Zygosity Diagnosis in the Absence of Genotypic Data: An Approach Using Latent Class Analysis

Heath, Andrew C., Nyholt, Dale R., Neuman, Rosalind, Madden, Pamela A. F., Bucholz, Kathleen K., Todd, Richard D., Nelson, Elliot C., Montgomery, Grant W. and Martin, Nicholas G. (2003) Zygosity Diagnosis in the Absence of Genotypic Data: An Approach Using Latent Class Analysis. Twin Research and Human Genetics, 6 1: 22-26.


Author Heath, Andrew C.
Nyholt, Dale R.
Neuman, Rosalind
Madden, Pamela A. F.
Bucholz, Kathleen K.
Todd, Richard D.
Nelson, Elliot C.
Montgomery, Grant W.
Martin, Nicholas G.
Title Zygosity Diagnosis in the Absence of Genotypic Data: An Approach Using Latent Class Analysis
Journal name Twin Research and Human Genetics   Check publisher's open access policy
ISSN 1369-0523
Publication date 2003
Sub-type Article (original research)
DOI 10.1375/136905203762687861
Volume 6
Issue 1
Start page 22
End page 26
Total pages 5
Editor N. G. Martin
K. M. Kirk
Place of publication Bowen Hills, Australia
Publisher Australian Academic Press
Collection year 2003
Language eng
Subject C1
321011 Medical Genetics
730107 Inherited diseases (incl. gene therapy)
Abstract For zygosity diagnosis in the absence of genotypic data, or in the recruitment phase of a twin study where only single twins from same-sex pairs are being screened, or to provide a test for sample duplication leading to the false identification of a dizygotic pair as monozygotic, the appropriate analysis of respondents' answers to questions about zygosity is critical. Using data from a young adult Australian twin cohort (N = 2094 complete pairs and 519 singleton twins from same-sex pairs with complete responses to all zygosity items), we show that application of latent class analysis (LCA), fitting a 2-class model, yields results that show good concordance with traditional methods of zygosity diagnosis, but with certain important advantages. These include the ability, in many cases, to assign zygosity with specified probability on the basis of responses of a single informant (advantageous when one zygosity type is being oversampled); and the ability to quantify the probability of misassignment of zygosity, allowing prioritization of cases for genotyping as well as identification of cases of probable laboratory error. Out of 242 twins (from 121 like-sex pairs) where genotypic data were available for zygosity confirmation, only a single case was identified of incorrect zygosity assignment by the latent class algorithm. Zygosity assignment for that single case was identified by the LCA as uncertain (probability of being a monozygotic twin only 76%), and the co-twin's responses clearly identified the pair as dizygotic (probability of being dizygotic 100%). In the absence of genotypic data, or as a safeguard against sample duplication, application of LCA for zygosity assignment or confirmation is strongly recommended.
Keyword Genetics & Heredity
Obstetrics & Gynecology
Reproductive Biology
Twin
Q-Index Code C1

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 32 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 37 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Access Statistics: 70 Abstract Views  -  Detailed Statistics
Created: Tue, 14 Aug 2007, 19:34:08 EST