ArticleBMC genetics2012
Fast and accurate haplotype frequency estimation for large haplotype vectors from pooled DNA data.
Article in BMC genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Estimating Organism Abundance Using Within-Sample Haplotype Frequencies of eDNA Data.Molecular ecology resources · 2026Article
- A joint use of pooling and imputation for genotyping SNPs.BMC bioinformatics · 2022Article
- Regionally Smoothed Meta-Analysis Methods for GWAS Datasets.Genetic epidemiology · 2016Article
- A sequential Monte Carlo framework for haplotype inference in CNV/SNP genotype data.EURASIP journal on bioinformatics & systems biology · 2014Article
- An EM algorithm based on an internal list for estimating haplotype distributions of rare variants from pooled genotype data.BMC genetics · 2013Article
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Authors and funding
3 authors.
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Abstract
backgroundTypically, the first phase of a genome wide association study (GWAS) includes genotyping across hundreds of individuals and validation of the most significant SNPs. Allelotyping of pooled genomic DNA is a common approach to reduce the overall cost of the study. Knowledge of haplotype structure can provide additional information to single locus analyses. Several methods have been proposed for estimating haplotype frequencies in a population from pooled DNA data.
resultsWe introduce a technique for haplotype frequency estimation in a population from pooled DNA samples focusing on datasets containing a small number of individuals per pool (2 or 3 individuals) and a large number of markers. We compare our method with the publicly available state-of-the-art algorithms HIPPO and HAPLOPOOL on datasets of varying number of pools and marker sizes. We demonstrate that our algorithm provides improvements in terms of accuracy and computational time over competing methods for large number of markers while demonstrating comparable performance for smaller marker sizes. Our method is implemented in the "Tree-Based Deterministic Sampling Pool" (TDSPool) package which is available for download at http://www.ee.columbia.edu/~anastas/tdspool.
conclusionsUsing a tree-based determinstic sampling technique we present an algorithm for haplotype frequency estimation from pooled data. Our method demonstrates superior performance in datasets with large number of markers and could be the method of choice for haplotype frequency estimation in such datasets.
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