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J Am Med Inform Assoc. 2018 Oct 1;25(10):1359-1365. doi: 10.1093/jamia/ocy056.

PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies.

Author information

1
Department of Statistics, The Ohio State University, Columbus, OH, USA.
2
Stuyvesant High School, New York City, NY, USA.
3
Center for Statistical Science, Tsinghua University, Beijing, China.
4
Department of Industrial Engineering, Tsinghua University, Beijing, China.
5
Univ. Bordeaux, ISPED, Inserm BPH 1219, Inria SISTM, Bordeaux, France.
6
Department of Biostatistics, Harvard University, Boston, MA, USA.
7
Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
8
Children's Hospital Boston, Boston, MA, USA.
9
Department of Medicine, Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, USA.

Abstract

Objective:

Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies.

Methods:

The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared.

Results:

Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001.

Conclusions:

PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies.

PMID:
29788308
DOI:
10.1093/jamia/ocy056
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