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Concordance of next generation sequence-based and sequence specific oligonucleotide probe-based HLA-DRB1 genotyping
Julie A Lane1, Jameel R Johnson1, Janelle A Noble1
1Children's Hospital Oakland Research Institute, Oakland, CA, United States.
Human Immunology
|August 7, 2015
Summary
Next generation sequencing (NGS) offers superior accuracy for human leukocyte antigen (HLA) genotyping compared to older methods. Optimizing DNA quality and using high-resolution technology significantly improves data reliability.
Area of Science:
- Immunogenetics
- Genomics
- Molecular Biology
Background:
- Human Leukocyte Antigen (HLA) genotyping is crucial for transplantation and disease association studies.
- Previous HLA genotyping often relied on moderate-resolution methods like sequence-specific oligonucleotide probe (SSOP) technology.
- Next-generation sequencing (NGS) offers higher resolution but requires validation against established methods.
Purpose of the Study:
- To compare the accuracy and concordance of HLA genotyping between NGS and SSOP methods.
- To assess the impact of NGS's higher resolution on existing genotype data.
- To identify sources of error in HLA genotyping and propose improvements.
Main Methods:
- Genotyping of HLA-DRB1, DRB3, DRB4, and DRB5 loci (exon 2) using NGS (Roche 454) on 993 African American newborn samples.
- Comparison of NGS genotype calls with previously generated SSOP data for the same samples.
- Analysis of discordant genotypes to determine the cause of discrepancies.
Main Results:
- A concordance rate of 92.3% was observed between NGS and SSOP genotyping.
- Only 3 out of 76 discordant genotypes were due to the higher resolution of NGS.
- The majority of discordances (73/76) resulted from preventable errors in sample handling, data interpretation, or entry.
- NGS-based methodology yielded data of superior quality and accuracy.
Conclusions:
- NGS provides more accurate HLA genotyping data compared to SSOP.
- Errors in sample handling and data management significantly impact genotyping accuracy.
- Improving genomic DNA quality, automating processes, and utilizing high-resolution technology are key to enhancing HLA data quality.
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