Detecting selection in low-coverage high-throughput sequencing data using principal component analysis
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Abstract
BACKGROUND: Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data. MATERIALS AND METHODS: We have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure. RESULTS: Here, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes. CONCLUSION: We show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.
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A previous study using principal components analysis found that samples were separated on the PC2 axis by read length rather than population, which could have led to misinformed interpretations.
“For example, a previous study using principal components analysis found that samples were separated on the PC2 axis by read length rather than population (Meisner et al. 2021), which could have led to misinformed interpretations.”
From Unravelling reference bias in ancient DNA datasets (2024) · cites this paper as Meisner et al. 2021
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Unravelling reference bias in ancient DNA datasets
Dolenz, S., van der Valk, T., Jin, C., Oppenheimer, J., Sharif, M. B., Orlando, L., Shapiro, B., Dalén, L., & Heintzman, P. D. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae436 · Cited by 33 · see citing works
- Authors
- Meisner, J., Albrechtsen, A., & Hanghøj, K.
- Affiliations
- University of Copenhagen (DK)
- Venue
- BMC Bioinformatics
- Published
- 2021-09-29
- Volume
- 22
- Issue
- 1
- Pages
- 470-470
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC8480091
- OpenAlex
- W3201939971
- Cited by
- 30
- References
- 32
- Citations by year
- 2026: 8 · 2025: 9 · 2024: 8 · 2023: 3 · 2022: 1 · 2021: 1
- FWCI
- 1.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Novo Nordisk Fonden (NNF20OC0061343)
- Det Frie Forskningsråd (DFF 0135-00211B)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- natural selection detection, low-pass sequencing, genotype likelihoods, principal component analysis, selection signatures, 1000 Genomes Project, European ancestry, East Asian ancestry, genotype uncertainty, false positive rate control
- MeSH terms
- Genetics, Population, Genotype, Humans, Genome, Polymorphism, Single Nucleotide, Principal Component Analysis, High-Throughput Nucleotide Sequencing
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