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Batch effects in population genomic studies with low-coverage whole genome sequencing data: Causes, detection and
Runyang Nicolas Lou1, Nina Overgaard Therkildsen1
1Department of Natural Resources, Cornell University, Ithaca, New York, USA.
Molecular Ecology Resources
|November 26, 2021
Summary
Batch effects in sequencing data can bias population genomic studies. Simple bioinformatic strategies can detect and mitigate these technical variations, enabling reliable data integration for robust genetic diversity and population structure analyses.
Area of Science:
- Genomics
- Bioinformatics
- Population Genetics
Background:
- Publicly available sequencing data has increased dramatically, offering opportunities for large-scale population genomic studies.
- Non-biological technical variations, known as batch effects, can confound biological signals in combined datasets.
- Low-coverage whole genome sequencing (lcWGS) data may be particularly susceptible to batch effects.
Purpose of the Study:
- To compare two batches of lcWGS data from Atlantic cod (Gadus morhua) to identify and address batch effects.
- To evaluate the impact of batch effects on genetic diversity estimates, population structure inference, and selection scans.
- To demonstrate bioinformatic strategies for mitigating batch effects in population genomic analyses.
Main Methods:
- Comparison of two lcWGS data batches from the same Atlantic cod populations.
- Assessment of a naive bioinformatic pipeline's susceptibility to batch effects.
- Identification of technical differences contributing to batch effects (e.g., sequencing chemistry, read type, DNA quality, depth).
- Application of bioinformatic strategies like read trimming and SNP filtering.
Main Results:
- A naive bioinformatic pipeline showed systematic bias in genetic diversity, population structure, and selection scans due to batch effects.
- Multiple technical factors, including sequencing chemistry, read type, and DNA degradation, were identified as sources of batch effects.
- Simple bioinformatic strategies effectively detected and substantially mitigated the impact of batch effects.
Conclusions:
- Combining sequencing datasets is powerful for population genomics when batch effects are explicitly managed.
- Bioinformatic strategies are crucial for ensuring the accuracy of genetic analyses when integrating diverse datasets.
- The findings are relevant for lcWGS and other sequencing strategies, emphasizing the need for batch effect control.
