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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
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 (
) 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
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.
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.
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