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Updated: Jul 1, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set

Huixiao Hong1, Zhenqiang Su, Weigong Ge

  • 1Division of Systems Toxicology, National Center for Toxicological Research, US Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA. Huixiao.Hong@fda.hhs.gov

BMC Bioinformatics
|September 20, 2008
PubMed
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Batch size and composition significantly impact genotype calling in genome-wide association studies (GWAS). Uniform, large batches and homogenous sample groupings improve genotype call consistency and downstream SNP association accuracy.

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWAS) identify genetic variants linked to traits.
  • Accurate genotype calling is crucial to avoid false genetic associations.
  • Large GWAS datasets are often processed in batches, but batch effects are not well understood.

Purpose of the Study:

  • To investigate the impact of batch size and composition on genotype calling accuracy in GWAS.
  • To assess how these batching factors affect downstream identification of associated SNPs.

Main Methods:

  • Utilized raw genotype data from 270 HapMap samples on the Affymetrix Human Mapping 500 K array.
  • Applied the BRLMM genotype calling algorithm.
  • Analyzed effects of three different batch sizes and three different batch compositions.

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Published on: April 13, 2012

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Main Results:

  • Both batch size and composition significantly affected genotype calling results and associated SNPs.
  • Effects were more pronounced for samples and SNPs with lower call rates and for heterozygous calls.
  • Inconsistencies in genotype calls propagated to the final list of significant SNPs.

Conclusions:

  • Batch size and composition are critical factors influencing BRLMM genotype calling in GWAS.
  • Larger variations in batch size and less homogenous sample batches lead to more severe effects.
  • Recommendations include using uniform, large batch sizes and grouping homogenous samples together for consistent genotype calls.