To identify protein-altering variants (PAVs) for glioma, we analysed Illumina HumanExome BeadChip exome-array data on 1882 glioma cases and 8079 controls from three independent European populations. In addition to single-variant tests we incorporated information on the predicted functional consequences of PAVs and analysed sets of genes with a higher likelihood of having a role in glioma on the basis of the profile of somatic mutations documented by large-scale sequencing initiatives. Globally there was a strong relationship between effect size and PAVs predicted to be damaging (P=2.29 × 10(-49)); however, these variants which are most likely to impact on risk, are rare (MAF<5%). Although no single variant showed an association which was statistically significant at the genome-wide threshold a number represented promising associations - BRCA2:c.9976A>T, p.(Lys3326Ter), which has been shown to influence breast and lung cancer risk (odds ratio (OR)=2.3, P=4.00 × 10(-4) for glioblastoma (GBM)) and IDH2:c.782G>A, p.(Arg261His) (OR=3.21, P=7.67 × 10(-3), for non-GBM). Additionally, gene burden tests revealed a statistically significant association for HARS2 and risk of GBM (P=2.20 × 10(-6)). Genome scans of low-frequency PAVs represent a complementary strategy to identify disease-causing variants compared with scans based on tagSNPs. Strategies to lessen the multiple testing burden by restricting analysis to PAVs with higher priors affords an opportunity to maximise study power.