Characterizing bias in population genetic inferences from low-coverage sequencing data
Open accessAbstract
The site frequency spectrum (SFS) is of primary interest in population genetic studies, because the SFS compresses variation data into a simple summary from which many population genetic inferences can proceed. However, inferring the SFS from sequencing data is challenging because genotype calls from sequencing data are often inaccurate due to high error rates and if not accounted for, this genotype uncertainty can lead to serious bias in downstream analysis based on the inferred SFS. Here, we compare two approaches to estimate the SFS from sequencing data: one approach infers individual genotypes from aligned sequencing reads and then estimates the SFS based on the inferred genotypes (call-based approach) and the other approach directly estimates the SFS from aligned sequencing reads by maximum likelihood (direct estimation approach). We find that the SFS estimated by the direct estimation approach is unbiased even at low coverage, whereas the SFS by the call-based approach becomes biased as coverage decreases. The direction of the bias in the call-based approach depends on the pipeline to infer genotypes. Estimating genotypes by pooling individuals in a sample (multisample calling) results in underestimation of the number of rare variants, whereas estimating genotypes in each individual and merging them later (single-sample calling) leads to overestimation of rare variants. We characterize the impact of these biases on downstream analyses, such as demographic parameter estimation and genome-wide selection scans. Our work highlights that depending on the pipeline used to infer the SFS, one can reach different conclusions in population genetic inference with the same data set. Thus, careful attention to the analysis pipeline and SFS estimation procedures is vital for population genetic inferences.
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Genotyping error could reduce power and increase the false-positive rate, especially for low-coverage sequencing, which can lead to biased reconstructions of the SFS.
“Genotyping error could reduce power and increase the false-positive rate, especially for low-coverage sequencing, which can lead to biased reconstructions of the SFS (Han et al. 2014).”
From Finding Hybrid Incompatibilities Using Genome Sequences from Hybrid Populations (2021) · cites this paper as Han et al. 2014
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- Authors
- Han, E., Sinsheimer, J. S., & Novembre, J.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Novembre, John
- Venue
- Molecular Biology and Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-11-27
- Volume
- 31
- Issue
- 3
- Pages
- 723-735
- ISSN
- 0737-4038 (ISSN-L)
- 1537-1719
- PMC
- PMC3935184
- OpenAlex
- W2151286417
- Cited by
- 94
- References
- 40
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 5 · 2023: 6 · 2022: 8 · 2021: 12 · 2020: 1 · 2019: 5 · 2018: 7 · 2017: 9 · 2016: 12 · 2015: 10 · 2014: 9
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Molecular Biology and Evolution (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (R01HG007089, HG007089, T32HG002536)
- National Institute of General Medical Sciences (GM-053275, R01GM053275)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- site frequency spectrum, low-pass sequencing, genotype calling, demographic parameter estimation, sequencing errors
- MeSH terms
- Base Sequence, Genetics, Population, Humans, Models, Genetic, Population Dynamics, Bias, Genome, Sequence Analysis, DNA, Databases, Nucleic Acid
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