The case-control design is one of the most commonly used designs in genome-
wide asociation studies. When we increase the sample size of either the
controls or, more importantly, the cases, the power of whatever test we use
will certainly increase. However increasing the sample size, means that addi-
tional individuals need to be genotyped and this implies extra financial costs.
However, nowadays with the emergence of genetic studies, a large number
of genetic data are available at low or no extra cost. Even though those
data may not be completely relevant to the current study, they can still be
used to increase the probability to identify true associations. Furthermore,
additional information, non-necessarily genetic, can also be used to improve
the power of a method.
In this thesis we extend the case-control design in order to take ad-
vantage of such types of additional data and/or information. We discuss
three designs; the case-cohort-control, the kin-cohort and the super-case–
case–control–super-control designs. For each of these, we present methods
that are adjusted or modified versions of standard case-control methods but
we also propose novel ones developed with those extended designs in mind.
Ultimately, we describe how those methods can be used in order to increase
the power of association tests, especially compared to similar methods of the