Discordant associations of educational attainment with ASD and ADHD implicate a polygenic form of pleiotropy

AbstractAutism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are complex co-occurring neurodevelopmental conditions. Their genetic architectures reveal striking similarities but also differences, including strong, discordant polygenic associations with educational attainment (EA). To study genetic mechanisms that present as ASD-related positive and ADHD-related negative genetic correlations with EA, we carry out multivariable regression analyses using genome-wide summary statistics (N = 10,610–766,345). Our results show that EA-related genetic variation is shared across ASD and ADHD architectures, involving identical marker alleles. However, the polygenic association profile with EA, across shared marker alleles, is discordant for ASD versus ADHD risk, indicating independent effects. At the single-variant level, our results suggest either biological pleiotropy or co-localisation of different risk variants, implicating MIR19A/19B microRNA mechanisms. At the polygenic level, they point to a polygenic form of pleiotropy that contributes to the detectable genome-wide correlation between ASD and ADHD and is consistent with effect cancellation across EA-related regions.

Download Full-text

Shared risk alleles with discordant polygenic effects: Disentangling the genetic overlap between ASD and ADHD

10.1101/580365 ◽

2019 ◽

Author(s):

Ellen Verhoef ◽

Jakob Grove ◽

Chin Yang Shapland ◽

Ditte Demontis ◽

Stephen Burgess ◽

...

Keyword(s):

Autism Spectrum ◽

Association Patterns ◽

Hyperactivity Disorder ◽

Risk Alleles ◽

Genome Wide ◽

Genetic Overlap ◽

Multivariable Regression ◽

Shared Risk ◽

Insight Into ◽

Polygenic Variation

AbstractInsight into shared polygenetic architectures affects our understanding of neurodevelopmental disorders. Here, we investigate evidence for pleiotropic mechanisms that may explain the comorbidity between Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). These complex neurodevelopmental conditions often co-occur, but differ in their polygenetic association patterns, especially with educational attainment (EA), showing discordant association effects. Using multivariable regression analyses and existing genome-wide summary statistics based on 10,610 to 766,345 individuals, we demonstrate that EA-related polygenic variation is shared between ASD and ADHD. We show that different combinations of the same ASD and ADHD risk-increasing alleles can simultaneously re-capture known ASD-related positive and ADHD-related negative associations with EA. Such patterns, although to a lesser degree, were also present for combinations of other psychiatric disorders. These findings suggest pleiotropic mechanisms, where the same polygenic sites can encode multiple independent, even discordant, association patterns without involving distinct loci, and have implications for cross-disorder investigations.

Download Full-text

Combining multivariate genomic approaches to elucidate the comorbidity between ASD and ADHD

10.1101/2020.04.22.054825 ◽

2020 ◽

Author(s):

Hugo Peyre ◽

Tabea Schoeler ◽

Chaoyu Liu ◽

Camille Michèle Williams ◽

Nicolas Hoertel ◽

...

Keyword(s):

Structural Equation ◽

Association Studies ◽

Autism Spectrum ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Hyperactivity Disorder ◽

Risk Variants ◽

Genome Wide ◽

Summary Data ◽

Shared Genetic

ABSTRACTBackgroundSeveral lines of evidence point toward the presence of shared genetic factors underlying Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD). However, Genome-Wide Association Studies (GWAS) have yet to identify risk variants (i.e. Single-Nucleotide Polymorphisms, SNPs) shared by ADHD and ASD.MethodsTwo complementary multivariate analyses – genomic structural equation modelling (SEM) and colocalization analysis – were exploited to identify the shared SNPs for ASD and ADHD, using summary data from two independent GWAS of ASD (N=46,350) and ADHD individuals (N=55,374).ResultsGenomic SEM identified 7 novel SNPs shared between ASD and ADHD (pgenome-wide<5e-8), including three SNPs that were not identified in any of the original univariate GWAS of ASD and ADHD (rs227378, rs2391769 and rs325506). We also mapped 4 novel genes (MANBA, DPYD, INSM1, and PAX1) to SNPs shared by ASD and ADHD, as well as 4 genes that had already been mapped to SNPs identified in either ASD or ADHD GWAS (SORCS3, XRN2, PTBP2 and NKX2-4). All the shared genes between ADHD and ASD were more prominently expressed in the brain than the genes mapped to SNPs specific to ASD or ADHD. Colocalization analyses revealed that 44% percent of the SNPs associated with ASD (p<1e-6) colocalized with ADHD SNPs and 26% of the SNPs associated with ADHD (p<1e-6) colocalized with ASD SNPs.ConclusionsUsing multivariate genomic analyses, the present study reveals the shared genetic pathways that underlie ASD and ADHD. Further investigation of these pathways may help identify new targets for treatment of these disorders.

Download Full-text

Genetic effects on planum temporale asymmetry and their limited relevance to neurodevelopmental disorders, intelligence or educational attainment

10.1101/622381 ◽

2019 ◽

Cited By ~ 2

Author(s):

Amaia Carrion-Castillo ◽

Antonietta Pepe ◽

Xiang-Zhen Kong ◽

Simon E Fisher ◽

Bernard Mazoyer ◽

...

Keyword(s):

Educational Attainment ◽

Neurodevelopmental Disorders ◽

Large Scale ◽

Genetic Correlations ◽

Autism Spectrum ◽

Genome Wide Association ◽

Planum Temporale ◽

Genome Wide ◽

Significant Heritability ◽

Left Right Asymmetry

AbstractPrevious studies have suggested that altered asymmetry of the planum temporale (PT) is associated with neurodevelopmental disorders, including dyslexia, schizophrenia, and autism. Shared genetic factors have been suggested to link PT asymmetry to these disorders. In a dataset of unrelated subjects from the general population (UK Biobank, N= 18,057), we found that PT volume asymmetry had a significant heritability of roughly 14%. In genome-wide association analysis, two loci were significantly associated with PT asymmetry, including a coding polymorphism within the gene ITIH5 that is predicted to affect the protein’s function and to be deleterious (rs41298373, P=2.01×10-15), and a locus that affects the expression of the genes BOK and DTYMK (rs7420166, P=7.54×10-10). DTYMK showed left-right asymmetry of mRNA expression in post mortem PT tissue. Cortex-wide mapping of these SNP effects revealed influences on asymmetry that went somewhat beyond the PT. Using publicly available genome-wide association statistics from large-scale studies, we saw no significant genetic correlations of PT asymmetry with autism spectrum disorder, attention deficit hyperactivity disorder, schizophrenia, educational attainment or intelligence. Of the top two individual loci associated with PT asymmetry, rs41298373 showed a tentative association with intelligence (unadjusted P=0.025), while the locus at BOK/DTYMK showed tentative association with educational attainment (unadjusted Ps<0.05). These findings provide novel insights into the genetic contributions to human brain asymmetry, but do not support a substantial polygenic association of PT asymmetry with cognitive variation and mental disorders, as far as can be discerned with current sample sizes.

Download Full-text

Common genetic risk variants identified in the SPARK cohort implicate DDHD2 as a novel autism risk gene

10.1101/2020.01.13.20017319 ◽

2020 ◽

Cited By ~ 3

Author(s):

Nana Matoba ◽

Dan Liang ◽

Huaigu Sun ◽

Nil Aygün ◽

Jessica C. McAfee ◽

...

Keyword(s):

Gene Regulation ◽

Association Study ◽

Genetic Risk ◽

Molecular Mechanisms ◽

Autism Spectrum ◽

Risk Variants ◽

Genome Wide ◽

Common Genetic Variants ◽

A Genome ◽

Gene Regulatory

AbstractBackgroundAutism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Large genetically informative cohorts of individuals with ASD have led to the identification of three common genome-wide significant (GWS) risk loci to date. However, many more common genetic variants are expected to contribute to ASD risk given the high heritability. Here, we performed a genome-wide association study (GWAS) using the Simons Foundation Powering Autism Research for Knowledge (SPARK) dataset to identify additional common genetic risk factors and molecular mechanisms underlying risk for ASD.MethodsWe performed an association study on 6,222 case-pseudocontrol pairs from SPARK and meta-analyzed with a previous GWAS. We integrated gene regulatory annotations to map non-coding risk variants to their regulated genes. Further, we performed a massively parallel reporter assay (MPRA) to identify causal variant(s) within a novel risk locus.ResultsWe identified one novel GWS locus from the SPARK GWAS. The meta-analysis identified four significant loci, including an additional novel locus. We observed significant enrichment of ASD heritability within regulatory regions of the developing cortex, indicating that disruption of gene regulation during neurodevelopment is critical for ASD risk. The MPRA identified one variant at the novel locus with strong impacts on gene regulation (rs7001340), and expression quantitative trait loci data demonstrated an association between the risk allele and decreased expression of DDHD2 (DDHD domain containing 2) in both adult and pre-natal brains.ConclusionsBy integrating genetic association data with multi-omic gene regulatory annotations and experimental validation, we fine-mapped a causal risk variant and demonstrated that DDHD2 is a novel gene associated with ASD risk.

Download Full-text

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

European Psychiatry ◽

10.1192/j.eurpsy.2019.6 ◽

2020 ◽

Vol 63 (1) ◽

Author(s):

Shiqiang Cheng ◽

Fanglin Guan ◽

Mei Ma ◽

Lu Zhang ◽

Bolun Cheng ◽

...

Keyword(s):

Psychiatric Disorders ◽

Plasma Proteins ◽

Genome Wide Association Study ◽

Genetic Relationships ◽

Genetic Correlations ◽

Tumor Necrosis Factor Receptor ◽

Autism Spectrum ◽

Human Blood Plasma ◽

Plasma Proteome ◽

Genome Wide

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

Download Full-text

An Atlas of Genetic Correlations across Human Diseases and Traits

10.1101/014498 ◽

2015 ◽

Cited By ~ 25

Author(s):

Brendan Bulik-Sullivan ◽

Hilary K Finucane ◽

Verneri Anttila ◽

Alexander Gusev ◽

Felix R Day ◽

...

Keyword(s):

Anorexia Nervosa ◽

Educational Attainment ◽

Genetic Correlation ◽

Complex Traits ◽

Genome Wide Association Study ◽

Genetic Correlations ◽

Significant Snps ◽

Modeling Framework ◽

Genome Wide ◽

Meta Analyses

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use our method to estimate 300 genetic correlations among 25 traits, totaling more than 1.5 million unique phenotype measurements. Our results include genetic correlations between anorexia nervosa and schizophrenia/ body mass index and associations between educational attainment and several diseases. These results highlight the power of a polygenic modeling framework, since there currently are no genome-wide significant SNPs for anorexia nervosa and only three for educational attainment.

Download Full-text

Geographic Confounding in Genome-Wide Association Studies

10.21203/rs.3.rs-362358/v1 ◽

2021 ◽

Author(s):

Abdel Abdellaoui ◽

Karin Verweij ◽

Michel G Nivard

Keyword(s):

Educational Attainment ◽

Social Stratification ◽

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Geographic Region ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Gene Environment ◽

Genome Wide

Abstract Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N = 69,772–271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

Download Full-text

Generalizability of GWA-Identified Genetic Risk Variants for Metabolic Traits to Populations from the Arabian Peninsula

Genes ◽

10.3390/genes12101637 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1637

Author(s):

Prashantha Hebbar ◽

Mohamed Abu-Farha ◽

Jehad Abubaker ◽

Arshad Mohamed Channanath ◽

Fahd Al-Mulla ◽

...

Keyword(s):

Large Scale ◽

Arabian Peninsula ◽

Genetic Correlations ◽

Human Migration ◽

Metabolic Risk ◽

Arab Population ◽

Metabolic Traits ◽

Risk Variants ◽

Genome Wide ◽

Gwa Studies

The Arabian Peninsula, located at the nexus of Africa, Europe, and Asia, was implicated in early human migration. The Arab population is characterized by consanguinity and endogamy leading to inbreeding. Global genome-wide association (GWA) studies on metabolic traits under-represent the Arab population. Replicability of GWA-identified association signals in the Arab population has not been satisfactorily explored. It is important to assess how well GWA-identified findings generalize if their clinical interpretations are to benefit the target population. Our recent study from Kuwait, which performed genome-wide imputation and meta-analysis, observed 304 (from 151 genes) of the 4746 GWA-identified metabolic risk variants replicable in the Arab population. A recent large GWA study from Qatar found replication of 30 GWA-identified lipid risk variants. These complementing studies from the Peninsula increase the confidence in generalizing metabolic risk loci to the Arab population. However, both the studies reported a low extent of transferability. In this review, we examine the observed low transferability in the context of differences in environment, genetic correlations (allele frequencies, linkage disequilibrium, effect sizes, and heritability), and phenotype variance. We emphasize the need for large-scale GWA studies on deeply phenotyped cohorts of at least 20,000 Arab individuals. The review further presents GWA-identified metabolic risk variants generalizable to the Arab population.

Download Full-text

Genetically regulated expression in late-onset Alzheimer’s disease implicates risk genes within known and novel loci

Translational Psychiatry ◽

10.1038/s41398-021-01677-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hung-Hsin Chen ◽

Lauren E. Petty ◽

Jin Sha ◽

Yi Zhao ◽

Amanda Kuzma ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Late Onset ◽

Meta Analysis ◽

Data Sets ◽

Phenotypic Variance ◽

Risk Variants ◽

Genome Wide ◽

Regulated Expression ◽

Genetic Mechanisms

AbstractLate-onset Alzheimer disease (LOAD) is highly polygenic, with a heritability estimated between 40 and 80%, yet risk variants identified in genome-wide studies explain only ~8% of phenotypic variance. Due to its increased power and interpretability, genetically regulated expression (GReX) analysis is an emerging approach to investigate the genetic mechanisms of complex diseases. Here, we conducted GReX analysis within and across 51 tissues on 39 LOAD GWAS data sets comprising 58,713 cases and controls from the Alzheimer’s Disease Genetics Consortium (ADGC) and the International Genomics of Alzheimer’s Project (IGAP). Meta-analysis across studies identified 216 unique significant genes, including 72 with no previously reported LOAD GWAS associations. Cross-brain-tissue and cross-GTEx models revealed eight additional genes significantly associated with LOAD. Conditional analysis of previously reported loci using established LOAD-risk variants identified eight genes reaching genome-wide significance independent of known signals. Moreover, the proportion of SNP-based heritability is highly enriched in genes identified by GReX analysis. In summary, GReX-based meta-analysis in LOAD identifies 216 genes (including 72 novel genes), illuminating the role of gene regulatory models in LOAD.

Download Full-text

Geographic Confounding in Genome-Wide Association Studies

10.1101/2021.03.18.435971 ◽

2021 ◽

Author(s):

Abdel Abdellaoui ◽

Karin J.H. Verweij ◽

Michel G. Nivard

Keyword(s):

Educational Attainment ◽

Social Stratification ◽

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Geographic Region ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Gene Environment ◽

Genome Wide

Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N=69,772-271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

Download Full-text