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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

Multiple linear regression for index SNP selection on unphased genotypes.

Jingwu He¹, A Zelikovsky

¹Fac. Comput. Sci., Georgia State Univ., Atlanta, GA 30318, USA. jingwu@cs.gsu.com

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This study introduces a new method for selecting index single nucleotide polymorphisms (SNPs) to efficiently represent large genotype datasets. The approach uses multiple linear regression for accurate data compression, reducing the number of SNPs needed for genetic analysis.

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Infinium Assay for Large-scale SNP Genotyping Applications

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Published on: November 19, 2013

Area of Science:

Genetics and Genomics
Bioinformatics
Computational Biology

Background:

High-throughput genotyping generates vast amounts of single nucleotide polymorphism (SNP) data.
Identifying informative SNPs (index SNPs) is crucial for efficient analysis of complex diseases.
Existing methods for index SNP selection require further optimization for large, unphased genotype datasets.

Purpose of the Study:

To propose a novel method for index SNP selection using unphased genotype data.
To achieve significant data compression while maintaining high prediction accuracy.
To compare the proposed method against state-of-the-art techniques.

Main Methods:

Utilized multiple linear regression (MLR) for SNP prediction on unphased genotypes.
Developed an index SNP selection algorithm based on MLR.
Evaluated algorithm performance by comparing actual SNPs with MLR-predicted SNPs.
Tested the method on ENCODE regions from HapMap data.

Main Results:

Achieved excellent prediction rates and data compression.
Demonstrated high accuracy (e.g., 93.5%) using only 2% of SNPs for a region of 123 SNPs.
Showed that the proposed method uses significantly fewer index SNPs (up to two times less) than existing methods to reach 90% prediction accuracy.

Conclusions:

The proposed MLR-based index SNP selection is effective for unphased genotype data.
This method offers superior data compaction and accuracy compared to current approaches.
Facilitates feasible fine genotype analysis by reducing the dimensionality of large genetic datasets.