Thorburn, Doko‐Miles J., Sagonas, Kostas, Binzer‐Panchal, Mahesh, Chain, Frederic J. J., Feulner, Philine G. D., Bornberg‐Bauer, Erich, Reusch, Thorsten B. H. , Samonte‐Padilla, Irene E., Milinski, Manfred, Lenz, Tobias L. and Eizaguirre, Christophe (2023) Origin matters: Using a local reference genome improves measures in population genomics. Molecular Ecology Resources, 23 . pp. 1706-1723. DOI 10.1111/1755-0998.13838.

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Abstract

Genome sequencing enables answering fundamental questions about the genetic basis of adaptation, population structure and epigenetic mechanisms. Yet, we usually need a suitable reference genome for mapping population-level resequencing data. In some model systems, multiple reference genomes are available, giving the challenging task of determining which reference genome best suits the data. Here, we compared the use of two different reference genomes for the three-spined stickleback (Gasterosteus aculeatus), one novel genome derived from a European gynogenetic individual and the published reference genome of a North American individual. Specifically, we investigated the impact of using a local reference versus one generated from a distinct lineage on several common population genomics analyses. Through mapping genome resequencing data of 60 sticklebacks from across Europe and North America, we demonstrate that genetic distance among samples and the reference genomes impacts downstream analyses. Using a local reference genome increased mapping efficiency and genotyping accuracy, effectively retaining more and better data. Despite comparable distributions of the metrics generated across the genome using SNP data (i.e. π, Tajima's D and FST), window-based statistics using different references resulted in different outlier genes and enriched gene functions. A marker-based analysis of DNA methylation distributions had a comparably high overlap in outlier genes and functions, yet with distinct differences depending on the reference genome. Overall, our results highlight how using a local reference genome decreases reference bias to increase confidence in downstream analyses of the data. Such results have significant implications in all reference-genome-based population genomic analyses.

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