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Statistical Methods for Disease Risk Prediction with Genotype Data

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Statistical Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2629))

Abstract

Single-nucleotide polymorphism (SNP) is the basic unit to understand the heritability of complex traits. One attractive application of the susceptible SNPs is to construct prediction models for assessing disease risk. Here, we introduce prediction methods for human traits using SNPs data, including the polygenic risk score (PRS), linear mixed models (LMMs), penalized regressions, and methods for controlling population stratification.

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Authors and Affiliations

  1. JC School of Public Health and Primary Care, the Chinese University of Hong Kong (CUHK), Shatin, Hong Kong

    Xiaoxuan Xia & Maggie Haitian Wang

  2. Department of Statistics, the Chinese University of Hong Kong (CUHK), Shatin, Hong Kong

    Xiaoxuan Xia & Yingying Wei

  3. CUHK Shenzhen Institute, Shenzhen, China

    Yexian Zhang & Maggie Haitian Wang

Authors
  1. Xiaoxuan Xia
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  2. Yexian Zhang
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  3. Yingying Wei
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  4. Maggie Haitian Wang
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Corresponding author

Correspondence to Maggie Haitian Wang .

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Editors and Affiliations

  1. Moffitt Cancer Center, Tampa, FL, USA

    Brooke Fridley

  2. Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

    Xuefeng Wang

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Xia, X., Zhang, Y., Wei, Y., Wang, M.H. (2023). Statistical Methods for Disease Risk Prediction with Genotype Data. In: Fridley, B., Wang, X. (eds) Statistical Genomics. Methods in Molecular Biology, vol 2629. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2986-4_15

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  • DOI: https://doi.org/10.1007/978-1-0716-2986-4_15

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2985-7

  • Online ISBN: 978-1-0716-2986-4

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