Model parameters with and without genetic factors.

1– The intercept and the predictor variables in the model. – see Statistical Analysis and Modeling section for description of how the variables were coded.2– Binary logit regression estimates for the parameters in the model. In the logistic regression equation log[p/(1-p)] = a+βx where p is the probability that nephropathy = 1, the estimate of each variable contributes to β.3– Standard errors of the individual regression coefficients.4– Test statistic; the squared ratio of the Estimate to the SE of the respective predictor.5- The probability that a particular Chi-Square test statistic (1 df) is as extreme as, or more so, than what has been observed under the null hypothesis; the null hypothesis is that all of the regression coefficients in the model are equal to zero. The numbers in the column are the associated p-values.6– The logistic regression estimate when all variables in the model are evaluated at zero. In the above equation intercept contributes to the α-coefficient.