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Updated: Oct 18, 2025

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Detecting selection in low-coverage high-throughput sequencing data using principal component analysis.

Jonas Meisner¹, Anders Albrechtsen¹, Kristian Hanghøj²

¹Department of Biology, The Bioinformatics Centre, University of Copenhagen, Copenhagen, Denmark.

BMC Bioinformatics

|September 30, 2021

Summary

This summary is machine-generated.

New methods allow for accurate detection of selection signatures in continuous populations using low-coverage sequencing data. This advance enables robust population genetic studies without requiring high-quality called genotypes, improving analysis of closely related groups.

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Area of Science:

Population Genetics
Genomics
Bioinformatics

Background:

Identifying selection signatures is crucial for understanding population genetics.
High-throughput sequencing enables larger sample sizes but poses challenges for continuous populations.
Existing methods struggle with genotype uncertainty in low-coverage data.

Purpose of the Study:

To extend principal component analysis (PCA)-based selection statistics for genotype likelihood data.
To enable selection scans in continuous populations using low-coverage sequencing data.
To address limitations of existing methods that require called genotypes.

Main Methods:

Extended two PCA-based selection statistics to handle genotype likelihoods.
Implemented these methods within the PCAngsd framework.
Applied the methods to low-coverage sequencing data from European and East Asian populations.

Main Results:

Developed two selection statistics that account for genotype uncertainty.
Demonstrated control of the false positive rate in selection scans.
Identified selection signatures comparable to state-of-the-art software using high-quality genotypes.

Conclusions:

Selection scans using low-coverage sequencing data from similar ancestries perform comparably to high-quality genotype data.
PCAngsd outperforms selection statistics derived from called genotypes in low-coverage data.
The new methods do not require ad-hoc filtering for analysis.