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SUMMARY:Human brain functional genomics and the mechanisms underlying gene
 tic risk for schizophrenia - Dr Michael Gandal
DTSTART:20210520T140000Z
DTEND:20210520T150000Z
UID:TALK160324@talks.cam.ac.uk
CONTACT:Sarah Morgan
DESCRIPTION:Our understanding of the pathophysiology of neuropsychiatric d
 isorders\, including schizophrenia (SCZ)\, lags greatly behind other field
 s of medicine. Defining genetic contributions to disease risk can provide 
 a rigorous foothold for mechanistic understanding\, and over the past deca
 de\, large-scale genetic studies have successfully identified hundreds of 
 genetic variants robustly associated with SCZ. However\, mechanistic insig
 ht and clinical translation continue to lag the pace of risk variant ident
 ification\, hindered by the sheer number of targets and their predominant 
 noncoding localization\, as well as pervasive pleiotropy and incomplete pe
 netrance. Successful next steps require identification of "causal" genetic
  variants and their proximal biological consequences\; placing variants wi
 thin biologically defined functional contexts\, reflecting specific molecu
 lar pathways\, cell types\, circuits\, and developmental windows\; and cha
 racterizing the downstream\, convergent neurobiological impact of polygeni
 city within an individual. Comprehensive transcriptomic profiling in human
  brain can provide a quantitative biological context for interpreting the 
 molecular effects of disease-associated genetic variants and for identifyi
 ng shared and distinct molecular pathways disrupted across major neuropsyc
 hiatric disorders. Here\, I will discuss our recent work as part of the Ps
 ychENCODE Consortium to generate a large-scale functional genomic resource
  of the human cortex\, integrating genotype and RNA seq data from more tha
 n 2000 samples\, including over 500 derived from individuals with SCZ. We 
 find pervasive differential splicing and expression\, with changes at the 
 transcript isoform-level -- as opposed to the gene level -- showing the la
 rgest effect sizes\, genetic enrichments\, and disease specificity. Coexpr
 ession networks identify a glial-immune signal demonstrating shared disrup
 tion of the blood-brain-barrier and up-regulation of NFkB-associated genes
 \, as well as disease-specific alterations in microglial-\, astrocyte-\, a
 nd interferon-response modules. Finally\, we leverage the transcriptome-wi
 de association (TWAS) approach to identify 64 high confidence candidate ri
 sk genes.  This large-scale integration of genomic data in human brain ena
 bles a comprehensive systems-level view of the neurobiological architectur
 e of major neuropsychiatric illness and provides a resource for mechanisti
 c insight and therapeutic development.
LOCATION:Online
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