D.M.-F.). “
“(Neuron 83, 1354–1368; September 17, 2014) In the original version of this paper, we inadvertently listed the reference and corresponding citation of Lacoste et al. (2014) as Baptiste et al. (2014). The reference and citation have now been corrected in the article online. “
“There is now a series of standard tools for the generation and analysis of -ome-scale human genotype data. Many scientific PF-06463922 questions have been asked using these methods, but most have focused on the traditional case-control genome-wide association (GWA) study (Hardy and Singleton, 2009). In the context of Alzheimer’s disease (AD) such approaches have successfully identified a considerable number of
loci that harbor risk variants for disease (Harold et al., 2009; Hollingworth et al., 2011; Lambert et al., 2009; Naj et al.,
2011; Seshadri et al., 2010). A fairly standard approach to getting more out of these data is to analyze increasingly larger cohorts; increasing n leads to greater power and a finer resolution of the genetic basis of disease. In this issue of Neuron, Cruchaga et al. (2013) describe an alternate approach, leveraging the power of genetics to find variability that modulates a disease-relevant endophenotype, and in turn using these results to identify risk alleles for AD. The notion of endophenotype as a route AZD9291 in vitro to understanding the basis of disease is one that has received considerable attention in recent years, particularly in the field of psychiatric genetics (Cruchaga et al., 2012; Hinrichs et al., 2010; Kauwe et al., 2007, 2008). The concept is simple and centers on addressing a problem that is particularly acute in late onset disorders: the temporal distance between initiation of a disease process and clinical presentation is a long one. In this time, myriad factors may alter the underlying disease process, including those within genetic, epigenetic, environmental, and stochastic spheres; consequently, the ultimate outcome (i.e., disease presentation) can be
highly variable. The endophenotype is meant to sit in the gap between this alpha and omega, and in the best case is a measure that directly—rather than indirectly—reflects the progression of the underlying disease process. over Measuring an intermediate disease-related event offers critical advantages over readouts of clinical presentation; such intermediate events can serve as preclinical biomarkers of disease or as a method to track progression. Because it is more proximal to the underlying disease process, and probably a more objective readout, intermediate events also may have less innate variance. In the current work, Cruchaga et al. (2013) performed a GWA study on ∼1,300 cerebrospinal fluid (CSF) samples measured for both tau and tau phosphorylated at threonine 181 (ptau), both of which are established biomarkers for AD. As expected, Cruchaga et al.