AIMC Topic: Quantitative Trait Loci
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Leveraging molecular-QTL co-association to predict novel disease-associated genetic loci using a graph convolutional neural network.
PloS one
Jan 1, 2025
Genome-wide association studies (GWAS) have successfully uncovered numerous associations between genetic variants and disease traits to date. Yet, identifying significantly associated loci remains a considerable challenge due to the concomitant multi...
Sub-sampling graph neural networks for genomic prediction of quantitative phenotypes.
G3 (Bethesda, Md.)
Nov 6, 2024
In genomics, use of deep learning (DL) is rapidly growing and DL has successfully demonstrated its ability to uncover complex relationships in large biological and biomedical data sets. With the development of high-throughput sequencing techniques, g...
DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.
Biostatistics (Oxford, England)
Apr 15, 2024
Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regre...
DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.
Bioinformatics (Oxford, England)
Dec 13, 2022
MOTIVATION: Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited...
TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.
Bioinformatics (Oxford, England)
Oct 14, 2022
MOTIVATION: Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of h...
Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.
Briefings in bioinformatics
Nov 5, 2021
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the risk ADHD-associated variants and genes with significant P-values, they may neglect the combined eff...
Deep learning-based high-throughput phenotyping accelerates gene discovery for stomatal traits.
Plant physiology
Nov 3, 2021
Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.
Plant physiology
Nov 3, 2021
Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping ep...
Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.
Bioinformatics (Oxford, England)
Jun 16, 2021
MOTIVATION: Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-co...
Statistical and Machine Learning Methods for eQTL Analysis.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2020
An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequencing capabilities and better genotyping methods, we are now able to more fully appreciate how regula...
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