Quantifying genomic imprinting in the presence of linkage.
Genomic imprinting decreases the power of classical linkage analysis, in which paternal and maternal transmissions of marker alleles are equally weighted. Several methods have been proposed for taking genomic imprinting into account in the model-free linkage analysis of binary traits. However, none of these methods are suitable for the formal identification and quantification of genomic imprinting in the presence of linkage. In addition, the available methods are designed for use with pure sib-pairs, requiring artificial decomposition in cases of larger sibships, leading to a loss of power. We propose here the maximum likelihood binomial method adaptive for imprinting (MLB-I), which is a unified analytic framework giving rise to specific tests in sibships of any size for (i) linkage adaptive to imprinting, (ii) genomic imprinting in the presence of linkage, and (iii) partial versus complete genomic imprinting. In addition, we propose an original measure for quantifying genomic imprinting. We have derived and validated the distribution of the three tests under their respective null hypotheses for various genetic models, and have assessed the power of these tests in simulations. This method can readily be applied to genome-wide scanning, as illustrated here for leprosy sibships. Our approach provides a novel tool for dissecting genomic imprinting in model-free linkage analysis, and will be of considerable value for identifying and evaluating the contribution of imprinted genes to complex diseases.
Vincent
Quentin
Q
Laboratory of Human Genetics of Infectious Diseases, University of Paris René Descartes-INSERM U.550, Necker Medical School, Paris, 75015, France.
Alcaïs
Alexandre
A
Alter
Andrea
A
Schurr
Erwin
E
Abel
Laurent
L
D001699:Biometry; D005260:Female; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease; D018392:Genomic Imprinting; D006801:Humans; D007918:Leprosy / Q000235:genetics; D016013:Likelihood Functions; D008297:Male; D008957:Models, Genetic; D015233:Models, Statistical