Quantitative Trait Loci
Showing 51 to 60 of 98 articles
Sensitivity analysis based on the random forest machine learning algorithm identifies candidate genes for regulation of innate and adaptive immune response of chicken.
Poultry science
Sep 12, 2020
Two categories of immune responses-innate and adaptive immunity-have both polygenic backgrounds and a significant environmental component. The goal of the reported study was to define candidate genes and mutations for the immune traits of interest in...
An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.
Genetic epidemiology
Jul 22, 2020
Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative fal...
Cross-species regulatory sequence activity prediction.
PLoS computational biology
Jul 20, 2020
Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, mod...
Humans
Neural Networks, Computer
Machine Learning
Mice
Genomics
Computational Biology
Epigenomics
Software
Algorithms
Animals
Gene Expression Regulation
Genome, Human
Species Specificity
Quantitative Trait Loci
Models, Statistical
Mutation
Hepatocytes
Genetic Variation
Models, Genetic
Databases, Genetic
Sequence Analysis, DNA
Morphological traits of drought tolerant horse gram germplasm: classification through machine learning.
Journal of the science of food and agriculture
Jul 3, 2020
BACKGROUND: Horse gram (Macrotyloma uniflorum (Lam.) Verdc.) is an underutilized pulse crop with good drought resistance traits. It is a rich source of protein. Conventional breeding methods for high yielding and abiotic stress tolerant germplasm are...
DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning.
PLoS computational biology
Feb 3, 2020
Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses...
TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.
Scientific reports
Dec 2, 2019
One of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods a...
QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice.
G3 (Bethesda, Md.)
Oct 7, 2019
Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine ma...
Multivariate Cluster-Based Multifactor Dimensionality Reduction to Identify Genetic Interactions for Multiple Quantitative Phenotypes.
BioMed research international
Jul 11, 2019
To understand the pathophysiology of complex diseases, including hypertension, diabetes, and autism, deleterious phenotypes are unlikely due to the effects of single genes, but rather, gene-gene interactions (GGIs), which are widely analyzed by multi...
A directed learning strategy integrating multiple omic data improves genomic prediction.
Plant biotechnology journal
Apr 14, 2019
Genomic prediction (GP) aims to construct a statistical model for predicting phenotypes using genome-wide markers and is a promising strategy for accelerating molecular plant breeding. However, current progress of phenotype prediction using genomic d...
Identification of optimal prediction models using multi-omic data for selecting hybrid rice.
Heredity
Mar 25, 2019
Genomic prediction benefits hybrid rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available fo...
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