Nanopore sequencing with unique molecular identifiers enables accurate mutation analysis and haplotyping in the complex lipoprotein(a) KIV-2 VNTR

Abstract Background Repetitive genome regions, such as variable number of tandem repeats (VNTR) or short tandem repeats (STR), are major constituents of the uncharted dark genome and evade conventional sequencing approaches. The protein-coding LPA kringle IV type-2 (KIV-2) VNTR (5.6 kb per unit, 1–40 units per allele) is a medically highly relevant example with a particularly intricate structure, multiple haplotypes, intragenic homologies, and an intra-VNTR STR. It is the primary regulator of plasma lipoprotein(a) [Lp(a)] concentrations, an important cardiovascular risk factor. Lp(a) concentrations vary widely between individuals and ancestries. Multiple variants and functional haplotypes in the LPA gene and especially in the KIV-2 VNTR strongly contribute to this variance. Methods We evaluated the performance of amplicon-based nanopore sequencing with unique molecular identifiers (UMI-ONT-Seq) for SNP detection, haplotype mapping, VNTR unit consensus sequence generation, and copy number estimation via coverage-corrected haplotypes quantification in the KIV-2 VNTR. We used 15 human samples and low-level mixtures (0.5 to 5%) of KIV-2 plasmids as a validation set. We then applied UMI-ONT-Seq to extract KIV-2 VNTR haplotypes in 48 multi-ancestry 1000 Genome samples and analyzed at scale a poorly characterized STR within the KIV-2 VNTR. Results UMI-ONT-Seq detected KIV-2 SNPs down to 1% variant level with high sensitivity, specificity, and precision (0.977 ± 0.018; 1.000 ± 0.0005; 0.993 ± 0.02) and accurately retrieved the full-length haplotype of each VNTR unit. Human variant levels were highly correlated with next-generation sequencing (R 2 = 0.983) without bias across the whole variant level range. Six reads per UMI produced sequences of each KIV-2 unit with Q40 quality. The KIV-2 repeat number determined by coverage-corrected unique haplotype counting was in close agreement with droplet digital PCR (ddPCR), with 70% of the samples falling even within the narrow confidence interval of ddPCR. We then analyzed 62,679 intra-KIV-2 STR sequences and explored KIV-2 SNP haplotype patterns across five ancestries. Conclusions UMI-ONT-Seq accurately retrieves the SNP haplotype and precisely quantifies the VNTR copy number of each repeat unit of the complex KIV-2 VNTR region across multiple ancestries. This study utilizes the KIV-2 VNTR, presenting a novel and potent tool for comprehensive characterization of medically relevant complex genome regions at scale.