Sampling strategies are a critical part of hypothesis testing: Specifically, how one samples determines what one can observe. Rather than simply seeking what was already known by considering autism and language impairment as two distinct clinical conditions, based on an arbitrary classification system, Bartlett et al. chose to test their hypotheses by using a clever sampling strategy and then combining distinct individuals with distinct diagnoses, autism and specific language impairment, into a single group of “affected” individuals. Considering seemingly distinct clinical syndromes as existing in a single “affected” state is a relatively new effort in genetic research; this stems from the observation that identical genetic variants consistently lead to distinct clinical syndromes. Among many examples, we might consider 16p11.2 copy number variations (CNVs) that lead to multiple phenotypes, such as autism, schizophrenia, intellectual disability, and language impairment (3–13). Such cross-condition sampling strategies are beginning to yield interesting and useful results. For example, the Psychiatric Genomics Consortium examined cross-disorder effects of genome-wide significant loci previously identified for bipolar disorder and schizophrenia. Investigators used genome-wide association study data on single nucleotide polymorphisms (SNPs) in 33,332 individuals with ASD, ADHD, bipolar disorder, schizophrenia, and major depressive disorders, along with 27,888 comparison subjects. The study showed genome-wide significance in intronic SNPs within ITIH3 and AS3MT, along with SNPs at two l-type voltage-gated calcium channel subunits, CACNA1C and CACNB2 (14). These results provide empirical evidence that these disorders have at least some level of shared genetic etiology.