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Related Experiment Video

Updated: Jul 18, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Genome-wide tagging SNPs with entropy-based Monte Carlo method.

Zhenqiu Liu¹, Shili Lin, Ming Tan

¹Division of Biostatistics, University of Maryland Greenebaum Cancer Center, Baltimore, Maryland, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 7, 2006

Summary

This study introduces a novel iterative Cross Entropy Monte Carlo (CEMC) algorithm for selecting single nucleotide polymorphisms (SNPs). The CEMC algorithm efficiently identifies informative SNPs across the entire genome without needing predefined haplotype blocks.

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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

Area of Science:

Genomics and Bioinformatics
Statistical Genetics
Computational Biology

Background:

Millions of common single nucleotide polymorphisms (SNPs) exist in the human genome, crucial for understanding complex diseases and drug responses.
High-throughput SNP data generation presents significant challenges for genome-wide association studies.
Existing SNP selection algorithms often rely on haplotype block definitions, limiting their applicability to whole-genome analyses.

Purpose of the Study:

To present an adaptation of the cross entropy (CE) method for efficient SNP selection.
To propose an iterative CE Monte Carlo (CEMC) algorithm for tagging SNP selection.
To develop a block-free algorithm applicable to whole-genome SNP selection.

Main Methods:

Developed an iterative Cross Entropy Monte Carlo (CEMC) algorithm.
The CEMC algorithm is independent of haplotype block structures.
Applied the block-free algorithm to large-scale datasets (thousands of SNPs, including simulated and real data).

Main Results:

Demonstrated the computational feasibility of CEMC for whole-genome SNP selection.
CEMC significantly outperformed random SNP selection.
CEMC showed superior performance compared to another block-free selection algorithm on the considered dataset.

Conclusions:

The proposed CEMC algorithm is a computationally feasible and effective method for whole-genome SNP selection.
The block-free nature of CEMC enhances its applicability across diverse genomic regions.
CEMC represents a significant advancement over existing SNP selection strategies.