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Quantitative Trait Loci

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Prediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation study.

PloS one
The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant bre...

DeepBSA: A deep-learning algorithm improves bulked segregant analysis for dissecting complex traits.

Molecular plant
Bulked segregant analysis (BSA) is a rapid, cost-effective method for mapping mutations and quantitative trait loci (QTLs) in animals and plants based on high-throughput sequencing. However, the algorithms currently used for BSA have not been systema...

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

Proceedings of the National Academy of Sciences of the United States of America
There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challengin...

TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.

Bioinformatics (Oxford, England)
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...

DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.

Bioinformatics (Oxford, England)
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...

deepGBLUP: joint deep learning networks and GBLUP framework for accurate genomic prediction of complex traits in Korean native cattle.

Genetics, selection, evolution : GSE
BACKGROUND: Genomic prediction has become widespread as a valuable tool to estimate genetic merit in animal and plant breeding. Here we develop a novel genomic prediction algorithm, called deepGBLUP, which integrates deep learning networks and a geno...

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.

British journal of clinical pharmacology
Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. However, interpreting the biological implications of these associations remains a challenge. This revie...

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.

BMC genomic data
BACKGROUND: Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for ...

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.

Biostatistics (Oxford, England)
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...

Integrating Bioinformatics and Machine Learning for Genomic Prediction in Chickens.

Genes
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...