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

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MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes.

Yun Li¹, Cristen J Willer, Jun Ding

¹Department of Genetics, Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.

Genetic Epidemiology

|November 9, 2010

Summary

Genotype imputation refines genome-wide association studies (GWAS) by accurately estimating unobserved genetic variants. This method enhances the power of genetic association studies and facilitates cross-study comparisons for complex disease research.

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Area of Science:

Genetics and Genomics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) identify common alleles linked to complex disease susceptibility.
Evaluating the effects of most common single nucleotide polymorphisms (SNPs) requires indirect methods using proxy markers or haplotypes.
The MaCH software package offers a Markov Chain framework for genotype imputation and haplotyping.

Purpose of the Study:

To evaluate the accuracy and utility of the MaCH software's genotype imputation and haplotyping framework.
To assess the impact of different genotyping panels, reference panel configurations, and shotgun sequencing designs.
To demonstrate how genotype imputation improves genetic association studies and facilitates cross-study analyses.

Main Methods:

Utilized simulations and experimental genotypes to assess the accuracy of the genotype imputation approach.
Evaluated various genotyping panels, reference panel configurations, and shotgun sequencing strategies.
Employed a Markov Chain framework to estimate unobserved genotypes and haplotypes from available data.

Main Results:

Genotype imputation using HapMap haplotypes as a reference is highly accurate for common variants with genome-wide SNP data or fine-mapping data.
The imputation approach is applicable across diverse populations.
Genotype imputation not only aids cross-study analyses but also significantly increases the statistical power of genetic association studies.

Conclusions:

Genotype imputation is a powerful tool for enhancing genetic association studies and meta-analyses of GWAS.
Advances in reference panels (e.g., larger HapMap panels) and whole genome shotgun sequencing will further improve association analyses of unobserved variants.
The MaCH framework provides a computationally efficient and accurate method for genotype imputation and haplotyping.