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.
No entities extracted from this document yet.
No uploaded files.
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Auditory Event-Related Potentials in Two Rat Models of Attention-Deficit Hyperactivity Disorder: Evidence of Automatic Attention Deficits in Spontaneously Hypertensive Rats but Not in Latrophilin-3 Knockout Rats. | Brewer LM et al. | — | 2025 | → |
| Effects of Alcohol on EEG Activity: A Systematic Review Focused on Sex-Related Differences in Youth. | Elliott AS et al. | — | 2025 | → |
| Examining associations between genetic and neural risk for externalizing behaviors in adolescence and early adulthood. | Brislin SJ et al. | — | 2024 | → |
| Faster bi-stable visual switching in psychosis. | Killebrew KW et al. | — | 2024 | → |
| Mismatch negativity and polygenic risk scores for schizophrenia and bipolar disorder. | Pentz AB et al. | — | 2024 | → |
| Added value of neurotechnology for forensic psychiatric and psychological assessment. | Kempes M | — | 2023 | → |
| Endophenotype trait domains for advancing gene discovery in autism spectrum disorder. | Mosconi MW et al. | — | 2023 | → |
| Inferring the Genetic Influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition. | Hatoum AS et al. | — | 2023 | → |
| Longitudinal stability and change in time-frequency measures from an oddball task during adolescence and early adulthood. | Malone SM et al. | — | 2023 | → |
| The genomics of visuospatial neurocognition in obsessive-compulsive disorder: A preliminary GWAS. | Alemany-Navarro M et al. | — | 2023 | → |
| The psychosis human connectome project: Design and rationale for studies of visual neurophysiology. | Schallmo MP et al. | — | 2023 | → |
| A nonparametric Bayesian model for estimating spectral densities of resting-state EEG twin data. | Hart B et al. | — | 2022 | → |
| Associating complex traits with genetic variants: polygenic risk scores, pleiotropy and endophenotypes. | Fisch GS | — | 2022 | → |
| Genetic and environmental etiology of drinking motives in college students. | Savage JE et al. | — | 2022 | → |
| Genetics in the ADHD Clinic: How Can Genetic Testing Support the Current Clinical Practice? | Balogh L et al. | — | 2022 | → |
| Using multivariate endophenotypes to identify psychophysiological mechanisms associated with polygenic scores for substance use, schizophrenia, and education attainment. | Harper J et al. | — | 2022 | → |
| What Has Been Learned from Using EEG Methods in Research of ADHD? | McLoughlin G et al. | — | 2022 | → |
| Forgetting Unwanted Memories: Active Forgetting and Implications for the Development of Psychological Disorders. | Costanzi M et al. | — | 2021 | → |
| Orbitofrontal cortex thickness and substance use disorders in emerging adulthood: causal inferences from a co-twin control/discordant twin study. | Harper J et al. | — | 2021 | → |
| Parietal P3 and midfrontal theta prospectively predict the development of adolescent alcohol use. | Harper J et al. | — | 2021 | → |
| Physical exercise-related endophenotypes in anorexia nervosa. | Di Lodovico L et al. | — | 2021 | → |
| Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression. | Jones C et al. | — | 2021 | → |
| Social endophenotypes in autism spectrum disorder: A scoping review. | Tiede GM et al. | — | 2021 | → |
| The Effects of Alcohol and Cannabis Use on the Cortical Thickness of Cognitive Control and Salience Brain Networks in Emerging Adulthood: A Co-twin Control Study. | Harper J et al. | — | 2021 | → |
| Transcriptome-wide association study reveals two genes that influence mismatch negativity. | Bhat A et al. | — | 2021 | → |
| EEG connectivity as the possible endophenotype in adult ADHD. | Ishii R et al. | — | 2020 | → |
| Look duration at the face as a developmental endophenotype: elucidating pathways to autism and ADHD. | Gui A et al. | — | 2020 | → |
| Social attention: What is it, how can we measure it, and what can it tell us about autism and ADHD? | Braithwaite EK et al. | — | 2020 | → |
| The polygenic risk for obsessive-compulsive disorder is associated with the personality trait harm avoidance. | Bey K et al. | — | 2020 | → |
| Validating Online Measures of Cognitive Ability in Genes for Good, a Genetic Study of Health and Behavior. | Liu M et al. | — | 2020 | → |
| Endophenotype Research in Psychiatry-The Grasshopper Grows Up. | Roffman JL | — | 2019 | → |
| Error-related brain activity as a transdiagnostic endophenotype for obsessive-compulsive disorder, anxiety and substance use disorder. | Riesel A et al. | — | 2019 | → |
| Minnesota Center for Twin and Family Research. | Wilson S et al. | — | 2019 | → |
| Reduced premovement positivity during the stimulus-response interval precedes errors: Using single-trial and regression ERPs to understand performance deficits in ADHD. | Burwell SJ et al. | — | 2019 | → |
| Target-related parietal P3 and medial frontal theta index the genetic risk for problematic substance use. | Harper J et al. | — | 2019 | → |
| The erring brain: Error-related negativity as an endophenotype for OCD-A review and meta-analysis. | Riesel A | — | 2019 | → |
| The Utility of Event-Related Potentials in Clinical Psychology. | Hajcak G et al. | — | 2019 | → |
| Behavioral and EEG responses to social evaluation: A two-generation family study on social anxiety. | Harrewijn A et al. | — | 2018 | → |
| Classification and treatment of antisocial individuals: From behavior to biocognition. | Brazil IA et al. | — | 2018 | → |
| Conflict-related medial frontal theta as an endophenotype for alcohol use disorder. | Harper J et al. | — | 2018 | → |
| Developmental dynamics of autonomic function in childhood. | Gatzke-Kopp L et al. | — | 2018 | → |
| Endophenotypes in psychiatric disease: prospects and challenges. | Iacono WG | — | 2018 | → |
| Heart rate variability as candidate endophenotype of social anxiety: A two-generation family study. | Harrewijn A et al. | — | 2018 | → |
| Mapping Risk from Genes to Behavior: The Enduring and Evolving Influence of Irving Gottesman's Endophenotype Concept. | Dick DM | — | 2018 | → |
| Reduced Medial Frontal Positivity During the Stimulus-Response Interval Precedes Action Errors and Explains Task Deficits in Attention-Deficit Hyperactivity Disorder | Burwell SJ et al. | — | 2018 | — |
| Misunderstanding RDoC. | Lake JI et al. | — | 2017 | → |
| Psychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci. | Liu M et al. | — | 2017 | → |
| Rigor and replication: Toward improved best practices in human electrophysiology research. | Larson MJ et al. | — | 2017 | → |
| Testing the effects of adolescent alcohol use on adult conflict-related theta dynamics. | Harper J et al. | — | 2017 | → |
| Translating advances in the molecular basis of schizophrenia into novel cognitive treatment strategies. | O'Tuathaigh CMP et al. | — | 2017 | → |
| What can time-frequency and phase coherence measures tell us about the genetic basis of P3 amplitude? | Malone SM et al. | — | 2017 | → |