Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.
Current phenotype classifiers for large biobanks with coupled electronic health records EHR and multi-omic data rely on ICD-10 codes for definition. However, ICD-10 codes are primarily designed for billing purposes, and may be insufficient for research. Nuanced phenotypes composed of a patients’ experience in the EHR will allow us to create precision psychiatry to predict disease risk, severity, and trajectories in EHR and clinical populations. Here, we create a phenotype risk score (PheRS) for major depressive disorder (MDD) using 2,086 cases and 31,000 individuals from Mount Sinai’s biobank BioMe ™. Rather than classifying individuals as ‘cases’ and ‘controls’, PheRS provide a whole-phenome estimate of each individual’s likelihood of having a given complex trait. These quantitative scores substantially increase power in EHR analyses and may identify individuals with likely ‘missing’ diagnoses (for example, those with large numbers of comorbid diagnoses and risk factors, but who lack explicit MDD diagnoses).Our approach applied ten-fold cross validation and elastic net regression to select comorbid ICD-10 codes for inclusion in our PheRS. We identified 158 ICD-10 codes significantly associated with Moderate MDD (F33.1). Phenotype Risk Score were significantly higher among individuals with ICD-10 MDD diagnoses compared to the rest of the population (Kolgorov-Smirnov p<2.2e-16), and were significantly correlated with MDD polygenic risk scores (R2>0.182). Accurate classifiers are imperative for identification of genetic associations with psychiatric disease; therefore, moving forward research should focus on algorithms that can better encompass a patient’s phenome.
42Causal genes and variants within genome-wide association study (GWAS) loci 43 can be identified by integrating GWAS statistics with expression quantitative trait 44 loci (eQTL) and determining which SNPs underlie both GWAS and eQTL signals. 45Most analyses, however, consider only the marginal eQTL signal, rather than 46 dissecting this signal into multiple independent eQTL for each gene. Here we 47 frequently shared across tissues than primary eQTL 9 and, like tissue and cell 87 type specific eQTL, are often found more distally to the genes they regulate. 9; 12; 88 13 These lines of evidence suggest that conditionally independent eQTL may 89 contribute to tissue-or other context-specific gene regulation (e.g. specific to a 90 particular cell type, developmental stage, or stimulation condition). 91 92 Here, we leveraged genotype and dorsolateral prefrontal cortex (DLPFC) 93 expression data provided by the CommonMind Consortium (CMC) to elucidate 94 the role of conditional eQTL in the etiology of schizophrenia (SCZ). Currently 95 comprising the largest existing postmortem brain genomic resource at nearly 600 96 samples, the CMC is generating and making publicly available an unprecedented 97 array of functional genomic data, including gene expression (RNA-sequencing), 98 histone modification (chromatin immunoprecipitation, ChIP-seq), and SNP 99 genotypes, from individuals with psychiatric disorders as well as unaffected 100 controls. 14 We utilized SNP dosage and RNA-sequencing (RNA-seq) data from 101 the CMC to identify primary and conditionally independent eQTL. We then 102 characterized the resulting eQTL on various genomic attributes including 103 distance to transcription start site, and their genes' specificity across tissues, cell-104 types, and developmental periods. In addition, we quantified enrichment of 105 primary and conditional eQTL in promoter and enhancer functional genomic 106 elements inferred from epigenomic data. Finally, we isolated each independent 107 eQTL signal by conducting a series of "all-but-one" conditional analyses for 108 genes with multiple independent eQTL, and assessed the overlap between all 109 eQTL association signals and the SCZ GWAS signals. 110 111 MATERIAL AND METHODS 112 113 CommonMind Consortium Data 114 115 We used pre-QC'ed genotype and expression data made available from the 116 CommonMind Consortium, and detailed information on quality control, data 117 adjustment and normalization procedures can be found in Fromer et. al. 14 Briefly, 118 samples were genotyped at 958,178 markers using the Illumina Infinium 119HumanOmniExpressExome array, and markers were removed on the basis of 120 having no alternate alleles, having a genotyping call rate 0.98, or a Hardy-121Weinberg P-value < 5x10 -5 . After phasing and imputation using the 1000 122Genomes Phase 1 integrated reference then filtering out variants with INFO < 0.8 123 or MAF < 0.05, the total number of markers included in the analysis increased to 124 approximately 6.4 million. Gene expression was assayed via RNA-se...
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