Using the R Package crlmm for Genotyping and Copy Number Estimation

Area of Science:

• Genomics
• Bioinformatics
• Statistical Genetics

Background:

• Genotyping platforms assess genotype-phenotype and copy number-phenotype associations.
• Copy number analysis tools show variability and discordance, potentially due to batch effects from different processing times or labs.
• Analysis algorithms lacking batch effect adjustment yield spurious association measures.

Purpose of the Study:

• To present a workflow for estimating allele-specific copy number using the R package crlmm.
• To integrate marker-level copy number estimates with Bioconductor software for inferring copy number variations.
• To address discordance in copy number change assessments by adjusting for batch effects.

Main Methods:

• Utilized the R package crlmm, which implements a multilevel model to adjust for batch effects.
• Estimated allele-specific copy number at millions of markers.
• Integrated marker-level estimates with Bioconductor software for copy number gain/loss region inference.
• Performed all analyses within the R statistical environment.

Main Results:

• The crlmm package provides allele-specific copy number estimates.
• The proposed workflow facilitates the inference of copy number variation regions.
• Adjusting for batch effects mitigates spurious associations in copy number analysis.

Conclusions:

• The crlmm R package and associated workflow offer a robust method for allele-specific copy number estimation.
• This approach improves the accuracy of copy number variation analysis, particularly in the presence of batch effects.
• The integration with Bioconductor software enhances the ability to identify copy number alterations crucial for genotype-phenotype studies.