Ranking of the importance of input variables (clinical parameters and SNPs) was achieved by ranking their influence on neural network error score.

If the presence of a particular SNP or clinical variable (among the neural network’s input variables) reduced the error score, that SNP or variable can be considered to make a positive contribution to the performance of the network (ie, it is of useful predictive value). The BMES cohort consisted of 1986 individuals with follow-up phenotype data at either the 5-year, 10-year, or both visits with genotypes available (Table 2). Of the 1986 participants, check details there were 67 incident OAG cases over the full 10-year follow-up period. At baseline, the incident OAG cases were significantly older than controls Alisertib order (P < .001) and had a higher proportion of female subjects (P = .009). IOP and VCDR at the baseline visit were also significantly different between those who later developed OAG and those who did not ( Table 2), as was systolic blood pressure. These features of this cohort have been previously reported. 11 Association analysis indicates that incident OAG was associated with SNPs at 3 of the 5 loci tested (Table 3). Significant association under an allelic test was seen at rs1412892 (P = .006) at the 9p21 locus

as well as rs10483727 (P = .004) at the SIX1/SIX6 locus. Additional SNPs at 9p21 and also at TMCO1 were nominally significant but did not survive after correction for multiple comparisons. The SNPs at the 8q22 and CAV1/CAV2 loci did not Thymidine kinase show association with incident glaucoma. Adjustment for covariates under an additive genetic model showed association at the same SNPs, although only SIX1/SIX6 remained significant after correction for testing 7 SNPs (P ≤ .007) ( Table 3). When all covariates and

the 3 associated loci (TMCO1, 9p21, and SIX1/SIX6) were included in a single regression model, all variables except blood pressure contributed significantly to the model ( Table 4). The population of neural networks was used to compare the rank importance of variables in the predictive model both with and without age matching between controls and incident cases (Table 5). As expected, when not age matched, vertical cup-to-disc ratio, age, and intraocular pressure rank the highest for predicting incident OAG. The top-ranked SNP in this analysis is at the SIX1/SIX6 locus, which also showed the strongest genetic association. When cases and controls were closely age matched the rank order of variables changed, likely indicating an interaction between age and the other variable, although vertical cup-to-disc ratio and intraocular pressure are still the most predictive variables. In this situation the SNP at the TMCO1 locus was most predictive. Of note, in both analyses, all SNPs significantly associated with incident OAG under the traditional statistics contribute positively to the neural network and improve its ability to predict incident OAG.