Through 2011, GWASs have identified 33 genetic loci that are linked to blood pressure. Data from the 1000 Genomes Project were used to examine these loci. By searching nonsynonymous SNPs, promoter SNPs, splicing site SNPs, and gain- or loss-of-stop codon SNPs in 1000 Genomes Project data, we identified 2,113 functional variants in 66 genes in the 33 loci: 613 nonsynonymous SNPs, 1,425 promoter SNPs, 114 splice SNPs, and 15 gain- or loss-of-stop SNPs. There were no frameshift variations. Four hundred four of 613 nonsynonymous SNPs were predicted to be deleterious, based on 1000 Genomes Project data, and 1,114 of 1,425 promoter SNPs were predicted to influence the binding of transcription factors, using TFSearch. To determine whether these functional variants were causative factors of blood pressure, we analyzed KARE data, comprising 7,551 Korean individuals. The 24,962 SNPs in the 33 loci were imputed from the 1000 Genomes Project data into the KARE data. One hundred fourteen of 2,113 functional variants were successfully imputed and analyzed for their association with systolic blood pressure, diastolic blood pressure, and hypertension in the KARE cohort. As a result, 15 SNPs—3 nonsynonymous SNPs, 11 promoter SNPs, and 1 splice site SNP—showed association signals. These results, despite the low percentage of functional variants that were analyzed, provide valuable data on the candidate variants that govern blood pressure GWAS signals.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, Yoon D, Lee MH, Kim DJ, Park M et al (2009) A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 41:527–534
Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, Ritchey JK, Young MA, Lamprecht T, McLellan MD et al (2012) Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481:506–510
Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, Chasman DI, Smith AV, Tobin MD, Verwoert GC, Hwang SJ et al (2011) Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478:103–109
Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing. Nature 467:1061–1073
Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106:9362–9367
Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5:e1000529
Howie B, Marchini J, Stephens M (2011) Genotype imputation with thousands of genomes. G3 Bethesda 1:457–470
Huang J, Ellinghaus D, Franke A, Howie B, Li Y (2012) 1000 Genomes-based imputation identifies novel and refined associations for the Welcome Trust Case Control Consortium phase 1 Data. Eur J Hum Genet 20:801–805
Kato N, Takeuchi F, Tabara Y, Kelly TN, Go MJ, Sim X, Tay WT, Chen CH, Zhang Y, Yamamoto K et al (2011) Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat Genet 43:531–538
Kumar P, Henikoff S, Ng PC (2009) Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 4:1073–1081
Liu JZ, Tozzi F, Waterworth DM, Pillai SG, Muglia P, Middleton L, Berrettini W, Knouff CW, Yuan X, Waeber G et al (2010) Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet 42:436–440
Rabbee N, Speed TP (2006) A genotype calling algorithm for affymetrix SNP arrays. Bioinformatics 22:7–12
Ramensky V, Bork P, Sunyaev S (2002) Human non-synonymous SNPs: server and survey. Nucleic Acids Res 30:3894–3900
Rivas MA, Beaudoin M, Gardet A, Stevens C, Sharma Y, Zhang CK, Boucher G, Ripke S, Ellinghaus D, Burtt N et al (2011) Deep resequencing of GWAS loci identifies independent rare variants associated with inflammatory bowel disease. Nat Genet 43:1066–1073
Trynka G, Hunt KA, Bockett NA, Romanos J, Mistry V, Szperl A, Bakker SF, Bardella MT, Bhaw-Rosun L, Castillejo G et al (2011) Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat Genet 43:1193–1201
Wolfrum C, Poy MN, Stoffel M (2005) Apolipoprotein M is required for prebeta-HDL formation and cholesterol efflux to HDL and protects against atherosclerosis. Nat Med 11:418–422
Yi W and Fuli Y (2011) HSGC Software: SNPTools. http://www.hgsc.bcm.tmc.edu/cascade-tech-software-ti.hgsc
This work was supported by the Basic Science Research Program through a National Research Foundation of Korea grant, funded by the Korean government (MEST) (2010-0012080). This research was performed within Consortium for Large-Scale Genome-Wide Association Study III (2011E7300400), which was supported by genotype data (the Korean Genome Analysis Project, 4845-301) and phenotype data (the Korean Genome Epidemiology Study, 4851-302) from the Korea Center for Disease Control.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Lim, J.E., Shin, YA., Hong, KW. et al. Characterization of functional variants in 33 blood pressure loci using 1000 genomes project data. Genes Genom 35, 387–393 (2013). https://doi.org/10.1007/s13258-012-0054-4
- 1000 Genomes Project
- Blood pressure
- Functional variants
- Association study