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      Sibship reconstruction from genetic data with typing errors.

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      Genetics
      Oxford University Press (OUP)

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          Abstract

          Likelihood methods have been developed to partition individuals in a sample into full-sib and half-sib families using genetic marker data without parental information. They invariably make the critical assumption that marker data are free of genotyping errors and mutations and are thus completely reliable in inferring sibships. Unfortunately, however, this assumption is rarely tenable for virtually all kinds of genetic markers in practical use and, if violated, can severely bias sibship estimates as shown by simulations in this article. I propose a new likelihood method with simple and robust models of typing error incorporated into it. Simulations show that the new method can be used to infer full- and half-sibships accurately from marker data with a high error rate and to identify typing errors at each locus in each reconstructed sib family. The new method also improves previous ones by adopting a fresh iterative procedure for updating allele frequencies with reconstructed sibships taken into account, by allowing for the use of parental information, and by using efficient algorithms for calculating the likelihood function and searching for the maximum-likelihood configuration. It is tested extensively on simulated data with a varying number of marker loci, different rates of typing errors, and various sample sizes and family structures and applied to two empirical data sets to demonstrate its usefulness.

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          Author and article information

          Journal
          Genetics
          Genetics
          Oxford University Press (OUP)
          0016-6731
          0016-6731
          Apr 2004
          : 166
          : 4
          Affiliations
          [1 ] Institute of Zoology, Zoological Society of London, London NW1 4RY, United Kingdom. jinliang.wang@ioz.ac.uk
          Article
          166/4/1963
          10.1534/genetics.166.4.1963
          1470831
          15126412
          19b08842-f727-4606-8687-1ee5d820f8fe
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