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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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‘Highly‐Informative’ Genetic Markers Can Bias Conclusions: Examples and General Solutions
ABSTRACT High‐grading bias is the overestimation power in a subset of loci caused by model overfitting. Using both empirical and simulated datasets, we show that high‐grading bias can cause severe overestimation of population structure, and thus mislead investigators, whenever highly informative or high‐FSTmarkers are chosen (i.e., ascertained) and used for subsequent assessments, a common practice in population genetic studies. This problem can occur in panmictic populations with no local adaptation.Biased results from choosing high‐FSTmarkers may have severe downstream implications for management and conservation, such as erroneous conservation unit delineation, which could squander limited conservation resources to protect incorrectly defined ‘populations’. Furthermore, we caution that high‐grading is not limited toFSTapproaches; high‐grading bias is a concern whenever a small subset of markers are first chosen to explain differences among groups based on their degree of difference and are subsequently reused to estimate the degree of difference among those groups. For example, selecting highFSTloci for use in a GT‐seq panel or using differentially expressed genes to plot sample membership in multivariate space can both result in spurious structure when none exists. We illustrate that using statistically based outlier tests in place of arbitraryFSTcut‐offs can reduce bias. Alternatively, permutation tests or cross‐evaluation can be used to detect high‐grading bias. We provide an R package, PCAssess, to help researchers detect and prevent high‐grading bias in genetic datasets by automating permutation tests and principal component analyses (https://github.com/hemstrow/PCAssess).
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- Award ID(s):
- 1924537
- PAR ID:
- 10614517
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology Resources
- Volume:
- 25
- Issue:
- 7
- ISSN:
- 1755-098X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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