Endophenotype best practices

Abstract

This review examines the current state of electrophysiological endophenotype research and recommends best practices that are based on knowledge gleaned from the last decade of molecular genetic research with complex traits. Endophenotype research is being oversold for its potential to help discover psychopathology relevant genes using the types of small samples feasible for electrophysiological research. This is largely because the genetic architecture of endophenotypes appears to be very much like that of behavioral traits and disorders: they are complex, influenced by many variants (e.g., tens of thousands) within many genes, each contributing a very small effect. Out of over 40 electrophysiological endophenotypes covered by our review, only resting heart, a measure that has received scant advocacy as an endophenotype, emerges as an electrophysiological variable with verified associations with molecular genetic variants. To move the field forward, investigations designed to discover novel variants associated with endophenotypes will need extremely large samples best obtained by forming consortia and sharing data obtained from genome wide arrays. In addition, endophenotype research can benefit from successful molecular genetic studies of psychopathology by examining the degree to which these verified psychopathology-relevant variants are also associated with an endophenotype, and by using knowledge about the functional significance of these variants to generate new endophenotypes. Even without molecular genetic associations, endophenotypes still have value in studying the development of disorders in unaffected individuals at high genetic risk, constructing animal models, and gaining insight into neural mechanisms that are relevant to clinical disorder.

Keywords: Biomarker; Candidate gene; Data sharing; Endophenotype; GREML; GWAS; Genes; Heritability.

Figure 1. GWAS-significant Effect Sizes for Phenotypes, Endophenotypes, and Biomarkers

Plotted here are GWAS-significant loci from large-scale GWAS meta-analyses of serum urate, cotinine levels (a nicotine metabolite) in smokers, total cholesterol, bone mineral density, cigarettes per day, BMI, height, brain anatomy volumes from structural MRI, resting heart rate, glycemic traits, neuroticism, depressive symptoms, subjective wellbeing, months of educational attainment, and antisaccade eye movements. Phenotypes are ordered by the maximum reported effect size except for Antisaccade, which was based on a single study and is undoubtedly an overestimate. The effect sizes for each trait illustrate the effect size distribution differences between the more “biological” measures such as cholesterol levels, brain volumes, and antisaccade eye movements, and genetically distal phenotypes such as BMI and height. Except for the three blood-derived phenotypes serum urate, cotinine and total cholesterol, all variants account for less than 1% of the variance in the corresponding trait.

Figure 2

Power calculations for GREML analyses of SNP heritability and genetic correlations. In Panel A, power is plotted against sample size for three di_erent levels of SNP heritability (the total phenotypic variance accounted for by measured SNPs and SNPs in LD with them): h2 of 0.20 (plotted in red), 0.40 (plotted in blue), and 0.60 (plotted in green). The dashed horizontal line represents power of 80%. Dropping an imaginary vertical line to the x-axis from the point where each curve crosses this line provides an estimate of the sample size needed to have adequate power (80% power) to detect a SNP heritability of the corresponding magnitude. Panel B plots power against sample size for detecting genetic correlations, the proportion of variance shared by two phenotypes due to measured SNPs. The SNP heritability is assumed to be the same for both phenotypes, and the same three levels are used as in Panel A. Power is estimated for four di_erent phenotypic correlations, r = .10 to r = .40. The true genetic correlation is assumed to account for 80% of the phenotypic correlation. All power estimates were conducted using R code provided by Jian Yang on the GCTA software discussion board (http://gcta.freeforums.net/board/1/gctadiscussion-board).

Figure 3. Prioritizing Candidate Genes/Variants for Follow-up Study

The usual set of candidate variants studied in psychiatric genetics and psychiatric endophenotype candidate gene research is represented in the upper left-hand corner. They are variants with plausible mechanisms based on behavioral neuroscience but inconsistent evidence for association. All candidates with high evidence for association are worthy of followup, especially those with highly plausible mechanisms of effect.