Mapping anorexia nervosa genes to clinical phenotypes.

BACKGROUND: Anorexia nervosa (AN) is a psychiatric disorder with complex etiology, with a significant portion of disease risk imparted by genetics. Traditional genome-wide association studies (GWAS) produce principal evidence for the association of genetic variants with disease. Transcriptomic imputation (TI) allows for the translation of those variants into regulatory mechanisms, which can then be used to assess the functional outcome of genetically regulated gene expression (GReX) in a broader setting through the use of phenome-wide association studies (pheWASs) in large and diverse clinical biobank populations with electronic health record phenotypes. METHODS: Here, we applied TI using S-PrediXcan to translate the most recent PGC-ED AN GWAS findings into AN-GReX. For significant genes, we imputed AN-GReX in the Mount Sinai BioMe™ Biobank and performed pheWASs on over 2000 outcomes to test the clinical consequences of aberrant expression of these genes. We performed a secondary analysis to assess the impact of body mass index (BMI) and sex on AN-GReX clinical associations. RESULTS: Our S-PrediXcan analysis identified 53 genes associated with AN, including what is, to our knowledge, the first-genetic association of AN with the major histocompatibility complex. AN-GReX was associated with autoimmune, metabolic, and gastrointestinal diagnoses in our biobank cohort, as well as measures of cholesterol, medications, substance use, and pain. Additionally, our analyses showed moderation of AN-GReX associations with measures of cholesterol and substance use by BMI, and moderation of AN-GReX associations with celiac disease by sex. CONCLUSIONS: Our BMI-stratified results provide potential avenues of functional mechanism for AN-genes to investigate further.

Landscape of Conditional eQTL in Dorsolateral Prefrontal Cortex and Co-localization with Schizophrenia GWAS.

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which variants underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissect this signal into multiple conditionally independent signals for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (n = 467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context-specific (e.g., tissue-, cell type-, or developmental time point-specific) regulation of gene expression. Integrating the 2014 Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC primary and conditional eQTL data reveals 40 loci with strong evidence for co-localization (posterior probability > 0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes, identify novel genes associated with schizophrenia risk, and provide specific hypotheses for their functional follow-up.

Collagenous Colitis Is Associated With HLA Signature and Shares Genetic Risks With Other Immune-Mediated Diseases.

BACKGROUND & AIMS: Collagenous colitis (CC) is an inflammatory bowel disorder with unknown etiopathogenesis involving HLA-related immune-mediated responses and environmental and genetic risk factors. We carried out an array-based genetic association study in a cohort of patients with CC and investigated the common genetic basis between CC and Crohn's disease (CD), ulcerative colitis (UC), and celiac disease. METHODS: DNA from 804 CC formalin-fixed, paraffin-embedded tissue samples was genotyped with Illumina Immunochip. Matching genotype data on control samples and CD, UC, and celiac disease cases were provided by the respective consortia. A discovery association study followed by meta-analysis with an independent cohort, polygenic risk score calculation, and cross-phenotype analyses were performed. Enrichment of regulatory expression quantitative trait loci among the CC variants was assessed in hemopoietic and intestinal cells. RESULTS: Three HLA alleles (HLA-B∗08:01, HLA-DRB1∗03:01, and HLA-DQB1∗02:01), related to the ancestral haplotype 8.1, were significantly associated with increased CC risk. We also identified an independent protective effect of HLA-DRB1∗04:01 on CC risk. Polygenic risk score quantifying the risk across multiple susceptibility loci was strongly associated with CC risk. An enrichment of expression quantitative trait loci was detected among the CC-susceptibility variants in various cell types. The cross-phenotype analysis identified a complex pattern of polygenic pleiotropy between CC and other immune-mediated diseases. CONCLUSIONS: In this largest genetic study of CC to date with histologically confirmed diagnosis, we strongly implicated the HLA locus and proposed potential non-HLA mechanisms in disease pathogenesis. We also detected a shared genetic risk between CC, celiac disease, CD, and UC, which supports clinical observations of comorbidity.

Association of the V122I Hereditary Transthyretin Amyloidosis Genetic Variant With Heart Failure Among Individuals of African or Hispanic/Latino Ancestry.

Importance: Hereditary transthyretin (TTR) amyloid cardiomyopathy (hATTR-CM) due to the TTR V122I variant is an autosomal-dominant disorder that causes heart failure in elderly individuals of African ancestry. The clinical associations of carrying the variant, its effect in other African ancestry populations including Hispanic/Latino individuals, and the rates of achieving a clinical diagnosis in carriers are unknown. Objective: To assess the association between the TTR V122I variant and heart failure and identify rates of hATTR-CM diagnosis among carriers with heart failure. Design, Setting, and Participants: Cross-sectional analysis of carriers and noncarriers of TTR V122I of African ancestry aged 50 years or older enrolled in the Penn Medicine Biobank between 2008 and 2017 using electronic health record data from 1996 to 2017. Case-control study in participants of African and Hispanic/Latino ancestry with and without heart failure in the Mount Sinai BioMe Biobank enrolled between 2007 and 2015 using electronic health record data from 2007 to 2018. Exposures: TTR V122I carrier status. Main Outcomes and Measures: The primary outcome was prevalent heart failure. The rate of diagnosis with hATTR-CM among TTR V122I carriers with heart failure was measured. Results: The cross-sectional cohort included 3724 individuals of African ancestry with a median age of 64 years (interquartile range, 57-71); 1755 (47%) were male, 2896 (78%) had a diagnosis of hypertension, and 753 (20%) had a history of myocardial infarction or coronary revascularization. There were 116 TTR V122I carriers (3.1%); 1121 participants (30%) had heart failure. The case-control study consisted of 2307 individuals of African ancestry and 3663 Hispanic/Latino individuals; the median age was 73 years (interquartile range, 68-80), 2271 (38%) were male, 4709 (79%) had a diagnosis of hypertension, and 1008 (17%) had a history of myocardial infarction or coronary revascularization. There were 1376 cases of heart failure. TTR V122I was associated with higher rates of heart failure (cross-sectional cohort: n = 51/116 TTR V122I carriers [44%], n = 1070/3608 noncarriers [30%], adjusted odds ratio, 1.7 [95% CI, 1.2-2.4], P = .006; case-control study: n = 36/1376 heart failure cases [2.6%], n = 82/4594 controls [1.8%], adjusted odds ratio, 1.8 [95% CI, 1.2-2.7], P = .008). Ten of 92 TTR V122I carriers with heart failure (11%) were diagnosed as having hATTR-CM; the median time from onset of symptoms to clinical diagnosis was 3 years. Conclusions and Relevance: Among individuals of African or Hispanic/Latino ancestry enrolled in 2 academic medical center-based biobanks, the TTR V122I genetic variant was significantly associated with heart failure.

Disproportionate Contributions of Select Genomic Compartments and Cell Types to Genetic Risk for Coronary Artery Disease.

Large genome-wide association studies (GWAS) have identified many genetic loci associated with risk for myocardial infarction (MI) and coronary artery disease (CAD). Concurrently, efforts such as the National Institutes of Health (NIH) Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) Consortium have provided unprecedented data on functional elements of the human genome. In the present study, we systematically investigate the biological link between genetic variants associated with this complex disease and their impacts on gene function. First, we examined the heritability of MI/CAD according to genomic compartments. We observed that single nucleotide polymorphisms (SNPs) residing within nearby regulatory regions show significant polygenicity and contribute between 59-71% of the heritability for MI/CAD. Second, we showed that the polygenicity and heritability explained by these SNPs are enriched in histone modification marks in specific cell types. Third, we found that a statistically higher number of 45 MI/CAD-associated SNPs that have been identified from large-scale GWAS studies reside within certain functional elements of the genome, particularly in active enhancer and promoter regions. Finally, we observed significant heterogeneity of this signal across cell types, with strong signals observed within adipose nuclei, as well as brain and spleen cell types. These results suggest that the genetic etiology of MI/CAD is largely explained by tissue-specific regulatory perturbation within the human genome.

Schizophrenia risk conferred by rare protein-truncating variants is conserved across diverse human populations.

Schizophrenia (SCZ) is a chronic mental illness and among the most debilitating conditions encountered in medical practice. A recent landmark SCZ study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This recent study-and most other large-scale human genetics studies-was mainly composed of individuals of European (EUR) ancestry, and the generalizability of the findings in non-EUR populations remains unclear. To address this gap, we designed a custom sequencing panel of 161 genes selected based on the current knowledge of SCZ genetics and sequenced a new cohort of 11,580 SCZ cases and 10,555 controls of diverse ancestries. Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1.48; P = 5.4 × 10-6). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.

A tissue-level phenome-wide network map of colocalized genes and phenotypes in the UK Biobank.

Phenome-wide association studies identified numerous loci associated with traits and diseases. To help interpret these associations, we constructed a phenome-wide network map of colocalized genes and phenotypes. We generated colocalized signals using the Genotype-Tissue Expression data and genome-wide association results in UK Biobank. We identified 9151 colocalized genes for 1411 phenotypes across 48 tissues. Then, we constructed bipartite networks using the colocalized signals in each tissue, and showed that the majority of links were observed in a single tissue. We applied the biLouvain clustering algorithm in each tissue-specific network to identify co-clusters of genes and phenotypes. We observed significant enrichments of these co-clusters with known biological and functional gene classes. Overall, the phenome-wide map provides links between genes, phenotypes and tissues, and can yield biological and clinical discoveries.

Exome-wide evaluation of rare coding variants using electronic health records identifies new gene-phenotype associations.

The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease1. By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants for each individual gene on human disease on an exome-wide scale, as assessed using a set of diverse EHR phenotypes. After discovering 97 genes with exome-by-phenome-wide significant phenotype associations (P < 10-6), we replicated 26 of these in the Penn Medicine Biobank, as well as in three other medical biobanks and the population-based UK Biobank. Of these 26 genes, five had associations that have been previously reported and represented positive controls, whereas 21 had phenotype associations not previously reported, among which were genes implicated in glaucoma, aortic ectasia, diabetes mellitus, muscular dystrophy and hearing loss. These findings show the value of aggregating rare predicted loss-of-function variants into 'gene burdens' for identifying new gene-disease associations using EHR phenotypes in a medical biobank. We suggest that application of this approach to even larger numbers of individuals will provide the statistical power required to uncover unexplored relationships between rare genetic variation and disease phenotypes.

Tissue-specific genetic features inform prediction of drug side effects in clinical trials.

Adverse side effects often account for the failure of drug clinical trials. We evaluated whether a phenome-wide association study (PheWAS) of 1167 phenotypes in >360,000 U.K. Biobank individuals, in combination with gene expression and expression quantitative trait loci (eQTL) in 48 tissues, can inform prediction of drug side effects in clinical trials. We determined that drug target genes with five genetic features-tissue specificity of gene expression, Mendelian associations, phenotype- and tissue-level effects of genome-wide association (GWA) loci driven by eQTL, and genetic constraint-confer a 2.6-fold greater risk of side effects, compared to genes without such features. The presence of eQTL in multiple tissues resulted in more unique phenotypes driven by GWA loci, suggesting that drugs delivered to multiple tissues can induce several side effects. We demonstrate the utility of PheWAS and eQTL data from multiple tissues for informing drug side effect prediction and highlight the need for tissue-specific drug delivery.

mTADA is a framework for identifying risk genes from de novo mutations in multiple traits.

Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. Studies of neuropsychiatric disorders and congenital heart disease (CHD) which use de novo mutations (DNMs) from parent-offspring trios have reported multiple putatively causal genes. However, a joint analysis method designed to integrate DNMs from multiple studies has yet to be implemented. We here introduce multiple-trait TADA (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. We first demonstrate that mTADA is able to leverage genetic overlaps to increase the statistical power of risk-gene identification. We then apply mTADA to large datasets of >13,000 trios for five neuropsychiatric disorders and CHD. We report additional risk genes for schizophrenia, epileptic encephalopathies and CHD. We outline some shared and specific biological information of intellectual disability and CHD by conducting systems biology analyses of genes prioritized by mTADA.

Limitations of Contemporary Guidelines for Managing Patients at High Genetic Risk of Coronary Artery Disease.

BACKGROUND: Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear. OBJECTIVES: This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention. METHODS: A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy. RESULTS: Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p < 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p < 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor. CONCLUSIONS: Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.

Analysis of Genetically Regulated Gene Expression Identifies a Prefrontal PTSD Gene, SNRNP35, Specific to Military Cohorts.

To reveal post-traumatic stress disorder (PTSD) genetic risk influences on tissue-specific gene expression, we use brain and non-brain transcriptomic imputation. We impute genetically regulated gene expression (GReX) in 29,539 PTSD cases and 166,145 controls from 70 ancestry-specific cohorts and identify 18 significant GReX-PTSD associations corresponding to specific tissue-gene pairs. The results suggest substantial genetic heterogeneity based on ancestry, cohort type (military versus civilian), and sex. Two study-wide significant PTSD associations are identified in European and military European cohorts; ZNF140 is predicted to be upregulated in whole blood, and SNRNP35 is predicted to be downregulated in dorsolateral prefrontal cortex, respectively. In peripheral leukocytes from 175 marines, the observed PTSD differential gene expression correlates with the predicted differences for these individuals, and deployment stress produces glucocorticoid-regulated expression changes that include downregulation of both ZNF140 and SNRNP35. SNRNP35 knockdown in cells validates its functional role in U12-intron splicing. Finally, exogenous glucocorticoids in mice downregulate prefrontal Snrnp35 expression.

Global landscape and genetic regulation of RNA editing in cortical samples from individuals with schizophrenia.

RNA editing critically regulates neurodevelopment and normal neuronal function. The global landscape of RNA editing was surveyed across 364 schizophrenia cases and 383 control postmortem brain samples from the CommonMind Consortium, comprising two regions: dorsolateral prefrontal cortex and anterior cingulate cortex. In schizophrenia, RNA editing sites in genes encoding AMPA-type glutamate receptors and postsynaptic density proteins were less edited, whereas those encoding translation initiation machinery were edited more. These sites replicate between brain regions, map to 3'-untranslated regions and intronic regions, share common sequence motifs and overlap with binding sites for RNA-binding proteins crucial for neurodevelopment. These findings cross-validate in hundreds of non-overlapping dorsolateral prefrontal cortex samples. Furthermore, ~30% of RNA editing sites associate with cis-regulatory variants (editing quantitative trait loci or edQTLs). Fine-mapping edQTLs with schizophrenia risk loci revealed co-localization of eleven edQTLs with six loci. The findings demonstrate widespread altered RNA editing in schizophrenia and its genetic regulation, and suggest a causal and mechanistic role of RNA editing in schizophrenia neuropathology.

Synergistic effects of common schizophrenia risk variants.

The mechanisms by which common risk variants of small effect interact to contribute to complex genetic disorders are unclear. Here, we apply a genetic approach, using isogenic human induced pluripotent stem cells, to evaluate the effects of schizophrenia (SZ)-associated common variants predicted to function as SZ expression quantitative trait loci (eQTLs). By integrating CRISPR-mediated gene editing, activation and repression technologies to study one putative SZ eQTL (FURIN rs4702) and four top-ranked SZ eQTL genes (FURIN, SNAP91, TSNARE1 and CLCN3), our platform resolves pre- and postsynaptic neuronal deficits, recapitulates genotype-dependent gene expression differences and identifies convergence downstream of SZ eQTL gene perturbations. Our observations highlight the cell-type-specific effects of common variants and demonstrate a synergistic effect between SZ eQTL genes that converges on synaptic function. We propose that the links between rare and common variants implicated in psychiatric disease risk constitute a potentially generalizable phenomenon occurring more widely in complex genetic disorders.

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

Publisher Correction: Gene expression imputation across multiple brain regions provides insights into schizophrenia risk.

In the HTML version of the article originally published, the author group 'The Schizophrenia Working Group of the Psychiatric Genomics Consortium' was displayed incorrectly. The error has been corrected in the HTML version of the article.

Population-Specific Resequencing Associates the ATP-Binding Cassette Subfamily C Member 4 Gene With Gout in New Zealand Maori and Pacific Men.

OBJECTIVE: There is no evidence for a genetic association between organic anion transporters 1-3 (SLC22A6, SLC22A7, and SLC22A8) and multidrug resistance protein 4 (MRP4; encoded by ABCC4) with the levels of serum urate or gout. The Maori and Pacific (Polynesian) population of New Zealand has the highest prevalence of gout worldwide. The aim of this study was to determine whether any Polynesian population-specific genetic variants in SLC22A6-8 and ABCC4 are associated with gout. METHODS: All participants had ≥3 self-reported Maori and/or Pacific grandparents. Among the total sample set of 1,808 participants, 191 hyperuricemic and 202 normouricemic individuals were resequenced over the 4 genes, and the remaining 1,415 individuals were used for replication. Regression analyses were performed, adjusting for age, sex, and Polynesian ancestry. To study the functional effect of nonsynonymous variants of ABCC4, transport assays were performed in Xenopus laevis oocytes. RESULTS: A total of 39 common variants were detected, with an ABCC4 variant (rs4148500) significantly associated with hyperuricemia and gout. This variant was monomorphic for the urate-lowering allele in Europeans. There was evidence for an association of rs4148500 with gout in the resequenced samples (odds ratio [OR] 1.62 [P = 0.012]) that was replicated (OR 1.25 [P = 0.033]) and restricted to men (OR 1.43 [P = 0.001] versus OR 0.98 [P = 0.89] in women). The gout risk allele was associated with fractional excretion of uric acid in male individuals (ß = -0.570 [P = 0.01]). A rare population-specific allele (P1036L) with predicted strong functional consequence reduced the uric acid transport activity of ABCC4 by 30%. CONCLUSION: An association between ABCC4 and gout and fractional excretion of uric acid is consistent with the established role of MRP4 as a unidirectional renal uric acid efflux pump.

Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders.

BACKGROUND: Integrating rare variation from trio family and case-control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified. METHODS: We used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls). RESULTS: For SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq. CONCLUSIONS: We have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs ( https://github.com/hoangtn/extTADA ). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.