Biology subjects

Mota, N. R.

Publications and source records attributed to Mota, N. R..

6 recordsLinked to original sources

bioRxiv2021

Uncovering the Genetic Architecture of Broad Antisocial Behavior through a Genome-Wide Association Study Meta-analysis.

Despite the substantial heritability of antisocial behavior (ASB), specific genetic variants robustly associated with the trait have not been identified. The present study by the Broad Antisocial Behavior Consortium (BroadABC) meta-analyzed data from 28 discovery samples (N = 85,359) and five independent replication samples (N = 8,058) with genotypic data and broad measures of ASB. We identified the first significant genetic associations with broad ASB, involving common intronic variants in the forkhead box protein P2 (FOXP2) gene (lead SNP rs12536335, P = 6.32 x 10-10). Furthermore, we observed intronic variation in Foxp2 and one of its targets (Cntnap2) distinguishing a mouse model of pathological aggression (BALB/cJ strain) from controls (BALB/cByJ strain). The SNP-based heritability of ASB was 8.4% (s.e.= 1.2%). Polygenic-risk-score (PRS) analyses in independent samples revealed that the genetic risk for ASB was associated with several antisocial outcomes across the lifespan, including diagnosis of conduct disorder, official criminal convictions, and trajectories of antisocial development. We found substantial genetic correlations of ASB with mental health (depression rg{square}={square}0.63, insomnia rg = 0.47), physical health (overweight rg = 0.19, waist-to-hip ratio rg = 0.32), smoking (rg{square}={square}0.54), cognitive ability (intelligence rg= -0.40), educational attainment (years of schooling rg = -0.46) and reproductive traits (age at first birth rg={square}- 0.58, fathers age at death rg= -0.54). Our findings provide a starting point towards identifying critical biosocial risk mechanisms for the development of ASB.

genetics↗

bioRxiv2020

Dissecting the heterogeneous subcortical brain volume of Autism spectrum disorder (ASD) using community detection

Structural brain alterations found in Autism Spectrum Disorder (ASD) have previously been very heterogeneous, with overall limited effect sizes for every region implicated. In this study, we aimed at exploring the existence of subgroups in ASD, based on neuroanatomic profiles; we hypothesized that effect sizes of case/control difference would be increased in defined subgroups. Using the dataset from the ENIGMA-ASD Working Group (n=2661), exploratory factor analysis (EFA) was applied on seven subcortical volumes of individuals with ASD and controls to uncover the underlying organization of subcortical structures. Based on earlier findings in ADHD patients and controls as well as data availability, we focused on three age groups: boys (aged 4-14 years), male adolescents (aged 14-22 years), and adult men (aged >=22 years). The resulting factor scores were used in a community detection (CD) analysis, to cluster participants into subgroups. Three factors were found in each sample, with the factor structure in adult men differing from that in boys and male adolescents. From the patterns in these factors, CD uncovered four distinct communities in boys and three communities in adolescents and adult men, irrespective of ASD diagnostic status. The effect sizes of case/control comparisons appeared more pronounced than in the whole sample in some communities. Based on subcortical volumes, we succeeded in stratifying our participants into more homogeneous subgroups with similar brain structural patterns. The stratification enhanced our ability to observe case/control differences of subcortical brain volumes in ASD, and may help explain some of the heterogeneity of previous findings in ASD.

neuroscience↗

bioRxiv2019

Characterizing neuroanatomic heterogeneity in people with and without ADHD based on subcortical brain volumes

BackgroundAttention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in children and adults. Neuroanatomic heterogeneity limits our understanding of the etiology of ADHD. This study aimed to parse neuroanatomic heterogeneity of ADHD, and to determine whether subgroups could be discerned in patients based on subcortical volumes. MethodsUsing the dataset from the ENIGMA-ADHD Working Group, we applied exploratory factor analysis (EFA) to subcortical volumes of 993 boys with and without ADHD, and to subsamples of 653 adult men, 400 girls, and 447 women. Factor scores derived from the EFA were used to build networks. A community detection (CD) algorithm clustered participants into subgroups based on the networks. ResultsThree factors (basal ganglia, limbic system, and thalamus) were found in boys and men with and without ADHD. The factor structures for girls and women differed from those in males. Given sample size considerations, we concentrated subsequent analyses on males. Male participants could be separated into four communities, though Community 3 was absent in healthy men. Significantly case-control differences of subcortical volumes were observed within communities in boys with increased effect sizes, but not in men. While we found no significant differences in ADHD symptom severity between communities in boys or men; affected men in Community 1 and 4 presented comorbidities more frequently than those in other communities. ConclusionOur results indicate that neuroanatomic heterogeneity in subcortical volumes exists, irrespective of ADHD diagnosis. Effect sizes of case-control differences appear more pronounced at least in some of the subgroups.

neuroscience↗

bioRxiv2019

Genetic underpinnings of sociability in the UK Biobank

Difficulties with sociability include a tendency to avoid social contacts and activities, and to prefer being alone rather than being with others. While sociability is a continuously distributed trait in the population, decreased sociability represent a common early manifestation of multiple neuropsychiatric disorders such as Schizophrenia (SCZ), Bipolar Disorder (BP), Major Depressive Disorder (MDD), Autism Spectrum Disorders (ASDs), and Alzheimers disease (AD). We aimed to investigate the genetic underpinnings of sociability as a continuous trait in the general population. In this respect, we performed a genome-wide association study (GWAS) using a sociability score based on 4 social functioning-related self-report questions in the UK Biobank sample (n=342,461) to test the effect of individual genetic variants. This was followed by LD score analyses to investigate the genetic correlation with psychiatric disorders (SCZ, BP, MDD, ASDs) and a neurological disorder (AD) as well as related phenotypes (Loneliness and Social Anxiety). The phenotypic data indeed showed that the sociability score was decreased in individuals with ASD, (probable) MDD, BP and SCZ, but not in individuals with AD. Our GWAS showed 604 genome-wide significant SNPs, coming from 18 independent loci (SNP-based h2=0.06). Genetic correlation analyses showed significant correlations with SCZ (rg=0.15, p=9.8e-23), MDD (rg=0.68, p=6.6e-248) and ASDs (rg=0.27, p=4.5e-28), but no correlation with BP (rg=0.01, p=0.45) or AD (rg=0.04, p=0.55). Our sociability trait was also genetically correlated with Loneliness (rg=0.45, p=2.4e-8) and Social Anxiety (rg=0.48, p=0.002). Our study shows that there is a significant genetic component to variation in population levels of sociability, which is relevant to some psychiatric disorders (SCZ, MDD, ASDs), but not to BP and AD.

genetics↗

bioRxiv2019

RICOPILI: Rapid Imputation for COnsortias PIpeLIne

MotivationGenome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open sourced Perl-based pipeline was developed to address the challenges of rapidly processing large scale multi-cohort GWAS studies including quality control, imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing (HPC) environments.\n\nSummaryRICOPILI was created as the Psychiatric Genomics Consortium (PGC) pipeline for GWAS and has been adopted by other users. The pipeline features i) technical and genomic quality control in case-control and trio cohorts ii) genome-wide phasing and imputation iv) association analysis v) meta-analysis vi) polygenic risk scoring and vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all of the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.\n\nAvailability and ImplementationRICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home\n\nContactsripke@broadinstitute.org\n\nSupplementary informationSupplementary data are available.

genetics↗

bioRxiv2019

Cross-disorder genetic analyses implicate dopaminergic signaling as a biological link between Attention-Deficit/Hyperactivity Disorder and obesity measures

Attention-Deficit/Hyperactivity Disorder (ADHD) and obesity are frequently comorbid, genetically correlated, and share brain substrates. The biological mechanisms driving this association are unclear, but candidate systems, like dopaminergic neurotransmission and circadian rhythm, have been suggested. Our aim was to identify the biological mechanisms underpinning the genetic link between ADHD and obesity measures and investigate associations of overlapping genes with brain volumes. We tested the association of dopaminergic and circadian rhythm gene sets with ADHD, body mass index (BMI), and obesity (using GWAS data of N=53,293, N=681,275, and N=98,697, respectively). We then conducted genome-wide ADHD-BMI and ADHD-obesity gene-based meta-analyses, followed by pathway enrichment analyses. Finally, we tested the association of ADHD-BMI overlapping genes with brain volumes (primary GWAS data N=10,720-10,928; replication data N=9,428). The dopaminergic gene set was associated with both ADHD (P=5.81x10-3) and BMI (P=1.63x10-5), the circadian rhythm was associated with BMI (P=1.28x10-3). The genome-wide approach also implicated the dopaminergic system, as the Dopamine-DARPP32 Feedback in cAMP Signaling pathway was enriched in both ADHD-BMI and ADHD-obesity results. The ADHD-BMI overlapping genes were associated with putamen volume (P=7.7x10-3; replication data P=3.9x10-2) - a brain region with volumetric reductions in ADHD and BMI and linked to inhibitory control. Our findings suggest that dopaminergic neurotransmission, partially through DARPP-32-dependent signaling and involving the putamen, is a key player underlying the genetic overlap between ADHD and obesity measures. Uncovering shared etiological factors underlying the frequently observed ADHD-obesity comorbidity may have important implications in terms of prevention and/or efficient treatment of these conditions.

genetics↗