BLink-seq delivers population-scale haplotypes without long reads: a scalable framework for non-model genomics

Authors

Iqbal, A. R., Dimens, P. V., Rick, J. A., Munn, P. R., McNairn, A. J., Landis, J. B., Schembri, R., Chan, Y. F., Kucka, M., Therkildsen, N. O., Grenier, J. K.

Abstract

Information about segregating haplotypes and structural variation (SV) can be extremely rich for a variety of applications in population genomics but remains largely inaccessible for many non-model species. Of the available methods, linked-read sequencing is especially promising for its low cost and scalability, but its adoption remains limited. One existing linked-read method is Haplotagging, which barcodes sequencing reads to reconstruct long molecules that encode haplotype information, with the potential to generate phased whole-genome data and detect structural variants. In this study, we present BLink-seq, a novel Haplotagging method that is compatible with standard short-read next-generation sequencing platforms, is locally reproducible with low-cost reagents, and is scalable for high-throughput sample processing. We optimized library preparation parameters, explored their relationship to linked-read library metrics, and validated phasing performance and structural variant detection in two evolutionary extremes: an experimental Drosophila melanogaster cross of inbred lines carrying known inversions, and four Atlantic silverside (Menidia menidia) parent-offspring trios sourced from highly outbred, wild-caught populations. We then applied our protocol to a cohort of 376 silversides to demonstrate its scalability and potential for SV detection and genotype imputation. Using BLink-seq, we generated chromosome-scale phased blocks and identified known inversions in both validation datasets. We discovered previously uncharacterized structural complexity within a known adaptive inversion on silverside chromosome 11, demonstrating that linked-read data can refine our understanding of SV architecture beyond what short reads alone can resolve. Finally, we provide a user guide for researchers interested in using BLink-seq.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 08 Aug 2026.

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