Fine mapping of a region of chromosome 11q13 reveals multiple independent loci associated with risk of prostate cancer.

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 (r(2)= 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, r(2)= 0.17; rs10896449-rs10896438, r(2)= 0.10; rs12793759-rs10896438, r(2)= 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.

Using principal components of genetic variation for robust and powerful detection of gene-gene interactions in case-control and case-only studies.

Many popular methods for exploring gene-gene interactions, including the case-only approach, rely on the key assumption that physically distant loci are in linkage equilibrium in the underlying population. These methods utilize the presence of correlation between unlinked loci in a disease-enriched sample as evidence of interactions among the loci in the etiology of the disease. We use data from the CGEMS case-control genome-wide association study of breast cancer to demonstrate empirically that the case-only and related methods have the potential to create large-scale false positives because of the presence of population stratification (PS) that creates long-range linkage disequilibrium in the genome. We show that the bias can be removed by considering parametric and nonparametric methods that assume gene-gene independence between unlinked loci, not in the entire population, but only conditional on population substructure that can be uncovered based on the principal components of a suitably large panel of PS markers. Applications in the CGEMS study as well as simulated data show that the proposed methods are robust to the presence of population stratification and are yet much more powerful, relative to standard logistic regression methods that are also commonly used as robust alternatives to the case-only type methods.

Efficient p-value evaluation for resampling-based tests.

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.

Application of a novel score test for genetic association incorporating gene-gene interaction suggests functionality for prostate cancer susceptibility regions.

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.

Common genetic variation in the sex hormone metabolic pathway and endometrial cancer risk: pathway-based evaluation of candidate genes.

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.

Long-term survival after heart failure: a contemporary population-based perspective.

BACKGROUND: Heart failure (HF) is a major public health problem that is associated with substantial morbidity, impaired quality of life, and diminished survival. Despite the considerable prevalence of HF in the United States, there are limited published data describing the contemporary long-term prognosis of patients hospitalized with decompensated HF. METHODS: A total of 2445 residents in the Worcester metropolitan area discharged from 11 greater Worcester hospitals after confirmed acute HF during 2000 comprised the study sample. Follow-up of discharged hospital survivors was carried out through 2005. RESULTS: The mean age of the study population was 76 years, 43.4% were men, and approximately three quarters had been previously diagnosed as having HF. Among discharged hospital patients, 37.3% died during the first year after hospital discharge, while 78.5% died during the 5-year follow-up period. Several subgroups of patients were at significantly increased risk for dying during the first year after hospital discharge. This included older persons (> or =85 years) (adjusted odds ratio [OR], 2.11; 95% confidence interval [CI], 1.35-3.29), patients with a history of chronic obstructive pulmonary disease (OR, 1.39; 95% CI, 1.15-1.69) or HF (OR, 1.26; 95% CI, 1.00-1.59), and patients with elevated serum urea nitrogen levels during hospitalization (OR, 1.02; 95% CI, 1.01-1.03). CONCLUSIONS: The results of our community-wide study demonstrate the poor long-term prognosis of patients surviving hospitalization for decompensated HF. Despite advances in the therapeutic management of these patients, their long-term survival remains guarded. Efforts are needed to improve the long-term survival of patients with this clinical syndrome.

Medical findings in outpatients with anorexia nervosa.

BACKGROUND: Approximately 0.5% to 1% of college-aged women have anorexia nervosa and most of them live in the community. However, few clinical data exist regarding community-dwelling women with anorexia nervosa. The objective of this study was to determine the prevalences of common medical findings for these women. METHODS: Cross-sectional, community-based study of 214 women with anorexia nervosa as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Participants were recruited through advertisements and community-based referrals to a study investigating skeletal health in outpatients with anorexia nervosa. RESULTS: The prevalences of medical findings among the 214 participants were as follows: anemia, 38.6%; leukocytopenia, 34.4%; hyponatremia, 19.7%; hypokalemia, 19.7%; bradycardia, 41.3%; hypotension, 16.1%; hypothermia, 22.4%; elevation of alanine aminotransferase concentration, 12.2%; osteopenia, 51.7%; osteoporosis, 34.6%; and primary amenorrhea, 14.8%. Moreover, 30% of the women reported histories of bone fractures. Except for leukocytopenia (P = .01), bone loss (P = .04), and bradycardia (P = .01), the probability of specific medical findings could not be predicted by the degree of undernutrition. CONCLUSIONS: These results demonstrate a high prevalence of medical findings in community-dwelling women with anorexia nervosa. Therefore, women with anorexia nervosa should be carefully followed up with regular physical examinations and laboratory assessments. In addition, low weight, particularly in conjunction with the abnormalities reported, should prompt the consideration of a diagnosis of anorexia nervosa.

Large-scale exploration of gene-gene interactions in prostate cancer using a multistage genome-wide association study.

Recent genome-wide association studies have identified independent susceptibility loci for prostate cancer 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 (SNP) across the genome to generate hypotheses about the functionality of prostate cancer susceptibility regions. We used data from Cancer Genetic Markers of Susceptibility: Stage I included 523,841 SNPs in 1,175 cases and 1,100 controls; Stage II included 27,383 SNPs in an additional 3,941 cases and 3,964 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 prostate cancer 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 prostate cancer 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 prostate cancer but also in other epithelial cancers that are associated with the 8q24 locus.

Identification of a new prostate cancer susceptibility locus on chromosome 8q24.

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. We identify a new association with prostate cancer risk on chromosome 8q24 (rs620861, P = 1.3 x 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 locus associated with prostate cancer susceptibility on 8q24.