High frequency of rare variants with a moderate-to-high predicted biological effect in protocadherin genes of extremely obese

Relatively rare variants with a moderate-to-high biological effect may contribute to the genetic predisposition of common disorders. To investigate this for obesity, we performed exome sequencing for 30 young (mean age: 29.7 years) extremely obese Caucasian subjects (mean body mass index: 51.1 kg/m²; m/f = 11/29). Rare variants with a moderate-to-high predicted biological effect were assembled and subjected to functional clustering analysis. It showed that the 55 clustered protocadherin genes on chromosome 5q31 have a significantly (P = 0.002) higher frequency of rare variants than a set of 325 reference genes. Since the protocadherin genes are expressed in the hypothalamus, we tested another 167 genes related to the function of the hypothalamus, but in those genes, the frequency of rare variants was not different from that of the reference genes. To verify the relation of variation in the protocadherin genes with extreme obesity, we analyzed data from more than 4,000 European Americans present on the Exome Variant Server, representing a sample of the general population. The significant enrichment of rare variants in the protocadherin genes was only observed with the group of extremely obese individuals but not in the “general population”, indicating an association between rare variants in the protocadherin cluster genes and extreme obesity.     

Functional gene clustering via gene annotation sentences, MeSH and GO keywords from biomedical literature

Gene function annotation remains a key challenge in modern biology. This is especially true for high-throughput techniques such as gene expression experiments. Vital information about genes is available electronically from biomedical literature in the form of full texts and abstracts. In addition, various publicly available databases (such as GenBank, Gene Ontology and Entrez) provide access to gene-related information at different levels of biological organization, granularity and data format. This information is being used to assess and interpret the results from high-throughput experiments. To improve keyword extraction for annotational clustering and other types of analyses, we have developed a novel text mining approach, which is based on keywords identified at the level of gene annotation sentences (in particular sentences characterizing biological function) instead of entire abstracts. Further, to improve the expressiveness and usefulness of gene annotation terms, we investigated the combination of sentence-level keywords with terms from the Medical Subject Headings (MeSH) and Gene Ontology (GO) resources. We find that sentence-level keywords combined with MeSH terms outperforms the typical 'baseline' set-up (term frequencies at the level of abstracts) by a significant margin, whereas the addition of GO terms improves matters only marginally. We validated our approach on the basis of a manually annotated corpus of 200 abstracts generated on the basis of 2 cancer categories and 10 genes per category. We applied the method in the context of three sets of differentially expressed genes obtained from pediatric brain tumor samples. This analysis suggests novel interpretations of discovered gene expression patterns.