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Regularized principal components of heritability

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  • Yixin Fang
  • Yang Feng
  • Ming Yuan

Abstract

In family studies with multiple continuous phenotypes, heritability can be conveniently evaluated through the so-called principal-component of heredity (PCH, for short; Ott and Rabinowitz in Hum Hered 49:106–111, 1999 ). Estimation of the PCH, however, is notoriously difficult when entertaining a large collection of phenotypes which naturally arises in dealing with modern genomic data such as those from expression QTL studies. In this paper, we propose a regularized PCH method to specifically address such challenges. We show through both theoretical studies and data examples that the proposed method can accurately assess the heritability of a large collection of phenotypes. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Yixin Fang & Yang Feng & Ming Yuan, 2014. "Regularized principal components of heritability," Computational Statistics, Springer, vol. 29(3), pages 455-465, June.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:3:p:455-465
    DOI: 10.1007/s00180-013-0444-3
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    References listed on IDEAS

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    1. Vivian G. Cheung & Richard S. Spielman & Kathryn G. Ewens & Teresa M. Weber & Michael Morley & Joshua T. Burdick, 2005. "Mapping determinants of human gene expression by regional and genome-wide association," Nature, Nature, vol. 437(7063), pages 1365-1369, October.
    2. Jianqing Fan & Yang Feng & Xin Tong, 2012. "A road to classification in high dimensional space: the regularized optimal affine discriminant," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 745-771, September.
    3. Man Jin & Yixin Fang, 2011. "Variable Selection in Canonical Discriminant Analysis for Family Studies," Biometrics, The International Biometric Society, vol. 67(1), pages 124-132, March.
    4. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    5. Michael Morley & Cliona M. Molony & Teresa M. Weber & James L. Devlin & Kathryn G. Ewens & Richard S. Spielman & Vivian G. Cheung, 2004. "Genetic analysis of genome-wide variation in human gene expression," Nature, Nature, vol. 430(7001), pages 743-747, August.
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    Cited by:

    1. Nickolay Trendafilov & Martin Kleinsteuber & Hui Zou, 2014. "Sparse matrices in data analysis," Computational Statistics, Springer, vol. 29(3), pages 403-405, June.

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