Copulas in QTL mapping.

The standard variance components method for mapping quantitative trait loci is derived on the assumption of normality. Unsurprisingly, statistical tests based on this method do not perform so well if this assumption is not satisfied. We use the statistical concept of copulas to relax the assumption of normality and derive a test that can perform well under any distribution of the continuous trait. In particular, we discuss bivariate normal copulas in the context of sib-pair studies. Our approach is illustrated by a linkage analysis of lipoprotein(a) levels, whose distribution s highly skewed.... Mehr ...

Verfasser: Basrak, B.
Klaassen, C.
Beekman, M.
Martin, N.G.
Boomsma, D.I.
Dokumenttyp: Artikel
Erscheinungsdatum: 2004
Reihe/Periodikum: Basrak , B , Klaassen , C , Beekman , M , Martin , N G & Boomsma , D I 2004 , ' Copulas in QTL mapping. ' , Behavior Genetics , vol. 34 , no. 2 , pp. 161-171 . https://doi.org/10.1023/B:BEGE.0000013730.63991.ba
Schlagwörter: /dk/atira/pure/keywords/cohort_studies/netherlands_twin_register_ntr_ / name=Netherlands Twin Register (NTR)
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29213968
Datenquelle: BASE; Originalkatalog
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Link(s) : https://research.vu.nl/en/publications/6aa9f7d6-ae7c-4800-a51a-06a6bc5c43e6

The standard variance components method for mapping quantitative trait loci is derived on the assumption of normality. Unsurprisingly, statistical tests based on this method do not perform so well if this assumption is not satisfied. We use the statistical concept of copulas to relax the assumption of normality and derive a test that can perform well under any distribution of the continuous trait. In particular, we discuss bivariate normal copulas in the context of sib-pair studies. Our approach is illustrated by a linkage analysis of lipoprotein(a) levels, whose distribution s highly skewed. We demonstrate that the asymptotic critical levels of the test can still be calculated using the interval mapping approach. The new method can be extended to more general pedigrees and multivariate phenotypes in a similar way as the original variance components method.