ArticleG3 (Bethesda, Md.)2020
Improving Imputation Quality in BEAGLE for Crop and Livestock Data.
Article in G3 (Bethesda, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Imputation in the Wild: Genome-Wide Robustness and Fine-Scale Limitations of Low-Coverage Genomes in Endangered Species.Molecular ecology resources · 2026Article
- Association mapping for hop cone chemistry and morphology identifies natural beneficial allele stacks.The plant genome · 2026Article
- MMGS: a novel genomic prediction framework to integrate genotype, environment and their interactions for multi-environment breeding trials.Horticulture research · 2026Article
- Genotyping errors can affect indirect predictions of young selection candidates: A simulation study.JDS communications · 2026Article
- Genome-Wide Homozygosity Predicts Inbreeding Depression in the Hihi/Stitchbird (Evolutionary applications · 2026Article
- Identification and association mapping of sources of stem rust resistance in the wild barley diversity panel effective against virulent isolates from the Pacific Northwest.G3 (Bethesda, Md.) · 2026Article
- Robust imputation-based method for eye, hair, and skin colour prediction from low-coverage ancient DNA.Scientific reports · 2026Article
- Identification of key gut microbiota and microbial QTL in contributing to the significant differences in body weight between two Chinese local chicken breeds.Animal genetics · 2026Article
- A Pan-TE Map Reveals the Important Role of Transposable Elements in Gene Expression and Phenotypic Diversity in 2,311 Rapeseed Accessions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Genome-Wide, High-Density Genotyping Approaches for Plant Germplasm Characterisation (Methods and Applications).International journal of molecular sciences · 2025Review
- Population Genomics Reveals Local Adaptation Related to Temperature Variation in Two Stream Frog Species: Implications for Vulnerability to Climate Warming.Molecular ecology · 2025Article
- Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds.BMC genomics · 2025Article
- High Imputation Accuracy Can Be Achieved Using a Small Reference Panel in a Natural Population With Low Genetic Diversity.Molecular ecology resources · 2025Article
- Genome landscape and genetic architecture of recombination in domestic goats (Capra hircus).Genetics, selection, evolution : GSE · 2025Article
- Resequencing and phenotyping of the first highly inbred eggplant multiparent population revealHorticulture research · 2025Article
- KBeagle: An Adaptive Strategy and Tool for Improving Imputation Accuracy and Computation Time.International journal of molecular sciences · 2025Article
- Study of the Association Between SNPs and External Pelvimetry Measurements in Romanian Simmental Cattle.Animals : an open access journal from MDPI · 2025Article
- Evaluation of Low-Coverage Sequencing Strategies for Whole-Genome Imputation in Pacific AbaloneInternational journal of molecular sciences · 2025Article
- Using genotype imputation to integrate Canola populations for genome-wide association and genomic prediction of blackleg resistance.BMC genomics · 2025Article
- Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning.Animals : an open access journal from MDPI · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.
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