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Association studies assessing the relationship between a common polymorphism and disease generally compare allele frequencies in cases and controls. In such studies, a limited amount of information is often available about disease incidence in relatives. We hypothesised that more power could be obtained by incorporating the constraints imposed by the properties of a genetic polymorphism, and that power could be further increased by using family history (FH) information. We have developed a simple method for incorporating basic FH information from cases and controls into a genetic association study, assuming Hardy-Weinberg equilibrium (HWE) in the general population. We model the likelihood of the data in terms of the allele frequency and its relative risk (RR) of disease and perform likelihood ratio tests. Using simulations, we compared the power to detect an association using this approach with that of a 2 x 2 chi-squared test, for a range of disease models. The sample size required to detect an association is consistently lower for tests including the HWE constraint, with the largest reduction for more common alleles. The required sample size is reduced further by stratifying by FH. Stratifying by FH also improves the precision of the RR estimates. In situations where basic FH data are already available, this study shows that efficiency can be improved by the inclusion of even this small amount of extra information.

This paper summarizes the results of searching for evidence of loci contributing to simulated quantitative traits which are associated with a common genetic disease using two multipoint identity by descent (IBD) sharing methods: MIM and MAPMAKER/SIBS. In brief, by varying the lod-score threshold from 1 to 3, we found that MIM and MAPMAKER/SIBS have similar power to detect linkage, but MAPMAKER/SIBS consistently produces a higher number of false positives.

The multipoint identity-by-descent method (MIM) was used to analyze simulated data for quantitative traits from GAW9. A two-stage method of implementation was used. First, polymorphic markers spaced 6-12 cM apart were used to identify chromosomes of interest for each trait Q1-Q4; and second, for each of these chromosomes, markers spaced 2 cM apart were used to confirm the linkage detected and refine the region for the susceptibility loci. MIM performed well at both levels of mapping, correctly detecting major genes for trait Q3 on chromosome 2, trait Q2 on chromosome 1, and trait Q4 on chromosome 5.

The multipoint identity-by-descent method was developed to detect linkage to a specific chromosomal region through partitioning the genetic variance. This method has previously been applied to quantitative traits, and here is extended to a qualitative trait, where a dichotomous affected/unaffected status variable is transformed to a quantitative variable by incorporating covariates. This method is applied to the Alzheimer's disease data sets from Genetic Analysis Workshop 8, to investigate putative linkage to chromosomes 19 and 21. The multipoint identity-by-descent method is used to test for linkage through the qualitative trait, and for excess sharing of the chromosomal region among affected sibs. Results are compared to those of the affected-pedigree-member method and classical linkage analysis. None of these methods gave results showing clear linkage, with the only marginally significant results occurring for the Boston data set on chromosome 19 and the Duke data set for chromosome 21 using the multipoint identity-by-descent method.

The corporation of a linkage disequilibrium parameter, delta, into linkage analysis is illustrated for data from Genetic Analysis Workshop II. Points from a joint likelihood surface are calculated and displayed on a recombination fraction-linkage disequilibrium grid using a simple modification of LIPED. The approach is shown to increase the power of linkage analysis and the power of tests for heterogeneity of linkage for the simulated examples.