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Abstract Dietary habits are essential in the mean age at menarche (AAM). However, the causal relationship between these factors remains unclear. Therefore, this study aimed to elucidate the genetic relationship between dietary habits and AAM. Genetic summary statistics for dietary habits were obtained from the UK Biobank. GWAS summary data for AAM was obtained from the ReproGen Consortium. Linkage disequilibrium score regression was used to test genetic correlations between dietary habits and AAM. The Mendelian randomization (MR) analyses used the inverse-variance weighted method. Genetic correlations with AAM were identified for 29 candi-date dietary habits, such as milk type (skimmed, semi-skimmed, full cream; coefficient = 0.2704, P ldsc = 1.13 × 10−14). MR evaluations revealed that 19 dietary habits were associated with AAM, including bread type (white vs. any other; OR 1.71, 95% CI 1.28–2.29, P mr = 3.20 × 10−4), tablespoons of cooked vegetables (OR 0.437, 95% CI 0.29–0.67; P mr = 1.30 × 10−4), and cups of coffee per day (OR 0.72, 95% CI 0.57–0.92, P mr = 8.31 × 10−3). These results were observed to be stable under the sensitivity analysis. Our study provides potential insights into the genetic mechanisms underlying AAM and evidence that dietary habits are associated with AAM.

Abstract Aim Juvenile idiopathic arthritis (JIA) is the most common chronic rheumatic disease of childhood, with genetic susceptibility and pathological processes such as autoimmunity and autoinflammation, but its pathogenesis is unclear. We conducted a transcriptome-wide association study (TWAS) using expression interpolation from a large-scale genome-wide association study (GWAS) dataset to identify genes, biological pathways, and environmental chemicals associated with JIA. Methods We obtained published GWAS data on JIA for TWAS and used mRNA expression profiling to validate the genes identified by TWAS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. A protein–protein interaction (PPI) network was generated, and central genes were obtained using Molecular Complex Detection (MCODE). Finally, chemical gene expression datasets were obtained from the Comparative Toxicogenomics database for chemical genome enrichment analysis. Results TWAS identified 1481 genes associated with JIA, and 154 differentially expressed genes were identified based on mRNA expression profiles. After comparing the results of TWAS and mRNA expression profiles, we obtained eight overlapping genes. GO and KEGG enrichment analyses of the genes identified by TWAS yielded 163 pathways, and PPI network analysis as well as MCODE resolution identified a total of eight clusters. Through chemical gene set enrichment analysis, 287 environmental chemicals associated with JIA were identified. Conclusion By integrating TWAS and mRNA expression profiles, genes, biological pathways, and environmental chemicals associated with JIA were identified. Our findings provide new insights into the pathogenesis of JIA, including candidate genetic and environmental factors contributing to its onset and progression.

Celiac disease (CeD) is one of the most common intestinal inflammatory diseases, and its incidence and prevalence have increased over time. CeD affects multiple organs and systems in the body, and environmental factors play a key role in its complex pathogenesis. Although gluten exposure is known to be the causative agent, many unknown environmental factors can trigger or exacerbate CeD. In this study, we investigated the influence of genetic and environmental factors on CeD. Data from a CeD genome-wide association study that included 12,041 CeD cases and 12,228 controls were used to conduct a transcriptome-wide association study (TWAS) using FUSION software. Gene expression reference data were obtained for the small intestine, whole blood, peripheral blood, and lymphocytes. We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the significant genes identified by the TWAS and conducted a protein–protein interaction network analysis based on the STRING database to detect the function of TWAS-identified genes for CeD. We also performed a chemical-related gene set enrichment analysis (CGSEA) using the TWAS-identified genes to test the relationships between chemicals and CeD. The TWAS identified 8,692 genes, including 101 significant genes (padjusted < 0.05). The CGSEA identified 2,559 chemicals, including 178 chemicals that were significantly correlated with CeD. This study performed a TWAS (for genetic factors) and CGSEA (for environmental factors) and identified several CeD-associated genes and chemicals. The findings expand our understanding of the genetic and environmental factors related to immune-mediated diseases.

Menarche is the first occurrence of menstrual bleeding and one of the most important events of female puberty. Alarmingly, over the last several decades, the mean age at menarche (AAM) has decreased. Environmental endocrine disruptors (EEDs) are chemicals that may interfere with the endocrine system, resulting in adverse developmental, immunological, neurological, and reproductive effects in humans. Thus, the effects of EEDs on fertility and reproduction are growing concerns in modern societies. In this study, we aimed to determine the influence of genetic and environmental factors on AAM. We used data from an AAM genome-wide association study of 329,345 women to conduct a transcriptome-wide association study (TWAS) with FUSION software. As references, we determined the gene-expression levels in the hypothalamus, pituitary gland, ovaries, uterus, and whole blood. We performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses using the significantly dysregulated genes identified by the TWAS. Using the STRING database, we also generated a protein–protein-interaction network to analyze common AAM-specific genes identified by the TWAS with different tissues. We performed chemical-related gene set enrichment analysis (CGSEA) and identified significant TWAS genes to uncover relationships between different chemicals and AAM. The TWAS identified 9,848 genes; among these, 1580 genes were significant (P < 0.05), and 11 genes were significant among the hypothalamus, pituitary, ovary, uterus, and whole blood. CGSEA identified 1,634 chemicals, including 120 chemicals significantly correlated with AAM. In summary, we performed a TWAS (for genetic factors) and CGSEA (for environmental factors) focusing on AAM and identified several AAM-associated genes and EEDs. The results of this study expand our understanding of genetic and environmental factors related to the onset of female puberty.