oa A Web Server for Visualization and Annotation of Genetic Variants Using Genomic Data from Qatar

In the last decade, starting from family-based association analysis and progressing to population-based association analysis have helped in understanding etiology of many rare and complex disorders. As of Nov. 2013, National Human Genome Research institute (NHGRI) Catalog of published Genome-Wide Association Studies (GWAS) contained 1,751 manually curated publications with 11,912 SNP-trait associations. Despite large number of studies reporting different loci for complex traits, the aggregate variance explained by these loci is relatively low and more efforts are needed to look for loci with larger variance. These loci with larger variance are difficult to find as they tend to be at low frequency in general population. Previously, 1000 Genome project have shown common variants ( ≥  10) were found in all the populations, but 17 of low frequency variants (0.5–5) were seen in single ancestry group and 53 of rare variants ( <  0.5) were present In a single population. Therefore, more number of sequencing studies is required especially on understudied populations which are not part of big sequencing projects like 1000 genome project or UK10K to unearth low frequency and rare variants which aid in our understanding of missing heritability in etiology of common disorders.

The Arabian Peninsula located at cross roads between Africa and Eurasia is not well represented in global sequencing projects. This limits our understanding of human genetic diversity as these populations are recipients of constant gene flow over generations from Africa and Eurasia. The State of Qatar located on northeastern coast of the Arabian Peninsula with alarmingly high rate of obesity and diabetes among nationals has started new initiatives to understand genetic causes for these common disorders. But, to further extrapolate information from these genetic studies to molecular disease mechanisms and generating significant biomarkers for clinical settings a comprehensive data resource is required integrating existing information from informatics and experimental approaches. This central resource requires annotations about the variant, the gene, epigenetics, gene expression and protein expression. Many resources like UCSC, Ensembl and NCBI provides these annotations but with limited capabilities in analyzing large number of variants. Variant centric resources which provide capabilities for annotation of large number of variants limit themselves to a specific category of annotation like amino acid changes, associations to traits or regulatory effects. Further, these large resources and variant centric resources share a common drawback towards difficulty in retrieving population specific annotations especially for populations which are not part of global sequencing projects. The annotation data from these understudied populations get lost in wealth of data. Therefore, population specific annotation resources are needed integrating annotations from various sources which can be used to compare population specific differences. For e.g. Genome browser part of Singapore Genome Variation Project (SGVP) integrates annotations from sources like dbSNP, NHGRI GWAS catalogue, Reactome into Malay population specific linkage disequilibrium (LD) data. In this work, we present a Qatar specific webserver for visualization and annotation of genetic variants implicated in association studies conducted in Qatari population. The webserver seamlessly integrates genomic annotations obtained from public databases with Qatar specific pre-computed genomic characteristics like Linkage Disequilibrium (LD), Recombination Rates and Allele Frequencies using already published genomic data from Qatar. It also provides user-friendly starting points for annotating single and list of genetic variants, LD blocks or genetic regions of interest. In conclusion, this webserver combines various annotation sources with genomic information from Qatari population with varied uses in field of genomic research.