TY - JOUR
T1 - Independent component analysis of SNPs reflects polygenic risk scores for schizophrenia
AU - Chen, Jiayu
AU - Calhoun, Vince D.
AU - Pearlson, Godfrey D.
AU - Perrone-Bizzozero, Nora I.
AU - Turner, Jessica A.
AU - Ehrlich, Stefan
AU - Ho, Beng Choon
AU - Liu, Jingyu
N1 - Funding Information:
This project was funded by the National Institutes of Health grants P20GM103472 , R01EB005846 , 1R01EB006841 and 1R01MH094524-01A1 , as well as an NSF EPSCoR grant # 1539067 .
Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2017/3/1
Y1 - 2017/3/1
N2 - Schizophrenia is a psychiatric disorder with high heritability. Recent genome-wide association studies have provided a list of risk loci reliably derived from unprecedentedly large samples. However, further delineation of the diagnosis-associated susceptibility variants is needed to better characterize the genetic architecture given the disease's complex nature. In this sense, a data-driven approach might hold promise for identifying functionally related clusters of genetic variants that might not be captured by hypothesis-based models. In the current study, independent component analysis (ICA) was applied to the Psychiatric Genomics Consortium's schizophrenia-related single nucleotide polymorphisms (SNPs) in 104 schizophrenia patients and 142 healthy controls of European Ancestry. We found that, for 13 out of 16 extracted independent components, the associated loadings correlated highly (r > 0.5) with the polygenic risk scores for SZ of the corresponding SNPs. These correlations were likely not inflated by the linkage disequilibrium structure (permutation p < 0.001). In brief, we demonstrate an example of ICA analysis on SNP data yielding functionally meaningful clusters, which motivates further application of data-driven approaches as a complimentary tool for hypothesis-based methods to enrich our knowledge on the genetic basis of complex disorders.
AB - Schizophrenia is a psychiatric disorder with high heritability. Recent genome-wide association studies have provided a list of risk loci reliably derived from unprecedentedly large samples. However, further delineation of the diagnosis-associated susceptibility variants is needed to better characterize the genetic architecture given the disease's complex nature. In this sense, a data-driven approach might hold promise for identifying functionally related clusters of genetic variants that might not be captured by hypothesis-based models. In the current study, independent component analysis (ICA) was applied to the Psychiatric Genomics Consortium's schizophrenia-related single nucleotide polymorphisms (SNPs) in 104 schizophrenia patients and 142 healthy controls of European Ancestry. We found that, for 13 out of 16 extracted independent components, the associated loadings correlated highly (r > 0.5) with the polygenic risk scores for SZ of the corresponding SNPs. These correlations were likely not inflated by the linkage disequilibrium structure (permutation p < 0.001). In brief, we demonstrate an example of ICA analysis on SNP data yielding functionally meaningful clusters, which motivates further application of data-driven approaches as a complimentary tool for hypothesis-based methods to enrich our knowledge on the genetic basis of complex disorders.
KW - ICA
KW - PGC
KW - Polygenic risk score
KW - Schizophrenia
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U2 - 10.1016/j.schres.2016.09.011
DO - 10.1016/j.schres.2016.09.011
M3 - Article
C2 - 27637363
AN - SCOPUS:84994882475
SN - 0920-9964
VL - 181
SP - 83
EP - 85
JO - Schizophrenia Research
JF - Schizophrenia Research
ER -