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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders.

Leanna M Hernandez1, Minsoo Kim2, Gil D Hoftman3

  • 1Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Intellectual and Developmental Disabilities Research Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.

Biological Psychiatry
|August 15, 2020
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Summary

High-throughput transcriptomics in the human brain helps pinpoint genetic risk mechanisms for psychiatric disorders. Understanding gene expression across development is key to unraveling complex polygenic contributions to these conditions.

Keywords:
AutismBrainCoexpressionGWASGenomicsSchizophreniaTranscriptomics

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Area of Science:

  • Neurogenetics
  • Psychiatric Genomics
  • Transcriptomics

Background:

  • Large-scale genetic studies identified hundreds of risk variants for psychiatric disorders.
  • Mechanistic insight and clinical translation lag due to variant complexity (noncoding, pleiotropy, incomplete penetrance).
  • Identifying causal variants and their biological context is crucial for progress.

Purpose of the Study:

  • To discuss the utility of high-throughput transcriptomic profiling in the human brain.
  • To explore how transcriptomics can address challenges in psychiatric genetics, particularly polygenicity.
  • To outline how transcriptomic data can inform the molecular pathology of psychiatric disorders.

Main Methods:

  • Transcriptome-wide association studies (TWAS) integrating genome-wide association studies (GWAS) with expression quantitative trait loci (eQTLs).
  • Analysis of transcriptomic results to understand molecular pathology in autism spectrum disorder, bipolar disorder, major depressive disorder, and schizophrenia.
  • Systems-level integration of genetic, genomic, and phenotypic data, including spatially resolved gene expression and neuroimaging.

Main Results:

  • Transcriptomic approaches offer a scalable solution to investigate polygenicity in psychiatric disorders.
  • Gene expression (dys)regulation during human brain development is a significant factor in psychiatric disease pathogenesis.
  • Both common variants (as eQTLs) and rare variants (in regulatory pathways) contribute to disease risk.

Conclusions:

  • High-throughput transcriptomics is essential for understanding the genetic architecture of psychiatric disorders.
  • Integrating multi-level data, including spatial transcriptomics and neuroimaging, provides a comprehensive view of disease mechanisms.
  • Focusing on gene expression regulation across development is critical for advancing psychiatric research and treatment.