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The decay of linkage disequilibrium under random union of gametes: How to calculate Bennett’s principal components. (English) Zbl 1011.92038
Summary: How rapidly does an arbitrary pattern of statistical association among a set of loci decay under meiosis and random union of gametes? This problem is non-trivial, even in the case of an infinitely large population where selection and other forces are absent. J.H. Bennett [Ann. Hum. Genet. 18, 311-317 (1954) found that, for an arbitrary number of loci with an arbitrary linkage map, it is possible to define measures of linkage disequilibrium that decay geometrically with time. He found a recursive method for deriving expressions for these variables in terms of “allelic moments” (the factorial moments about the origin of the “allelic indicators”), and expressions for the allelic moments in terms of his new variables. However, Bennett nowhere stated his recursive algorithm explicitly, nor did he give a general formula for his measures of linkage disequilibrium, for an arbitrary number of loci. Recursive definitions of Bennett’s variables were obtained by Yu.I. Lyubich [see “Mathematical structures in population genetics.” (1992; Zbl 0747.92019)]. However, the expressions generated by these recursions are not the same as those found by Bennett. (They do not express Bennett’s variables as functions of the allelic moments.) Lyubich’s derivations employ genetic algebras.
Here, I present a method for obtaining explicit expressions for Bennett’s variables in terms of the allelic moments. I show that the transformation from the allelic moments to Bennett’s variables and the inverse transformation always have the form that Bennett claimed. (This transformation and its inverse have essentially the same form.) I present general recursions for calculating the coefficients in the forward transformation and the coefficients in the inverse transformation. My derivations involve combinatorial arguments and ordinary algebra only. The special case of unlinked loci is briefly discussed.

92D10 Genetics and epigenetics
62P10 Applications of statistics to biology and medical sciences; meta analysis
Full Text: DOI
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