Assessment and development of computational strategies for assembling the human FCGR2/3 locus using Nanopore long-read sequencing data 2309911

Abstract

Abstract Introduction The human FCGR locus encodes Fc gamma receptors (FcγRs) that mediate antibody effector functions and shape responses to infection, autoimmunity, and therapeutic antibodies. Genotyping this region is difficult due to copy number variations (CNVs), small nucleotide variants (SNVs), and highly homologous paralogs in strong linkage disequilibrium. We assessed and developed computational strategies to haplotype the FCGR2/3 locus using Nanopore long-read sequencing. Methods We analyzed 25 individuals from five superpopulations from the 1000 Genome Project (1kGP) Nanopore whole-genome sequencing dataset and assembled FCGR2/3 haplotypes. Performance was evaluated through comparisons to known haplotypes, depth-based copy-number estimates, published assemblies, trio-based validation when available, and independently assembled PacBio HiFi haplotypes from the same individuals. Nucleotide-level assessment was also performed by mapping FcγR genes annotated on human reference genome (T2T-CHM13) to each haplotype assembly to confirm complete coding sequences (CDS). Results We generated contiguous, structurally validated FCGR2/3 haplotypes for 24 individuals. High-copy number FCGR2/3 haplotypes were present in all individuals, with 62% carrying two copies of haplotype 1 (H1) and 20.8% carrying one H1 and one haplotype 5 (H5). Low-copy number haplotypes were rare, observed in 12.5% of individuals carrying one H1 and haplotype 2 (H2). For a subset of individuals, we validated complete, full-length CDSs and generated variant sets relative to T2T-CHM13. Conclusion This work provides a validated computational framework for assembling FCGR2/3 haplotypes, and in some cases improved, germline references for 1kGP individuals. Future studies will focus on applying this framework to efficiently genotype the FCGR2/3 locus in individuals enrolled in human studies. Funding Source NIH/NIAID, R01AI187318 Topic Categories Technological Innovations in Immunology (TECH)