A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Abstract

Background Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. Methods We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. Results We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. Conclusions Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

A comprehensive comparison of multilocus association methods with summary statistics in genome-wide association studies

“…The enrichment analysis demonstrated that some of these detected genes were significantly enriched in the pancreas, brain, and liver tissues (Additional file 2 : Fig. S5), consistent with prior findings [ 34 , 35 ].

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“…In addition, we found that approximately 10.7% of identified genes showed pleiotropic association with at least two disorders. For example, there were 43 genes showing simultaneously significant association with SCZ and bipolar disorder (BIP), which was consistent with the highly common genetic foundation underlying the two disorders [ 34 , 35 , 105 , 115 – 119 ]. We discovered that HMP identified the most associated genes for four disorders including attention-deficit/hyperactivity disorder (ADHD: 27 genes), cannabis use (CU: 14 genes), BIP (81 genes) and SCZ (307 genes), while SKAT detected more associated genes for the remaining two disorders including autism spectrum disorder (ASD: 4 genes) and MDD (10 genes) (Table 6 ).…”

“…Again, we leveraged genotypes of 503 European descents from the 1000 Genomes Project as the reference panel when the LD matrix was required. To avoid numerical instability, we only considered genes with at least ten SNPs following our prior work [ 34 , 35 ], and further performed an enrichment analysis for all identified genes using FUMA [ 108 ].…”

“…Sixth, SNP-set analysis can easily take functional information into account [21,[28][29][30][31][32][33], which hence improves power and facilitates interpretation of GWAS discoveries. Finally, besides its own importance, SNP-set analysis is a critical step toward many other post-GWAS functional explorations, including gene-centric pleiotropy identification [34,35], TWAS with bulk-cell sequencing RNA data [36,37] and integrative gene analysis of GWAS with single-cell RNA sequencing data [38,39].…”

Depression and bipolar disorder subtypes differ in their genetic correlations with biological rhythms

“…Also, a study investigating sleep regulation via the EGFR signalling pathway found genetic variants in ERBB4 to be associated with excessive daytime sleepiness 59 . BIP-I and sleep duration were both associated with MSRA , which is engaged in protein repair processes; MSRA was associated with nine psychiatric disorders in a study investigating pleiotropic effects 60 ; it has also been linked to neuroticism 61 and sleep behaviour 62 . CADM2 , which was overlapping in BIP-I and daytime sleepiness, belongs to the immunoglobulin superfamily and has been implicated in various genetic studies investigating psychological and physical traits, such as obesity 63 , cognitive function 64 , and physical activity 65 .…”