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Updated: Feb 7, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Integrating genome-wide association study, chromosomal enhancer maps and element-gene interaction networks detected
Mei Ma1, Xiao Liang1, Shiqiang Cheng1
1Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China.
Objective:
To explore the associations between chemical elements and attention deficit hyperactivity disorder (ADHD)/intelligence quotient (IQ).
Methods:
We applied elements related gene set enrichment analysis (ERGSEA) to explore the relationships between elements and ADHD/IQ. The GWAS dataset of ADHD was derived from the Psychiatric Genomics Consortium, involving 55,374 individuals. The GWAS dataset of IQ was derived from UK Biobank web-based measure (n = 17,862), UK Biobank touchscreen measure (n = 36,257), CHIC consortium (n = 12,441) and five additional cohorts (n = 11,748). Enhancer-gene datasets of eight brain tissues consist of 935 individuals. Utilizing the published GWAS summary and eight brain region-related chromosomal enhancer maps to obtain the SNP association testing signals. The element-gene interaction datasets of 21 elements were downloaded from the comparative toxicogenomics database (CTD).
Results:
ERGSEA observed significant associations between 4 elements and ADHD, such as Al at Hippocampus Middle (P value = 0.040), As at Angular Gyrus (P value = 0.007) and Na at Hippocampus Middle (P value = 0.026). Additionally, ERGSEA identified that 5 elements were associated with IQ, mainly including Al at Dorsolateral Prefrontal Cortex (P value = 0.017), As at Dorsolateral Prefrontal Cortex (P value = 0.004) and Pb at Germinal Matrix (P value = 0.045).
Conclusion:
Our study results provide novel clues for understanding the associations between elements and ADHD/IQ. This study also illustrated the good performance of ERGSEA approach for complex diseases.
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