Numerous studies have explored the health impacts of individual metal exposures, yet the effects of metal mixtures on human endogenous metabolism remain largely unexplored. We aimed to assess the serum metabolic signatures of people exposed to metal mixtures. Serum and urine samples were collected from 186 workers at a steel factory in Anhui, China, in September 2019. Inductively coupled plasma mass spectrometry was used to analyze the concentrations of 23 metal elements. The serum metabolome was determined by liquid chromatography-mass spectrometry (LC-MS). A metabolome-wide association study (MWAS) was performed across the metal exposures and metabolism using quantile g-computation modeling. Pathway enrichment analysis was performed using MetaboAnalyst. We identified 226 metabolites associated with metal mixtures, primarily involving lipid metabolism (glycerophospholipids, sphingolipids), amino acid metabolism (arginine and proline, alanine, aspartate and glutamate metabolism) and caffeine metabolic pathways. Exposure to metal mixtures is mainly associated with alterations in lipid metabolism and amino acid metabolism, particularly in the glycerophospholipid and arginine and proline metabolism pathways.

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Observational studies revealed altered gut microbial composition in patients with allergic diseases, which illustrated a strong association between the gut microbiome and the risk of allergies. However, whether such associations reflect causality remains to be well-documented. Two-sample mendelian randomization (2SMR) was performed to estimate the potential causal effect between the gut microbiota and the risk of allergic diseases. 3, 12, and 16 SNPs at the species, genus, and family levels respectively of 15 microbiome features were obtained as the genetic instruments of the exposure dataset from a previous study. GWAS summary data of a total of 17 independent studies related to allergic diseases were collected from the IEU GWAS database for the outcome dataset. Significant causal relationships were obtained between gut microbiome features including Ruminococcaceae, Eggerthella, Bifidobacterium, Faecalibacterium, and Bacteroides and the risk of allergic diseases. Furthermore, our results also pointed out a number of putative associations between the gut microbiome and allergic diseases. Taken together, this study was the first study using the approach of 2SMR to elucidate the association between gut microbiome and allergic diseases.

Background: Accumulating evidence shows that DNA methylation plays a role in antipsychotic response. However, the mechanisms by which DNA methylation changes are associated with antipsychotic responses remain largely unknown. Methods: We performed a methylome-wide association study (MWAS) to evaluate the association between DNA methylation and the response to risperidone in schizophrenia. Genomic DNA methylation patterns were assessed using the Agilent Human DNA Methylation Microarray. Results: We identified numerous differentially methylated positions (DMPs) and regions (DMRs) associated with antipsychotic response. CYP46A1, SPATS2, and ATP6V1E1 had the most significant DMPs, with p values of 2.50 × 10-6, 3.53 × 10-6, and 5.71 × 10-6, respectively. The top-ranked DMR was located on chromosome 7, corresponding to the PTPRN2 gene with a Šidák-corrected p-value of 9.04 × 10-13. Additionally, a significant enrichment of synaptic function and neurotransmitters was found in the differentially methylated genes after gene ontology and pathway analysis. Conclusion: The identified DMP- and DMR-overlapping genes associated with antipsychotic response are related to synaptic function and neurotransmitters. These findings may improve understanding of the mechanisms underlying antipsychotic response and guide the choice of antipsychotic in schizophrenia.

The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78−0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66−0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41−0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs.

As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being under genetic regulation, may be imputable from SNP genotypes. Here, we investigate prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits. We start by examining how well CpG methylation can be predicted from SNP genotypes, comparing three penalised regression approaches and examining whether changing the window size improves prediction accuracy. Although methylation at most CpG sites cannot be accurately predicted from SNP genotypes, for a subset it can be predicted well. We next apply our methylation prediction models (trained using the optimal method and window size) to carry out a methylome-wide association study (MWAS) of primary biliary cholangitis. We intersect the regions identified via MWAS with those identified via TWAS, providing insight into the interplay between CpG methylation, gene expression and disease status. We conclude that MWAS has the potential to improve understanding of biological mechanisms in complex traits.

Analysis of the genetic control of small metabolites provides powerful information on the regulation of the endpoints of genome expression. We carried out untargeted liquid chromatography−high-resolution mass spectrometry in 273 individuals characterized for pathophysiological elements of the cardiometabolic syndrome. We quantified 3013 serum lipidomic features, which we used in both genome-wide association studies (GWAS), using a panel of over 2.5 M imputed single-nucleotide polymorphisms (SNPs), and metabolome-wide association studies (MWAS) with phenotypes. Genetic analyses showed that 926 SNPs at 551 genetic loci significantly (q-value < 10−8) regulate the abundance of 74 lipidomic features in the group, with evidence of monogenic control for only 22 of these. In addition to this strong polygenic control of serum lipids, our results underscore instances of pleiotropy, when a single genetic locus controls the abundance of several distinct lipid features. Using the LIPID MAPS database, we assigned putative lipids, predominantly fatty acyls and sterol lipids, to 77% of the lipidome signals mapped to the genome. We identified significant correlations between lipids and clinical and biochemical phenotypes. These results demonstrate the power of untargeted lipidomic profiling for high-density quantitative molecular phenotyping in human-genetic studies and illustrate the complex genetic control of lipid metabolism.

Lung, breast, prostate, and colorectal cancers are among the most common and fatal malignancies worldwide. They are mainly caused by multifactorial mechanisms and are genetically heterogeneous. We investigated the genetic architecture of these cancers through genome-wide association, pathway-based, and summary-based transcriptome-/methylome-wide association analyses using three independent cohorts. Our genome-wide association analyses identified the associations of 33 single-nucleotide polymorphisms (SNPs) at P < 5E - 06, of which 32 SNPs were not previously reported and did not have proxy variants within their ± 1 Mb flanking regions. Moreover, other polymorphisms mapped to their closest genes were not previously associated with the same cancers at P < 5E - 06. Our pathway enrichment analyses revealed associations of 32 pathways; mainly related to the immune system, DNA replication/transcription, and chromosomal organization; with the studied cancers. Also, 60 probes were associated with these cancers in our transcriptome-wide and methylome-wide analyses. The ± 1 Mb flanking regions of most probes had not attained P < 5E - 06 in genome-wide association studies. The genes corresponding to the significant probes can be considered as potential targets for further functional studies. Two genes (i.e., CDC14A and PMEL) demonstrated stronger evidence of associations with lung cancer as they had significant probes in both transcriptome-wide and methylome-wide association analyses. The novel cancer-associated SNPs and genes identified here would advance our understanding of the genetic heterogeneity of the common cancers.

Ex situ (captivity in zoos) is regarded as an important form of conservation for endangered animals. Many studies have compared differences in the gut microbiome between captive and wild animals, but few have explained those differences at the functional level due to the limited amount of 16S rRNA data. Here, we compared the gut microbiome of captive and wild Rhinopithecus roxellana, whose high degree of dietary specificity makes it a good subject to observe the effects of the captive environment on their gut microbiome, by performing a metagenome-wide association study (MWAS). The Chao1 index was significantly higher in the captive R. roxellana cohort than in the wild cohort, and the Shannon index of captive R. roxellana was higher than that of the wild cohort but the difference was not significant. A significantly increased ratio of Prevotella/Bacteroides, which revealed an increased ability to digest simple carbohydrates, was found in the captive cohort. A significant decrease in the abundance of Firmicutes and enrichment of genes related to the pentose phosphate pathway were noted in the captive cohort, indicating a decreased ability of captive monkeys to digest fiber. Additionally, genes required for glutamate biosynthesis were also significantly more abundant in the captive cohort than in the wild cohort. These changes in the gut microbiome correspond to changes in the composition of the diet in captive animals, which has more simple carbohydrates and less crude fiber and protein than the diet of the wild animals. In addition, more unique bacteria in captive R. roxellana were involved in antibiotic resistance (Acinetobacter) and diarrhea (Desulfovibrio piger), and in the prevention of diarrhea (Phascolarctobacterium succinatutens) caused by Clostridioides difficile. Accordingly, our data reveal the cause-and-effect relationships between changes in the exact dietary composition and changes in the gut microbiome on both the structural and functional levels by comparing of captive and wild R. roxellana.

Although expression quantitative trait loci (eQTLs) have been powerful in identifying susceptibility genes from genome-wide association study (GWAS) findings, most trait-associated loci are not explained by eQTLs alone. Alternative QTLs, including DNA methylation QTLs (meQTLs), are emerging, but cell-type-specific meQTLs using cells of disease origin have been lacking. Here, we established an meQTL dataset by using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization with meQTLs, eQTLs, and mRNA splice-junction QTLs from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified candidate susceptibility genes at 63% of melanoma GWAS loci. Among the three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes is preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTLs and melanoma meQTLs identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTLs identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIP-seq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTLs.