Chimerism Analysis Through Shallow Whole-Genome Sequencing and Single-Nucleotide Polymorphism Profiling

Due to limited sensitivity and technical artifacts that confound the interpretation of short tandem repeat (STR)-based chimerism analysis, a more robust and reliable next-generation sequencing (NGS)-based chimerism-analysis method is needed.

We developed a novel NGS protocol based on Genome-wide Low-depth ANalysis for Chimerism Estimation (GLANCE) and evaluated its performance. GLANCE uses single-nucleotide polymorphism (SNP) profiles gathered from whole-genome sequencing data for hematopoietic stem cell transplant (HSCT) recipients and donors. We employed in silico simulations and testing with serially diluted samples to assess performance; optimize parameters; and calculate the read depth, optimal number of SNP markers, linearity, precision, and sensitivity. We compared the chimerism results with those from STR-PCR and an amplicon-based NGS chimerism test utilizing an SNP panel.

An average read depth greater than 0.5× and at least 3,000 unique SNP markers were required for acceptable performance. GLANCE displayed excellent linearity (R²>0.99) over the expected chimerism range of 1-100%, and the overall intra-assay coefficient of variation was 1.7%. The respective detection and quantification limits were 0.3% and 0.9%. GLANCE correlated strongly with STR-PCR (Pearson's r=0.988) and an NGS-based chimerism assay using adjacent SNPs in linkage disequilibrium (Pearson's r=0.981 for HSCT).

GLANCE performed similarly to STR-PCR and amplicon-based chimerism methods, with slightly lower sensitivity and superior precision. GLANCE expands the pool of SNP markers that can be used, delivering secondary data to enhance post-transplant patient monitoring and relapse surveillance.