Endophenotype best practices.
- Authors
- Iacono, William G; Malone, Stephen M; Vrieze, Scott I
- Year
- 2017
- Journal
- International journal of psychophysiology : official journal of the International Organization of Psychophysiology
- PMID
- 27473600
- DOI
- 10.1016/j.ijpsycho.2016.07.516
- PMCID
- PMC5219856
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.
GWAS-significant Effect Sizes for Phenotypes, Endophenotypes, and BiomarkersPlotted 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.
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).
Prioritizing Candidate Genes/Variants for Follow-up StudyThe 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.
| # | Section | Preview |
|---|---|---|
| 100 | 7.0 Recommendations to Advance Endophenotype Genetics — 7.3 Adequate power to detect individual effects is crucial but almost never attained in existing endophenotype genetic association studies — 7.3.2 Power in GREML | For the sake of simplicity, power estimates for genetic correlations assume that the heritability of… |
| 101 | 7.0 Recommendations to Advance Endophenotype Genetics — 7.3 Adequate power to detect individual effects is crucial but almost never attained in existing endophenotype genetic association studies — 7.3.2 Power in GREML | the heritability of the two traits is similar affect power both positively and negatively, but the… |
| 102 | 7.0 Recommendations to Advance Endophenotype Genetics — 7.4 Summary of recommendations | The above recommendations for variant discovery are simple but important; our key points are… |
| 103 | 8.0 Moving from GWAS-implicated Loci to Causal Variants | Up to now we have focused primarily on recommendations for improving the discovery of new genes… |
| 104 | 8.0 Moving from GWAS-implicated Loci to Causal Variants | GWAS takes advantage of the LD structure of the genome, which reflects the fact that through… |
| 105 | 8.0 Moving from GWAS-implicated Loci to Causal Variants | A common technique to understand a locus is to fine map it, or genotype/sequence all variants within… |
| 106 | 8.0 Moving from GWAS-implicated Loci to Causal Variants | Once most or even all variants within a locus have been genotyped (e.g., through sequencing), one… |
| 107 | 8.0 Moving from GWAS-implicated Loci to Causal Variants | A potentially more powerful approach to disentangle LD from truly independently associated effects… |
| 108 | 8.0 Moving from GWAS-implicated Loci to Causal Variants | Another approach is to test putatively functional variants within a locus for association. One… |
| 109 | 8.0 Moving from GWAS-implicated Loci to Causal Variants | epigenomic annotations (e.g., from ENCODE or NIH Roadmap) indicating that the intronic locus in FTO… |
| 110 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes | In this section we review the literature on electrophysiological biomarkers, with an emphasis on… |
| 111 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes | it is likely that the failure of endophenotypes in general to lead to verified molecular genetic… |
| 112 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes — 9.1 A review of how well candidate endophenotypes satisfy threshold criteria | Table 5 lists a variety of electrophysiological measures that are either considered biomarkers or… |
| 113 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes — 9.1 A review of how well candidate endophenotypes satisfy threshold criteria | possible to be exhaustive. We did not include measures reflecting neurobiological systems that… |
| 114 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes — 9.1 A review of how well candidate endophenotypes satisfy threshold criteria | The sheer number of entries in Table 5 attests to the level of interest in endophenotypes and… |
| 115 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes — 9.1 A review of how well candidate endophenotypes satisfy threshold criteria | measures that are more recently available to endophenotype researchers. In other cases, such as the… |
| 116 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes — 9.1 A review of how well candidate endophenotypes satisfy threshold criteria | The second column of Table 5 contains our evaluation of the strength of evidence for considering the… |
| 117 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes — 9.1 A review of how well candidate endophenotypes satisfy threshold criteria | in healthy first-degree relatives, such as PFC broadband noise and the feedback positivity. We… |
| 118 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes — 9.1 A review of how well candidate endophenotypes satisfy threshold criteria | While necessarily imprecise and somewhat subjective, we feel that this scheme is nevertheless… |
| 119 | 9.0 Selective Review of Electrophysiological Biomarkers as Candidate Endophenotypes — 9.2 Selective review of molecular genetic studies of endophenotypes | The vast majority of molecular genetic studies of putative endophenotypes have been candidate gene… |
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