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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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Updated: May 19, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Multivariate phenotype association analysis by marker-set kernel machine regression.

Arnab Maity1, Patrick F Sullivan, Jun-Ying Tzeng

  • 1Department of Statistics, North Carolina State University, Raleigh, USA.

Genetic Epidemiology
|August 18, 2012
PubMed
Summary
This summary is machine-generated.

Jointly analyzing multiple phenotypes in genetic studies enhances gene detection power. A new multivariate kernel machine method effectively analyzes correlated traits, outperforming other methods when genetic effects are shared or correlations are strong.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Basics of Multivariate Analysis in Neuroimaging Data
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Genetic studies often collect multiple, correlated phenotypes for complex diseases.
  • Joint analysis of these traits can increase statistical power to detect disease-associated genes.
  • Existing methods may not fully leverage shared genetic mechanisms across multiple phenotypes.

Purpose of the Study:

  • To develop a novel multivariate kernel machine regression method for joint analysis of multiple phenotypes.
  • To evaluate the performance of this new method against existing strategies using simulations.
  • To assess the impact of phenotype correlation and genetic effect patterns on statistical power.

Main Methods:

  • Constructed a multivariate regression model based on kernel machine for joint evaluation of multimarker effects on multiple phenotypes.
  • Derived a multivariate kernel machine test using a score-like statistic.
  • Conducted simulations to compare the proposed method with multiple univariate kernel machine tests (using original phenotypes or principal components).

Main Results:

  • The multivariate kernel machine test demonstrated varying power depending on phenotype correlation and genetic effect patterns.
  • No single method showed uniformly superior performance across all scenarios.
  • The multivariate approach showed strong performance, especially with stronger phenotype correlations or when genes influenced multiple traits.

Conclusions:

  • The multivariate kernel machine method is a robust approach for joint genetic analysis of multiple phenotypes.
  • It offers advantages in statistical power when phenotypes are correlated or share common genetic underpinnings.
  • The method was successfully applied to a real-world dataset, demonstrating its practical utility.