Assessing the utility of intermediate phenotypes for genetic mapping of psychiatric disease

Abstract

Intermediate phenotypes are traits positioned somewhere between genetic variation and disease. They represent a target for attempts to find disease-associated genetic variants and elucidation of mechanisms. Psychiatry has been particularly enamoured with intermediate phenotypes, due to uncertainty about disease aetiology, inconclusive results in early psychiatric genetic studies, and their appeal relative to traditional diagnostic categories. In this review, we argue that new genetic findings are relevant to the question of the utility of these constructs. In particular, results from genome-wide association studies of psychiatric disorders now allow an assessment of the potential role of particular intermediate phenotypes. Based on such an analysis, as well as other recent results, we conclude that intermediate phenotypes are likely to be most valuable in understanding mechanism.

Box Figure. Genotype by Recall

A – Schematic representation of how recall by genotype studies mirror randomized controlled trial designs and thus allow for the development of causal inference. B - Power to detect differences across homozygotes for a genotype “x” for a model phenotypic measurement following the recall of 200 participants either at random (given a range of minor allele frequencies, 0.1-0.4) or where balanced groups each of 100 homozygotes have been selected for assessment (“recall fixed”). Total sample size = 200, α= 0.05. For the purposes of illustration, high-cost target data are polysomngraphy-recorded neuro-oscillatory brain activity characteristics taken from [95].

Figure 1. Statistical Power

Simulated data are plotted to show power (vertical axis) for different sample sizes (horizontal axis) for six different effect sizes, expressed as odds ratios. The simulations use two million SNPs and are taken from [97], which contains details of the parameters used. The significance level was set at 5×10−8. Sample size is shown for the number of cases required; the simulations assume an equal number of controls.