Susceptibility to sporadic Parkinson's disease (PD) is thought to be influenced by both genetic and environmental factors and their interaction with each other. Statistical models including multiple variants in axon guidance pathway genes have recently been purported to be capable of predicting PD risk, survival free of the disease and age at disease onset; however the specific models have not undergone independent validation. Here we tested the best proposed risk panel of 23 single nucleotide polymorphisms (SNPs) in two PD sample sets, with a total of 525 cases and 518 controls. By single marker analysis, only one marker was significantly associated with PD risk in one of our sample sets (rs6692804: P = 0.03). Multi-marker analysis using the reported model found a mild association in one sample set (two sided P = 0.049, odds ratio for each score change = 1.07) but no significance in the other (two sided P = 0.98, odds ratio = 1), a stark contrast to the reported strong association with PD risk (P = 4.64x10(-38), odds ratio as high as 90.8). Following a procedure similar to that used to build the reported model, simulated multi-marker models containing SNPs from randomly chosen genes in a genome wide PD dataset produced P-values that were highly significant and indistinguishable from similar models where disease status was permuted (3.13x10(-23) to 4.90x10(-64)), demonstrating the potential for overfitting in the model building process. Together, these results challenge the robustness of the reported panel of genetic markers to predict PD risk in particular and a role of the axon guidance pathway in PD genetics in general.