Dataset Information

Effects of marker type and filtering criteria on Q ST-F ST comparisons.

ABSTRACT: Comparative studies of quantitative and neutral genetic differentiation (Q ST-F ST tests) provide means to detect adaptive population differentiation. However, Q ST-F ST tests can be overly liberal if the markers used deflate F ST below its expectation, or overly conservative if methodological biases lead to inflated F ST estimates. We investigated how marker type and filtering criteria for marker selection influence Q ST-F ST comparisons through their effects on F ST using simulations and empirical data on over 18 000 in silico genotyped microsatellites and 3.8 million single-locus polymorphism (SNP) loci from four populations of nine-spined sticklebacks (Pungitius pungitius). Empirical and simulated data revealed that F ST decreased with increasing marker variability, and was generally higher with SNPs than with microsatellites. The estimated baseline F ST levels were also sensitive to filtering criteria for SNPs: both minor alleles and linkage disequilibrium (LD) pruning influenced F ST estimation, as did marker ascertainment. However, in the case of stickleback data used here where Q ST is high, the choice of marker type, their genomic location, ascertainment and filtering made little difference to outcomes of Q ST-F ST tests. Nevertheless, we recommend that Q ST-F ST tests using microsatellites should discard the most variable loci, and those using SNPs should pay attention to marker ascertainment and properly account for LD before filtering SNPs. This may be especially important when level of quantitative trait differentiation is low and levels of neutral differentiation high.

SUBMITTER: Li Z

PROVIDER: S-EPMC6894560 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Effects of marker type and filtering criteria on Q ST-F ST comparisons.

Li Zitong Z Löytynoja Ari A Fraimout Antoine A Merilä Juha J

Royal Society open science 20191106 11

Comparative studies of quantitative and neutral genetic differentiation (Q ST-F ST tests) provide means to detect adaptive population differentiation. However, Q ST-F ST tests can be overly liberal if the markers used deflate F ST below its expectation, or overly conservative if methodological biases lead to inflated F ST estimates. We investigated how marker type and filtering criteria fo ...[more]

PMID: 31827824

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Project description:Background: The accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Removing contaminants and filtering rare features are two common approaches to deal with this problem. While contaminant detection methods use auxiliary sequencing process information to identify known contaminants, filtering methods remove taxa that are present in a small number of samples and have small counts in the samples where they are observed. The latter approach reduces the extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured "mock" datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation as well as its impact on identifying taxa that discriminate between disease states. Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where the same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs while preserving the between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially abundant across groups of samples, and random forest models were used to rank features with the largest contribution toward disease classification. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between the filtering and the contaminant removal method shows that they have complementary effects and are advised to be used in conjunction. Conclusions: Filtering reduces the complexity of microbiome data while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.

Dataset Information

Effects of marker type and filtering criteria on Q ST-F ST comparisons.

Publications

Effects of marker type and filtering criteria on <i>Q</i> <sub>ST</sub>-<i>F</i> <sub>ST</sub> comparisons.

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