Genome-wide association analysis of copy number variations in subarachnoid aneurysmal hemorrhage

Bae, Joon Seol; Cheong, Hyun Sub; Park, Byung Lae; Kim, Lyoung Hyo; Park, Tae Joon; Kim, Jason Yongha; Pasaje, Charisse Flerida A.; Lee, Jin Sol; Cui, Tailin; Inoue, Ituro; Shin, Hyoung Doo

doi:10.1038/jhg.2010.97

Cited by 8 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CNV can contribute to disease susceptibility by influencing the gene expression level [11] . CNV has been revealed to be associated with complex human diseases including autism, inflammatory autoimmune disorders, lung cancer, osteoporosis, subarachnoid aneurysmal hemorrhage, sporadic amyotrophic lateral sclerosis, avellino corneal dystrophy, and schizophrenia [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] . Recent studies have focused on how CNV in the human genome affects various inherited phenotypes, including disease susceptibility [22] , [23] .…”

Section: Introductionmentioning

confidence: 99%

The Genetic Effect of Copy Number Variations on the Risk of Type 2 Diabetes in a Korean Population

et al. 2011

Self Cite

View full text Add to dashboard Cite

BackgroundUnlike Caucasian populations, genetic factors contributing to the risk of type 2 diabetes mellitus (T2DM) are not well studied in Asian populations. In light of this, and the fact that copy number variation (CNV) is emerging as a new way to understand human genomic variation, the objective of this study was to identify type 2 diabetes–associated CNV in a Korean cohort.Methodology/Principal FindingsUsing the Illumina HumanHap300 BeadChip (317,503 markers), genome-wide genotyping was performed to obtain signal and allelic intensities from 275 patients with type 2 diabetes mellitus (T2DM) and 496 nondiabetic subjects (Total n = 771). To increase the sensitivity of CNV identification, we incorporated multiple factors using PennCNV, a program that is based on the hidden Markov model (HMM). To assess the genetic effect of CNV on T2DM, a multivariate logistic regression model controlling for age and gender was used. We identified a total of 7,478 CNVs (average of 9.7 CNVs per individual) and 2,554 CNV regions (CNVRs; 164 common CNVRs for frequency>1%) in this study. Although we failed to demonstrate robust associations between CNVs and the risk of T2DM, our results revealed a putative association between several CNVRs including chr15:45994758–45999227 (P = 8.6E-04, Pcorr = 0.01) and the risk of T2DM. The identified CNVs in this study were validated using overlapping analysis with the Database of Genomic Variants (DGV; 71.7% overlap), and quantitative PCR (qPCR). The identified variations, which encompassed functional genes, were significantly enriched in the cellular part, in the membrane-bound organelle, in the development process, in cell communication, in signal transduction, and in biological regulation.Conclusion/SignificanceWe expect that the methods and findings in this study will contribute in particular to genome studies of Asian populations.

show abstract

Section: Introductionmentioning

confidence: 99%

The Genetic Effect of Copy Number Variations on the Risk of Type 2 Diabetes in a Korean Population

et al. 2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…In another study, high-density (300K Illumina) SNP-microarrays from a GWAS of 191 Japanese patients with aneurysmal subarachnoid hemorrhage and 282 controls were used for CNV exploration. 45 CNV-findings were carefully validated by visual examination of the genoplot images and overlapping analysis with the Database of Genomic Variants (DGV - http://dgv.tcag.ca/dgv/app/home ). Moreover, selected findings were validated by quantitative PCR.…”

Section: Cnv In Stroke Patientsmentioning

confidence: 99%

Copy Number Variation and Risk of Stroke

Grond‐Ginsbach

Erhart

Chen

et al. 2018

Stroke

View full text Add to dashboard Cite

“…They hypothesized that CNVs can predict the risk of SAH [47]. The authors identified a total of 4.574 CNVs from a Japanese population sample ( n = 473) and discovered 1.644 unique CNV regions containing 1.232 genes.…”

Section: Cnvs In Hemorrhagic Strokementioning

confidence: 99%

Copy number variations and stroke

2016

View full text Add to dashboard Cite

Stroke is the third leading cause of death worldwide after heart disease and all forms of cancers. Monogenic disorders, genetic, and environmental risk factors contribute to damaging cerebral blood vessels and, consequently, cause stroke. Developments in genomic research led to the discovery of numerous copy number variants (CNVs) that have been recently identified as a new tool for understanding the genetic basis of many diseases. This review discusses the current understanding of the types of stroke, the existing knowledge on the involvement of specific CNVs in stroke as well as the limitations of the methods used for detecting CNVs like SNP-microarray. To confirm an unequivocally association between CNVs and stroke and extend the current findings, it would be desirable to use another methodology to detect smaller CNVs or CNVs in genomic regions poorly covered by this technique, for instance, CGH-array.

show abstract

Genome-wide association analysis of copy number variations in subarachnoid aneurysmal hemorrhage

Cited by 8 publications

References 33 publications

The Genetic Effect of Copy Number Variations on the Risk of Type 2 Diabetes in a Korean Population

The Genetic Effect of Copy Number Variations on the Risk of Type 2 Diabetes in a Korean Population

Copy Number Variation and Risk of Stroke

Copy number variations and stroke

Contact Info

Product

Resources

About