  An instance of a group testing problem is a set of objects $\cO$ and an
unknown subset $P$ of $\cO$. The task is to determine $P$ by using queries of
the type ``does $P$ intersect $Q$'', where $Q$ is a subset of $\cO$. This
problem occurs in areas such as fault detection, multiaccess communications,
optimal search, blood testing and chromosome mapping. Consider the two stage
algorithm for solving a group testing problem. In the first stage a
predetermined set of queries are asked in parallel and in the second stage, $P$
is determined by testing individual objects. Let $n=\cardof{\cO}$. Suppose that
$P$ is generated by independently adding each $x\in \cO$ to $P$ with
probability $p/n$. Let $q_1$ ($q_2$) be the number of queries asked in the
first (second) stage of this algorithm. We show that if
$q_1=o(\log(n)\log(n)/\log\log(n))$, then $\Exp(q_2) = n^{1-o(1)}$, while there
exist algorithms with $q_1 = O(\log(n)\log(n)/\log\log(n))$ and $\Exp(q_2) =
o(1)$. The proof involves a relaxation technique which can be used with
arbitrary distributions. The best previously known bound is $q_1+\Exp(q_2) =
\Omega(p\log(n))$. For general group testing algorithms, our results imply that
if the average number of queries over the course of $n^\gamma$ ($\gamma>0$)
independent experiments is $O(n^{1-\epsilon})$, then with high probability
$\Omega(\log(n)\log(n)/\log\log(n))$ non-singleton subsets are queried. This
settles a conjecture of Bill Bruno and David Torney and has important
consequences for the use of group testing in screening DNA libraries and other
applications where it is more cost effective to use non-adaptive algorithms
and/or too expensive to prepare a subset $Q$ for its first test.
