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◆ Genome biology2025-10-15· Biology

Reference genome bias in light of species-specific chromosomal reorganization and translocations

Marius F. Maurstad, Siv Nam Khang Hoff, José Cerca, Mark Ravinet, Ian Bradbury, Kjetill S. Jakobsen, Kim Præbel, Sissel Jentoft

原始摘要(英文原文)· Original abstract
Abstract Background Whole-genome sequencing efforts, have during the past decade, unveiled the central role of genomic rearrangements—such as chromosomal inversions—in evolutionary processes, including local adaptation in a wide range of taxa. However, employment of reference genomes from distantly or even closely related species for mapping and the subsequent variant calling can lead to errors and/or biases in the datasets generated for downstream analyses. Results Here, we capitalize on the recently generated chromosome-anchored genome assemblies for Arctic cod ( Arctogadus glacialis ), polar cod ( Boreogadus saida ), and Atlantic cod ( Gadus morhua ) to evaluate the extent and consequences of reference bias on population sequencing datasets (approx. 15–20 × coverage) for both Arctic cod and polar cod. Our findings demonstrate that the choice of reference genome impacts the mapping statistics, including mapping depth and mapping quality, as well as core population genetic estimates, such as heterozygosity levels, nucleotide diversity (π), and cross-species genetic divergence (D XY ). Furthermore, using a more distantly related reference genome can lead to inaccurate detection and characterization of chromosomal inversions, i.e., in terms of size (length) and location (position), due to inter-chromosomal reorganizations between species. Additionally, we observe that some of the verified species-specific inversions are split across multiple genomic regions when mapped against a heterospecific reference. Conclusions Inaccurate identification of chromosomal rearrangements as well as biased population genetic measures could potentially lead to erroneous interpretation of species-specific genomic diversity, impede the resolution of local adaptation, and thus, impact predictions of their genomic potential to respond to climatic and other environmental perturbations.
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