Adherence to Life's Essential 8 is associated with delayed white matter aging.

Importance: The American Heart Association introduced Life's Essential 8 (LE8) as a checklist of healthy lifestyle factors to help older individuals maintain and improve cardiovascular health and live longer. How LE8 can foster healthy brain aging and interact with genetic risk factors to render the aging brain less vulnerable to dementia is not well understood.

Objective: To investigate the impact of LE8 on the white matter brain aging and the moderating effects of the allele.

Nongenetic and Genetic Factors Associated with White Matter Brain Aging: Exposome-Wide and Genome-Wide Association Study.

Background/objectives: Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g.

dCCA: detecting differential covariation patterns between two types of high-throughput omics data.

Motivation: The advent of multimodal omics data has provided an unprecedented opportunity to systematically investigate underlying biological mechanisms from distinct yet complementary angles. However, the joint analysis of multi-omics data remains challenging because it requires modeling interactions between multiple sets of high-throughput variables. Furthermore, these interaction patterns may vary across different clinical groups, reflecting disease-related biological processes.

Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank.

Background And Aims: Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging.

Design: Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB).

Meta-Analysis of Transcriptome-Wide Association Studies across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction.

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci, but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as a reference panel to detect associations with the phenotype at the gene level and have been gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking.

A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures.

The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables.

Biomarker Categorization in Transcriptomic Meta-Analysis by Concordant Patterns With Application to Pan-Cancer Studies.

With the increasing availability and dropping cost of high-throughput technology in recent years, many-omics datasets have accumulated in the public domain. Combining multiple transcriptomic studies on related hypothesis via meta-analysis can improve statistical power and reproducibility over single studies. For differential expression (DE) analysis, biomarker categorization by DE pattern across studies is a natural but critical task following biomarker detection to help explain between study heterogeneity and classify biomarkers into categories with potentially related functionality.

Detecting survival-associated biomarkers from heterogeneous populations.

Detection of prognostic factors associated with patients' survival outcome helps gain insights into a disease and guide treatment decisions. The rapid advancement of high-throughput technologies has yielded plentiful genomic biomarkers as candidate prognostic factors, but most are of limited use in clinical application. As the price of the technology drops over time, many genomic studies are conducted to explore a common scientific question in different cohorts to identify more reproducible and credible biomarkers.