Dataset Information

A Maximum-Likelihood Approach to Estimating the Insertion Frequencies of Transposable Elements from Population Sequencing Data.

ABSTRACT: Transposable elements (TEs) contribute to a large fraction of the expansion of many eukaryotic genomes due to the capability of TEs duplicating themselves through transposition. A first step to understanding the roles of TEs in a eukaryotic genome is to characterize the population-wide variation of TE insertions in the species. Here, we present a maximum-likelihood (ML) method for estimating allele frequencies and detecting selection on TE insertions in a diploid population, based on the genotypes at TE insertion sites detected in multiple individuals sampled from the population using paired-end (PE) sequencing reads. Tests of the method on simulated data show that it can accurately estimate the allele frequencies of TE insertions even when the PE sequencing is conducted at a relatively low coverage (=5X). The method can also detect TE insertions under strong selection, and the detection ability increases with sample size in a population, although a substantial fraction of actual TE insertions under selection may be undetected. Application of the ML method to genomic sequencing data collected from a natural Daphnia pulex population shows that, on the one hand, most (>90%) TE insertions present in the reference D. pulex genome are either fixed or nearly fixed (with allele frequencies >0.95); on the other hand, among the nonreference TE insertions (i.e., those detected in some individuals in the population but absent from the reference genome), the majority (>70%) are still at low frequencies (<0.1). Finally, we detected a substantial fraction (?9%) of nonreference TE insertions under selection.

SUBMITTER: Jiang X

PROVIDER: S-EPMC6188571 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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A Maximum-Likelihood Approach to Estimating the Insertion Frequencies of Transposable Elements from Population Sequencing Data.

Jiang Xiaoqian X Tang Haixu H Mohammed Ismail Wazim W Lynch Michael M

Molecular biology and evolution 20181001 10

Transposable elements (TEs) contribute to a large fraction of the expansion of many eukaryotic genomes due to the capability of TEs duplicating themselves through transposition. A first step to understanding the roles of TEs in a eukaryotic genome is to characterize the population-wide variation of TE insertions in the species. Here, we present a maximum-likelihood (ML) method for estimating allele frequencies and detecting selection on TE insertions in a diploid population, based on the genotyp ...[more]

PMID: 30099533

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Project description:BackgroundThe UN's Sustainable Development Goals are devoted to eradicate a range of infectious diseases to achieve global well-being. These efforts require monitoring disease transmission at a level that differentiates between pathogen variants at the genetic/molecular level. In fact, the advantages of genetic (molecular) measures like multiplicity of infection (MOI) over traditional metrics, e.g., R0, are being increasingly recognized. MOI refers to the presence of multiple pathogen variants within an infection due to multiple infective contacts. Maximum-likelihood (ML) methods have been proposed to derive MOI and pathogen-lineage frequencies from molecular data. However, these methods are biased.Methods and findingsBased on a single molecular marker, we derive a bias-corrected ML estimator for MOI and pathogen-lineage frequencies. We further improve these estimators by heuristical adjustments that compensate shortcomings in the derivation of the bias correction, which implicitly assumes that data lies in the interior of the observational space. The finite sample properties of the different variants of the bias-corrected estimators are investigated by a systematic simulation study. In particular, we investigate the performance of the estimator in terms of bias, variance, and robustness against model violations. The corrections successfully remove bias except for extreme parameters that likely yield uninformative data, which cannot sustain accurate parameter estimation. Heuristic adjustments further improve the bias correction, particularly for small sample sizes. The bias corrections also reduce the estimators' variances, which coincide with the Cramér-Rao lower bound. The estimators are reasonably robust against model violations.ConclusionsApplying bias corrections can substantially improve the quality of MOI estimates, particularly in areas of low as well as areas of high transmission-in both cases estimates tend to be biased. The bias-corrected estimators are (almost) unbiased and their variance coincides with the Cramér-Rao lower bound, suggesting that no further improvements are possible unless additional information is provided. Additional information can be obtained by combining data from several molecular markers, or by including information that allows stratifying the data into heterogeneous groups.

Dataset Information

A Maximum-Likelihood Approach to Estimating the Insertion Frequencies of Transposable Elements from Population Sequencing Data.

Publications

A Maximum-Likelihood Approach to Estimating the Insertion Frequencies of Transposable Elements from Population Sequencing Data.

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