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. 2019 Oct 24;12(1):143.
doi: 10.1186/s12920-019-0593-5.

Gene-based analysis of ADHD using PASCAL: a biological insight into the novel associated genes

Aitana Alonso-Gonzalez  1 , Manuel Calaza  1 , Cristina Rodriguez-Fontenla  2 , Angel Carracedo  3   4
Affiliations

Gene-based analysis of ADHD using PASCAL: a biological insight into the novel associated genes

Aitana Alonso-Gonzalez et al. BMC Med Genomics. .

Abstract

Background: Attention-Deficit Hyperactivity Disorder (ADHD) is a complex neurodevelopmental disorder (NDD) which may significantly impact on the affected individual's life. ADHD is acknowledged to have a high heritability component (70-80%). Recently, a meta-analysis of GWAS (Genome Wide Association Studies) has demonstrated the association of several independent loci. Our main aim here, is to apply PASCAL (pathway scoring algorithm), a new gene-based analysis (GBA) method, to the summary statistics obtained in this meta-analysis. PASCAL will take into account the linkage disequilibrium (LD) across genomic regions in a different way than the most commonly employed GBA methods (MAGMA or VEGAS (Versatile Gene-based Association Study)). In addition to PASCAL analysis a gene network and an enrichment analysis for KEGG and GO terms were carried out. Moreover, GENE2FUNC tool was employed to create gene expression heatmaps and to carry out a (DEG) (Differentially Expressed Gene) analysis using GTEX v7 and BrainSpan data.

Results: PASCAL results have revealed the association of new loci with ADHD and it has also highlighted other genes previously reported by MAGMA analysis. PASCAL was able to discover new associations at a gene level for ADHD: FEZF1 (p-value: 2.2 × 10- 7) and FEZF1-AS1 (p-value: 4.58 × 10- 7). In addition, PASCAL has been able to highlight association of other genes that share the same LD block with some previously reported ADHD susceptibility genes. Gene network analysis has revealed several interactors with the associated ADHD genes and different GO and KEGG terms have been associated. In addition, GENE2FUNC has demonstrated the existence of several up and down regulated expression clusters when the associated genes and their interactors were considered.

Conclusions: PASCAL has been revealed as an efficient tool to extract additional information from previous GWAS using their summary statistics. This study has identified novel ADHD associated genes that were not previously reported when other GBA methods were employed. Moreover, a biological insight into the biological function of the ADHD associated genes across brain regions and neurodevelopmental stages is provided.

Keywords: ADHD (attention-deficit hyperactivity disorder); DEG (differentially expressed gene) analysis; GBA (gene-based analysis); GWAS (genome wide association study); Gene-network analysis; NDDs (neurodevelopmental disorders); PASCAL (pathway scoring algorithm); PGC (Psychiatric Genomics Consortium).

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
Regional association plots for ADHD GWAS meta-analysis (chromosomes 1, 7 and 11). PASCAL has revealed novel associations at a gene level for MED8 (chr1), FEZF1 and FEZF1-AS1 (chr 7) and NS3BP and PDDC1 (chr11). Regional plot for chromosome 1 has been constructed with rs11810109 as index SNP (r2 = 0.99 with the lead SNP, rs11420276) due to the lack of LD data for this marker
Fig. 2
Fig. 2
Regional association plots for ADHD GWAS meta-analysis; females (chromosome 16) and males (chromosome 1). Both regions contain PASCAL associated genes
Fig. 3
Fig. 3
ADHD gene-networks constructed with PASCAL associated genes and its FunCoup interactors. Main query and interactor partners which form each network are represented. as blue circles. Query genes are also circled by black lines. Node sizes scale to emphasize gene importance in the whole network while participating nodes for each KEGG pathway are marked in black: a cell cycle; b oocyte meiosis; c progesterone-mediated oocyte maturation; d Ubiquitin mediated proteolysis
Fig. 4
Fig. 4
ADHD female gene-networks constructed with PASCAL associated genes and its FunCoup interactors. Mainquery and interactor partners which form each network are represented. as blue circles. Query genes are also circled by black lines. Node sizes scale to emphasize gene importance in the whole network while participating nodes for each KEGG pathway are marked in black: a GAP junction; b Protein processing in endoplasmic reticulum: c Ubiquitin mediated proteolysis; d Phagosome
Fig. 5
Fig. 5
ADHD gene expression heatmaps constructed with GTEX v7 (53 tissues) (left) and BrainSpan 29 different ages of brain samples data (right).Genes and tissues are ordered by clusters for the GTEX heatmap. In the case of BrainSpan heatmap, genes are ordered by expression clusters and neurodevelopmental stages are chronologically ordered fom prenatal to postnatal stages
Fig. 6
Fig. 6
ADHD DEG plots constructed with GTEX v7 (53 tissues) (left) and BrainSpan 29 different ages of brain samples RNA seq data (right). Significantly enriched DEG sets (Pbon < 0.05) are highlighted in red
Fig. 7
Fig. 7
Gene expression heatmaps for ADHD females genes, constructed with GTEX v7 (53 tissues) (left) and BrainSpan 29 different ages of brain samples data (right).Genes and tissues are ordered by clusters for the GTEX heatmap. In the case of BrainSpan heatmap, genes are ordered by expression clusters and neurodevelopmental stages are chronologically ordered fom prenatal to postnatal stages
Fig. 8
Fig. 8
ADHD DEG plots for the females group constructed with GTEX v7 (53 tissues) (left) and BrainSpan 29 different ages of brain samples. No significantly enriched DEG set was found
See this image and copyright information in PMC

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