ArticleGenetics2010
The use of family relationships and linkage disequilibrium to impute phase and missing genotypes in up to whole-genome sequence density genotypic data.
Article in Genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data Integration, Imputation, and Meta-analysis for Genome-Wide Association Studies.Methods in molecular biology (Clifton, N.J.) · 2022Pooled it
- Genomic estimates of Identity-By-Descent relationships in large scale data sets.Genetics, selection, evolution : GSE · 2026Article
- A vision of how low-coverage sequence data should contribute to genetic evaluation in the future.Journal of animal science · 2025Article
- Targeted genotyping-by-sequencing of potato and data analysis with R/polyBreedR.The plant genome · 2024Article
- Genome-Wide Analysis of Milk Production Traits and Selection Signatures in Serbian Holstein-Friesian Cattle.Animals : an open access journal from MDPI · 2024Article
- Advances in understanding the genetic architecture of antibody response to paratuberculosis in sheep by heritability estimate and LDLA mapping analyses and investigation of candidate regions using sequence-based data.Genetics, selection, evolution : GSE · 2024Article
- Comparing Methods to Select Candidates for Re-Genotyping to Impute Higher-Density Genotype Data in a Japanese Black Cattle Population: A Case Study.Animals : an open access journal from MDPI · 2023Article
- Association analysis and functional annotation of imputed sequence data within genomic regions influencing resistance to gastro-intestinal parasites detected by an LDLA approach in a nucleus flock of Sarda dairy sheep.Genetics, selection, evolution : GSE · 2022Article
- Imputation to whole-genome sequence and its use in genome-wide association studies for pork colour traits in crossbred and purebred pigs.Frontiers in genetics · 2022Article
- Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Genetic variation in recombination rate in the pig.Genetics, selection, evolution : GSE · 2021Article
- AlphaFamImpute: high-accuracy imputation in full-sib families from genotype-by-sequencing data.Bioinformatics (Oxford, England) · 2020Article
- Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling.Genetics, selection, evolution : GSE · 2020Article
- Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations.Genetics, selection, evolution : GSE · 2020Article
- Management of Genetic Diversity in the Era of Genomics.Frontiers in genetics · 2020Article
- Mapping genomic regions affecting milk traits in Sarda sheep by using the OvineSNP50 Beadchip and principal components to perform combined linkage and linkage disequilibrium analysis.Genetics, selection, evolution : GSE · 2019Article
- Sequence imputation from low density single nucleotide polymorphism panel in a black poplar breeding population.BMC genomics · 2019Article
- Rediscovering the value of families for psychiatric genetics research.Molecular psychiatry · 2019Review
- Parentage assignment with genotyping-by-sequencing data.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2019Article
- A Random Forests Framework for Modeling Haplotypes as Mosaics of Reference Haplotypes.Frontiers in genetics · 2019Article
Corrections and comments
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A novel method, called linkage disequilibrium multilocus iterative peeling (LDMIP), for the imputation of phase and missing genotypes is developed. LDMIP performs an iterative peeling step for every locus, which accounts for the family data, and uses a forward-backward algorithm to accumulate information across loci. Marker similarity between haplotype pairs is used to impute possible missing genotypes and phases, which relies on the linkage disequilibrium between closely linked markers. After this imputation step, the combined iterative peeling/forward-backward algorithm is applied again, until convergence. The calculations per iteration scale linearly with number of markers and number of individuals in the pedigree, which makes LDMIP well suited to large numbers of markers and/or large numbers of individuals. Per iteration calculations scale quadratically with the number of alleles, which implies biallelic markers are preferred. In a situation with up to 15% randomly missing genotypes, the error rate of the imputed genotypes was <1% and approximately 99% of the missing genotypes were imputed. In another example, LDMIP was used to impute whole-genome sequence data consisting of 17,321 SNPs on a chromosome. Imputation of the sequence was based on the information of 20 (re)sequenced founder individuals and genotyping their descendants for a panel of 3000 SNPs. The error rate of the imputed SNP genotypes was 10%. However, if the parents of these 20 founders are also sequenced, >99% of missing genotypes are imputed correctly.
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