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Related Concept Videos

Pedigree Analysis01:35

Pedigree Analysis

Overview
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.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

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Related Experiment Video

Updated: Jun 7, 2026

A Computational Pipeline for Intergenic/Intragenic Enhancer RNA Quantification in Mouse Embryonic Stem Cells
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Published on: October 28, 2025

A machine learning pipeline for quantitative phenotype prediction from genotype data.

Giorgio Guzzetta1, Giuseppe Jurman, Cesare Furlanello

  • 1Fondazione Bruno Kessler, Trento, Italy.

BMC Bioinformatics
|November 2, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning pipeline for quantitative phenotype prediction using genetic data. The L1L2 method effectively selects genetic markers and improves prediction accuracy in complex traits.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Systems biology and biomedicine
  • Genetics and genomics
  • Computational biology

Background:

  • Quantitative phenotypes are crucial in systems biology and biomedicine, especially for complex diseases with high individual variability.
  • Machine learning enhances Genome-Wide Association Studies (GWAS) by focusing on predictive accuracy and feature selection in multivariate genetic data.
  • Reproducible and stringent Data Analysis Protocols (DAP) are essential for controlling variability and ensuring reliable results in genotype-phenotype mapping.

Purpose of the Study:

  • To present a genome-to-phenotype machine learning pipeline for quantitative trait prediction.
  • To apply the pipeline for fitting complex phenotypic traits in heterogeneous stock mice using single nucleotide polymorphisms (SNPs).
  • To evaluate the pipeline's effectiveness in marker selection and prediction accuracy compared to existing methods.

Main Methods:

  • A machine learning pipeline centered on the L1L2 regularization method (naïve elastic net) for regression and dimensionality reduction.
  • SNP marker selection using a DAP developed in the MAQC-II initiative, adapted for microarray data and applied to SNP data.
  • Comparison of the L1L2 approach with Support Vector Regression (SVR) and Monte Carlo Markov Chain (MCMC), employing algebraic indicators for model selection and a 'saturation' procedure for marker panel refinement.

Main Results:

  • The L1L2 pipeline achieved prediction accuracies comparable to MCMC and SVR methods.
  • Selected SNPs by the L1L2 algorithm showed good agreement with candidate loci identified through standard GWAS.
  • The combined L1L2 feature selection and saturation procedure effectively addressed the issue of neglecting highly correlated features.

Conclusions:

  • The L1L2 pipeline demonstrates efficacy in both genetic marker selection and prediction accuracy for quantitative phenotypes.
  • Machine learning techniques, when supported by adequate Data Analysis Protocols (DAP), can significantly aid quantitative phenotype prediction.
  • This approach is valuable for functional studies utilizing whole-genome information and for understanding complex genetic traits.