Next-generation data filtering in the genomics era

Hemstrom, William; Grummer, Jared A.; Luikart, Gordon; Christie, Mark R.

doi:10.1038/s41576-024-00738-6

Cited by 77Genetic diversity and population structureGenomics and Phylogenetic StudiesGenetic Associations and Epidemiology

Next-generation data filtering in the genomics era

Hemstrom, William, Grummer, Jared A., Luikart, Gordon, Christie, Mark R.·2024·Nature Reviews Genetics

DOI 10.1038/s41576-024-00738-6

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Abstract

Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (F ST), nucleotide diversity (π) and effective population size (N e).

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A refined analysis of Neanderthal-introgressed sequences in modern humans with a complete reference genome
Liang, S.-A., Ren, T., Zhang, J., He, J., Wang, X., Jiang, X., He, Y., McCoy, R. C., Fu, Q., Akey, J. M., et al. (2025) · Genome Biology · doi:10.1186/s13059-025-03502-z · Cited by 8 · see citing works

Authors: Hemstrom, W., Grummer, J. A., Luikart, G., & Christie, M. R.
Affiliations: Purdue University West Lafayette (US)
University of Montana (US)
Corresponding author: Hemstrom, William
Christie, Mark R.
ORCID: Hemstrom, William
Grummer, Jared A.
Luikart, Gordon
Christie, Mark R.
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2024-06-14
Volume: 25
Issue: 11
Pages: 750-767
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/s41576-024-00738-6
OpenAlex: W4399678281
Cited by: 77
References: 150
Citations by year: 2026: 34 · 2025: 38 · 2024: 5
FWCI: 5.31
Type: journal article
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Keywords: minor allele frequency, missing data, linkage disequilibrium, Hardy-Weinberg equilibrium, Tajima's D, FST, nucleotide diversity, effective population size, reproducibility, data quality, variant filtering
MeSH terms: Animals, Gene Frequency, Genetics, Population, Humans, Genetic Variation, Linkage Disequilibrium, Genomics, High-Throughput Nucleotide Sequencing

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