Search

Genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) that play important roles in the genetic heritability of traits and diseases. With most of these SNPs located on the non-coding part of the genome, it is currently assumed that these SNPs influence the expression of nearby genes on the genome. However, identifying which genes are targeted by these disease-associated SNPs remains challenging. In the past, protein knowledge graphs have often been used to identify genes that are associated with disease, also referred to as “disease genes”. Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by testing and comparing the performance of six existing methods for a protein knowledge graph, four of which were developed for disease gene identification. We compare our performance against two baselines: (1) an existing state-of-the-art method that is based on guilt-by-association, and (2) the leading assumption that SNPs target the nearest gene on the genome. We test these methods with four reference sets, three of which were obtained by different means. Furthermore, we combine methods to investigate whether their combination improves performance. We find that protein knowledge graphs that include predicate information perform comparable to the current state of the art, achieving an area under the receiver operating characteristic curve (AUC) of 79.6% on average across all four reference sets. Protein knowledge graphs that lack predicate information perform comparable to our other baseline (genetic distance) which achieved an AUC of 75.7% across all four reference sets. Combining multiple methods improved performance to 84.9% AUC. We conclude that methods for a protein knowledge graph can be used to identify which genes are targeted by disease-associated non-coding SNPs.

Objective: After stratifying for age, sex and multimorbidity at baseline, our aim is to analyse time trends in incident multimorbidity and polypharmacy in the 15-year clinical trajectories of individual patients in a family medicine setting. Methods: This study was carried out using data from the Registration Network Family Medicine in the South of the Netherlands. The clinical trajectories of 10037 subjects during the 15-year period (2000–2014) were analyzed in a repeated measurement of using a generalized estimating equations model as well as a multilevel random intercept model with repeated measurements to determine patterns of incident multimorbidity and polypharmacy. Hierarchical age-period-cohort models were used to generate age and cohort trajectories for comparison with prevalence trends in multimorbidity literature. Results: Multimorbidity was more common in females than in males throughout the duration of the 15-year trajectory (females: 39.6%; males: 33.5%). With respective ratios of 11.7 and 5.9 between the end and the beginning of the 15-year period, the youngest female and male groups showed a substantial increase in multimorbidity prevalence. Ratios in the oldest female and male groups were 2.2 and 1.9 respectively. Females had higher levels of multimorbidity than males in the 0-24-year and 25-44-year age groups, but the levels converged to a prevalence of 92.2% in the oldest male and 90.7% in the oldest female group. Similar, albeit, moderate differences were found in polypharmacy patterns. Conclusions: We sought to specify the progression of multimorbidity from an early age. As a result, our study adds to the multimorbidity literature by specifying changes in chronic disease accumulation with relation to polypharmacy, and by tracking differences in patient trajectories according to age and sex. Multimorbidity and polypharmacy are common and their prevalence is accelerating, with a relatively rapid increase in younger groups. From the point of view o