Kristen Brennand presentation at the live webinar of June 28, 2017 hosted by the Schizophrenia Research Forum (http://www.schizophreniaforum.org/forums/webinar-modeling-neuropsychiatric-disorders-using-vitro-models)

1. Growing complexity of SZ genetics
Flint & Munafò, Nature. 2014
Start of post-doc
@Gage lab
Start of lab
@ISMMS
Today
Up to 1000
SZ genes?

2. Modeling SZ with hiPSC neural cells
predisposition to
^
Schizophrenia
Brennand et al, Molecular Psychiatry. 2015

3. miR-9 is decreased in a subset of SZ NPCs
Topol, Zhu et al, Cell Reports. 2016
Gang Fang
• All SZ NPCs have miR-9
levels less than median
value of control NPCs
• Signal driven by 50% of SZ
NPCs with values less
than bottom quartile of
controls

4. miR-9 targets are associated with
common genetic risk for SZ
Hauberg et al, JAMA Psych, 2016.

5. Fromer et al, Nat. Neuro. 2016
Post-mortem (and hiPSC) case-control
studies of SZ remain underpowered
“With 25 cases and 25 controls, we find that even null data will lead to
estimates of differential expression > 2 (... or < 0.25), ... by chance for a
large number of genes (0-26%).”
“Genomewide, the median number of subjects with SCZ and controls
needed to obtain 80% power assuming 10,000 genes is ~28,500, well
beyond any available data set.”
258 SZ; 279 control
• Poor replication of findings from
previous (smaller) post-mortem studies
• No case-control signature in genes at
the 108 SZ loci

6. Summary
• Post-mortem and hiPSC-based studies will be
underpowered for the foreseeable future.
• eQTLs and differential expression signals
between post-mortem and hiPSC-based
studies show significant overlap.
• hiPSC-based isogenic approaches will power
meaningful analyses to verify causal variants
identified by GWAS and eQTL studies.