PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-6 (6)
 

Clipboard (0)
None

Select a Filter Below

Journals
Year of Publication
Document Types
author:("ciampi, Julia")
1.  Application of a Novel Score Test for Genetic Association Incorporating Gene-Gene Interaction Suggests Functionality for Prostate Cancer Susceptibility Regions 
Human Heredity  2011;72(3):182-193.
Aims
We introduce an innovative multilocus test for disease association. It is an extension of an existing score test that gains power over alternative methods by incorporating a parsimonious one-degree-of-freedom model for interaction. We use our method in applications designed to detect interactions that generate hypotheses about the functionality of prostate cancer (PRCA) susceptibility regions.
Methods
Our proposed score test is designed to gain additional power through the use of a retrospective likelihood that exploits an assumption of independence between unlinked loci in the underlying population. Its performance is validated through simulation. The method is used in conditional scans with data from stage II of the Cancer Genetic Markers of Susceptibility PRCA genome-wide association study.
Results
Our proposed method increases power to detect susceptibility loci in diverse settings. It identified two high-ranking, biologically interesting interactions: (1) rs748120 of NR2C2 and subregions of 8q24 that contain independent susceptibility loci specific to PRCA and (2) rs4810671 of SULF2 and both JAZF1 and HNF1B that are associated with PRCA and type 2 diabetes.
Conclusions
Our score test is a promising multilocus tool for genetic epidemiology. The results of our applications suggest functionality for poorly understood PRCA susceptibility regions. They motivate replication study.
doi:10.1159/000331222
PMCID: PMC3242702  PMID: 22086326
Gene-gene interaction; Score test; Prostate cancer
2.  Identification of a novel prostate cancer susceptibility locus on chromosome 8q24 
Nature genetics  2009;41(10):1055-1057.
We report a genome-wide association study in 10,286 cases and 9,135 controls of European ancestry, in the Cancer Genetic Markers of Susceptibility (CGEMS) initiative, identifying a new association with prostate cancer risk on chromosome 8q24 (rs620861, p=1.3×10-10, heterozygote OR = 1.17, 95% CI 1.10 – 1.24; homozygote OR = 1.33; 95% CI 1.21 – 1.45). This defines a new prostate locus on 8q24, Region 4, previously associated with breast cancer.
doi:10.1038/ng.444
PMCID: PMC3430510  PMID: 19767755
3.  Fine mapping of a region of chromosome 11q13 reveals multiple independent loci associated with risk of prostate cancer 
Human Molecular Genetics  2011;20(14):2869-2878.
Genome-wide association studies have identified prostate cancer susceptibility alleles on chromosome 11q13. As part of the Cancer Genetic Markers of Susceptibility (CGEMS) Initiative, the region flanking the most significant marker, rs10896449, was fine mapped in 10 272 cases and 9123 controls of European origin (10 studies) using 120 common single nucleotide polymorphisms (SNPs) selected by a two-staged tagging strategy using HapMap SNPs. Single-locus analysis identified 18 SNPs below genome-wide significance (P< 10−8) with rs10896449 the most significant (P= 7.94 × 10−19). Multi-locus models that included significant SNPs sequentially identified a second association at rs12793759 [odds ratio (OR) = 1.14, P= 4.76 × 10−5, adjusted P= 0.004] that is independent of rs10896449 and remained significant after adjustment for multiple testing within the region. rs10896438, a proxy of previously reported rs12418451 (r2= 0.96), independent of both rs10896449 and rs12793759 was detected (OR = 1.07, P= 5.92 × 10−3, adjusted P= 0.054). Our observation of a recombination hotspot that separates rs10896438 from rs10896449 and rs12793759, and low linkage disequilibrium (rs10896449–rs12793759, r2= 0.17; rs10896449–rs10896438, r2= 0.10; rs12793759–rs10896438, r2= 0.12) corroborate our finding of three independent signals. By analysis of tagged SNPs across ∼123 kb using next generation sequencing of 63 controls of European origin, 1000 Genome and HapMap data, we observed multiple surrogates for the three independent signals marked by rs10896449 (n= 31), rs10896438 (n= 24) and rs12793759 (n= 8). Our results indicate that a complex architecture underlying the common variants contributing to prostate cancer risk at 11q13. We estimate that at least 63 common variants should be considered in future studies designed to investigate the biological basis of the multiple association signals.
doi:10.1093/hmg/ddr189
PMCID: PMC3118760  PMID: 21531787
4.  Efficient p-value evaluation for resampling-based tests 
Biostatistics (Oxford, England)  2011;12(3):582-593.
The resampling-based test, which often relies on permutation or bootstrap procedures, has been widely used for statistical hypothesis testing when the asymptotic distribution of the test statistic is unavailable or unreliable. It requires repeated calculations of the test statistic on a large number of simulated data sets for its significance level assessment, and thus it could become very computationally intensive. Here, we propose an efficient p-value evaluation procedure by adapting the stochastic approximation Markov chain Monte Carlo algorithm. The new procedure can be used easily for estimating the p-value for any resampling-based test. We show through numeric simulations that the proposed procedure can be 100–500 000 times as efficient (in term of computing time) as the standard resampling-based procedure when evaluating a test statistic with a small p-value (e.g. less than 10 − 6). With its computational burden reduced by this proposed procedure, the versatile resampling-based test would become computationally feasible for a much wider range of applications. We demonstrate the application of the new method by applying it to a large-scale genetic association study of prostate cancer.
doi:10.1093/biostatistics/kxq078
PMCID: PMC3114653  PMID: 21209154
Bootstrap procedures; Genetic association studies; p-value; Resampling-based tests; Stochastic approximation Markov chain Monte Carlo
5.  Large Scale Exploration of Gene-Gene Interactions in Prostate Cancer Using a Multi-stage Genome-wide Association Study 
Cancer research  2011;71(9):3287-3295.
Recent genome-wide association studies have identified independent susceptibility loci for prostate cancer (CaP) that could influence risk through interaction with other, possibly undetected, susceptibility loci. We explored evidence of interaction between pairs of 13 known susceptibility loci and single nucleotide polymorphisms (SNPs) across the genome to generate hypotheses about the functionality of CaP susceptibility regions. We used data from Cancer Genetic Markers of Susceptibility: Stage I included 523,841 SNPs in 1175 cases and 1100 controls; Stage II included 27,383 SNPs in an additional 3941 cases and 3964 controls. Power calculations assessed the magnitude of interactions our study is likely to detect. Logistic regression was used with alternative methods that exploit constraints of gene-gene independence between unlinked loci to increase power. Our empirical evaluation demonstrated that an empirical Bayes (EB) technique is powerful and robust to possible violation of the independence assumption. Our EB analysis identified several noteworthy interacting SNP pairs, although none reached genome-wide significance. We highlight a Stage II interaction between the major CaP susceptibility locus in the subregion of 8q24 that contains POU5F1B and an intronic SNP in the transcription factor EPAS1, which has potentially important functional implications for 8q24. Another noteworthy result involves interaction of a known CaP susceptibility marker near the prostate protease genes KLK2 and KLK3 with an intronic SNP in PRXX2. Overall, the interactions we have identified merit follow-up study, particularly the EPAS1 interaction which has implications not only in CaP but also in other epithelial cancers that are associated with the 8q24 locus.
doi:10.1158/0008-5472.CAN-10-2646
PMCID: PMC3085580  PMID: 21372204
6.  Common genetic variation in the sex hormone metabolic pathway and endometrial cancer risk: pathway-based evaluation of candidate genes 
Carcinogenesis  2010;31(5):827-833.
Background. Estrogen plays a major role in endometrial carcinogenesis, suggesting that common variants of genes in the sex hormone metabolic pathway may be related to endometrial cancer risk. In support of this view, variants in CYP19A1 [cytochrome P450 (CYP), family 19, subfamily A, polypeptide 1] have been associated with both circulating estrogen levels and endometrial cancer risk. Associations with variants in other genes have been suggested, but findings have been inconsistent. Methods. We examined 36 sex hormone-related genes using a tagging approach in a population-based case–control study of 417 endometrial cancer cases and 407 controls conducted in Poland. We evaluated common variation in these genes in relation to endometrial cancer risk using sequential haplotype scan, variable-sized sliding window and adaptive rank-truncated product (ARTP) methods. Results. In our case–control study, the strongest association with endometrial cancer risk was for AR (androgen receptor; ARTP P = 0.006). Multilocus analyses also identified boundaries for a region of interest in AR and in CYP19A1 around a previously identified susceptibility loci. We did not find evidence for consistent associations between previously reported candidate single-nucleotide polymorphisms in this pathway and endometrial cancer risk. Discussion. In summary, we identified regions in AR and CYP19A1 that are of interest for further evaluation in relation to endometrial cancer risk in future haplotype and subsequent fine mapping studies in larger study populations.
doi:10.1093/carcin/bgp328
PMCID: PMC2864407  PMID: 20053928

Results 1-6 (6)