Example genodive input8/2/2023 ![]() ![]() We used library B to examine actual subdivision in our study populations. Similarly, when examining fine scale population subdivision, library B was robust to changing parameters but library A lost resolution depending on the parameter set. When detecting fine population differentiation, the population map influenced the number of loci and missing data, which generated artefacts in all downstream analyses tested. 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 F ST and ADMIXTURE, but not DAPC. ![]() 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. Each library was analyzed using the STACKS pipeline followed by three types of population structure assessment (F ST, 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. The fish were selected from populations with different levels of expected genetic subdivision. 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). 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. ![]()
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