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A Latent Variable Partial Least Squares Path Modeling Approach to Regional Association and Polygenic Effect with Applications to a Human Obesity Study

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Listed:
  • Fuzhong Xue
  • Shengxu Li
  • Jian'an Luan
  • Zhongshang Yuan
  • Robert N Luben
  • Kay-Tee Khaw
  • Nicholas J Wareham
  • Ruth J F Loos
  • Jing Hua Zhao

Abstract

Genetic association studies are now routinely used to identify single nucleotide polymorphisms (SNPs) linked with human diseases or traits through single SNP-single trait tests. Here we introduced partial least squares path modeling (PLSPM) for association between single or multiple SNPs and a latent trait that can involve single or multiple correlated measurement(s). Furthermore, the framework naturally provides estimators of polygenic effect by appropriately weighting trait-attributing alleles. We conducted computer simulations to assess the performance via multiple SNPs and human obesity-related traits as measured by body mass index (BMI), waist and hip circumferences. Our results showed that the associate statistics had type I error rates close to nominal level and were powerful for a range of effect and sample sizes. When applied to 12 candidate regions in data (N = 2,417) from the European Prospective Investigation of Cancer (EPIC)-Norfolk study, a region in FTO was found to have stronger association (rs7204609∼rs9939881 at the first intron P = 4.29×10−7) than single SNP analysis (all with P>10−4) and a latent quantitative phenotype was obtained using a subset sample of EPIC-Norfolk (N = 12,559). We believe our method is appropriate for assessment of regional association and polygenic effect on a single or multiple traits.

Suggested Citation

  • Fuzhong Xue & Shengxu Li & Jian'an Luan & Zhongshang Yuan & Robert N Luben & Kay-Tee Khaw & Nicholas J Wareham & Ruth J F Loos & Jing Hua Zhao, 2012. "A Latent Variable Partial Least Squares Path Modeling Approach to Regional Association and Polygenic Effect with Applications to a Human Obesity Study," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-10, February.
    • Handle: RePEc:plo:pone00:0031927
    DOI: 10.1371/journal.pone.0031927
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    References listed on IDEAS

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    1. Sun, Yan V. & Jacobsen, Douglas M. & Turner, Stephen T. & Boerwinkle, Eric & Kardia, Sharon L.R., 2009. "Fast implementation of a scan statistic for identifying chromosomal patterns of genome wide association studies," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1794-1801, March.
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    Cited by:

    1. Xiaoshuai Zhang & Xiaowei Yang & Zhongshang Yuan & Yanxun Liu & Fangyu Li & Bin Peng & Dianwen Zhu & Jinghua Zhao & Fuzhong Xue, 2013. "A PLSPM-Based Test Statistic for Detecting Gene-Gene Co-Association in Genome-Wide Association Study with Case-Control Design," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-8, April.

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