Genotype imputation improves GWAS resolution and facilitates candidate gene identification in pearl millet
- 1The University of Tokyo
Abstract
Pearl millet (Cenchrus americanus; syn. Pennisetum glaucum (L.) R. Br.) is an important cereal crop mainly grown in semi-arid areas. However, in pearl millet, genes underlying many agronomic traits remain unknown. Our previous genome-wide association study (GWAS) was based primarily on genotype data derived from restriction site-associated DNA sequencing (RAD-seq), which contained a high proportion of missing genotypes and may have limited mapping resolution. To address this limitation, we used whole-genome sequencing (WGS)-derived genotype data from 21 elite lines as a reference panel to impute missing genotypes in the RAD-seq-derived dataset. Genotype imputation reduced the proportion of missing genotypes from 82.42% to 0% and increased the mean density of non-missing genotypes from 569.61 to 1,158.09 genotypes/Mb. Leave-one-out validation yielded a mean genotype concordance of 89.9% (SD = 4.6%). GWAS using the imputed dataset showed more localized association signals for several traits. Homologs of rice SHORT INTERNODES1, Arabidopsis FLOWERING LOCUS Y, and rice EPIDERMAL PATTERNING FACTOR 2 were identified as candidates for genes regulating culm height, days to panicle emergence, and awn growth, respectively. These results demonstrate that reference-panel-based genotype imputation can increase the utility of reduced-representation genotype datasets and facilitate candidate gene identification in pearl millet.
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BibTeX
@misc{kambara2026genotype,
title = {{Genotype imputation improves GWAS resolution and facilitates candidate gene identification in pearl millet}},
author = {K. Kambara and S. K. Gupta and D. Tsugama},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.16.759592},
url = {https://www.biorxiv.org/content/10.64898/2026.10.16.759592v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Genotype imputation improves GWAS resolution and facilitates candidate gene identification in pearl millet
AU - K. Kambara
AU - S. K. Gupta
AU - D. Tsugama
PY - 2026
DA - 2026/10/16
PB - bioRxiv
DO - 10.64898/2026.10.16.759592
UR - https://www.biorxiv.org/content/10.64898/2026.10.16.759592v1
AB - Pearl millet (Cenchrus americanus; syn. Pennisetum glaucum (L.) R. Br.) is an important cereal crop mainly grown in semi-arid areas. However, in pearl millet, genes underlying many agronomic traits remain unknown. Our previous genome-wide association study (GWAS) was based primarily on genotype data derived from restriction site-associated DNA sequencing (RAD-seq), which contained a high proportion of missing genotypes and may have limited mapping resolution. To address this limitation, we used whole-genome sequencing (WGS)-derived genotype data from 21 elite lines as a reference panel to impute missing genotypes in the RAD-seq-derived dataset. Genotype imputation reduced the proportion of missing genotypes from 82.42% to 0% and increased the mean density of non-missing genotypes from 569.61 to 1,158.09 genotypes/Mb. Leave-one-out validation yielded a mean genotype concordance of 89.9% (SD = 4.6%). GWAS using the imputed dataset showed more localized association signals for several traits. Homologs of rice SHORT INTERNODES1, Arabidopsis FLOWERING LOCUS Y, and rice EPIDERMAL PATTERNING FACTOR 2 were identified as candidates for genes regulating culm height, days to panicle emergence, and awn growth, respectively. These results demonstrate that reference-panel-based genotype imputation can increase the utility of reduced-representation genotype datasets and facilitate candidate gene identification in pearl millet.
ER -