| Title: | Predicting Polygenic Risk of Psychiatric Disorders |
| Journal: | Biological Psychiatry |
| Published: | 28 Dec 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/30737014/ |
| DOI: | https://doi.org/10.1016/j.biopsych.2018.12.015 |
| URL: | https://europepmc.org/articles/pmc6599546?pdf=render |
| Citations: | 201 (38 in last 2 years) as of 8 Aug 2024 |
Publication 12023
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Abstract
Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.</p>
5 Authors
- Alicia R Martin
- Mark J Daly
- Elise B Robinson
- Steven E Hyman
- Benjamin M Neale
1 Application
| Application ID | Title |
|---|---|
| 31063 | Methodological extensions to estimate genetic heritability and shared risk factors for phenotypes of the UK Biobank |
Enabling scientific discoveries that improve human health