Banking with precision: transfusion medicine as a potential universal application in clinical genomics

Abstract

Purpose of review: To summarize the most recent scientific progress in transfusion medicine genomics and discuss its role within the broad genomic precision medicine model, with a focus on the unique computational and bioinformatic aspects of this emergent field.

Recent findings: Recent publications continue to validate the feasibility of using next-generation sequencing (NGS) for blood group prediction with three distinct approaches: exome sequencing, whole genome sequencing, and PCR-based targeted NGS methods. The reported correlation of NGS with serologic and alternative genotyping methods ranges from 92 to 99%. NGS has demonstrated improved detection of weak antigens, structural changes, copy number variations, novel genomic variants, and microchimerism. Addition of a transfusion medicine interpretation to any clinically sequenced genome is proposed as a strategy to enhance the cost-effectiveness of precision genomic medicine. Interpretation of NGS in the blood group antigen context requires not only advanced immunohematology knowledge, but also specialized software and hardware resources, and a bioinformatics-trained workforce.

Summary: Blood transfusions are a common inpatient procedure, making blood group genomics a promising facet of precision medicine research. Further efforts are needed to embrace transfusion bioinformatic challenges and evaluate its clinical utility.

Figure 1.. Schematic representation of NGS read depth.

Read depth refers to the number of aligned NGS reads that overlap with a given genomic coordinate. (A) Broken lines indicate the read depth at different genomic positions for a simulated low-depth NGS alignment. Low read depths are associated with lower nucleotide call precision and may miss heterozygous polymorphic sites. (B) Simulation of low read depth in an exome sequencing NGS dataset. Boxes represent predicted exons, a low read depth exon is colored in red. Possible interpretations include: genomic deletion, loss of sequences in the initial quality trimming process, or failure of exome capture strategy design. (C) Higher read depth in a select genomic region (marked by the red rectangle). This may represent misalignment of short reads that in reality derive from a homologous gene; in this case a real heterozygous polymorphism may manifest as significantly less than 50% of the nucleotide calls. Other important NGS quality parameters, not shown in the figure, include mapping quality and fraction of alternate allele.

Figure 2.. Simplified, schematic diagram of an NGS bioinformatic pipeline.

Processing steps in bold are discussed in the manuscript text. Pipelines for specific software may vary. Abbreviations: FASTQ = text file that contains nucleotide sequence and quality strings; sam = sequence alignment map; bam = binary, smaller version of a sam file; vcf = variant call file.