This paper compares five measures of linkage disequilibrium (LD) for fine-scale mapping: the correlation coefficient Δ, Lewontin's D', the robust formulation of population attributable risk δ, Yule's Q, and Kaplan and Weir's proportional difference d. The study investigates these measures under the assumption of initial complete LD between disease and marker loci. The results show that δ is the most effective measure for fine mapping because it is directly related to the recombination fraction between the disease and marker loci and is invariant when disease haplotypes are sampled at a rate higher than their population frequencies, as in case-control studies. D' yields results comparable to δ in many realistic settings. Q performs best among the remaining three measures. All measures show some sensitivity to marker allele frequencies, but Q, Δ, and d are most sensitive to variation in marker allele frequencies across loci. The study also discusses the performance of these measures in deterministic calculations and stochastic simulations. It shows that δ is most directly related to the recombination fraction θ and is the ideal measure for simple disequilibrium mapping. δ is also robust to case-control sampling, unlike D', which is affected by marker allele and haplotype frequencies. Q is less sensitive to marker allele frequencies than Δ and d but still shows deviation from symmetry. The study concludes that δ is the best measure for fine mapping, especially in case-control studies, and that its performance is not affected by the configuration of marker allele frequencies. The results suggest that δ is a more reliable measure for fine mapping than other LD measures.This paper compares five measures of linkage disequilibrium (LD) for fine-scale mapping: the correlation coefficient Δ, Lewontin's D', the robust formulation of population attributable risk δ, Yule's Q, and Kaplan and Weir's proportional difference d. The study investigates these measures under the assumption of initial complete LD between disease and marker loci. The results show that δ is the most effective measure for fine mapping because it is directly related to the recombination fraction between the disease and marker loci and is invariant when disease haplotypes are sampled at a rate higher than their population frequencies, as in case-control studies. D' yields results comparable to δ in many realistic settings. Q performs best among the remaining three measures. All measures show some sensitivity to marker allele frequencies, but Q, Δ, and d are most sensitive to variation in marker allele frequencies across loci. The study also discusses the performance of these measures in deterministic calculations and stochastic simulations. It shows that δ is most directly related to the recombination fraction θ and is the ideal measure for simple disequilibrium mapping. δ is also robust to case-control sampling, unlike D', which is affected by marker allele and haplotype frequencies. Q is less sensitive to marker allele frequencies than Δ and d but still shows deviation from symmetry. The study concludes that δ is the best measure for fine mapping, especially in case-control studies, and that its performance is not affected by the configuration of marker allele frequencies. The results suggest that δ is a more reliable measure for fine mapping than other LD measures.