Dataset Information

A computational approach for positive genetic identification and relatedness detection from low-coverage shotgun sequencing data.

ABSTRACT: Several methods exist for detecting genetic relatedness or identity by comparing DNA information. These methods generally require genotype calls, either single-nucleotide polymorphisms or short tandem repeats, at the sites used for comparison. For some DNA samples, like those obtained from bone fragments or single rootless hairs, there is often not enough DNA present to generate genotype calls that are accurate and complete enough for these comparisons. Here, we describe IBDGem, a fast and robust computational procedure for detecting genomic regions of identity-by-descent by comparing low-coverage shotgun sequence data against genotype calls from a known query individual. At less than 1× genome coverage, IBDGem reliably detects segments of relatedness and can make high-confidence identity detections with as little as 0.01× genome coverage.

SUBMITTER: Nguyen R

PROVIDER: S-EPMC10445519 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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A computational approach for positive genetic identification and relatedness detection from low-coverage shotgun sequencing data.

Nguyen Remy R Kapp Joshua D JD Sacco Samuel S Myers Steven P SP Green Richard E RE

The Journal of heredity 20230801 5

Several methods exist for detecting genetic relatedness or identity by comparing DNA information. These methods generally require genotype calls, either single-nucleotide polymorphisms or short tandem repeats, at the sites used for comparison. For some DNA samples, like those obtained from bone fragments or single rootless hairs, there is often not enough DNA present to generate genotype calls that are accurate and complete enough for these comparisons. Here, we describe IBDGem, a fast and robus ...[more]

PMID: 37381815

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Project description:BackgroundTransposable elements (TEs) and other repetitive elements are a large and dynamically evolving part of eukaryotic genomes, especially in plants where they can account for a significant proportion of genome size. Their dynamic nature gives them the potential for use in identifying and characterizing crop germplasm. However, their repetitive nature makes them challenging to study using conventional methods of molecular biology. Next generation sequencing and new computational tools have greatly facilitated the investigation of TE variation within species and among closely related species.Results(i) We generated low-coverage Illumina whole genome shotgun sequencing reads for multiple individuals of cacao (Theobroma cacao) and related species. These reads were analysed using both an alignment/mapping approach and a de novo (graph based clustering) approach. (ii) A standard set of ultra-conserved orthologous sequences (UCOS) standardized TE data between samples and provided phylogenetic information on the relatedness of samples. (iii) The mapping approach proved highly effective within the reference species but underestimated TE abundance in interspecific comparisons relative to the de novo methods. (iv) Individual T. cacao accessions have unique patterns of TE abundance indicating that the TE composition of the genome is evolving actively within this species. (v) LTR/Gypsy elements are the most abundant, comprising c.10% of the genome. (vi) Within T. cacao the retroelement families show an order of magnitude greater sequence variability than the DNA transposon families. (vii) Theobroma grandiflorum has a similar TE composition to T. cacao, but the related genus Herrania is rather different, with LTRs making up a lower proportion of the genome, perhaps because of a massive presence (c. 20%) of distinctive low complexity satellite-like repeats in this genome.Conclusions(i) Short read alignment/mapping to reference TE contigs provides a simple and effective method of investigating intraspecific differences in TE composition. It is not appropriate for comparing repetitive elements across the species boundaries, for which de novo methods are more appropriate. (ii) Individual T. cacao accessions have unique spectra of TE composition indicating active evolution of TE abundance within this species. TE patterns could potentially be used as a "fingerprint" to identify and characterize cacao accessions.

Dataset Information

A computational approach for positive genetic identification and relatedness detection from low-coverage shotgun sequencing data.

Publications

A computational approach for positive genetic identification and relatedness detection from low-coverage shotgun sequencing data.

Similar Datasets

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