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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Statistical Methods for Disease Risk Prediction with Genotype Data.

Xiaoxuan Xia1,2, Yexian Zhang3, Yingying Wei2

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

Methods in Molecular Biology (Clifton, N.J.)
|March 17, 2023
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Summary
This summary is machine-generated.

Single nucleotide polymorphisms (SNPs) help understand complex traits and disease risk. This study reviews methods like polygenic risk scores and linear mixed models for trait prediction using SNP data.

Keywords:
Complex disease predictionLinear mixed modelPenalized regressionPolygenic risk scorePopulation stratificationSingle-nucleotide polymorphism

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Single nucleotide polymorphisms (SNPs) are fundamental to understanding the genetic basis of complex human traits.
  • Identifying susceptible SNPs is crucial for developing predictive models of disease risk.

Purpose of the Study:

  • To introduce and review various prediction methods for human traits utilizing SNP data.
  • To highlight the application of SNPs in disease risk assessment.

Main Methods:

  • Polygenic Risk Score (PRS) construction.
  • Linear Mixed Models (LMMs) application.
  • Penalized regression techniques.
  • Methods for controlling population stratification.

Main Results:

  • The study outlines established and emerging methodologies for SNP-based trait prediction.
  • It emphasizes the utility of these methods in genetic epidemiology and personalized medicine.

Conclusions:

  • SNP data analysis offers powerful tools for predicting complex human traits and disease susceptibility.
  • Further development and application of these methods can advance precision medicine.