AIMC Topic: Quantitative Trait Loci
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Psychiatric Genomics 2025: State of the Art and the Path Forward.
The Psychiatric clinics of North America
Feb 28, 2025
Psychiatric genetics has evolved from candidate-gene studies to whole-genome sequencing efforts. With hundreds of disease-associated loci now identified, functional interpretation of the associated loci becomes the critical next step toward translati...
Machine Learning Methods for Classifying Multiple Sclerosis and Alzheimer's Disease Using Genomic Data.
International journal of molecular sciences
Feb 27, 2025
Complex diseases pose challenges in prediction due to their multifactorial and polygenic nature. This study employed machine learning (ML) to analyze genomic data from the UK Biobank, aiming to predict the genomic predisposition to complex diseases l...
A multi-modal transformer for cell type-agnostic regulatory predictions.
Cell genomics
Jan 29, 2025
Sequence-based deep learning models have emerged as powerful tools for deciphering the cis-regulatory grammar of the human genome but cannot generalize to unobserved cellular contexts. Here, we present EpiBERT, a multi-modal transformer that learns g...
Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning.
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Jan 24, 2025
Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic archit...
Rapid and accurate multi-phenotype imputation for millions of individuals.
Nature communications
Jan 4, 2025
Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been ...
Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis.
The Plant journal : for cell and molecular biology
Nov 27, 2024
Cannabis sativa L., known for its medicinal and psychoactive properties, has recently experienced rapid market expansion but remains understudied in terms of its fundamental biology due to historical prohibitions. This pioneering study implements GS ...
Genetic association of tertiary lymphoid structure-related gene signatures with HCC based on Mendelian randomization and machine learning and construction of prognosis model.
International immunopharmacology
Nov 19, 2024
BACKGROUND: Tertiary lymphoid structures (TLS) are formed in numerous cancer types. However, their value and significance in hepatocellular carcinoma (HCC) is unclear.
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...
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...
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