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Incomplete bioinformatic filtering and inadequate age and growth analysis lead to an incorrect inference of harvested-induced changes.

ABSTRACT: Understanding the evolutionary impacts of harvest on fish populations is important for informing fisheries management and conservation and has become a growing research topic over the last decade. However, the dynamics of fish populations are highly complex, and phenotypes can be influenced by many biotic and abiotic factors. Therefore, it is vital to collect robust data and explore multiple alternative hypotheses before concluding that fish populations are influenced by harvest. In their recently published manuscript, Bowles et al, Evolutionary Applications, 13(6):1128 conducted age/growth and genomic analysis of walleye (Sander vitreus) populations sampled 13-15 years (1-2.5 generations) apart and hypothesized that observed phenotypic and genomic changes in this time period were likely due to harvest. Specifically, Bowles et al. (2020) documented differential declines in size-at-age in three exploited walleye populations compared to a separate, but presumably less-exploited, reference population. Additionally, they documented population genetic differentiation in one population pair, homogenization in another, and outlier loci putatively under selection across time points. Based on their phenotypic and genetic results, they hypothesized that selective harvest had led to fisheries-induced evolution (referred to as nascent changes) in the exploited populations in as little as 1-2.5 generations. We re-analyzed their data and found that (a) sizes declined across both exploited and reference populations during the time period studied and (b) observed genomic differentiation in their study was the result of inadequate data filtering, including retaining individuals with high amounts of missing data and retaining potentially undersplit and oversplit loci that created false signals of differentiation between time points. This re-analysis did not provide evidence for phenotypic or genetic changes attributable to harvest in any of the study populations, contrasting the hypotheses presented by Bowles et al. (2020). Our comment highlights the potential pitfalls associated with conducting age/growth analyses with low sample sizes and inadequately filtering genomic datasets.

SUBMITTER: Larson WA

PROVIDER: S-EPMC7896720 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Incomplete bioinformatic filtering and inadequate age and growth analysis lead to an incorrect inference of harvested-induced changes.

Larson Wesley A WA Isermann Daniel A DA Feiner Zachary S ZS

Evolutionary applications 20200912 2

Understanding the evolutionary impacts of harvest on fish populations is important for informing fisheries management and conservation and has become a growing research topic over the last decade. However, the dynamics of fish populations are highly complex, and phenotypes can be influenced by many biotic and abiotic factors. Therefore, it is vital to collect robust data and explore multiple alternative hypotheses before concluding that fish populations are influenced by harvest. In their recent ...[more]

PMID: 33664775

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Project description:Here we describe a method for whole genome bisulfite sequencing in very small cell populations (µWGBS) and in single cells (scWGBS). Our method is optimized for profiling DNA methylation in many samples at low coverage, and we developed a bioinformatic method that uses collections of single-cell methylomes to infer cell state dynamics. Using these technological advances, we studied epigenetic cell state dynamics in three in vitro models of cellular differentiation and pluripotency and identified patterns of epigenetic remodeling and cell-to-cell heterogeneity. 1-cell or 4-cell samples were collected from a variety of human and mouse cell lines (KBM7, CCE, K562, 32D, HL60). We also included negative controls (no cells), and cross controls (cells of one species aligned to the other). K562 cells were treated with 5-azacytidine in a final concentration of 1µM to induce demethylation. HL60 cells were treated with 1α,25-dihydroxyvitamin D3 in a final concentration of 10nM to induce differentiation. Naïve pluripotency of CCE ES cells was induced by culturing the cells in ESGRO-2i medium with the addition of 1% FCS. Differentiation of CCE ES cells was induced by treating the cells with all-trans retinoic acid (ATRA) in a final concentration of 0,3µM every third day along with complete LIF-free medium exchange. Embryoid body formation from CCE ES cells was induced by hanging-drop culture for 5 days, followed by 9 days on gelatinized dishes (see treatment annotation). Each experiment, with treated and untreated cells, was performed in multiple biological replicates. We then measured DNA methylation in the individual 1-cell or 4-cell experiments with bisulfite treatment and high-throughput sequencing. For sequencing, most experiments pooled a 4-cell experiment from one species with a 1-cell experiment from the other, but some replicates were sequenced as a single cell (indicated with SC in the replicate annotation). Two samples (marked as “deep”) were sequenced much deeper than the others. See Farlik et al. (2015) Cell Reports for complete study details.

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Incomplete bioinformatic filtering and inadequate age and growth analysis lead to an incorrect inference of harvested-induced changes.

Publications

Incomplete bioinformatic filtering and inadequate age and growth analysis lead to an incorrect inference of harvested-induced changes.

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