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New Concepts for Distinguishing the Hidden Patterns of Linkage Disequilibrium Which Underlie Association Between Genotypes and Complex Phenotypes

Published online by Cambridge University Press:  21 February 2012

Jacqueline Wicks*
Affiliation:
Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia; Australian Research Council Centre for Complex Systems, School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia. jacki@itee.uq.edu.au
Susan A. Treloar
Affiliation:
Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia.
Nicholas G. Martin
Affiliation:
Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia.
David L. Duffy
Affiliation:
Genetic Epidemiology Laboratory, Queensland Institute of Medical Research, Brisbane, Australia.
*
*Address for correspondence: Jacki Wicks, ARC Centre for Complex Systems, School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.

Abstract

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Adisappointing feature of conventional methods for detecting association between DNA variation and a phenotype of interest is that they tell us little about the hidden pattern of linkage disequilibrium (LD) with the functional variant that is actually responsible for the association. This limitation applies to case-control studies and also to the transmission/disequilibrium test (TDT) and other family-based association methods. Here we present a fresh perspective on genetic association based on two novel concepts called ‘LD squares’ and ‘equi- risk alleles’. These describe and characterize the different patterns of gametic LD which underlie genetic association. These concepts lead to a general principle – the Equi-Risk Allele Segregation Principle – which captures the way in which underlying LD patterns affect the transmission patterns of genetic variants associated with a phenotype. This provides a basis for distinguishing the hidden LD patterns and might help to locate the functional variants responsible for the association.

Type
Articles
Copyright
Copyright © Cambridge University Press 2005