ArticleBMC bioinformatics2011
Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Modeling Biases from Low-Pass Genome Sequencing to Enable Accurate Population Genetic Inferences.Molecular biology and evolution · 2025Article
- Modeling biases from low-pass genome sequencing to enable accurate population genetic inferences.bioRxiv : the preprint server for biology · 2024Article
- Fast imputation using medium or low-coverage sequence data.BMC genetics · 2015Article
- GINDEL: accurate genotype calling of insertions and deletions from low coverage population sequence reads.PloS one · 2014Article
- HapFABIA: identification of very short segments of identity by descent characterized by rare variants in large sequencing data.Nucleic acids research · 2013Article
- Article
- An integrative variant analysis pipeline for accurate genotype/haplotype inference in population NGS data.Genome research · 2013Article
- Genotype calling from next-generation sequencing data using haplotype information of reads.Bioinformatics (Oxford, England) · 2012Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping.
resultsIn this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/.
conclusionsIntegration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies.
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Registered trials
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