Applications of computational algorithm tools to identify functional SNPs in cytokine genes.
Understanding the functions of single nucleotide polymorphisms (SNPs) can greatly help to understand the genetics of the human phenotype variation and especially the genetic basis of human complex diseases. However, how to identify functional SNPs from a pool containing both functional and neutral SNPs is challenging. In this study, we analyzed the genetic variations that can alter the expression and function of a group of cytokine proteins using computational tools. As a result, we extracted 4552 SNPs from 45 cytokine proteins from SNPper database. Of particular interest, 828 SNPs were in the 5'UTR region, 961 SNPs were in the 3' UTR region, and 85 SNPs were non-synonymous SNPs (nsSNPs), which cause amino acid change. Evolutionary conservation analysis using the SIFT tool suggested that 8 nsSNPs may disrupt the protein function. Protein structure analysis using the PolyPhen tool suggested that 5 nsSNPs might alter protein structure. Binding motif analysis using the UTResource tool suggested that 27 SNPs in 5' or 3'UTR might change protein expression levels. Our study demonstrates the presence of naturally occurring genetic variations in the cytokine proteins that may affect their expressions and functions with possible roles in complex human disease, such as immune diseases.
Shen
Jie
J
Tulane Cancer Center, Tulane University, New Orleans, LA, USA.
Deininger
Prescott L
PL
Zhao
Hua
H
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D016207:Cytokines / Q000235:genetics* / Q000502:physiology; D030561:Databases, Nucleic Acid; D006801:Humans; D020641:Polymorphism, Single Nucleotide / Q000502:physiology*
D016207:Cytokines