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Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Updated: Jun 15, 2026

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons
09:21

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons

Published on: July 7, 2023

New kernel methods for phenotype prediction from genotype data.

Ritsuko Onuki1, Tetsuo Shibuya, Minoru Kanehisa

  • 1Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokosko, Uji, Kyoto 611-0011, Japan. onuki@hgc.jp

Genome Informatics. International Conference on Genome Informatics
|March 19, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Support Vector Machine (SVM) method for phenotype prediction using genotype data. The approach enhances accuracy, particularly in regions with strong linkage disequilibrium (LD), by incorporating haplotype predictions.

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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Last Updated: Jun 15, 2026

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons
09:21

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

  • Computational genetics
  • Bioinformatics
  • Statistical genetics

Background:

  • Phenotype prediction from genotype data is a critical challenge in genetics.
  • Existing kernel methods often overlook haplotype information, potentially limiting predictive power.

Purpose of the Study:

  • To develop a novel kernel-based Support Vector Machine (SVM) method for improved phenotype prediction from genotype data.
  • To leverage haplotype information inferred using Hidden Markov Models (HMM) within the SVM framework.

Main Methods:

  • Inferred multiple suboptimal haplotype candidates from genotype data using Hidden Markov Models (HMM).
  • Computed a kernel matrix based on predicted haplotypes and their emission probabilities from HMM.
  • Validated the method on simulated (GeneArtisan) and real-world datasets (NAT2 gene, HapMap project, diseased individuals).

Main Results:

  • The proposed HMM-based haplotype prediction SVM method demonstrated superior performance compared to naive kernel methods.
  • Performance improvements were particularly notable in datasets exhibiting strong linkage disequilibrium (LD).

Conclusions:

  • Integrating haplotype information via HMM-enhanced kernel methods significantly improves phenotype prediction accuracy.
  • This approach offers a more robust tool for genotype-phenotype association studies, especially in complex genetic architectures.