Genome-wide association study of psychiatric and substance use comorbidity in Mexican individuals

The Prevalence of Symptomatology and Risk Factors in Mental Health in Mexico: The 2016-17 ENCODAT Cohort

There is little recent information about the prevalence of symptomatology of mental health disorders in representative population samples in Mexico. To determine the prevalence of mental health symptoms in Mexico and its comorbidity with tobacco, alcohol, and drug use disorder (SUD), we used the 2016-17 National Survey of Drug, Alcohol, and Tobacco Use (Encuesta Nacional de Consumo de Drogas, Alcohol y Tabaco, ENCODAT 2016-2017). The data were collected from households using a cross-sectional, stratified, multistage design, with a confidence level of 90% and a response rate of 73.6%. The final sample included 56,877 completed interviews of individuals aged 12-65, with a subsample of 13,130 who answered the section on mental health. Symptoms of mania and hypomania (7.9%), depression (6.4%), and post-traumatic stress (5.7%) were the three main problems reported. Of this subsample, 56.7% reported using a legal or illegal drug without SUD, 5.4% reported SUD at one time on alcohol, 0.8% on tobacco, and 1.3% on medical or illegal drugs, 15.9% reported symptoms related to mental health, and 2.9% comorbidity. The prevalence found is consistent with those reported in previous studies, except for an increase in post-traumatic stress, which is consistent with the country's increase in trauma.

Benchmarking post-GWAS analysis tools in major depression: Challenges and implications

Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery.

Genome-Wide Analysis of Disordered Eating Behavior in the Mexican Population

Alterations in eating behavior characterized eating disorders (ED). The genetic factors shared between ED diagnoses have been underexplored. The present study performed a genome-wide association study in individuals with disordered eating behaviors in the Mexican population, blood methylation quantitative trait loci (blood-meQTL), summary data-based Mendelian randomization (SMR) analysis, and in silico function prediction by different algorithms. The analysis included a total of 1803 individuals. We performed a genome-wide association study and blood-meQTL analysis by logistic and linear regression. In addition, we analyzed in silico functional variant prediction, phenome-wide, and multi-tissue expression quantitative trait loci. The genome-wide association study identified 44 single-nucleotide polymorphisms (SNP) associated at a nominal value and seven blood-meQTL at a genome-wide threshold. The SNPs show enrichment in genome-wide associations of the metabolic and immunologic domains. In the in silico analysis, the SNP rs10419198 (p-value = 4.85 × 10-5) located on an enhancer mark could change the expression of PRR12 in blood, adipocytes, and brain areas that regulate food intake. Additionally, we found an association of DNA methylation levels of SETBP1 (p-value = 6.76 × 10-4) and SEMG1 (p-value = 5.73 × 10-4) by SMR analysis. The present study supports the previous associations of genetic variation in the metabolic domain with ED.