Evidence map›Paper›PMID 33683359›Full record

ArticleGenetics2021

Distinct error rates for reference and nonreference genotypes estimated by pedigree analysis.

Richard J Wang, Predrag Radivojac, Matthew W Hahn

Abstract read
In one paragraph

Article in Genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

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11citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. PidTools: Algorithm and web tools for crop pedigree identification analysis.Computational and structural biotechnology journal · 2024
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Richard J WangDepartment of Biology, Indiana University, Bloomington, IN 47405, USA.
Predrag RadivojacKhoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
Matthew W HahnDepartment of Biology, Indiana University, Bloomington, IN 47405, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Errors in genotype calling can have perverse effects on genetic analyses, confounding association studies, and obscuring rare variants. Analyses now routinely incorporate error rates to control for spurious findings. However, reliable estimates of the error rate can be difficult to obtain because of their variance between studies. Most studies also report only a single estimate of the error rate even though genotypes can be miscalled in more than one way. Here, we report a method for estimating the rates at which different types of genotyping errors occur at biallelic loci using pedigree information. Our method identifies potential genotyping errors by exploiting instances where the haplotypic phase has not been faithfully transmitted. The expected frequency of inconsistent phase depends on the combination of genotypes in a pedigree and the probability of miscalling each genotype. We develop a model that uses the differences in these frequencies to estimate rates for different types of genotype error. Simulations show that our method accurately estimates these error rates in a variety of scenarios. We apply this method to a dataset from the whole-genome sequencing of owl monkeys (Aotus nancymaae) in three-generation pedigrees. We find significant differences between estimates for different types of genotyping error, with the most common being homozygous reference sites miscalled as heterozygous and vice versa. The approach we describe is applicable to any set of genotypes where haplotypic phase can reliably be called and should prove useful in helping to control for false discoveries.

Indexed as

GenotypePedigreeAnimalsAotidaeFemaleGenotyping TechniquesMaleReference StandardsReproducibility of Resultsgenotyping error ratehaplotype phasepedigree analysiswhole-genome sequencing

Identifiers

PMID33683359
PMCPMC8045694

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.