Sampling strategies for rare variant tests in case-control studies.

Advances in sequencing technology allow assessing the impact of rare variation on common disorders. For this purpose, methods combine rare variants across a gene and compare an aggregate statistic between cases and controls. However, sequencing many individuals is costly. Hence, it is necessary to identify case samples that are most likely to result in powerful tests under realistic model assumptions. Power can be increased by selecting cases that are highly likely to carry risk variants. As rare variants that contribute to the heritability of a disease co-segregate among affected family members, selecting cases that have affected family members may increase the power of rare variant tests considerably. Here I compare sequencing random cases to cases ascertained to have affected family members. I quantify the power of the different approaches and provide criteria for sample selection under different models of inheritance. Under a model of multiplicative gene-gene interaction, a sample of random cases has to be 2-16-fold larger to achieve the same power as a sample of cases ascertained to have affected family members. However, in traits with high heritability this power gain can be reduced or even reversed under models of additive gene-gene interaction. Hence study designs should depend on the studied disease's heritability and on the available sample size. I also show that selecting cases that share both chromosomes identical by descent with an affected sibling at candidate regions can result in a further power gain.