Fine-mapping genomic loci refines bipolar disorder risk genes

Abstract

Bipolar disorder is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 bipolar disorder risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci and prioritized 17 likely causal SNPs for bipolar disorder. We mapped these SNPs to genes and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci and results from rare variant exome sequencing in bipolar disorder. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment, including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, FKBP2, RASGRP1, FURIN, FES, MED24 and THRA among others in bipolar disorder. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of bipolar disorder polygenic risk scores across diverse populations and present a high-throughput fine-mapping pipeline.

Fig. 1. Schematic workflow of the fine-mapping pipeline developed for PGC3 BD GWAS risk loci.

Conditional analyses were performed within GWS loci using GCTA-COJO, based on the LD structure of the Haplotype Reference Consortium (HRC) reference panel. Fine-mapping was conducted using statistical (SuSiE and FINEMAP) and functionally-informed (PolyFun) methods, according to the LD structure of the HRC, UK Biobank (UKB) and a subset of the GWAS data (‘in-sample LD’), as well as implementing single-variant (no LD) fine-mapping. PolyFun functional priors were based on the published baseline-LF2.2 UKB model. Fine-mapping results were validated computationally via VEP annotations and functional consequences, overlap with epigenomic peaks from brain cell types, SMR with brain expression, splicing and methylation QTL data, convergence with rare variant associations from the BipEx sequencing collaboration and testing whether fine-mapping effect sizes improve PRSs (PRS-CS and PolyPred). Asterisk indicates that the MHC was fine-mapped using separate procedures (see ‘Fine-mapping the MHC locus’ section). VEP, variant effect predictor.

Fig. 2. Results and comparison of 16 fine-mapping analyses conducted.

The barplot displays the number of SNPs fine-mapped with PIP > 0.5 and part of a 95% credible set on the y axis and each fine-mapping analysis on the x axis. The black bordered bars indicate the number of SNPs fine-mapped with PIP > 0.95 and part of a 95% credible set. Each analysis is named according to (LD option)_(fine-mapping method). The heatmap displays the Jaccard index of concordance in results between each pair of fine-mapping analyses, calculated based on SNPs with PIP > 0.5 and part of a 95% credible set. Jaccard indices ranged from 0.25 to 1 (mean = 0.54, s.d. = 0.20), with higher values indicating more similar fine-mapping results.

Fig. 3. Plot of union consensus SNPs across all 16 fine-mapping analyses, including different LD options and fine-mapping methods.

The color of the points corresponds to the LD option used—UKB (pink), HRC (blue), in-sample LD (purple) and no LD (single-variant fine-mapping; gray). Circles indicate statistical fine-mapping methods and squares indicate functional fine-mapping methods. Small shapes denote SNPs with PIP between 0.50 and 0.90, while large shapes denote SNPs with PIP above 0.95. On the x axis, analyses are named according to (LD option)_(fine-mapping method). On the y axis, the PGC3 locus name is displayed in parentheses after each fine-mapped SNP and indicates the name assigned to identify the locus in the original PGC3 BD GWAS publication, which is not necessarily the causal gene.

Fig. 4. Summary of analyses performed to link each fine-mapped SNP to the relevant gene(s).

The y axis shows the 17 union consensus SNPs with the PGC3 locus name displayed in parentheses after each one, which indicates the name assigned to identify the locus in the original PGC3 BD GWAS publication and not necessarily the causal gene. On the x axis, the columns depict the results of eight analyses performed to link the fine-mapped SNPs to the relevant gene(s). The analysis method and the dataset used are labeled above and below the figure, respectively. Colored cells denote significant results and the relevant gene names are printed within each cell. For fine-mapped SNPs located in active enhancers, the relevant genes were obtained using data on PLAC-seq interactions with gene promoters. A colored cell includes no gene name when there was no known interaction between the enhancer and a promoter, or when the methylation probe was not annotated to any gene. Empty cells are those with nonsignificant results or where the SNP was not present in the dataset used.

Fig. 5. Phenotypic variance in BD explained by standard PRS (PRS-CS) and fine-mapping-informed PRS (SuSiE + PRS-CS and PolyPred-P) in target cohorts of diverse genetic ancestries.

The x axis displays the target cohorts, grouped by genetic ancestry, and the PRS method used. The name of each cohort and the number of BD cases and controls are shown below each barplot. The y axis shows the percentage variance explained on the liability scale (assuming a 2% population prevalence of BD) with error bars indicating the 95% confidence interval around each R² value. P values for the association of PRS with case versus control status are printed on top of each bar. Significant P values (P < 0.05) for the test of difference in variance explained by the fine-mapping informed PRS versus PRS-CS are provided above the horizontal lines, using the F test for nested models.