Evidence map›Paper›PMID 31978053›Full record

ArticlePloS one2020

How "simple" methodological decisions affect interpretation of population structure based on reduced representation library DNA sequencing: A case study using the lake whitefish.

Carly F Graham, Douglas R Boreham, Richard G Manzon, Wendylee Stott, Joanna Y Wilson, Christopher M Somers

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Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
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9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Carly F GrahamDepartment of Biology, University of Regina, Regina, Saskatchewan, Canada.ORCID 0000-0002-6455-4438
Douglas R BorehamMedical Sciences, Northern Ontario School of Medicine, Greater Sudbury, Ontario, Canada.
Richard G ManzonDepartment of Biology, University of Regina, Regina, Saskatchewan, Canada.
Wendylee StottDepartment of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, USA.
Joanna Y WilsonDepartment of Biology, McMaster University, Hamilton, Ontario, Canada.
Christopher M SomersDepartment of Biology, University of Regina, Regina, Saskatchewan, Canada.ORCID 0000-0003-2232-6856

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reduced representation (RRL) sequencing approaches (e.g., RADSeq, genotyping by sequencing) require decisions about how much to invest in genome coverage and sequencing depth, as well as choices of values for adjustable bioinformatics parameters. To empirically explore the importance of these "simple" methodological decisions, we generated two independent sequencing libraries for the same 142 individual lake whitefish (Coregonus clupeaformis) using a nextRAD RRL approach: (1) a larger number of loci at low sequencing depth based on a 9mer (library A); and (2) fewer loci at higher sequencing depth based on a 10mer (library B). The fish were selected from populations with different levels of expected genetic subdivision. Each library was analyzed using the STACKS pipeline followed by three types of population structure assessment (FST, DAPC and ADMIXTURE) with iterative increases in the stringency of sequencing depth and missing data requirements, as well as more specific a priori population maps. Library B was always able to resolve strong population differentiation in all three types of assessment regardless of the selected parameters, largely due to retention of more loci in analyses. In contrast, library A produced more variable results; increasing the minimum sequencing depth threshold (-m) resulted in a reduced number of retained loci, and therefore lost resolution at high -m values for FST and ADMIXTURE, but not DAPC. When detecting fine population differentiation, the population map influenced the number of loci and missing data, which generated artefacts in all downstream analyses tested. Similarly, when examining fine scale population subdivision, library B was robust to changing parameters but library A lost resolution depending on the parameter set. We used library B to examine actual subdivision in our study populations. All three types of analysis found complete subdivision among populations in Lake Huron, ON and Dore Lake, SK, Canada using 10,640 SNP loci. Weak population subdivision was detected in Lake Huron with fish from sites in the north-west, Search Bay, North Point and Hammond Bay, showing slight differentiation. Overall, we show that apparently simple decisions about library construction and bioinformatics parameters can have important impacts on the interpretation of population subdivision. Although potentially more costly on a per-locus basis, early investment in striking a balance between the number of loci and sequencing effort is well worth the reduced genomic coverage for population genetics studies. More conservative stringency settings on STACKS parameters lead to a final dataset that was more consistent and robust when examining both weak and strong population differentiation. Overall, we recommend that researchers approach "simple" methodological decisions with caution, especially when working on non-model species for the first time.

Indexed as

Gene LibraryGenetics, PopulationGenetic VariationGenomeAnimalsComputational BiologyGenetic SpeciationSalmonidaeSequence Analysis, DNA

Identifiers

PMID31978053
PMCPMC6980518

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.