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Schizophr Res. 2016 Jul;174(1-3):1-9. doi: 10.1016/j.schres.2016.04.011. Epub 2016 Apr 28.

Prioritizing schizophrenia endophenotypes for future genetic studies: An example using data from the COGS-1 family study.

Author information

1
VISN-20 Mental Illness Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, WA, USA; VISN-20 Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, WA, USA.
2
VISN-22 Mental Illness Research, Education, and Clinical Center, VA San Diego Healthcare System, San Diego, CA, USA; Department of Psychiatry, University of California, San Diego, San Diego, CA, USA.
3
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
4
Department of Psychiatry, University of California, San Diego, San Diego, CA, USA.
5
Department of Psychiatry, University of Colorado Health Sciences Center, Denver, CO, USA.
6
Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, USA; VA Greater Los Angeles Health Care System, Los Angeles, CA, USA.
7
Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA.
8
Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, USA.
9
Massachusetts Mental Health Center Public Psychiatry Division of the Beth Israel Deaconess Medical Center, Harvard Medical School Department of Psychiatry, Boston, MA, USA.
10
Department of Psychiatry, Mount Sinai School of Medicine, New York, NY, USA; VISN-3 Mental Illness Research, Education, and Clinical Center, James J. Peters VA Medical Center, New York, NY, USA.
11
Department of Psychiatry and Biobehavioral Sciences, Geffen School of Medicine at University of California, Los Angeles, Los Angeles, CA, USA; VA Greater Los Angeles Health Care System, Los Angeles, CA, USA; Department of Biostatistics, Fielding School of Public Health at University of California, Los Angeles, Los Angeles, CA, USA.
12
VISN-20 Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, WA, USA; Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.
13
VISN-20 Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, WA, USA; Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA. Electronic address: dwt1@uw.edu.

Abstract

Past studies describe numerous endophenotypes associated with schizophrenia (SZ), but many endophenotypes may overlap in information they provide, and few studies have investigated the utility of a multivariate index to improve discrimination between SZ and healthy community comparison subjects (CCS). We investigated 16 endophenotypes from the first phase of the Consortium on the Genetics of Schizophrenia, a large, multi-site family study, to determine whether a subset could distinguish SZ probands and CCS just as well as using all 16. Participants included 345 SZ probands and 517 CCS with a valid measure for at least one endophenotype. We used both logistic regression and random forest models to choose a subset of endophenotypes, adjusting for age, gender, smoking status, site, parent education, and the reading subtest of the Wide Range Achievement Test. As a sensitivity analysis, we re-fit models using multiple imputations to determine the effect of missing values. We identified four important endophenotypes: antisaccade, Continuous Performance Test-Identical Pairs 3-digit version, California Verbal Learning Test, and emotion identification. The logistic regression model that used just these four endophenotypes produced essentially the same results as the model that used all 16 (84% vs. 85% accuracy). While a subset of endophenotypes cannot replace clinical diagnosis nor encompass the complexity of the disease, it can aid in the design of future endophenotypic and genetic studies by reducing study cost and subject burden, simplifying sample enrichment, and improving the statistical power of locating those genetic regions associated with schizophrenia that may be the easiest to identify initially.

KEYWORDS:

Accuracy; Endophenotype; Logistic regression; Multiple imputation; ROC curve; Random forest; Schizophrenia; Sensitivity; Specificity

PMID:
27132484
PMCID:
PMC4912929
DOI:
10.1016/j.schres.2016.04.011
[Indexed for MEDLINE]
Free PMC Article

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