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Abstract BackgroundLow-depth sequencing allows researchers to increase sample size at the expense of lower accuracy. To incorporate uncertainties while maintaining statistical power, we introduce to analyze population structure of low-depth sequencing data. ResultsThe method optimizes the choice of nonlinear transformations of dosages to maximize the Ky Fan norm of the covariance matrix. The transformation incorporates the uncertainty in calling between heterozygotes and the common homozygotes for loci having a rare allele and is more linear when both variants are common. ConclusionsWe apply to samples from two indigenous Siberian populations and reveal hidden population structure accurately using only a single chromosome. The package is available onhttps://github.com/yiwenstat/MCPCA_PopGen.
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A population-level statistic for assessing Mendelian behavior of genotyping-by-sequencing data from highly duplicated genomes
Abstract BackgroundGiven the economic and environmental importance of allopolyploids and other species with highly duplicated genomes, there is a need for methods to distinguish paralogs, i.e. duplicate sequences within a genome, from Mendelian loci, i.e. single copy sequences that pair at meiosis. The ratio of observed to expected heterozygosity is an effective tool for filtering loci but requires genotyping to be performed first at a high computational cost, whereas counting the number of sequence tags detected per genotype is computationally quick but very ineffective in inbred or polyploid populations. Therefore, new methods are needed for filtering paralogs. ResultsWe introduce a novel statistic,Hind/HE, that uses the probability that two reads sampled from a genotype will belong to different alleles, instead of observed heterozygosity. The expected value ofHind/HEis the same across all loci in a dataset, regardless of read depth or allele frequency. In contrast to methods based on observed heterozygosity, it can be estimated and used for filtering loci prior to genotype calling. In addition to filtering paralogs, it can be used to filter loci with null alleles or high overdispersion, and identify individuals with unexpected ploidy and hybrid status. We demonstrate that the statistic is useful at read depths as low as five to 10, well below the depth needed for accurate genotype calling in polyploid and outcrossing species. ConclusionsOur methodology for estimatingHind/HEacross loci and individuals, as well as determining reasonable thresholds for filtering loci, is implemented in polyRAD v1.6, available athttps://github.com/lvclark/polyRAD. In large sequencing datasets, we anticipate that the ability to filter markers and identify problematic individuals prior to genotype calling will save researchers considerable computational time.
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- Award ID(s):
- 1661490
- PAR ID:
- 10364406
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- BMC Bioinformatics
- Volume:
- 23
- Issue:
- 1
- ISSN:
- 1471-2105
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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