Influence of autozygosity on common disease risk across the phenotypic spectrum.

Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (FROH) and common diseases in Genes & Health (n = 23,978 British South Asians), UK Biobank (n = 397,184), and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe (median n = 480,282). We estimated that autozygosity due to consanguinity accounts for 5%-18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity.

Chromatin accessibility during human first-trimester neurodevelopment.

The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.

On the interpretation of transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) aim to detect relationships between gene expression and a phenotype, and are commonly used for secondary analysis of genome-wide association study (GWAS) results. Results from TWAS analyses are often interpreted as indicating a genetic relationship between gene expression and a phenotype, but this interpretation is not consistent with the null hypothesis that is evaluated in the traditional TWAS framework. In this study we provide a mathematical outline of this TWAS framework, and elucidate what interpretations are warranted given the null hypothesis it actually tests. We then use both simulations and real data analysis to assess the implications of misinterpreting TWAS results as indicative of a genetic relationship between gene expression and the phenotype. Our simulation results show considerably inflated type 1 error rates for TWAS when interpreted this way, with 41% of significant TWAS associations detected in the real data analysis found to have insufficient statistical evidence to infer such a relationship. This demonstrates that in current implementations, TWAS cannot reliably be used to investigate genetic relationships between gene expression and a phenotype, but that local genetic correlation analysis can serve as a potential alternative.

Demystifying non-coding GWAS variants: an overview of computational tools and methods.

Genome-wide association studies (GWAS) have found the majority of disease-associated variants to be non-coding. Major efforts into the charting of the non-coding regulatory landscapes have allowed for the development of tools and methods which aim to aid in the identification of causal variants and their mechanism of action. In this review, we give an overview of current tools and methods for the analysis of non-coding GWAS variants in disease. We provide a workflow that allows for the accumulation of in silico evidence to generate novel hypotheses on mechanisms underlying disease and prioritize targets for follow-up study using non-coding GWAS variants. Lastly, we discuss the need for comprehensive benchmarks and novel tools for the analysis of non-coding variants.

Investigating genetically stratified subgroups to better understand the etiology of alcohol misuse.

Alcohol misuse (AM) is highly prevalent and harmful, with theorized subgroups differing on internalizing and externalizing dimensions. Despite known heterogeneity, genome-wide association studies (GWAS) are usually conducted on unidimensional phenotypes. These approaches have identified important genes related to AM but fail to capture a large part of the heritability, even with recent increases in sample sizes. This study aimed to address phenotypic heterogeneity in GWAS to aid gene finding and to uncover the etiology of different types of AM. Genetic and phenotypic data from 410,414 unrelated individuals of multiple ancestry groups (primarily European) in the UK Biobank were obtained. Mixture modeling was applied to measures of alcohol misuse and internalizing/externalizing psychopathology to uncover phenotypically homogenous subclasses, which were carried forward to GWAS and functional annotation. A four-class model emerged with "low risk", "internalizing-light/non-drinkers", "heavy alcohol use-low impairment", and "broad high risk" classes. SNP heritability ranged from 3 to 18% and both known AM signals and novel signals were captured by genomic risk loci. Class comparisons showed distinct patterns of regional brain tissue enrichment and genetic correlations with internalizing and externalizing phenotypes. Despite some limitations, this study demonstrated the utility of genetic research on homogenous subclasses. Not only were novel genetic signals identified that might be used for follow-up studies, but addressing phenotypic heterogeneity allows for the discovery and investigation of differential genetic vulnerabilities in the development of AM, which is an important step towards the goal of personalized medicine.

Shared genetic loci between Alzheimer's disease and multiple sclerosis: Crossroads between neurodegeneration and immune system.

BACKGROUND: Neuroinflammation is involved in the pathophysiology of Alzheimer's disease (AD), including immune-linked genetic variants and molecular pathways, microglia and astrocytes. Multiple Sclerosis (MS) is a chronic, immune-mediated disease with genetic and environmental risk factors and neuropathological features. There are clinical and pathobiological similarities between AD and MS. Here, we investigated shared genetic susceptibility between AD and MS to identify putative pathological mechanisms shared between neurodegeneration and the immune system. METHODS: We analysed GWAS data for late-onset AD (N cases = 64,549, N controls = 634,442) and MS (N cases = 14,802, N controls = 26,703). Gaussian causal mixture modelling (MiXeR) was applied to characterise the genetic architecture and overlap between AD and MS. Local genetic correlation was investigated with Local Analysis of [co]Variant Association (LAVA). The conjunctional false discovery rate (conjFDR) framework was used to identify the specific shared genetic loci, for which functional annotation was conducted with FUMA and Open Targets. RESULTS: MiXeR analysis showed comparable polygenicities for AD and MS (approximately 1800 trait-influencing variants) and genetic overlap with 20% of shared trait-influencing variants despite negligible genetic correlation (rg = 0.03), suggesting mixed directions of genetic effects across shared variants. conjFDR analysis identified 16 shared genetic loci, with 8 having concordant direction of effects in AD and MS. Annotated genes in shared loci were enriched in molecular signalling pathways involved in inflammation and the structural organisation of neurons. CONCLUSIONS: Despite low global genetic correlation, the current results provide evidence for polygenic overlap between AD and MS. The shared loci between AD and MS were enriched in pathways involved in inflammation and neurodegeneration, highlighting new opportunities for future investigation.

The association between delinquent peer affiliation and disruptive behavior interacts with functional brain correlates of reward sensitivity: a biosocial interaction study in adolescent delinquents.

BACKGROUND: Affiliating with delinquent peers may stimulate the development of antisocial behavior, especially for adolescents who are sensitive to social rewards. The current study examines whether the association between delinquent peer affiliation (DPA) and disruptive behavior interacts with functional brain correlates of reward sensitivity in early onset male adolescents delinquents. METHODS: Childhood arrestees (n = 126, mean age = 17.7 [s.d. 1.6]) completed a DPA questionnaire, and participated in an fMRI study in which reward sensitivity was operationalized through responsiveness of the ventral striatum (VS), amygdala, and medial prefrontal cortex (mPFC) during the monetary incentive delay paradigm (reward anticipation and outcome). Symptoms of disruptive behavior disorders (DBD) were assessed through structured psychiatric interviews (Diagnostic Interview Schedule for Children) with adolescents. RESULTS: DPA had a main effect on DBD symptoms. Adolescents with high VS reward responses showed a stronger significant positive association between DPA and DBD symptoms compared to low VS responders. No evidence for an interaction effect was found for the amygdala and mPFC. Post-hoc analyses revealed the positive association between DPA and DBD was only present in males, with a diminishing effect as age increased. CONCLUSIONS: We found evidence for a biosocial interaction between DPA and reward sensitivity of the VS in relation to DBD symptom severity. This study provides the first evidence of an interaction effect between a brain mechanism and an environmental factor in relation to DBD symptoms, implying that susceptibility to influences of delinquent peers may intertwine with individual biological differences.

Statistical testing in transcriptomic-neuroimaging studies: A how-to and evaluation of methods assessing spatial and gene specificity.

Multiscale integration of gene transcriptomic and neuroimaging data is becoming a widely used approach for exploring the molecular underpinnings of large-scale brain organization in health and disease. Proper statistical evaluation of determined associations between imaging-based phenotypic and transcriptomic data is key in these explorations, in particular to establish whether observed associations exceed "chance level" of random, nonspecific effects. Recent approaches have shown the importance of statistical models that can correct for spatial autocorrelation effects in the data to avoid inflation of reported statistics. Here, we discuss the need for examination of a second category of statistical models in transcriptomic-neuroimaging analyses, namely those that can provide "gene specificity." By means of a couple of simple examples of commonly performed transcriptomic-neuroimaging analyses, we illustrate some of the potentials and challenges of transcriptomic-imaging analyses, showing that providing gene specificity on observed transcriptomic-neuroimaging effects is of high importance to avoid reports of nonspecific effects. Through means of simulations we show that the rate of reported nonspecific effects (i.e., effects that cannot be specifically linked to a specific gene or gene-set) can run as high as 60%, with only less than 5% of transcriptomic-neuroimaging associations observed through ordinary linear regression analyses showing both spatial and gene specificity. We provide a discussion, a tutorial, and an easy-to-use toolbox for the different options of null models in transcriptomic-neuroimaging analyses.

Combined cellomics and proteomics analysis reveals shared neuronal morphology and molecular pathway phenotypes for multiple schizophrenia risk genes.

An enigma in studies of neuropsychiatric disorders is how to translate polygenic risk into disease biology. For schizophrenia, where > 145 significant GWAS loci have been identified and only a few genes directly implicated, addressing this issue is a particular challenge. We used a combined cellomics and proteomics approach to show that polygenic risk can be disentangled by searching for shared neuronal morphology and cellular pathway phenotypes of candidate schizophrenia risk genes. We first performed an automated high-content cellular screen to characterize neuronal morphology phenotypes of 41 candidate schizophrenia risk genes. The transcription factors Tcf4 and Tbr1 and the RNA topoisomerase Top3b shared a neuronal phenotype marked by an early and progressive reduction in synapse numbers upon knockdown in mouse primary neuronal cultures. Proteomics analysis subsequently showed that these three genes converge onto the syntaxin-mediated neurotransmitter release pathway, which was previously implicated in schizophrenia, but for which genetic evidence was weak. We show that dysregulation of multiple proteins in this pathway may be due to the combined effects of schizophrenia risk genes Tcf4, Tbr1, and Top3b. Together, our data provide new biological functions for schizophrenia risk genes and support the idea that polygenic risk is the result of multiple small impacts on common neuronal signaling pathways.

The statistical properties of gene-set analysis.

The rapid increase in loci discovered in genome-wide association studies has created a need to understand the biological implications of these results. Gene-set analysis provides a means of gaining such understanding, but the statistical properties of gene-set analysis are not well understood, which compromises our ability to interpret its results. In this Analysis article, we provide an extensive statistical evaluation of the core structure that is inherent to all gene- set analyses and we examine current implementations in available tools. We show which factors affect valid and successful detection of gene sets and which provide a solid foundation for performing and interpreting gene-set analysis.

Germline variants in HEY2 functional domains lead to congenital heart defects and thoracic aortic aneurysms.

PURPOSE: In this study we aimed to establish the genetic cause of a myriad of cardiovascular defects prevalent in individuals from a genetically isolated population, who were found to share a common ancestor in 1728. METHODS: Trio genome sequencing was carried out in an index patient with critical congenital heart disease (CHD); family members had either exome or Sanger sequencing. To confirm enrichment, we performed a gene-based association test and meta-analysis in two independent validation cohorts: one with 2685 CHD cases versus 4370 . These controls were also ancestry-matched (same as FTAA controls), and the other with 326 cases with familial thoracic aortic aneurysms (FTAA) and dissections versus 570 ancestry-matched controls. Functional consequences of identified variants were evaluated using expression studies. RESULTS: We identified a loss-of-function variant in the Notch target transcription factor-encoding gene HEY2. The homozygous state (n = 3) causes life-threatening congenital heart defects, while 80% of heterozygous carriers (n = 20) had cardiovascular defects, mainly CHD and FTAA of the ascending aorta. We confirm enrichment of rare risk variants in HEY2 functional domains after meta-analysis (MetaSKAT p = 0.018). Furthermore, we show that several identified variants lead to dysregulation of repression by HEY2. CONCLUSION: A homozygous germline loss-of-function variant in HEY2 leads to critical CHD. The majority of heterozygotes show a myriad of cardiovascular defects.

Correction: Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence.

A correction to this paper has been published and can be accessed via a link at the top of the paper.

Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence.

Schizophrenia (SCZ) and bipolar disorder (BD) are severe mental disorders associated with cognitive impairment, which is considered a major determinant of functional outcome. Despite this, the etiology of the cognitive impairment is poorly understood, and no satisfactory cognitive treatments exist. Increasing evidence indicates that genetic risk for SCZ may contribute to cognitive impairment, whereas the genetic relationship between BD and cognitive function remains unclear. Here, we combined large genome-wide association study data on SCZ (n = 82,315), BD (n = 51,710), and general intelligence (n = 269,867) to investigate overlap in common genetic variants using conditional false discovery rate (condFDR) analysis. We observed substantial genetic enrichment in both SCZ and BD conditional on associations with intelligence indicating polygenic overlap. Using condFDR analysis, we leveraged this enrichment to increase statistical power and identified 75 distinct genomic loci associated with both SCZ and intelligence, and 12 loci associated with both BD and intelligence at conjunctional FDR < 0.01. Among these loci, 20 are novel for SCZ, and four are novel for BD. Most SCZ risk alleles (61 of 75, 81%) were associated with poorer cognitive performance, whereas most BD risk alleles (9 of 12, 75%) were associated with better cognitive performance. A gene set analysis of the loci shared between SCZ and intelligence implicated biological processes related to neurodevelopment, synaptic integrity, and neurotransmission; the same analysis for BD was underpowered. Altogether, the study demonstrates that both SCZ and BD share genetic influences with intelligence, albeit in a different manner, providing new insights into their genetic architectures.

MIR137 schizophrenia-associated locus controls synaptic function by regulating synaptogenesis, synapse maturation and synaptic transmission.

The MIR137 locus is a replicated genetic risk factor for schizophrenia. The risk-associated allele is reported to increase miR-137 expression and miR-137 overexpression alters synaptic transmission in mouse hippocampus. We investigated the cellular mechanisms underlying these observed effects in mouse hippocampal neurons in culture. First, we correlated the risk allele to expression of the genes in the MIR137 locus in human postmortem brain. Some evidence for increased MIR137HG expression was observed, especially in hippocampus of the disease-associated genotype. Second, in mouse hippocampal neurons, we confirmed previously observed changes in synaptic transmission upon miR-137 overexpression. Evoked synaptic transmission and spontaneous release were 50% reduced. We identified defects in release probability as the underlying cause. In contrast to previous observations, no evidence was obtained for selective synaptic vesicle docking defects. Instead, ultrastructural morphometry revealed multiple effects of miR-137 overexpression on docking, active zone length and total vesicle number. Moreover, proteomic analyses of neuronal protein showed that expression of Syt1 and Cplx1, previously reported as downregulated upon miR-137 overexpression, was unaltered. Immunocytochemistry of synapses overexpressing miR-137 showed normal Synaptotagmin1 and Complexin1 protein levels. Instead, our proteomic analyses revealed altered expression of genes involved in synaptogenesis. Concomitantly, synaptogenesis assays revealed 31% reduction in synapse formation. Taken together, these data show that miR-137 regulates synaptic function by regulating synaptogenesis, synaptic ultrastructure and synapse function. These effects are plausible contributors to the increased schizophrenia risk associated with miR-137 overexpression.

Psychiatric Polygenic Risk Scores as Predictor for Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorder in a Clinical Child and Adolescent Sample.

Neurodevelopmental disorders such as attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable and influenced by many single nucleotide polymorphisms (SNPs). SNPs can be used to calculate individual polygenic risk scores (PRS) for a disorder. We aim to explore the association between the PRS for ADHD, ASD and for Schizophrenia (SCZ), and ADHD and ASD diagnoses in a clinical child and adolescent population. Based on the most recent genome wide association studies of ADHD, ASD and SCZ, PRS of each disorder were calculated for individuals of a clinical child and adolescent target sample (N = 688) and for adult controls (N = 943). We tested with logistic regression analyses for an association with (1) a single diagnosis of ADHD (N = 280), (2) a single diagnosis of ASD (N = 295), and (3) combining the two diagnoses, thus subjects with either ASD, ADHD or both (N = 688). Our results showed a significant association of the ADHD PRS with ADHD status (OR 1.6, P = 1.39 × 10-07) and with the combined ADHD/ASD status (OR 1.36, P = 1.211 × 10-05), but not with ASD status (OR 1.14, P = 1). No associations for the ASD and SCZ PRS were observed. In sum, the PRS of ADHD is significantly associated with the combined ADHD/ASD status. Yet, this association is primarily driven by ADHD status, suggesting disorder specific genetic effects of the ADHD PRS.

Biological annotation of genetic loci associated with intelligence in a meta-analysis of 87,740 individuals.

Variance in IQ is associated with a wide range of health outcomes, and 1% of the population are affected by intellectual disability. Despite a century of research, the fundamental neural underpinnings of intelligence remain unclear. We integrate results from genome-wide association studies (GWAS) of intelligence with brain tissue and single cell gene expression data to identify tissues and cell types associated with intelligence. GWAS data for IQ (N = 78,308) were meta-analyzed with a study comparing 1247 individuals with mean IQ ~170 to 8185 controls. Genes associated with intelligence implicate pyramidal neurons of the somatosensory cortex and CA1 region of the hippocampus, and midbrain embryonic GABAergic neurons. Tissue-specific analyses find the most significant enrichment for frontal cortex brain expressed genes. These results suggest specific neuronal cell types and genes may be involved in intelligence and provide new hypotheses for neuroscience experiments using model systems.

Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer's disease.

Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer's disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV-associated RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), triglycerides (TG), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TG, TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 90 SNPs on 19 different chromosomes that were jointly associated with AD and CV-associated outcomes. In meta-analyses across three independent cohorts, we found four novel loci within MBLAC1 (chromosome 7, meta-p = 1.44 × 10-9), MINK1 (chromosome 17, meta-p = 1.98 × 10-7) and two chromosome 11 SNPs within the MTCH2/SPI1 region (closest gene = DDB2, meta-p = 7.01 × 10-7 and closest gene = MYBPC3, meta-p = 5.62 × 10-8). In a large 'AD-by-proxy' cohort from the UK Biobank, we replicated three of the four novel AD/CV pleiotropic SNPs, namely variants within MINK1, MBLAC1, and DDB2. Expression of MBLAC1, SPI1, MINK1 and DDB2 was differentially altered within postmortem AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid-associated RFs. We pinpoint a subset of cardiovascular-associated genes that strongly increase the risk for AD. Our collective findings support a disease model in which cardiovascular biology is integral to the development of clinical AD in a subset of individuals.

The Biological Contributions to Gender Identity and Gender Diversity: Bringing Data to the Table.

The American Psychological Association defines gender identity as, "A person's deeply-felt, inherent sense of being a boy, a man, or a male; a girl, a woman, or a female; or an alternative gender (e.g., genderqueer, gender nonconforming, gender neutral) that may or may not correspond to a person's sex assigned at birth or to a person's primary or secondary sex characteristics" (American Psychological Association, Am Psychol 70(9):832-864, 2015). Here we review the evidence that gender identity and related socially defined gender constructs are influenced in part by innate factors including genes. Based on the data reviewed, we hypothesize that gender identity is a multifactorial complex trait with a heritable polygenic component. We argue that increasing the awareness of the biological diversity underlying gender identity development is relevant to all domains of social, medical, and neuroscience research and foundational for reducing health disparities and promoting human-rights protections for gender minorities.

Polygenic scores for schizophrenia and educational attainment are associated with behavioural problems in early childhood in the general population.

BACKGROUND: Genome-wide association studies in adults have identified numerous genetic variants related to psychiatric disorders and related traits, such as schizophrenia and educational attainment. However, the effects of these genetic variants on behaviour in the general population remain to be fully understood, particularly in younger populations. We investigated whether polygenic scores of five psychiatric disorders and educational attainment are related to emotional and behaviour problems during early childhood. METHODS: From the Generation R Study, we included participants with available genotype data and behavioural problems measured with the Child Behavior Checklist (CBCL) at the age of 3 (n = 1,902), 6 (n = 2,202) and 10 years old (n = 1,843). Polygenic scores were calculated for five psychiatric disorders and educational attainment. These polygenic scores were tested for an association with the broadband internalizing and externalizing problem scales and the specific CBCL syndrome scale scores. RESULTS: Analysis of the CBCL broadband scales showed that the schizophrenia polygenic score was associated with significantly higher internalizing scores at 3, 6 and 10 years and higher externalizing scores at age 3 and 6. The educational attainment polygenic score was associated with lower externalizing scores at all time points and lower internalizing scores at age 3. No associations were observed for the polygenic scores of bipolar disorder, major depressive disorder and autism spectrum disorder. Secondary analyses of specific syndrome scores showed that the schizophrenia polygenic score was strongly related to the Thought Problems scores. A negative association was observed between the educational attainment polygenic score and Attention Problems scores across all age groups. CONCLUSIONS: Polygenic scores for adult psychiatric disorders and educational attainment are associated with variation in emotional and behavioural problems already at a very early age.

Gene-based association studies report genetic links for clinical subtypes of frontotemporal dementia.

Genome-wide association studies in frontotemporal dementia showed limited success in identifying associated loci. This is possibly due to small sample size, allelic heterogeneity, small effect sizes of single genetic variants, and the necessity to statistically correct for testing millions of genetic variants. To overcome these issues, we performed gene-based association studies on 3348 clinically identified frontotemporal dementia cases and 9390 controls (discovery, replication and joint-cohort analyses). We report association of APOE and TOMM40 with behavioural variant frontotemporal dementia, and ARHGAP35 and SERPINA1 with progressive non-fluent aphasia. Further, we found the ɛ2 and ɛ4 alleles of APOE harbouring protective and risk increasing effects, respectively, in clinical subtypes of frontotemporal dementia against neurologically normal controls. The APOE-locus association with behavioural variant frontotemporal dementia indicates its potential risk-increasing role across different neurodegenerative diseases, whereas the novel genetic associations of ARHGAP35 and SERPINA1 with progressive non-fluent aphasia point towards a potential role of the stress-signalling pathway in its pathophysiology.