ArticleGenetics, selection, evolution : GSE2017
A method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals.
Article in Genetics, selection, evolution : GSE, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Evaluation of Low-Coverage Sequencing Strategies for Whole-Genome Imputation in Pacific AbaloneInternational journal of molecular sciences · 2025Article
- A vision of how low-coverage sequence data should contribute to genetic evaluation in the future.Journal of animal science · 2025Article
- Developing best practices for genotyping-by-sequencing analysis in the construction of linkage maps.GigaScience · 2022Article
- Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing.Genetics, selection, evolution : GSE · 2022Article
- Genomic prediction with whole-genome sequence data in intensely selected pig lines.Genetics, selection, evolution : GSE · 2022Article
- Rare and population-specific functional variation across pig lines.Genetics, selection, evolution : GSE · 2022Article
- Structural variant-based pangenome construction has low sensitivity to variability of haplotype-resolved bovine assemblies.Nature communications · 2022Article
- Comparison of the choice of animals for re-sequencing in two maternal pig lines.Genetics, selection, evolution : GSE · 2022Article
- Mining massive genomic data of two Swiss Braunvieh cattle populations reveals six novel candidate variants that impair reproductive success.Genetics, selection, evolution : GSE · 2021Article
- Accelerated deciphering of the genetic architecture of agricultural economic traits in pigs using a low-coverage whole-genome sequencing strategy.GigaScience · 2021Article
- Optimizing Sequencing Resources in Genotyped Livestock Populations Using Linear Programming.Frontiers in genetics · 2021Article
- Towards a Cost-Effective Implementation of Genomic Prediction Based on Low Coverage Whole Genome Sequencing in Dezhou Donkey.Frontiers in genetics · 2021Article
- The Future of Livestock Management: A Review of Real-Time Portable Sequencing Applied to Livestock.Genes · 2020Review
- Article
- 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
- Haplotype structure in commercial maize breeding programs in relation to key founder lines.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2020Article
- Assessing genomic diversity and signatures of selection in Original Braunvieh cattle using whole-genome sequencing data.BMC genomics · 2020Article
- Optimizing Selection of the Reference Population for Genotype Imputation From Array to Sequence Variants.Frontiers in genetics · 2019Article
- Sequence variation, evolutionary constraint, and selection at the CD163 gene in pigs.Genetics, selection, evolution : GSE · 2018Article
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4 authors.
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Abstract
backgroundThis paper describes a heuristic method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals. Low-coverage sequencing assembles high-coverage sequence information for every individual by accumulating data from the genome segments that they share with many other individuals into consensus haplotypes. Deriving the consensus haplotypes accurately is critical for achieving a high phasing and imputation accuracy. In order to enable accurate phasing and imputation of sequence information for the whole population, we allocate the available sequencing resources among individuals with existing phased genomic data by targeting the sequencing coverage of their haplotypes.
resultsOur method, called AlphaSeqOpt, prioritizes haplotypes using a score function that is based on the frequency of the haplotypes in the sequencing set relative to the target coverage. AlphaSeqOpt has two steps: (1) selection of an initial set of individuals by iteratively choosing the individuals that have the maximum score conditional on the current set, and (2) refinement of the set through several rounds of exchanges of individuals. AlphaSeqOpt is very effective for distributing a fixed amount of sequencing resources evenly across haplotypes, which results in a reduction of the proportion of haplotypes that are sequenced below the target coverage. AlphaSeqOpt can provide a greater proportion of haplotypes sequenced at the target coverage by sequencing less individuals, as compared with other methods that use a score function based on haplotype frequencies in the population. A refinement of the initially selected set can provide a larger more diverse set with more unique individuals, which is beneficial in the context of low-coverage sequencing. We extend the method with an approach for filtering rare haplotypes based on their flanking haplotypes, so that only those that are likely to derive from a recombination event are targeted.
conclusionsWe present a method for allocating sequencing resources so that a greater proportion of haplotypes are sequenced at a coverage that is sufficiently high for population-based imputation with low-coverage sequencing. The haplotype score function, the refinement step, and the new approach for filtering rare haplotypes make AlphaSeqOpt more effective for that purpose than previously reported methods for reducing sequencing redundancy.
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