AIMC Topic: Genome-Wide Association Study
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Machine learning derived retinal pigment score from ophthalmic imaging shows ethnicity is not biology.
Nature communications
Jan 2, 2025
Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as a surrogate marker for biological variability. We derived a continuous, measured metric, the retinal pigment score (RPS),...
Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders.
Science advances
Jan 1, 2025
DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory varian...
Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.
Cell
Dec 19, 2024
Psychiatric disorders are influenced by genetic and environmental factors. However, their study is hindered by limitations on precisely characterizing human behavior. New technologies such as wearable sensors show promise in surmounting these limitat...
Multiomic Network Analysis Identifies Dysregulated Neurobiological Pathways in Opioid Addiction.
Biological psychiatry
Nov 29, 2024
BACKGROUND: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. However, opioid addiction treatments have limited efficacy, meaning that additional...
Disentangling the Genetic Landscape of Peripartum Depression: A Multi-Polygenic Machine Learning Approach on an Italian Sample.
Genes
Nov 26, 2024
BACKGROUND: The genetic determinants of peripartum depression (PPD) are not fully understood. Using a multi-polygenic score approach, we characterized the relationship between genome-wide information and the history of PPD in patients with mood disor...
Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.
Nature communications
Nov 16, 2024
Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existi...
Gene-diet interactions in carbonated sugar-sweetened beverage consumption and metabolic syndrome risk: A machine learning analysis in a large hospital-based cohort.
Clinical nutrition ESPEN
Oct 11, 2024
BACKGROUND AND AIM: Carbonated sugar-sweetened beverages (CSSB) intake has been increasingly linked to metabolic diseases. To investigate the association between CSSB intake and metabolic syndrome (MetS) risk, and the interaction between genetic pred...
Integrated approach of machine learning, Mendelian randomization and experimental validation for biomarker discovery in diabetic nephropathy.
Diabetes, obesity & metabolism
Oct 6, 2024
AIM: To identify potential biomarkers and explore the mechanisms underlying diabetic nephropathy (DN) by integrating machine learning, Mendelian randomization (MR) and experimental validation.
Enhancing Gene Expression Predictions Using Deep Learning and Functional Annotations.
Genetic epidemiology
Sep 30, 2024
Transcriptome-wide association studies (TWAS) aim to uncover genotype-phenotype relationships through a two-stage procedure: predicting gene expression from genotypes using an expression quantitative trait locus (eQTL) data set, then testing the pred...
Valid inference for machine learning-assisted genome-wide association studies.
Nature genetics
Sep 30, 2024
Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to sample collection and precise phenotyping, ML-assisted genome-wide association study (GWAS), which us...
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