Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders

Abstract

Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium-schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genome-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.

Figure 1 –

Forest plots for the 4 most significant SNPs in SCZ mtCOJO analysis with larger conditional effect sizes

Figure 1 –

Forest plots for the 4 most significant SNPs in SCZ mtCOJO analysis with larger conditional effect sizes

Figure 1 –

Forest plots for the 4 most significant SNPs in SCZ mtCOJO analysis with larger conditional effect sizes

Figure 1 –

Forest plots for the 4 most significant SNPs in SCZ mtCOJO analysis with larger conditional effect sizes

Figure 2

Results from brain cell-type enrichment analyses of raw and conditional GWAS analyses