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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Phenotypically Enriched Genotypic Imputation in Genetic Association Tests.

Wei Vivian Zhuang¹, Joanne M Murabito, Kathryn L Lunetta

¹Department of Biostatistics, Boston University School of Public Health, Boston, Mass., USA.

|September 1, 2016

Summary

This summary is machine-generated.

Phenotypically Enriched Genotypic Imputation (PEGI) improves genetic association study power by incorporating phenotype data. This method enhances genotype imputation, outperforming traditional approaches when genetic data is incomplete.

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Area of Science:

Epidemiology
Genetic Epidemiology
Statistical Genetics

Background:

Longitudinal studies often have individuals with rich phenotype data but missing genotypes due to death or loss to follow-up.
Traditional methods like complete-case analysis (CC) exclude ungenotyped individuals, while genotype imputation (GI) uses relatives' genotypes but ignores phenotype data.
Existing strategies do not leverage phenotypic information to address missing genotype data.

Purpose of the Study:

To propose a novel method, phenotypically enriched genotypic imputation (PEGI), to incorporate observed phenotypes into genotype imputation.
To evaluate the performance of PEGI compared to existing methods (CC and GI) in genetic association studies with missing genotype data.

Main Methods:

Developed the PEGI method using an expectation-maximization (EM)-based maximum likelihood approach.
Incorporated observed phenotypic data directly into the genotype imputation process.
Simulated data with genotypes missing completely at random and analyzed the Framingham Heart Study dataset.

Main Results:

Simulations demonstrated that PEGI improves statistical power for detecting associations with single-nucleotide polymorphisms (SNPs) that have a moderate to strong effect on a phenotype, without increasing type I errors.
Comparison using the Framingham Heart Study data showed PEGI's ability to detect associations between SNPs and age at natural menopause.
PEGI outperformed both GI and CC methods in detecting these genetic associations.

Conclusions:

The PEGI method offers improved power for detecting genetic associations compared to both complete-case analysis and standard genotype imputation.
PEGI effectively utilizes phenotypic information to enhance genotype imputation, particularly beneficial in longitudinal epidemiological studies with missing genetic data.
This approach holds promise for increasing the efficiency and power of genetic association studies when dealing with incomplete genotype data.