genes,
UBR4 and PHLPP1, which are involved in neurogenesis and myelination,
respectively. Our findings show how integration of fossil skull data with archaic
genomics and neuroimaging can suggest developmental mechanisms that may
contribute to the unique modern human endocranial shape.

Abstract

Cranial growth and development is a complex process which affects the closely related traits of head circumference (HC) and intracranial volume (ICV). The underlying genetic influences affecting these traits during the transition from childhood to adulthood are little understood, but might include both age-specific genetic influences and low-frequency genetic variation. To understand these influences, we model the developmental genetic architecture of HC, showing this is genetically stable and correlated with genetic determinants of ICV. Investigating up to 46,000 children and adults of European descent, we identify association with final HC and/or final ICV+HC at 9 novel common and low-frequency loci, illustrating that genetic variation from a wide allele frequency spectrum contributes to cranial growth. The largest effects are reported for low-frequency variants within TP53, with 0.5 cm wider heads in increaser-allele carriers versus non-carriers during mid-childhood, suggesting a previously unrecognized role of TP53 transcripts in human cranial development.

Abstract

Alcohol use is correlated within spouse-pairs, but it is difficult to disentangle effects of alcohol consumption on mate-selection from social factors or the shared spousal environment. We hypothesised that genetic variants related to alcohol consumption may, via their effect on alcohol behaviour, influence mate selection. Here, we find strong evidence that an individual’s self-reported alcohol consumption and their genotype at rs1229984, a missense variant in ADH1B, are associated with their partner’s self-reported alcohol use. Applying Mendelian randomization, we estimate that a unit increase in an individual’s weekly alcohol consumption increases partner’s alcohol consumption by 0.26 units (95% C.I. 0.15, 0.38; P = 8.20 × 10−6). Furthermore, we find evidence of spousal genotypic concordance for rs1229984, suggesting that spousal concordance for alcohol consumption existed prior to cohabitation. Although the SNP is strongly associated with ancestry, our results suggest some concordance independent of population stratification. Our findings suggest that alcohol behaviour directly influences mate selection.

Abstract

Aim: To determine if nonsyndromic cleft lip with or without cleft palate (nsCL/P) genetic risk variants influence liability to nsCL/P through gene regulation pathways, such as those involving DNA methylation. Materials & methods: nsCL/P genetic summary data and methylation data from four studies were used in conjunction with Mendelian randomization and joint likelihood mapping to investigate potential mediation of nsCL/P genetic variants. Results & conclusion: Evidence was found at VAX1 (10q25.3), LOC146880 (17q23.3) and NTN1 (17p13.1), that liability to nsCL/P and variation in DNA methylation might be driven by the same genetic variant, suggesting that genetic variation at these loci may increase liability to nsCL/P by influencing DNA methylation. Follow-up analyses using different tissues and gene expression data provided further insight into possible biological mechanisms.

Abstract

The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.

Abstract

Synaesthesia is a neurological phenomenon affecting perception, where triggering stimuli (e.g. letters and numbers) elicit unusual secondary sensory experiences (e.g. colours). Family-based studies point to a role for genetic factors in the development of this trait. However, the contributions of common genomic variation to synaesthesia have not yet been investigated. Here, we present the SynGenes cohort, the largest genotyped collection of unrelated people with grapheme–colour synaesthesia (n = 723). Synaesthesia has been associated with a range of other neuropsychological traits, including enhanced memory and mental imagery, as well as greater sensory sensitivity. Motivated by the prior literature on putative trait overlaps, we investigated polygenic scores derived from published genome-wide scans of schizophrenia and autism spectrum disorder (ASD), comparing our SynGenes cohort to 2181 non-synaesthetic controls. We found a very slight association between schizophrenia polygenic scores and synaesthesia (Nagelkerke's R2 = 0.0047, empirical p = 0.0027) and no significant association for scores related to ASD (Nagelkerke's R2 = 0.00092, empirical p = 0.54) or body mass index (R2 = 0.00058, empirical p = 0.60), included as a negative control. As sample sizes for studying common genomic variation continue to increase, genetic investigations of the kind reported here may yield novel insights into the shared biology between synaesthesia and other traits, to complement findings from neuropsychology and brain imaging.

Abstract

Interpreting polygenic overlap between ADHD and both literacy-related and language-related impairments is challenging as genetic associations might be influenced by indirectly shared genetic factors. Here, we investigate genetic overlap between polygenic ADHD risk and multiple literacy-related and/or language-related abilities (LRAs), as assessed in UK children (N ≤ 5919), accounting for genetically predictable educational attainment (EA). Genome-wide summary statistics on clinical ADHD and years of schooling were obtained from large consortia (N ≤ 326,041). Our findings show that ADHD-polygenic scores (ADHD-PGS) were inversely associated with LRAs in ALSPAC, most consistently with reading-related abilities, and explained ≤1.6% phenotypic variation. These polygenic links were then dissected into both ADHD effects shared with and independent of EA, using multivariable regressions (MVR). Conditional on EA, polygenic ADHD risk remained associated with multiple reading and/or spelling abilities, phonemic awareness and verbal intelligence, but not listening comprehension and non-word repetition. Using conservative ADHD-instruments (P-threshold < 5 × 10−8), this corresponded, for example, to a 0.35 SD decrease in pooled reading performance per log-odds in ADHD-liability (P = 9.2 × 10−5). Using subthreshold ADHD-instruments (P-threshold < 0.0015), these effects became smaller, with a 0.03 SD decrease per log-odds in ADHD risk (P = 1.4 × 10−6), although the predictive accuracy increased. However, polygenic ADHD-effects shared with EA were of equal strength and at least equal magnitude compared to those independent of EA, for all LRAs studied, and detectable using subthreshold instruments. Thus, ADHD-related polygenic links with LRAs are to a large extent due to shared genetic effects with EA, although there is evidence for an ADHD-specific association profile, independent of EA, that primarily involves literacy-related impairments.

Abstract

PURPOSE: Time spent in "sports/outdoor activity" has shown a negative association with incident myopia during childhood. We investigated the association of incident myopia with time spent outdoors and physical activity separately. METHODS: Participants in the Avon Longitudinal Study of Parents and Children (ALSPAC) were assessed by noncycloplegic autorefraction at ages 7, 10, 11, 12, and 15 years, and classified as myopic (≤-1 diopters) or as emmetropic/hyperopic (≥-0.25 diopters) at each visit (N = 4,837-7,747). Physical activity at age 11 years was measured objectively using an accelerometer, worn for 1 week. Time spent outdoors was assessed via a parental questionnaire administered when children were aged 8-9 years. Variables associated with incident myopia were examined using Cox regression. RESULTS: In analyses using all available data, both time spent outdoors and physical activity were associated with incident myopia, with time outdoors having the larger effect. The results were similar for analyses restricted to children classified as either nonmyopic or emmetropic/hyperopic at age 11 years. Thus, for children nonmyopic at age 11, the hazard ratio (95% confidence interval, CI) for incident myopia was 0.66 (0.47-0.93) for a high versus low amount of time spent outdoors, and 0.87 (0.76-0.99) per unit standard deviation above average increase in moderate/vigorous physical activity. CONCLUSION: Time spent outdoors was predictive of incident myopia independently of physical activity level. The greater association observed for time outdoors suggests that the previously reported link between "sports/outdoor activity" and incident myopia is due mainly to its capture of information relating to time outdoors rather than physical activity.

Abstract

During aging, intracranial volume remains unchanged and represents maximally attained brain size, while various interacting biological phenomena lead to brain volume loss. Consequently, intracranial volume and brain volume in late life reflect different genetic influences. Our genome-wide association study (GWAS) in 8,175 community-dwelling elderly persons did not reveal any associations at genome-wide significance (P < 5 × 10(-8)) for brain volume. In contrast, intracranial volume was significantly associated with two loci: rs4273712 (P = 3.4 × 10(-11)), a known height-associated locus on chromosome 6q22, and rs9915547 (P = 1.5 × 10(-12)), localized to the inversion on chromosome 17q21. We replicated the associations of these loci with intracranial volume in a separate sample of 1,752 elderly persons (P = 1.1 × 10(-3) for 6q22 and 1.2 × 10(-3) for 17q21). Furthermore, we also found suggestive associations of the 17q21 locus with head circumference in 10,768 children (mean age of 14.5 months). Our data identify two loci associated with head size, with the inversion at 17q21 also likely to be involved in attaining maximal brain size.

Abstract

Craniofacial morphology is highly heritable, but little is known about which genetic variants influence normal facial variation in the general population. We aimed to identify genetic variants associated with normal facial variation in a population-based cohort of 15-year-olds from the Avon Longitudinal Study of Parents and Children. 3D high-resolution images were obtained with two laser scanners, these were merged and aligned, and 22 landmarks were identified and their x, y, and z coordinates used to generate 54 3D distances reflecting facial features. 14 principal components (PCs) were also generated from the landmark locations. We carried out genome-wide association analyses of these distances and PCs in 2,185 adolescents and attempted to replicate any significant associations in a further 1,622 participants. In the discovery analysis no associations were observed with the PCs, but we identified four associations with the distances, and one of these, the association between rs7559271 in PAX3 and the nasion to midendocanthion distance (n-men), was replicated (p = 4 × 10−7). In a combined analysis, each G allele of rs7559271 was associated with an increase in n-men distance of 0.39 mm (p = 4 × 10−16), explaining 1.3% of the variance. Independent associations were observed in both the z (nasion prominence) and y (nasion height) dimensions (p = 9 × 10−9 and p = 9 × 10−10, respectively), suggesting that the locus primarily influences growth in the yz plane. Rare variants in PAX3 are known to cause Waardenburg syndrome, which involves deafness, pigmentary abnormalities, and facial characteristics including a broad nasal bridge. Our findings show that common variants within this gene also influence normal craniofacial development.

Abstract

BACKGROUND: Epigenetic markings acquired in early life may have phenotypic consequences later in development through their role in transcriptional regulation with relevance to the developmental origins of diseases including obesity. The goal of this study was to investigate whether DNA methylation levels at birth are associated with body size later in childhood. PRINCIPAL FINDINGS: A study design involving two birth cohorts was used to conduct transcription profiling followed by DNA methylation analysis in peripheral blood. Gene expression analysis was undertaken in 24 individuals whose biological samples and clinical data were collected at a mean ± standard deviation (SD) age of 12.35 (0.95) years, the upper and lower tertiles of body mass index (BMI) were compared with a mean (SD) BMI difference of 9.86 (2.37) kg/m(2). This generated a panel of differentially expressed genes for DNA methylation analysis which was then undertaken in cord blood DNA in 178 individuals with body composition data prospectively collected at a mean (SD) age of 9.83 (0.23) years. Twenty-nine differentially expressed genes (>}1.2-fold and p{<10(-4)) were analysed to determine DNA methylation levels at 1-3 sites per gene. Five genes were unmethylated and DNA methylation in the remaining 24 genes was analysed using linear regression with bootstrapping. Methylation in 9 of the 24 (37.5%) genes studied was associated with at least one index of body composition (BMI, fat mass, lean mass, height) at age 9 years, although only one of these associations remained after correction for multiple testing (ALPL with height, p(Corrected) = 0.017). CONCLUSIONS: DNA methylation patterns in cord blood show some association with altered gene expression, body size and composition in childhood. The observed relationship is correlative and despite suggestion of a mechanistic epigenetic link between in utero life and later phenotype, further investigation is required to establish causality.

Abstract

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

Abstract

To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association studies (GWAS) (N = 10,768 individuals of European ancestry enrolled in pregnancy and/or birth cohorts) and followed up three lead signals in six replication studies (combined N = 19,089). rs7980687 on chromosome 12q24 (P = 8.1 × 10(-9)) and rs1042725 on chromosome 12q15 (P = 2.8 × 10(-10)) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height, their effects on infant head circumference were largely independent of height (P = 3.8 × 10(-7) for rs7980687 and P = 1.3 × 10(-7) for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P = 3.9 × 10(-6)). SNPs correlated to the 17q21 signal have shown genome-wide association with adult intracranial volume, Parkinson's disease and other neurodegenerative diseases, indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.

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.