The Norwegian Mother, Father, and Child cohort study (MoBa) genotyping data resource: MoBaPsychGen pipeline v.1

BACKRGROUND: The Norwegian Mother, Father, and Child Cohort Study (MoBa) is a population-based pregnancy cohort, which includes approximately 114,500 children, 95,200 mothers, and 75,200 fathers. Genotyping of MoBa has been conducted through multiple research projects, spanning several years; using varying selection criteria, genotyping arrays, and genotyping centres. MoBa contains numerous interrelated families, which necessitated the implementation of a family-based quality control (QC) pipeline that verifies and accounts for diverse types of relatedness. METHODS: The MoBaPsychGen pipeline, comprising pre-imputation QC, phasing, imputation, and post-imputation QC, was developed based on current best-practice protocols and implemented to account for the complex structure of the MoBa genotype data. The pipeline includes QC on both single nucleotide polymorphism (SNP) and individual level. Phasing and imputation were performed using the publicly available Haplotype Reference Consortium release 1.1 panel as a reference. Information from the Medical Birth Registry of Norway and MoBa questionnaires were used to identify biological sex, year of birth, reported parent-offspring (PO) relationships, and multiple births (only available in the offspring generation). RESULTS: In total, 207,569 unique individuals (90% of the unique individuals included in the study) and 6,981,748 SNPs passed the MoBaPsychGen pipeline. The relatedness checks performed throughout the pipeline allowed identification of within-generation and across-generation first-degree, second-degree, and third-degree relatives. The individuals passing post-imputation QC comprised 64,471 families ranging in size from singletons to 84 unique individuals (singletons are included as families as other family members may not have been genotyped, imputed, or passed post-imputation QC). The relationships identified include 287 monozygotic twin pairs, 22,884 full siblings, 117,004 PO pairs, 23,299 second-degree relative pairs, and 10,828 third-degree relative pairs. DISCUSSION: MoBa contains a highly complex relatedness structure, with a variety of family structures including singletons, PO duos, full (mother, father, child) PO trios, nuclear families, blended families, and extended families. The availability of robustly quality-controlled genetic data for such a large cohort with a unique extended family structure will allow many novel research questions to be addressed. Furthermore, the MoBaPsychGen pipeline has potential utility in similar cohorts

Genome wide association study identifies variants in NBEA associated with migraine in bipolar disorder

Methods We performed a genome-wide association analysis contrasting 460 bipolar migraneurs with 914 bipolar patients without migraine from the Bipolar Genome Study (BiGS).Results We identified one genome-wide significant association between migraine in bipolar disorder patients and rs1160720, an intronic single nucleotide polymorphism (SNP) in the NBEA gene (P=2.97×10-8, OR: 1.82, 95% CI: 1.47-2.25), although this was not replicated in a smaller sample of 289 migraine cases. Limitations Our study is based on self-reported migraine.Background Migraine is a common comorbidity among individuals with bipolar disorder, but the underlying mechanisms for this co-occurrence are poorly understood. The aim of this study was to investigate the genetic background of bipolar patients with and without migraine.Conclusions NBEA encodes neurobeachin, a scaffolding protein primarily expressed in the brain and involved in trafficking of vesicles containing neurotransmitter receptors. This locus has not previously been implicated in migraine per se. We found no evidence of association in data from the GWAS migraine meta-analysis consortium (n=118,710 participants) suggesting that the association might be specific to migraine co-morbid with bipolar disorder

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Genetic association study of childhood aggression across raters, instruments, and age

Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association metaanalysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis AGGoverall was 3.31% (SE= 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P= 1.6E-06), PCDH7 (P= 2.0E-06), and IPO13 (P= 2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations rg among rater-specific assessment of AGG ranged from rg= 0.46 between self- and teacher-assessment to rg= 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range |rg|: 0.19-1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg=∼-0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range |rg| : 0.46-0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.</p

A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder

Background Attention-deficit/hyperactivity disorder (ADHD) shows substantial heritability and is 2-7 times more common in males than females. We examined two putative genetic mechanisms underlying this sex bias: sex-specific heterogeneity and higher burden of risk in female cases. Methods We analyzed genome-wide autosomal common variants from the Psychiatric Genomics Consortium and iPSYCH Project (20,183 cases, 35,191 controls) and Swedish populationregister data (N=77,905 cases, N=1,874,637 population controls). Results Genetic correlation analyses using two methods suggested near complete sharing of common variant effects across sexes, with rg estimates close to 1. Analyses of population data, however, indicated that females with ADHD may be at especially high risk of certain comorbid developmental conditions (i.e. autism spectrum disorder and congenital malformations), potentially indicating some clinical and etiological heterogeneity. Polygenic risk score (PRS) analysis did not support a higher burden of ADHD common risk variants in female cases (OR=1.02 [0.98-1.06], p=0.28). In contrast, epidemiological sibling analyses revealed that the siblings of females with ADHD are at higher familial risk of ADHD than siblings of affected males (OR=1.14, [95% CI: 1.11-1.18], p=1.5E-15). Conclusions Overall, this study supports a greater familial burden of risk in females with ADHD and some clinical and etiological heterogeneity, based on epidemiological analyses. However, molecular genetic analyses suggest that autosomal common variants largely do not explain the sex bias in ADHD prevalence

QQ plot.

<p>This figure represents the distribution of p-values observed in the presented genome-wide association study of ADHD. The shaded area represents the 95% confidence interval.</p

Protein-protein interaction network build from proteins encoded in associated intervals.

<p>The colored, full circles represent proteins encoded in associated intervals (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122501#pone.0122501.s002" target="_blank">S2 Table</a>). The smaller, grey circles represent interactors of indirect connections. Functionally, the DAPPLE-constructed diagram can be divided into two main groups: group “A” mostly involved in the regulation of gene expression and inflammation; and group “B” mostly involved in cell adhesion.</p

List of SNPs with observed association p-value being less than 1.00E-05.

<p>Chromosomal position is specified in Build 36 (hg18). OR refers to odds ratio and 95%CI refers to 95% confidence interval. <i>TRIM36</i> refers to tripartite motif containing 36 gene. <i>ZBTB16</i> refers to zinc finger and BTB domain containing 16 gene.</p><p>List of SNPs with observed association p-value being less than 1.00E-05.</p

Manhattan plot.

<p>Red line represents genome-wide significance threshold of 5.00E-08, while the blue line corresponds to the suggestive threshold of p = 1.00E-05.</p