Genomics yields biological and phenotypic insights into bipolar disorder

Abstract

Bipolar disorder (BD) is a severe, highly heritable mental illness. The underlying mechanisms remain largely unknown. To gain greater insight, we performed the largest genome-wide association study (GWAS) meta-analyses of BD, combining clinical and community (biobank and self-report) samples of European, East Asian, African American and Latino ancestry. We detected 337 independent genome-wide significant variants mapped to 298 loci in the multi-ancestry meta-analysis, a 4-fold increase over previous findings, and a novel ancestral-specific locus in the East Asian cohort. Fine-mapping and integration of eQTL data implicated 47 credible genes in the etiology of BD. The genetic architecture of BD in community-based samples was more similar to BD type II than to BD type I, potentially reflecting a non-hospitalized, non-psychotic portion of the BD spectrum.

Fine-mapping genomic loci refines bipolar disorder risk genes

“…We assessed whether fine-mapping results could be used to improve the performance of BD PRS in 12 testing cohorts: three EUR cohorts that were independent of the BD GWAS, two East Asian cohorts, four admixed African American cohorts, and three Latino cohorts 46,49,50 . Standard PRS were calculated using the PRS-CS method, and fine-mapping informed PRS were calculated via PolyPred, to integrate statistical fine-mapping results (SuSiE+PRS-CS) or functional fine-mapping results (Polypred-P).…”

“…However, this enabled us to investigate the impact of LD reference panels on fine-mapping, which would be challenging for diverse ancestry data, given the limited availability of such panels at present. Increasing ancestral diversity in BD GWAS is an active area of research 46 , and in future the differences in LD structure between populations could be leveraged to aid fine-mapping 62 and PRS predictions 44 . Second, we approximated “in-sample LD” of the GWAS as we only had access to a subset of the individual-level data (73% of the total effective sample size), we used best guess genotypes to represent imputed dosages, and we merged genotypes across cohorts and calculated LD, in contrast to the GWAS, which was a meta-analysis between cohorts.…”

“…Fourth, three PRS were calculated for each individual in the testing cohorts, using PLINK v1.90 28 to weight SNPs by their effect sizes from PRS-CS, SuSiE+PRS-CS and Polypred-P respectively, and sum across all SNPs in each PRS. Finally, PRS were tested for association with case versus control status in each testing cohort using a logistic regression model including PCs 1-5 and any others as necessary to control for genetic ancestry 46 . In each testing cohort, the amount of phenotypic variance explained by the PRS (R 2 ) and the 95% confidence intervals were calculated on the liability scale 47 , using the r2redux R package 48 , assuming a lifetime prevalence of BD in the general population of 2%.…”

Genome-wide DNA methylation risk scores for schizophrenia derived from blood and brain tissues further explain the genetic risk in patients stratified by polygenic risk scores for schizophrenia and bipolar disorder

“…Based on the largest GWASs for SZ, BD and SZ versus BD from the PGC at the time, we extracted individuals with the genetic risk. However, the latest and largest-scale GWAS of BD that identified 298 loci has been recently preprinted 57. Further stratification using the latest GWAS of BD would be required.…”

“…However, the latest and largest-scale GWAS of BD that identified 298 loci has been recently preprinted. 57 Further stratification using the latest GWAS of BD would be required. We used available partial EWAS summary statistics or raw IDAT files to calculate the MRSs as a discovery EWAS.…”