Power comparison of parametric and nonparametric linkage tests in small pedigrees

Pak Chung Sham*, Ming Wei Lin, Jing Hua Zhao, David Curtis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


When the mode of inheritance of a disease is unknown, the LOD-score method of linkage analysis must take into account uncertainties in model parameters. We have previously proposed a parametric linkage test called 'MFLOD,' which does not require specification of disease model parameters. In the present study, we introduce two new model-free parametric linkage tests, known as 'MLOD' and 'MALOD.' These tests are defined, respectively, as the LOD score and the admixture LOD score, maximized (subject to the same constraints as MFLOD) over disease-model parameters. We compared the power of these three parametric linkage tests and that of two nonparametric linkage tests, NP(all) and NPL(pairs), which are implemented in GENEHUNTER. With the use of small pedigrees and a fully informative marker, we found the powers of MLOD, NPL(all), and NPL(pairs) to be almost equivalent to each other and not far below that of a LOD-score analysis performed under the assumption the correct genetic parameters. Thus, linkage analysis is not much hindered by uncertain mode of inheritance. The results also suggest that both parametric and nonparametric methods are suitable for linkage analysis of complex disorders in small pedigrees. However, whether these results apply to large pedigrees remains to be answered.

Original languageEnglish
Pages (from-to)1661-1668
Number of pages8
JournalAmerican Journal of Human Genetics
Issue number5
StatePublished - 2000


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