AIMC Topic: Quantitative Trait Loci

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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 Species Specificity Quantitative Trait Loci Genetic Variation Gene Expression Regulation Genome, Human Epigenomics Models, Genetic Databases, Genetic Sequence Analysis, DNA Models, Statistical Mutation Animals Hepatocytes Mice Genomics Computational Biology Machine Learning Software Algorithms
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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...
Machine Learning Stress, Physiological Phenotype Algorithms Quantitative Trait Loci Droughts Genotype Plant Breeding Seeds Fabaceae
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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...
Humans Multivariate Analysis Quantitative Trait Loci Deep Learning Genome-Wide Association Study Polymorphism, Single Nucleotide Genetic Association Studies
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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...
Genome, Plant Quantitative Trait Loci Arabidopsis Models, Genetic Multigene Family Linear Models Machine Learning Genome-Wide Association Study Genomics Polymorphism, Single Nucleotide
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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...
Quantitative Trait, Heritable Web Browser Arabidopsis Computational Biology Oryza Algorithms Quantitative Trait Loci Machine Learning Reproducibility of Results Genes, Plant Software ROC Curve
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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...
Epistasis, Genetic Quantitative Trait Loci Multifactor Dimensionality Reduction Algorithms Genome-Wide Association Study Polymorphism, Single Nucleotide Cluster Analysis Humans Software
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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...
Models, Statistical Metabolome Transcriptome Oryza Genomics Supervised Machine Learning Genetic Markers Polymorphism, Single Nucleotide Phenotype Quantitative Trait Loci Models, Genetic
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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...
Metabolomics Regression Analysis Oryza Crops, Agricultural Crosses, Genetic Quantitative Trait Loci Quantitative Trait, Heritable Genomics Models, Genetic Support Vector Machine Seeds Plant Breeding Chimera
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Improving eQTL Analysis Using a Machine Learning Approach for Data Integration: A Logistic Model Tree Solution.

Journal of computational biology : a journal of computational molecular cell biology Jul 27, 2018
Expression quantitative trait loci (eQTL) analysis is an emerging method for establishing the impact of genetic variations (such as single nucleotide polymorphisms) on the expression levels of genes. Although different methods for evaluating the impa...
Computational Biology Quantitative Trait Loci Gene Expression Profiling Humans Logistic Models Gene Regulatory Networks Machine Learning Gene Expression Regulation Polymorphism, Single Nucleotide Algorithms Genotype
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Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.

Nature genetics Jul 16, 2018
Key challenges for human genetics, precision medicine and evolutionary biology include deciphering the regulatory code of gene expression and understanding the transcriptional effects of genome variation. However, this is extremely difficult because ...
Polymorphism, Single Nucleotide Models, Genetic Gene Expression Mutation Humans Promoter Regions, Genetic Deep Learning Algorithms Quantitative Trait Loci Genome-Wide Association Study Computer Simulation Genetic Predisposition to Disease
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