ABO allele-level frequency estimation based on population-scale genotyping by next generation sequencing View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12

AUTHORS

Kathrin Lang, Ines Wagner, Bianca Schöne, Gerhard Schöfl, Kerstin Birkner, Jan A. Hofmann, Jürgen Sauter, Julia Pingel, Irina Böhme, Alexander H. Schmidt, Vinzenz Lange

ABSTRACT

BACKGROUND: The characterization of the ABO blood group status is vital for blood transfusion and solid organ transplantation. Several methods for the molecular characterization of the ABO gene, which encodes the alleles that give rise to the different ABO blood groups, have been described. However, the application of those methods has so far been restricted to selected samples and not been applied to population-scale analysis. RESULTS: We describe a cost-effective method for high-throughput genotyping of the ABO system by next generation sequencing. Sample specific barcodes and sequencing adaptors are introduced during PCR, rendering the products suitable for direct sequencing on Illumina MiSeq or HiSeq instruments. Complete sequence coverage of exons 6 and 7 enables molecular discrimination of the ABO subgroups and many alleles. The workflow was applied to ABO genotype more than a million samples. We report the allele group frequencies calculated on a subset of more than 110,000 sampled individuals of German origin. Further we discuss the potential of the workflow for high resolution genotyping taking the observed allele group frequencies into account. Finally, sequence analysis revealed 287 distinct so far not described alleles of which the most abundant one was identified in 174 samples. CONCLUSIONS: The described workflow delivers high resolution ABO genotyping at low cost enabling population-scale molecular ABO characterization. More... »

PAGES

374

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12864-016-2687-1

DOI

http://dx.doi.org/10.1186/s12864-016-2687-1

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https://app.dimensions.ai/details/publication/pub.1052662921

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27207383


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