ArticleFrontiers in genetics2019
Finding the Optimal Imputation Strategy for Small Cattle Populations.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data.International journal of molecular sciences · 2022Pooled it
- Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds.BMC genomics · 2025Article
- KBeagle: An Adaptive Strategy and Tool for Improving Imputation Accuracy and Computation Time.International journal of molecular sciences · 2025Article
- Evaluation of Low-Coverage Sequencing Strategies for Whole-Genome Imputation in Pacific AbaloneInternational journal of molecular sciences · 2025Article
- Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits.Genetics, selection, evolution : GSE · 2025Article
- A primer on sequencing and genotype imputation in cattle.Journal of animal science · 2025Review
- Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes.Genetics, selection, evolution : GSE · 2024Article
- Whole-Genome Sequencing Data Reveal New Loci Affecting Milk Production in German Black Pied Cattle (DSN).Genes · 2023Article
- Binned Data Provide Better Imputation of Missing Time Series Data from Wearables.Sensors (Basel, Switzerland) · 2023Article
- Imputation of low-density marker chip data in plant breeding: Evaluation of methods based on sugar beet.The plant genome · 2022Article
- Evaluation of Whole-Genome Sequence Imputation Strategies in Korean Hanwoo Cattle.Animals : an open access journal from MDPI · 2022Article
- Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation.Advanced genetics (Hoboken, N.J.) · 2022Article
- Development and validation of a horse reference panel for genotype imputation.Genetics, selection, evolution : GSE · 2022Article
- Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa.Tropical animal health and production · 2022Article
- Exploring the optimal strategy of imputation from SNP array to whole-genome sequencing data in farm animals.Frontiers in genetics · 2022Article
- Improving the resolution of canine genome-wide association studies using genotype imputation: A study of two breeds.Animal genetics · 2021Article
- Article
- How imputation can mitigate SNP ascertainment Bias.BMC genomics · 2021Article
- Influence of the kappa casein genotype on the technological properties of cow milk of Simmental and Alatau breeds.Functional & integrative genomics · 2021Article
- Inference of Ancestries and Heterozygosity Proportion and Genotype Imputation in West African Cattle Populations.Frontiers in genetics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The imputation from lower density SNP chip genotypes to whole-genome sequence level is an established approach to generate high density genotypes for many individuals. Imputation accuracy is dependent on many factors and for small cattle populations such as the endangered German Black Pied cattle (DSN), determining the optimal imputation strategy is especially challenging since only a low number of high density genotypes is available. In this paper, the accuracy of imputation was explored with regard to (1) phasing of the target population and the reference panel for imputation, (2) comparison of a 1-step imputation approach, where 50 k genotypes are directly imputed to sequence level, to a 2-step imputation approach that used an intermediate step imputing first to 700 k and subsequently to sequence level, (3) the software tools Beagle and Minimac, and (4) the size and composition of the reference panel for imputation. Analyses were performed for 30 DSN and 30 Holstein Frisian cattle available from the 1000 Bull Genomes Project. Imputation accuracy was assessed using a leave-one-out cross validation procedure. We observed that phasing of the target populations and the reference panels affects the imputation accuracy significantly. Minimac reached higher accuracy when imputing using small reference panels, while Beagle performed better with larger reference panels. In contrast to previous research, we found that when a low number of animals is available at the intermediate imputation step, the 1-step imputation approach yielded higher imputation accuracy compared to a 2-step imputation. Overall, the size of the reference panel for imputation is the most important factor leading to higher imputation accuracy, although using a larger reference panel consisting of a related but different breed (Holstein Frisian) significantly reduced imputation accuracy. Our findings provide specific recommendations for populations with a limited number of high density genotyped or sequenced animals available such as DSN. The overall recommendation when imputing a small population are to (1) use a large reference panel of the same breed, (2) use a large reference panel consisting of diverse breeds, or (3) when a large reference panel is not available, we recommend using a smaller same breed reference panel without including a different related breed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.