Estimating genotyping error rates from Mendelian errors in SNP array genotypes and their impact on inference

Saunders, Ian; Brohede, Jesper; Hannan, Garry N.

doi:10.1016/j.ygeno.2007.05.011

Cited by 69 publications

(70 citation statements)

References 12 publications

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“…A small number of Mendelian inconsistencies are expected due to de novo mutation, but the observation of many inconsistencies is a strong indicator of genotyping error. Mendelian errors can thus be used to calibrate methods and filter data (Saunders et al 2007).…”

Section: Ox1 3lb United Kingdommentioning

confidence: 99%

Indels, structural variation, and recombination drive genomic diversity in Plasmodium falciparum

et al. 2016

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The malaria parasite Plasmodium falciparum has a great capacity for evolutionary adaptation to evade host immunity and develop drug resistance. Current understanding of parasite evolution is impeded by the fact that a large fraction of the genome is either highly repetitive or highly variable and thus difficult to analyze using short-read sequencing technologies. Here, we describe a resource of deep sequencing data on parents and progeny from genetic crosses, which has enabled us to perform the first genome-wide, integrated analysis of SNP, indel and complex polymorphisms, using Mendelian error rates as an indicator of genotypic accuracy. These data reveal that indels are exceptionally abundant, being more common than SNPs and thus the dominant mode of polymorphism within the core genome. We use the high density of SNP and indel markers to analyze patterns of meiotic recombination, confirming a high rate of crossover events and providing the first estimates for the rate of non-crossover events and the length of conversion tracts. We observe several instances of meiotic recombination within copy number variants associated with drug resistance, demonstrating a mechanism whereby fitness costs associated with resistance mutations could be compensated and greater phenotypic plasticity could be acquired.

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Section: Ox1 3lb United Kingdommentioning

confidence: 99%

Indels, structural variation, and recombination drive genomic diversity in Plasmodium falciparum

et al. 2016

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“…In population-based samples, these errors are often detected when follow-up Sanger sequencing fails to validate calls. However, with family based data, mendelian inheritance errors (MIEs) can help identify erroneous sequencing calls given that mutation occurs infrequently [3-5]. Although filters have been developed for whole genome sequencing (WGS) to identify regions of high complexity often associated with errors [1], there are no consensus guidelines for quality control procedures.…”

Section: Introductionmentioning

confidence: 99%

Using Mendelian inheritance errors as quality control criteria in whole genome sequencing data set

Pilipenko

Kurowski

et al. 2014

BMC Proc

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Although the technical and analytic complexity of whole genome sequencing is generally appreciated, best practices for data cleaning and quality control have not been defined. Family based data can be used to guide the standardization of specific quality control metrics in nonfamily based data. Given the low mutation rate, Mendelian inheritance errors are likely as a result of erroneous genotype calls. Thus, our goal was to identify the characteristics that determine Mendelian inheritance errors. To accomplish this, we used chromosome 3 whole genome sequencing family based data from the Genetic Analysis Workshop 18. Mendelian inheritance errors were provided as part of the GAW18 data set. Additionally, for binary variants we calculated Mendelian inheritance errors using PLINK. Based on our analysis, nonbinary single-nucleotide variants have an inherently high number of Mendelian inheritance errors. Furthermore, in binary variants, Mendelian inheritance errors are not randomly distributed. Indeed, we identified 3 Mendelian inheritance error peaks that were enriched with repetitive elements. However, these peaks can be lessened with the inclusion of a single filter from the sequencing file. In summary, we demonstrated that erroneous sequencing calls are nonrandomly distributed across the genome and quality control metrics can dramatically reduce the number of mendelian inheritance errors. Appropriate quality control will allow optimal use of genetic data to realize the full potential of whole genome sequencing.

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“…Because alleles were randomly selected, an allele chosen to contain an error could be replaced with the same allele. We chose genotyping error rates of 0, 0.005, 0.01 and 0.03 because they encompass the average documented error rates for SNPs and microsatellites (Pompanon et al, 2005;Saunders et al, 2007).…”

Section: Validationmentioning

confidence: 99%

Bayesian parentage analysis with systematic accountability of genotyping error, missing data and false matching

2013

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Motivation: The goal of any parentage analysis is to identify as many parent-offspring relationships as possible, while minimizing incorrect assignments. Existing methods can achieve these ends, but they require additional information in the form of demographic data, thousands of markers and/or estimates of genotyping error rates. For many non-model systems, it is simply not practical, cost-effective or logistically feasible to obtain this information. Here, we develop a Bayesian parentage method that only requires the sampled genotypes to account for genotyping error, missing data and false matches. Results: Extensive testing with microsatellite and SNP datasets reveals that our Bayesian parentage method reliably controls for the number of false assignments, irrespective of the genotyping error rate. When the number of loci is limiting, our approach maximizes the number of correct assignments by accounting for the frequencies of shared alleles. Comparisons with exclusion and likelihood-based methods on an empirical salmon dataset revealed that our Bayesian method had the highest ratio of correct to incorrect assignments. Availability: Our program SOLOMON is available as an R package from the CRAN website. SOLOMON comes with a fully functional graphical user interface, requiring no user knowledge about the R programming environment. In addition to performing Bayesian parentage analysis, SOLOMON includes Mendelian exclusion and a priori power analysis modules. Further information and user support can be found at https://sites.google.com/site/parentagemethods/.

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Estimating genotyping error rates from Mendelian errors in SNP array genotypes and their impact on inference

Cited by 69 publications

References 12 publications

Indels, structural variation, and recombination drive genomic diversity in Plasmodium falciparum

Indels, structural variation, and recombination drive genomic diversity in Plasmodium falciparum

Using Mendelian inheritance errors as quality control criteria in whole genome sequencing data set

Bayesian parentage analysis with systematic accountability of genotyping error, missing data and false matching

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