AIMC Topic: Quantitative Trait Loci

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Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Nucleic acids research Dec 16, 2019
Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional fe...
Genome-Wide Association Study Genetic Predisposition to Disease Mental Disorders Risk Factors Quantitative Trait Loci Regulatory Sequences, Nucleic Acid Polymorphism, Single Nucleotide Humans Autoimmune Diseases Neural Networks, Computer
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TAGOOS: genome-wide supervised learning of non-coding loci associated to complex phenotypes.

Nucleic acids research Aug 22, 2019
Genome-wide association studies (GWAS) associate single nucleotide polymorphisms (SNPs) to complex phenotypes. Most human SNPs fall in non-coding regions and are likely regulatory SNPs, but linkage disequilibrium (LD) blocks make it difficult to dist...
Humans Phenotype Polymorphism, Single Nucleotide Computational Biology Genome-Wide Association Study Genetic Predisposition to Disease Linkage Disequilibrium Gene Expression Regulation Internet Regulatory Sequences, Nucleic Acid Quantitative Trait Loci Supervised Machine Learning
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Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB.

Nucleic acids research Jan 8, 2019
Successful development of biological databases requires accommodation of the burgeoning amounts of data from high-throughput genomics pipelines. As the volume of curated data in Animal QTLdb (https://www.animalgenome.org/QTLdb) increases exponentiall...
Knowledge Bases Quantitative Trait Loci Poultry Livestock Genome-Wide Association Study Databases, Genetic Animals
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Computational aspects underlying genome to phenome analysis in plants.

The Plant journal : for cell and molecular biology Jan 1, 2019
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promisi...
Phenomics Quantitative Trait Loci Genomics Machine Learning Plants Genetic Association Studies Phenotype Genome, Plant
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Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

GigaScience Oct 1, 2017
Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a...
Quantitative Trait Loci Plant Roots Triticum Image Processing, Computer-Assisted Machine Learning Seedlings Algorithms
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Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

GigaScience Oct 1, 2017
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation...
Phenotype Plant Roots Triticum Plants Machine Learning Plant Shoots Quantitative Trait Loci
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Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

Bioinformatics (Oxford, England) Jun 15, 2016
UNLABELLED: Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simu...
Linear Models Quantitative Trait Loci Phenotype Models, Genetic Plants Animals Polymorphism, Single Nucleotide Humans Machine Learning Genotype Algorithms
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