American journal of ophthalmology · 2019
Integration of Genetic and Biometric Risk Factors for Detection of Primary Angle Closure Glaucoma.
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Nongpiur Monisha E, Khor Chiea-Chuen, Cheng Ching-Yu, Husain Rahat, Boey Pui Yi, Chew Annabel, Ho Ching Lin, Wong Tina T, Perera Shamira, Wong Tien Yin, Vithana Eranga N, Aung Tin
Аннотация
PURPOSE: The purpose of this study was to investigate whether the addition of primary angle closure glaucoma (PACG)-associated genetic loci allows improved detection of PACG, compared to anterior segment parameters measured by imaging.
DESIGN: Case-control study.
METHODS: Genotype data of the 8 PACG single-nucleotide polymorphisms (SNPs) (rs11024102 at PLEKHA7, rs3753841 at COL11A1, rs1015213 located between PCMTD1 and ST18 on Chromosome 8q, rs3816415 at EPDR1, rs1258267 at CHAT, rs736893 at GLIS3, rs7494379 at FERMT2, and rs3739821 mapping in between DPM2 and FAM102A) were available. Customized software was used to measure anterior segment optical coherence tomography (ASOCT) parameters, namely, anterior chamber depth, width, and area (ACD, ACW, and ACA) and lens vault (LV). Statistical analysis for positive predictive values was modeled using the receiver operating characteristic curve (AUC). Statistical significance comparing predictive power of the different parameters was calculated using permutation.
RESULTS: A total of 388 PACG subjects and 751 controls with both ASOCT and genetic data were available for analysis. Anterior segment parameters including ACD, ACA, and LV had excellent predictive value (AUCs >0.94), except ACW (AUC=0.65), for identifying PACG. The inclusion of genetic risk alleles (either singly or as a composite genetic risk score for 8 genomewide association study SNPs) to ACD only provided a +0.50% improvement in reclassifying PACG cases and controls over and above the discriminatory value of ACD. This +0.50% improvement was not statistically significant (P > .05).
CONCLUSIONS: Although significant on their own, the incorporation of genetic data in the context of anterior segment imaging parameters like ACD provided only a marginal improvement of PACG detection.
DESIGN: Case-control study.
METHODS: Genotype data of the 8 PACG single-nucleotide polymorphisms (SNPs) (rs11024102 at PLEKHA7, rs3753841 at COL11A1, rs1015213 located between PCMTD1 and ST18 on Chromosome 8q, rs3816415 at EPDR1, rs1258267 at CHAT, rs736893 at GLIS3, rs7494379 at FERMT2, and rs3739821 mapping in between DPM2 and FAM102A) were available. Customized software was used to measure anterior segment optical coherence tomography (ASOCT) parameters, namely, anterior chamber depth, width, and area (ACD, ACW, and ACA) and lens vault (LV). Statistical analysis for positive predictive values was modeled using the receiver operating characteristic curve (AUC). Statistical significance comparing predictive power of the different parameters was calculated using permutation.
RESULTS: A total of 388 PACG subjects and 751 controls with both ASOCT and genetic data were available for analysis. Anterior segment parameters including ACD, ACA, and LV had excellent predictive value (AUCs >0.94), except ACW (AUC=0.65), for identifying PACG. The inclusion of genetic risk alleles (either singly or as a composite genetic risk score for 8 genomewide association study SNPs) to ACD only provided a +0.50% improvement in reclassifying PACG cases and controls over and above the discriminatory value of ACD. This +0.50% improvement was not statistically significant (P > .05).
CONCLUSIONS: Although significant on their own, the incorporation of genetic data in the context of anterior segment imaging parameters like ACD provided only a marginal improvement of PACG detection.