Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness. Here we address this question using a partitioning-based heritability analysis of genome-wide single-nucleotide polymorphism (SNP) and neuroimaging data from 1750 healthy individuals. We find that schizophrenia-associated genetic variants explain a significantly enriched proportion of trait heritability in eight brain phenotypes (false discovery rate=10%). In particular, intracranial volume and left superior frontal gyrus thickness exhibit significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross-disorder comparison suggests that the neurogenetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders. Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder.
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Neuroimaging genetics data were provided by the Brain Genomics Superstruct Project (GSP) of Harvard University and MGH, with support from the Center for Brain Science Neuroinformatics Research Group, Athinoula A Martinos Center for Biomedical Imaging, Center for Human Genetic Research, and Stanley Center for Psychiatric Research. Twenty individual investigators at Harvard and MGH generously contributed data to the overall project. The QTIM study is supported by grants from NIH (R01 HD050735) and the NHMRC (389875, 486682 and 1009064). We thank the twins and siblings for their participation, Marlene Grace and Ann Eldridge for twin recruitment, Aiman Al Najjar and other radiographers for scanning, Kerrie McAloney and Daniel Park for research support, and Anjali Henders and staff for DNA sample processing and preparation. PMT, NJ and MJW were supported in part by a Consortium grant (U54 EB020403 to PMT) from the NIH Institutes contributing to the Big Data to Knowledge (BD2K) Initiative. This research was also funded in part by NIH Grants K99MH101367 (to PHL); K23MH104515 (to JTB), K01MH099232 (to AJH); K24MH094614 and R01 MH101486 (to JWS); JWS is a Tepper Family MGH Research Scholar.
Project conception and experiment design: PHL. Statistical analysis and interpretation of findings: PHL, JTB, J-YJ, JWS, DO and DSM. Imaging and genetics data generation and processing: AJH, PHL, RB, JR, JWS, TG, NJ, DPH, JF, KLM, GIZ, NGM, MJW and PMT. Writing of the manuscript: PHL, JTB, JWS, TG and YC. Revision of the manuscript: AJH, JTB, JWS, TG, NJ and PMT.
PLINK (ver. 1.9): https://www.cog-genomics.org/plink2/
EIGENSOFT (ver. 6.1): http://www.hsph.harvard.edu/alkes-price/software/
The GCTA-GREML Power Calculator: http://cnsgenomics.com/shiny/gctaPower/
Genome-wide Complex Trait Analysis (GCTA ver. 1.24.4): http://www.complextraitgenomics.com/software/gcta/
Psychiatric Genomic Consortium (PGC) SCZ, BIP, ASD, ADHD, MDD summary statistics: http://www.med.unc.edu/pgc/downloads
Crohn’s disease summary statistics: http://www.ibdgenetics.org/downloads.html
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on the Molecular Psychiatry website
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Lee, P., Baker, J., Holmes, A. et al. Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia. Mol Psychiatry 21, 1680–1689 (2016) doi:10.1038/mp.2016.164
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