  Affected relatives are essential for pedigree linkage analysis, however, they
cause a violation of the independent sample assumption in case-control
association studies. To avoid the correlation between samples, a common
practice is to take only one affected sample per pedigree in association
analysis. Although several methods exist in handling correlated samples, they
are still not widely used in part because these are not easily implemented, or
because they are not widely known. We advocate the effective sample size method
as a simple and accessible approach for case-control association analysis with
correlated samples. This method modifies the chi-square test statistic,
p-value, and 95% confidence interval of the odds-ratio by replacing the
apparent number of allele or genotype counts with the effective ones in the
standard formula, without the need for specialized computer programs. We
present a simple formula for calculating effective sample size for many types
of relative pairs and relative sets. For allele frequency estimation, the
effective sample size method captures the variance inflation exactly. For
genotype frequency, simulations showed that effective sample size provides a
satisfactory approximation. A gene which is previously identified as a type 1
diabetes susceptibility locus, the interferon-induced helicase gene (IFIH1), is
shown to be significantly associated with rheumatoid arthritis when the
effective sample size method is applied. This significant association is not
established if only one affected sib per pedigree were used in the association
analysis. Relationship between the effective sample size method and other
methods -- the generalized estimation equation, variance of eigenvalues for
correlation matrices, and genomic controls -- are discussed.
