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. 2023 Nov 3;24(1):415.
doi: 10.1186/s12859-023-05554-z.

Estimating microhaplotype allele frequencies from low-coverage or pooled sequencing data

Thomas A Delomas  1 , Stuart C Willis  2
Affiliations

Estimating microhaplotype allele frequencies from low-coverage or pooled sequencing data

Thomas A Delomas et al. BMC Bioinformatics. .

Abstract

Background: Microhaplotypes have the potential to be more cost-effective than SNPs for applications that require genetic panels of highly variable loci. However, development of microhaplotype panels is hindered by a lack of methods for estimating microhaplotype allele frequency from low-coverage whole genome sequencing or pooled sequencing (pool-seq) data.

Results: We developed new methods for estimating microhaplotype allele frequency from low-coverage whole genome sequence and pool-seq data. We validated these methods using datasets from three non-model organisms. These methods allowed estimation of allele frequency and expected heterozygosity at depths routinely achieved from pooled sequencing.

Conclusions: These new methods will allow microhaplotype panels to be designed using low-coverage WGS and pool-seq data to discover and evaluate candidate loci. The python script implementing the two methods and documentation are available at https://www.github.com/delomast/mhFromLowDepSeq .

Keywords: Genotype panel design; Low-depth whole genome sequencing; Microhaplotype; Pool-seq; Skim-seq.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Distribution of error in estimated allele frequencies from the oyster dataset binned by the number of reads contributing to the estimate. Sample sizes in bins from left to right: 194,204, 125,410, 54,818, 81,027, 96,920, 90,745, 75,136, 63,388, 113,454, 97,540
Fig. 2
Fig. 2
Mean square error in estimated allele frequencies from the oyster dataset binned by the number of reads contributing to the estimate. Sample sizes in bins from left to right: 194,204, 125,410, 54,818, 81,027, 96,920, 90,745, 75,136, 63,388, 113,454, 97,540
Fig. 3
Fig. 3
Distribution of error in expected heterozygosity from the oyster dataset binned by the number of reads contributing to the estimate. Sample sizes in bins from left to right: 194,204, 125,410, 54,818, 81,027, 96,920, 90,745, 75,136, 63,388, 113,454, 97,540
Fig. 4
Fig. 4
Mean square error in expected heterozygosity from the oyster dataset binned by the number of reads contributing to the estimate. Sample sizes in bins from left to right: 194,204, 125,410, 54,818, 81,027, 96,920, 90,745, 75,136, 63,388, 113,454, 97,540
See this image and copyright information in PMC

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