Rethinking ovarian cancer genomics: where genome-wide association studies stand?

AbstractAccumulating evidence suggests a relationship between endometrial cancer and epithelial ovarian cancer. For example, endometrial cancer and epithelial ovarian cancer share epidemiological risk factors and molecular features observed across histotypes are held in common (e.g. serous, endometrioid and clear cell). Independent genome-wide association studies (GWAS) for endometrial cancer and epithelial ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. Using GWAS summary statistics, we explored the shared genetic etiology between endometrial cancer and epithelial ovarian cancer. Genetic correlation analysis using LD Score regression revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e. inverse-variance meta-analysis, co-localization, and M-values), and performed analyses by stratified by subtype. We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel regions at 7p22.2, 7q22.1, 9p12 and 11q13.3 were identified at a sub-genome wide threshold (P < 5 × 10−7). Integration with promoter-associated HiChIP chromatin loops from immortalized endometrium and epithelial ovarian cell lines, and expression quantitative trait loci (eQTL) data highlighted candidate target genes for further investigation.

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Prostate cancer genomics, biology, and risk assessment through genome-wide association studies

Cancer Science ◽

10.1111/j.1349-7006.2011.02193.x ◽

2012 ◽

Vol 103 (4) ◽

pp. 607-613 ◽

Cited By ~ 22

Author(s):

Hidewaki Nakagawa ◽

Shusuke Akamatsu ◽

Ryo Takata ◽

Atsushi Takahashi ◽

Michiaki Kubo ◽

...

Keyword(s):

Prostate Cancer ◽

Risk Assessment ◽

Cancer Genomics ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Prostate cancer genomics by high-throughput technologies: genome-wide association study and sequencing analysis

Endocrine Related Cancer ◽

10.1530/erc-13-0113 ◽

2013 ◽

Vol 20 (4) ◽

pp. R171-R181 ◽

Cited By ~ 5

Author(s):

Hidewaki Nakagawa

Keyword(s):

Prostate Cancer ◽

High Throughput ◽

High Throughput Sequencing ◽

Genome Wide Association Study ◽

Cancer Genomics ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Sequencing Analysis ◽

Genome Wide

Prostate cancer (PC) is the most common malignancy in males. It is evident that genetic factors at both germline and somatic levels play critical roles in prostate carcinogenesis. Recently, genome-wide association studies (GWAS) by high-throughput genotyping technology have identified more than 70 germline variants of various genes or chromosome loci that are significantly associated with PC susceptibility. They include multiple 8q24 loci, prostate-specific genes, and metabolism-related genes. Somatic alterations in PC genomes have been explored by high-throughput sequencing technologies such as whole-genome sequencing and RNA sequencing, which have identified a variety of androgen-responsive events and fusion transcripts represented by E26 transformation-specific (ETS) gene fusions. Recent innovations in high-throughput genomic technologies have enabled us to analyze PC genomics more comprehensively, more precisely, and on a larger scale in multiple ethnic groups to increase our understanding of PC genomics and biology in germline and somatic studies, which can ultimately lead to personalized medicine for PC diagnosis, prevention, and therapy. However, these data indicate that the PC genome is more complex and heterogeneous than we expected from GWAS and sequencing analyses.

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Abstract 4579: Variants in long non-coding RNAs are associated with epithelial ovarian cancer risk in a pooled analysis of three genome-wide association studies.

10.1158/1538-7445.am2013-4579 ◽

2013 ◽

Author(s):

Yian Ann Chen ◽

Zhihua Chen ◽

Jennifer Permuth-Wey ◽

Ya-Yu Tsai ◽

Hui-Yi Lin ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Risk ◽

Epithelial Ovarian Cancer ◽

Association Studies ◽

Pooled Analysis ◽

Genome Wide Association ◽

Ovarian Cancer Risk ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Non Coding Rnas

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Genome-Wide Association Study for Ovarian Cancer Susceptibility Using Pooled DNA

Twin Research and Human Genetics ◽

10.1017/thg.2012.38 ◽

2012 ◽

Vol 15 (5) ◽

pp. 615-623 ◽

Cited By ~ 8

Author(s):

Yi Lu ◽

Xiaoqing Chen ◽

Jonathan Beesley ◽

Sharon E. Johnatty ◽

Anna deFazio ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Susceptibility ◽

Association Studies ◽

Small Sample ◽

Genome Wide Association ◽

Ovarian Cancer Risk ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Pooled Dna ◽

Stage 1

Recent Genome-Wide Association Studies (GWAS) have identified four low-penetrance ovarian cancer susceptibility loci. We hypothesized that further moderate- or low-penetrance variants exist among the subset of single-nucleotide polymorphisms (SNPs) not well tagged by the genotyping arrays used in the previous studies, which would account for some of the remaining risk. We therefore conducted a time- and cost-effective stage 1 GWAS on 342 invasive serous cases and 643 controls genotyped on pooled DNA using the high-density Illumina 1M-Duo array. We followed up 20 of the most significantly associated SNPs, which are not well tagged by the lower density arrays used by the published GWAS, and genotyping them on individual DNA. Most of the top 20 SNPs were clearly validated by individually genotyping the samples used in the pools. However, none of the 20 SNPs replicated when tested for association in a much larger stage 2 set of 4,651 cases and 6,966 controls from the Ovarian Cancer Association Consortium. Given that most of the top 20 SNPs from pooling were validated in the same samples by individual genotyping, the lack of replication is likely to be due to the relatively small sample size in our stage 1 GWAS rather than due to problems with the pooling approach. We conclude that there are unlikely to be any moderate or large effects on ovarian cancer risk untagged by less dense arrays. However, our study lacked power to make clear statements on the existence of hitherto untagged small-effect variants.

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