Evidence map›Paper›PMID 17851696›Full record

ArticleHuman genetics2007

Methods to impute missing genotypes for population data.

Zhaoxia Yu, Daniel J Schaid

Abstract read
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Article in Human genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Article
  2. Article
  3. Structured Matrix Completion with Applications to Genomic Data Integration.Journal of the American Statistical Association · 2016
    Article
  4. Lung Cancer Risk Prediction Using Common SNPs Located in GWAS-Identified Susceptibility Regions.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2015
    Article
  5. Article
  6. Coverage and efficiency in current SNP chips.European journal of human genetics : EJHG · 2014
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Effects of missing marker and segregation distortion on QTL mapping in F2 populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2010
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Zhaoxia YuDepartment of Statistics, University of California, Irvine, CA 92697, USA. yu.zhaoxia@ics.uci.edu
Daniel J Schaid

Funding

Quantitative Methods for Genetic EpidemiologyR01GM065450 · NIGMS · MAYO CLINIC ROCHESTER · PI SCHAID, DANIEL J. · 2002 to 2020
$5.6M
NIGMS NIH HHS GM065450NIGMS NIH HHS R01 GM065450
6 · The paper itself

Abstract

For large-scale genotyping studies, it is common for most subjects to have some missing genetic markers, even if the missing rate per marker is low. This compromises association analyses, with varying numbers of subjects contributing to analyses when performing single-marker or multi-marker analyses. In this paper, we consider eight methods to infer missing genotypes, including two haplotype reconstruction methods (local expectation maximization-EM, and fastPHASE), two k-nearest neighbor methods (original k-nearest neighbor, KNN, and a weighted k-nearest neighbor, wtKNN), three linear regression methods (backward variable selection, LM.back, least angle regression, LM.lars, and singular value decomposition, LM.svd), and a regression tree, Rtree. We evaluate the accuracy of them using single nucleotide polymorphism (SNP) data from the HapMap project, under a variety of conditions and parameters. We find that fastPHASE has the lowest error rates across different analysis panels and marker densities. LM.lars gives slightly less accurate estimate of missing genotypes than fastPHASE, but has better performance than the other methods.

Indexed as

GenotypeGenetic MarkersGenetics, PopulationHaplotypesHumansLinear ModelsLinkage DisequilibriumModels, GeneticModels, StatisticalPolymorphism, Single NucleotideStatistics, NonparametricGenetic Markers

Identifiers

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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.