PASCAL algorithm has revealed novel ADHD associated genes ADHD in comparison with those genes previously reported by MAGMA [10]. The fact that both GBA methods employ similar but slightly different statistical approaches, might explain the differences found between the results reported by both methods. Thus, the vast majority of genes shared between both studies are located near to genome-wide significant index variants identified by Demontis et al. (cluster of genes within chromosome 1).
However PASCAL has been able to unveil new associated genes that do not physically overlap with individual genome wide significant SNPs. This is the case of the region located on chromosome 7 that encompass FEZF1, FEZF1-AS1 and part of CADPS2 including its 3’UTR. The top SNP, rs2845270, is located between the 3’UTR of CADPS2 and just before the TSS (transcription starting site) of FEZF1. It should be noted that rs2845270 did not reach the required GWAS significance level (p-value < 5 × 10− 8). However, PASCAL algorithm was able to rescue FEZF1 and FEZF1-AS1 as associated genes when the p-values of neighbor SNPs were considered (all SNPs included between both recombination peaks). It is worth noting that the enrichment analysis has not detected any interactor for FEZF1. In addition, FEZF1-AS1, which encodes a lncRNA, was not recognized by FunCoup. This implies a limitation when it comes to describe the biological processes that could be underlying ADHD etiology in relation with this gene. Moreover, expression heatmaps have not revealed differences when FEZF1 and FEZF1-AS1 expression patterns were analyzed across pre- and post-natal developmental stages. However, GTEX expression analyses have identified two ADHD expression clusters (one downregulated and another one upregulated). Thus, FEZF1 and FEZF1-AS1 have shown a high relative expression in brain together with other ADHD associated genes and their interactors (ELOVL2, CCNA1,CHD8 and SORCS3). It should be noted that previous genetic and functional studies have linked this genomic region to other neurodevelopmental phenotypes. Therefore, FEZF1 was identified as a strong candidate gene for ASD in a family sequencing study and the region spans the autism susceptibly locus 1 (AUTS1) [16, 17]. Moreover, FEZF1 expression was mainly found in the forebrain region during early embriogenesis. FEZF1 and FEZF2 are both related with the differentiation of neuron stem cells and a proper cortical development [18,19,20]. In addition it was proved that the downregulation of lncRNA FEZF1-AS1, suppresses the activation of the Wnt/β-catenin signaling pathway in tumor progression. Although its functional role in neurons has not been proved to date, genes within this canonical pathway has been repeatedly linked to ASD and ADHD phenotypes [21, 22]. Moreover, CADPS2 plays an important function on the synaptic circuits throughout the activity-dependent release of neurotrophic factor (BDNF). Indeed, genetic variants in CADPS2 gene were associated to ASD and CADPS2 KO mice have shown impairments in behavioral phenotypes [23,24,25,26].
In addition, two novel loci were associated with ADHD, NS3BP and PDDC1, both located on chromosome 11. Previous GBA carried out by MAGMA has revealed association of a nearby locus, PIDD1, but the proxy SNP was not identified since the required GWAS significance level (p < 5 × 10 − 8) was not surpassed [10]. However, PASCAL was able to identify as associated both genes located on the same genetic region around the top SNP (rs28633403). FunCoup was unable to recognize NS3BP which belongs to an uncharacterized LOC171391 gene which entails a limitation in the enrichment analysis. Moreover, no gene interaction was reported for PDDC1. Thus, it should be noted that it has not been possible to directly relate PDDC1 (glutamine amidotransferase like class 1 domain containing 1) and NS3BP (pseudogene) with any biological term. Moreover, there is a lack of previous studies reporting their biological function. However, PDDC1 is located within the brain-downregulated cluster together with other ADHD associated genes and their interactors (KDM4A, KDM4A-AS1, CDC20, AURKA, NEK2, BUB1 and BUB1B among others). Curiously, some of the genes that have shown downregulation across GTEX adult brain were upregulated during early neurodevelopmental stages (8–9 pcw) and they have shown a high relative expression in testis. Precisely, most of those genes overexpressed in testis are interactor partners of CDC20. CDC20 together with KDM4A and KDM4A-AS1 has shown enrichment in KEGG and GO terms related with cell cycle as well as with positive and negative regulation of cellular processes. Specifically, CDC20 has been previously reported as a coactivator of the ubiquitin ligase anaphase-promoting complex (APC). The APC-CDC20 complex has essential functions in regulating mitosis but it has also been described nonmitotic functions in neurons. Thus, APC-CDC20 complex plays a role in dendrite morphogenesis during brain development [27].
Novel associated genes were reported in both males and females when PASCAL analysis was carried out. Until now, no GBA has been done with this data. However, a genetic investigation of sex bias in ADHD including this GWAS meta-analyses has revealed different single SNPs associated for male and female meta-analyses [28]. Thus, PASCAL has revealed the association of 9 genes located on chromosome 16. The lead SNP of the region (rs4984677) lies within FBXL16 and it has been pointed as one of the top SNPs associated (p-value:1.9 × 10− 7) for females in the sex-specific meta-analysis [28]. However, it should be noted that the number of SNPs covering these genes is relatively lower in comparison with other associated genes. Moreover, it is also necessary to highlight that the female cohort only includes 4945 cases versus 14,154 cases included in the male cohort. Therefore, these associations should be carefully considered.
Network analysis for the whole GBA, has detected 9 associated genes but it was only able to identify interactors for three genes: NARFL, HAGHL and STUB. In addition, enrichment analyses seem to point to different biological processes from those previously reported for the whole ADHD analysis. Gene expression heatmap (GTEx data) has shown a higher expression in brain for FBXL16, MTRN, CCDC78 and HAGHL compared to other tissue types. It is worth noting that the expression of FBXL16, HAGHL and MTRN cluster together across different neurodevelopmental stages (prenatal and postnatal). The function of these genes during neurodevelopment is unknown except in the case of MTRN. MTRN encodes for a neurotrophic factor that plays important roles both in the glial cell differentiation and the formation of axonal networks [29].
The genetic and functional annotation results seem to point to a differential role of the associated genes in males versus females. Genetic differences between genders in ADHD etiology could explain the sex bias reported in the prevalence for this NDD. Thus, males have shown a rate of ADHD diagnosis seven times higher than females [30]. However, the study conducted by the PGC did not found any evidence that point towards the explanation of this sex bias by the association of common variants [28]. Further research will be needed to clarify this question. Probably, to gather a much larger sample size for males and females GWAS would be helpful for future studies.
In conclusion, PASCAL algorithm was used to carry out a novel ADHD GBA, employing summary statistics from the latest PGC meta-analysis. This has helped to identify novel gene associations for ADHD different from those reported by MAGMA. Thus, our results prove that both tools might be used as complementary GBA approaches to highlight genes associated to this disorder.
Although PASCAL has solved many limitations found in other GBA as the algorithmic efficiency and the optimization of the correlation matrix, novel improvements could be added to the method. Thus, the incorporation of functional annotation data, eQTLs or methylation status for each SNP could help to prioritize and report different associated genes.
Moreover, gene-network and functional annotation approaches including gene expression heatmaps and DEG have helped to understand these genetic findings in a biological context. This is extremely useful to select the most suitable candidates genes for future functional studies.