Network based analysis of genome wide association data provides novel candidate genes for lipoprotein traits

Amitabh Sharma

: J Comput Sci Syst Biol

Abstract :

Presently genome wide association studies (GWASs) have generated plethora of data that need to be interpreted with diverse biological dimensions. Here, we have designed a network-based approach to predict additional candidate genes using GWAS meta-analysis data of >100,000 individuals for lipid- and lipoprotein traits (Global Lipids Genetics Consortium, GLGC). Starting with seed genes located near SNPs with p<5x10 -8 in GLGC GWAS, we applied a multi-step prioritization scheme to identify candidate genes that have moderate p-values but nevertheless might play a role in lipid and lipoprotein metabolism. The method involved selecting candidate genes from the human interactome that cluster, co-express and share comorbidity patterns with seed genes. Furthermore, we assumed that addition of population-based comorbidity data with molecular- and genetic information provides additional power to uncover the other disease relations to the GWAS findings. The final candidate genes harbour SNPs with p-value<0.05 in GWAS meta-analysis data. We selected four SNPs for validation in Malm� Diet and Cancer Cardiovascular Cohort based on their location and conservation status, and found significant association of a synonymous SNP rs234706 in cystathionine beta-synthase gene ( CBS ) with total cholesterol (p=0.003) and LDL cholesterol (p=0.00001) levels. Further, the minor allele of rs234706 associated significantly with mRNA level of CBS in liver samples of 206 subjects (p=0.04). Despite CBS known biological role in lipid metabolism, SNPs in this locus have not yet been identified as associated with lipoprotein traits by GWAS.

Biography :

Amitabh Sharma has completed his Ph.D at the age of 30 years from Pune University and postdoctoral studies from Department of Clinical Sciences, CRC, Lund University, Malm? University Hospital, S-205 02 Malm?, Sweden. He is working as Research associate at Center for complex network research, Dept. of Physics, Northeastern University, Boston, USA-02115, a premier center for Network research. He has published more than 13 papers in reputed journals and involved in implementing the network medicine approach for understanding the complex diseases.