AIMC Topic: Quantitative Trait Loci
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Identification of MEG3 and MAPK3 as potential therapeutic targets for osteoarthritis through multiomics integration and machine learning.
Scientific reports
Jul 2, 2025
Knee osteoarthritis (KOA) is a prevalent degenerative joint disorder, yet its underlying molecular mechanisms remain puzzling. This study aimed to uncover the genes with a causal relationship to KOA using Mendelian randomization (MR), transcriptomic ...
Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.
NAR genomics and bioinformatics
Jun 13, 2025
Large-scale quantitative studies have identified significant genetic associations for various neurological disorders. Expression quantitative trait locus (eQTL) studies have shown the effect of single-nucleotide polymorphisms (SNPs) on the differenti...
Generative prediction of causal gene sets responsible for complex traits.
Proceedings of the National Academy of Sciences of the United States of America
Jun 12, 2025
The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generally . The challenge arises from complex traits being determined by a combination of multiple genes (or loci), whi...
Transcripts and genomic intervals associated with variation in metabolite abundance in maize leaves under field conditions.
BMC genomics
May 1, 2025
Plants exhibit extensive environment-dependent intraspecific metabolic variation, which likely plays a role in determining variation in whole plant phenotypes. However, much of the work seeking to use natural variation to link genes and transcript's ...
Exploring new drug treatment targets for immune related bone diseases using a multi omics joint analysis strategy.
Scientific reports
Mar 27, 2025
In the field of treatment and prevention of immune-related bone diseases, significant challenges persist, necessitating the urgent exploration of new and effective treatment methods. However, most existing Mendelian randomization (MR) studies are con...
Improving genetic variant identification for quantitative traits using ensemble learning-based approaches.
BMC genomics
Mar 12, 2025
BACKGROUND: Genome-wide association studies (GWAS) are rapidly advancing due to the improved resolution and completeness provided by Telomere-to-Telomere (T2T) and pangenome assemblies. While recent advancements in GWAS methods have primarily focused...
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...
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