The Relative Power of Family-Based and Case-Control Designs
for Linkage Disequilibrium Studies of Complex Human Diseases
I. DNA Pooling
Neil Risch1,2,3,4 and Jun Teng3
Departments of 1 Genetics and
2 Health Research and Policy,
Stanford University School of Medicine and
3
Department of Statistics, Stanford University, Stanford,
California 94305 USA
4
Corresponding author.
Genome Research,
8(12):1273-1288 (1998)
Abstract
We consider statistics for analyzing a variety of
family-based and nonfamily-based designs for detecting
linkage disequilibrium of a marker with a disease
susceptibility locus. These designs include sibships with
parents, sibships without parents, and use of unrelated
controls. We also provide formulas for and evaluate the
relative power of different study designs using these
statistics. In this first paper in the series, we derive statistical
tests based on data derived from DNA pooling experiments
and describe their characteristics. Although designs based
on affected and unaffected sibs without parents are usually
robust to population stratification, they suffer a loss of power
compared with designs using parents or unrelateds as
controls. Although increasing the number of unaffected sibs
improves power, the increase is generally not substantial.
Designs including sibships with multiple affected sibs are
typically the most powerful, with any of these control
groups, when the disease allele frequency is low. When the
allele frequency is high, however, designs with unaffected
sibs as controls do not retain this advantage. In designs with
parents, having an affected parent has little impact on the
power, except for rare dominant alleles, where the power is
increased compared with families with no affected parents.
Finally, we also demonstrate that for sibships with parents,
only the parents require individual genotyping to derive the
TDT statistic, whereas all the offspring can be pooled. This
can potentially lead to considerable savings in genotyping,
especially for multiplex sibships. The formulas and tables
we derive should provide some guidance to investigators
designing nuclear family-based linkage disequilibrium
studies for complex diseases.
Association Studies