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High Imputation Accuracy Can Be Achieved Using a Small Reference Panel in a Natural Population With Low Genetic
Hui Zhen Tan1,2, Katarina C Stuart1, Tram Vi1,2
1School of Biological Sciences, University of Auckland, Auckland, New Zealand.
Molecular Ecology Resources
|August 13, 2025
Summary
Genotype imputation using small reference panels is effective for improving genetic data quality in wild populations, even those with low genetic diversity. This study demonstrates high accuracy for imputing missing genotypes in hihi/stitchbirds.
Area of Science:
- Genomics
- Population Genetics
- Bioinformatics
Background:
- Genotype imputation enhances genetic datasets by inferring missing genotypes using reference haplotypes.
- While typically used for large datasets, imputation can benefit smaller natural populations, especially those with low genetic diversity.
- The hihi/stitchbird (Notiomystis cincta) presents a valuable case study for assessing imputation in such populations.
Purpose of the Study:
- To determine best practices and effectiveness of genotype imputation using small reference panels for species with low genetic diversity.
- To evaluate imputation accuracy and the impact of various parameters on wild populations.
- To provide a framework for optimizing imputation in other understudied wild populations.
Main Methods:
- A leave-one-out method was employed on 30 high-coverage hihi individuals, masking and imputing single nucleotide polymorphisms (SNPs) using Beagle v5.4.
- Imputation accuracy was quantified using r², the correlation between imputed and true genotype dosages.
- Multiple imputation parameters, linkage maps, reference panel compositions, and SNP densities were systematically tested.
Main Results:
- Mean imputation accuracy (r²) exceeded 0.95 in most tests, even with a small reference panel of high-fecundity individuals.
- Linkage maps did not improve imputation accuracy, and very low SNP densities decreased accuracy.
- Filtering based on r² significantly impacted heterozygosity and site frequency spectrum (SFS) at low SNP densities, but runs of homozygosity (ROH) inference remained robust.
Conclusions:
- Genotype imputation is a viable and accurate tool for enhancing genetic data in wild populations with limited diversity.
- Optimizing imputation parameters, particularly SNP density and filtering thresholds, is crucial for downstream analyses like heterozygosity and SFS.
- The study offers a practical template for implementing and refining genotype imputation in other wild species.
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