Psychiatric disorders are a group of complex psychological syndromes with high prevalence. It has been reported that gut microbiota has a dominant influence on the risks of psychiatric disorders through gut microbiota–brain axis. We extended the classic gene set enrichment analysis (GSEA) approach to detect the association between gut microbiota and complex diseases using published genome-wide association study (GWAS) and GWAS of gut microbiota summary data. We applied our approach to real GWAS data sets of five psychiatric disorders, including attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (AUT), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD). To evaluate the performance of our approach, we also tested the genetic correlations of obesity and type 2 diabetes with gut microbiota. We identified several significant associations between psychiatric disorders and gut microbiota, such as ADHD and genus Desulfovibrio (P = 0.031), order Clostridiales (P = 0.034). For AUT, association signals were observed for genera Bacteroides (P = 0.012) and Desulfovibrio (P = 0.033). Genus Desulfovibrio (P = 0.005) appeared to be associated with BD. For MDD, association signals were observed for genus Desulfovibrio (P = 0.003), order Clostridiales (P = 0.004), family Lachnospiraceae (P = 0.007) and genus Bacteroides (P = 0.007). Genus Desulfovibrio (P = 0.012) and genus Bacteroides (P = 0.038) appeared to be associated with SCZ. Our study results provide novel clues for revealing the roles of gut microbiota in psychiatric disorders. This study also illustrated the good performance of GSEA approach for exploring the relationships between gut microbiota and complex diseases.

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Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with strong genetic components. To identity novel risk variants for ALS, utilizing the latest genome-wide association studies (GWAS) and eQTL study data, we conducted a genome-wide expression association analysis by summary data-based Mendelian randomization (SMR) method. Summary data were derived from a large-scale GWAS of ALS, involving 12577 cases and 23475 controls. The eQTL annotation dataset included 923,021 cis-eQTL for 14,329 genes and 4732 trans-eQTL for 2612 genes. Genome-wide single gene expression association analysis was conducted by SMR software. To identify ALS-associated biological pathways, the SMR analysis results were further subjected to gene set enrichment analysis (GSEA). SMR single gene analysis identified one significant and four suggestive genes associated with ALS, including C9ORF72 (P value = 7.08 × 10), NT5C3L (P value = 1.33 × 10), GGNBP2 (P value = 1.81 × 10), ZNHIT3(P value = 2.94 × 10), and KIAA1600(P value = 9.97 × 10). GSEA identified 7 significant biological pathways, such as PEROXISOME (empirical P value = 0.006), GLYCOLYSIS_GLUCONEOGENESIS (empirical P value = 0.043), and ARACHIDONIC_ACID_ METABOLISM (empirical P value = 0.040). Our study provides novel clues for the genetic mechanism studies of ALS.