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Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants

Author

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  • Melissa G Naylor
  • Xihong Lin
  • Scott T Weiss
  • Benjamin A Raby
  • Christoph Lange

Abstract

Background: Discovering genetic associations between genetic markers and gene expression levels can provide insight into gene regulation and, potentially, mechanisms of disease. Such analyses typically involve a linkage or association analysis in which expression data are used as phenotypes. This approach leads to a large number of multiple comparisons and may therefore lack power. We assess the potential of applying canonical correlation analysis to partitioned genomewide data as a method for discovering regulatory variants. Methodology/Principal Findings: Simulations suggest that canonical correlation analysis has higher power than standard pairwise univariate regression to detect single nucleotide polymorphisms when the expression trait has low heritability. The increase in power is even greater under the recessive model. We demonstrate this approach using the Childhood Asthma Management Program data. Conclusions/Significance: Our approach reduces multiple comparisons and may provide insight into the complex relationships between genotype and gene expression.

Suggested Citation

  • Melissa G Naylor & Xihong Lin & Scott T Weiss & Benjamin A Raby & Christoph Lange, 2010. "Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants," PLOS ONE, Public Library of Science, vol. 5(5), pages 1-6, May.
    • Handle: RePEc:plo:pone00:0010395
    DOI: 10.1371/journal.pone.0010395
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    References listed on IDEAS

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    1. Tao Lu & Ying Pan & Shyan-Yuan Kao & Cheng Li & Isaac Kohane & Jennifer Chan & Bruce A. Yankner, 2004. "Gene regulation and DNA damage in the ageing human brain," Nature, Nature, vol. 429(6994), pages 883-891, June.
    2. Waaijenborg Sandra & Verselewel de Witt Hamer Philip C. & Zwinderman Aeilko H, 2008. "Quantifying the Association between Gene Expressions and DNA-Markers by Penalized Canonical Correlation Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-29, January.
    3. Parkhomenko Elena & Tritchler David & Beyene Joseph, 2009. "Sparse Canonical Correlation Analysis with Application to Genomic Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-36, January.
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    Cited by:

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    2. Mariusz Malinowski, 2022. "Financial Situation of Local Government Units as a Determinant of the Standards of Living for the Polish Population," Energies, MDPI, vol. 15(15), pages 1-24, July.
    3. Joanna Smoluk-Sikorska & Mariusz Malinowski & Władysława Łuczka, 2020. "Identification of the Conditions for Organic Agriculture Development in Polish Districts—An Implementation of Canonical Analysis," Agriculture, MDPI, vol. 10(11), pages 1-31, October.
    4. Joanna Smoluk-Sikorska & Mariusz Malinowski, 2021. "An Attempt to Apply Canonical Analysis to Investigate the Dependencies between the Level of Organic Farming Development in Poland and the Chosen Environmental Determinants," Energies, MDPI, vol. 14(24), pages 1-26, December.
    5. Mariusz Malinowski, 2021. "“Green Energy” and the Standard of Living of the EU Residents," Energies, MDPI, vol. 14(8), pages 1-35, April.
    6. Malinowski, Mariusz, 2023. "Multidimensional relationships between the financial condition of rural communes and the standard of living of their inhabitants," International Journal of Agricultural Sciences and Technology (IJAGST), SvedbergOpen, vol. 199(2), August.

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