AIMC Topic: Quantitative Trait Loci
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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...
Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits.
PloS one
Aug 28, 2024
We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidops...
Analyzing Medicago spp. seed morphology using GWAS and machine learning.
Scientific reports
Jul 30, 2024
Alfalfa is widely recognized as an important forage crop. To understand the morphological characteristics and genetic basis of seed morphology in alfalfa, we screened 318 Medicago spp., including 244 Medicago sativa subsp. sativa (alfalfa) and 23 oth...
Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to .
International journal of molecular sciences
Jun 25, 2024
(Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor gene...
Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens.
Genes
May 26, 2024
Genomic prediction plays an increasingly important role in modern animal breeding, with predictive accuracy being a crucial aspect. The classical linear mixed model is gradually unable to accommodate the growing number of target traits and the increa...
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