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

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A new genotype calling method for affymetrix SNP arrays.

Bilin Fu¹, Jin Xu

¹Department of Statistics and Actuarial Science, East China Normal University, 500 Dongchuan Road, Shanghai 200241, PR China. fubilin@gmail.com

Journal of Bioinformatics and Computational Biology

|November 16, 2011

Summary

A new, fast genotype calling method for Affymetrix SNP chips offers comparable accuracy to existing methods. This computationally simple algorithm performs well, especially in small sample cases, and provides confidence scores for each genotype call.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Current genotype calling methods like RLMM and CRLMM are accurate but computationally intensive.
These methods face challenges with accuracy in small sample sizes.
Existing methods require complex preprocessing steps, increasing computational cost.

Purpose of the Study:

To develop a fast and accurate genotype calling method for Affymetrix 100 k and 500 k SNP chips.
To address the computational expense and accuracy limitations of current methods, particularly in small sample scenarios.
To provide a standalone algorithm that can utilize self-training without external data.

Main Methods:

A two-stage classification scheme combining unsupervised and supervised learning.
Stage 1: Unsupervised classification for rapid, high-accuracy discrimination of most SNPs.
Stage 2: Supervised classification incorporating allele frequency information (HapMap or self-training).

Main Results:

The new method demonstrates performance comparable to CRLMM on HapMap data.
The algorithm shows superior accuracy in small sample cases.
The method is computationally efficient and provides confidence scores for genotype calls.

Conclusions:

The developed two-stage genotype calling algorithm is a computationally efficient alternative for Affymetrix SNP chips.
It offers robust performance across different sample sizes, excelling in small sample scenarios.
The algorithm's standalone capability via self-training enhances its utility and accessibility.