Fibromuscular Dysplasia and Abdominal Aortic Aneurysms Are Dimorphic Sex-Specific Diseases With Shared Complex Genetic Architecture.

BACKGROUND: The risk of arterial diseases may be elevated among family members of individuals having multifocal fibromuscular dysplasia (FMD). We sought to investigate the risk of arterial diseases in families of individuals with FMD. METHODS: Family histories for 73 probands with FMD were obtained, which included an analysis of 463 total first-degree relatives focusing on FMD and related arterial disorders. A polygenic risk score for FMD (PRSFMD) was constructed from prior genome-wide association findings of 584 FMD cases and 7139 controls and evaluated for association with an abdominal aortic aneurysm (AAA) in a cohort of 9693 AAA cases and 294 049 controls. A previously published PRSAAA was also assessed among the FMD cases and controls. RESULTS: Of all first degree relatives of probands, 9.3% were diagnosed with FMD, aneurysms, and dissections. Aneurysmal disease occurred in 60.5% of affected relatives and 5.6% of all relatives. Among 227 female first-degree relatives of probands, 4.8% (11) had FMD, representing a relative risk (RR)FMD of 1.5 ([95% CI, 0.75-2.8]; P=0.19) compared with the estimated population prevalence of 3.3%, though not of statistical significance. Of all fathers of FMD probands, 11% had AAAs resulting in a RRAAA of 2.3 ([95% CI, 1.12-4.6]; P=0.014) compared with population estimates. The PRSFMD was found to be associated with an AAA (odds ratio, 1.03 [95% CI, 1.01-1.05]; P=2.6×10-3), and the PRSAAA was found to be associated with FMD (odds ratio, 1.53 [95% CI, 1.2-1.9]; P=9.0×10-5) as well. CONCLUSIONS: FMD and AAAs seem to be sex-dimorphic manifestations of a heritable arterial disease with a partially shared complex genetic architecture. Excess risk of having an AAA according to a family history of FMD may justify screening in family members of individuals having FMD.

Genome-wide association study of aromatase inhibitor discontinuation due to musculoskeletal symptoms.

OBJECTIVE: Aromatase inhibitors (AIs) are commonly used to treat hormone receptor positive (HR +) breast cancer. AI-induced musculoskeletal syndrome (AIMSS) is a common toxicity that causes AI treatment discontinuation. The objective of this genome-wide association study (GWAS) was to identify genetic variants associated with discontinuation of AI therapy due to AIMSS and attempt to replicate previously reported associations. METHODS: In the Exemestane and Letrozole Pharmacogenetics (ELPh) study, postmenopausal patients with HR + non-metastatic breast cancer were randomized to letrozole or exemestane. Genome-wide genotyping of germline DNA was conducted followed by imputation. Each imputed variant was tested for association with time-to-treatment discontinuation due to AIMSS using a Cox proportional hazards model assuming additive genetic effects and adjusting for age, baseline pain score, prior taxane treatment, and AI arm. Secondary analyses were conducted within each AI arm and analyses of candidate variants previously reported to be associated with AIMSS risk. RESULTS: Four hundred ELPh participants were included in the combined analysis. Two variants surpassed the genome-wide significance level in the primary analysis (p value < 5 × 10-8), an intronic variant (rs79048288) within CCDC148 (HR = 4.42, 95% CI: 2.67-7.33) and an intergenic variant (rs912571) upstream of PPP1R14C (HR = 0.30, 95% CI: 0.20-0.47). In the secondary analysis, rs74418677, which is known to be associated with expression of SUPT20H, was significantly associated with discontinuation of letrozole therapy due to AIMSS (HR = 5.91, 95% CI: 3.16-11.06). We were able to replicate associations for candidate variants previously reported to be associated with AIMSS in this cohort, but were not able to replicate associations for any other variants previously reported in other patient cohorts. CONCLUSIONS: Our GWAS findings identify several candidate variants that may be associated with AIMSS risk from AI generally or letrozole specifically. Validation of these associations in independent cohorts is needed before translating these findings into clinical practice to improve treatment outcomes in patients with HR + breast cancer.

Subclinical Cardiac Dysfunction is Associated with Reduced Cardiorespiratory Fitness and Cardiometabolic Risk Factors in Firefighters.

BACKGROUND: Past studies have documented the ability of cardiopulmonary exercise testing to detect cardiac dysfunction in symptomatic patients with coronary artery disease. Firefighters are at high risk for work-related cardiac events. This observational study investigated the association of subclinical cardiac dysfunction detected by cardiopulmonary exercise testing with modifiable cardiometabolic risk factors in asymptomatic firefighters. METHODS: As part of mandatory firefighter medical evaluations, study subjects were assessed at 2 occupational health clinics serving 21 different fire departments. Mixed effects logistic regression analyses were used to estimate odds ratios (ORs) and account for clustering by fire department. RESULTS: Of the 967 male firefighters (ages 20-60 years; 84% non-Hispanic white; 14% on cardiovascular medications), nearly two-thirds (63%) had cardiac dysfunction despite having normal predicted cardiorespiratory fitness (median peak VO2 = 102%). In unadjusted analyses, cardiac dysfunction was significantly associated with advanced age, obesity, diastolic hypertension, high triglycerides, low high-density lipoprotein (HDL) cholesterol, and reduced cardiorespiratory fitness (all P values < .05). After adjusting for age and ethnicity, the odds of having cardiac dysfunction were approximately one-third higher among firefighters with obesity and diastolic hypertension (OR = 1.39, 95% confidence interval [CI] = 1.03-1.87 and OR = 1.36, 95% CI = 1.03-1.80) and more than 5 times higher among firefighters with reduced cardiorespiratory fitness (OR = 5.41, 95% CI = 3.29-8.90). CONCLUSION: Subclinical cardiac dysfunction detected by cardiopulmonary exercise testing is a common finding in career firefighters and is associated with substantially reduced cardiorespiratory fitness and cardiometabolic risk factors. These individuals should be targeted for aggressive risk factor modification to increase cardiorespiratory fitness as part of an outpatient prevention strategy to improve health and safety.

Genome-wide association study of letrozole plasma concentrations identifies non-exonic variants that may affect CYP2A6 metabolic activity.

OBJECTIVES: Letrozole is a nonsteroidal aromatase inhibitor used to treat hormone-receptor-positive breast cancer. Variability in letrozole efficacy and toxicity may be partially attributable to variable systemic drug exposure, which may be influenced by germline variants in the enzymes responsible for letrozole metabolism, including cytochrome P450 2A6 (CYP2A6). The objective of this genome-wide association study (GWAS) was to identify polymorphisms associated with steady-state letrozole concentrations. METHODS: The Exemestane and Letrozole Pharmacogenetics (ELPh) Study randomized postmenopausal patients with hormone-receptor-positive nonmetastatic breast cancer to letrozole or exemestane treatment. Germline DNA was collected pretreatment and blood samples were collected after 1 or 3 months of treatment to measure steady-state letrozole (and exemestane) plasma concentrations via HPLC/MS. Genome-wide genotyping was conducted on the Infinium Global Screening Array (>650 000 variants) followed by imputation. The association of each germline variant with age- and BMI-adjusted letrozole concentrations was tested in self-reported white patients via linear regression assuming an additive genetic model. RESULTS: There were 228 patients who met the study-specific inclusion criteria and had both DNA and letrozole concentration data for this GWAS. The association for one genotyped polymorphism (rs7937) with letrozole concentration surpassed genome-wide significance (P = 5.26 × 10-10), explaining 13% of the variability in untransformed steady-state letrozole concentrations. Imputation around rs7937 and in silico analyses identified rs56113850, a variant in the CYP2A6 intron that may affect CYP2A6 expression and activity. rs7937 was associated with age- and BMI-adjusted letrozole levels even after adjusting for genotype-predicted CYP2A6 metabolic phenotype (P = 3.86 × 10-10). CONCLUSION: Our GWAS findings confirm that steady-state letrozole plasma concentrations are partially determined by germline polymorphisms that affect CYP2A6 activity, including variants near rs7937 such as the intronic rs56113850 variant. Further research is needed to confirm whether rs56113850 directly affects CYP2A6 activity and to integrate nonexonic variants into CYP2A6 phenotypic activity prediction systems.

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures.

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.

Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk.

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5 × 10(-8)) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B and SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23 and TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease-susceptibility loci.

Mutational landscape of candidate genes in familial prostate cancer.

BACKGROUND: Family history is a major risk factor for prostate cancer (PCa), suggesting a genetic component to the disease. However, traditional linkage and association studies have failed to fully elucidate the underlying genetic basis of familial PCa. METHODS: Here, we use a candidate gene approach to identify potential PCa susceptibility variants in whole exome sequencing data from familial PCa cases. Six hundred ninety-seven candidate genes were identified based on function, location near a known chromosome 17 linkage signal, and/or previous association with prostate or other cancers. Single nucleotide variants (SNVs) in these candidate genes were identified in whole exome sequence data from 33 PCa cases from 11 multiplex PCa families (3 cases/family). RESULTS: Overall, 4,856 candidate gene SNVs were identified, including 1,052 missense and 10 nonsense variants. Twenty missense variants were shared by all three family members in each family in which they were observed. Additionally, 15 missense variants were shared by two of three family members and predicted to be deleterious by five different algorithms. Four missense variants, BLM Gln123Arg, PARP2 Arg283Gln, LRCC46 Ala295Thr and KIF2B Pro91Leu, and one nonsense variant, CYP3A43 Arg441Ter, showed complete co-segregation with PCa status. Twelve additional variants displayed partial co-segregation with PCa. CONCLUSIONS: Forty-three nonsense and shared, missense variants were identified in our candidate genes. Further research is needed to determine the contribution of these variants to PCa susceptibility.

A method to prioritize quantitative traits and individuals for sequencing in family-based studies.

Owing to recent advances in DNA sequencing, it is now technically feasible to evaluate the contribution of rare variation to complex traits and diseases. However, it is still cost prohibitive to sequence the whole genome (or exome) of all individuals in each study. For quantitative traits, one strategy to reduce cost is to sequence individuals in the tails of the trait distribution. However, the next challenge becomes how to prioritize traits and individuals for sequencing since individuals are often characterized for dozens of medically relevant traits. In this article, we describe a new method, the Rare Variant Kinship Test (RVKT), which leverages relationship information in family-based studies to identify quantitative traits that are likely influenced by rare variants. Conditional on nuclear families and extended pedigrees, we evaluate the power of the RVKT via simulation. Not unexpectedly, the power of our method depends strongly on effect size, and to a lesser extent, on the frequency of the rare variant and the number and type of relationships in the sample. As an illustration, we also apply our method to data from two genetic studies in the Old Order Amish, a founder population with extensive genealogical records. Remarkably, we implicate the presence of a rare variant that lowers fasting triglyceride levels in the Heredity and Phenotype Intervention (HAPI) Heart study (p = 0.044), consistent with the presence of a previously identified null mutation in the APOC3 gene that lowers fasting triglyceride levels in HAPI Heart study participants.

Determinants of blood pressure response to low-salt intake in a healthy adult population.

Although the beneficial effects of lowering salt intake in hypertensive patients are widely appreciated, the impact of promoting dietary salt restriction for blood pressure (BP) reduction at the population level remains controversial. The authors used 24-hour ambulatory BP monitoring to characterize the determinants of systolic BP (SBP) response to low-salt intake in a large, relatively healthy Amish population. Patients received a high- and low-sodium diet for 6 days each, separated by a 6- to 14-day washout period. Variance component analysis was used to assess the association of several variables with SBP response to low-salt diet. Mean SBP was 0.7 ± 5.8 mm Hg and 1.3 ± 6.1 mm Hg lower on the low-salt compared with the high-salt diet during daytime (P=.008) and nighttime (P<.0001), respectively. SBP response to a low-salt diet was significantly associated with increasing age and pre-intervention SBP, in both daytime and nighttime, while the association with female sex and SBP response to cold pressor test (CPT) was significant only during nighttime. Our results suggest that salt reduction may have greater BP-lowering effects on women, older individuals, individuals with higher SBP, and individuals with higher SBP response to CPT.

Absence of CHEK2*1100delC mutation in families with hereditary breast cancer in North America.

The CHEK2*1100delC mutation has been reported to confer a twofold increased risk of breast cancer among carriers. The frequency of the mutation varies among populations. The highest frequency has been described in Northern and Eastern European countries; the frequency may be much lower in North America. In this study, our aim was to determine the frequency of CHEK2*1100delC in members of breast cancer families who tested negative for a deleterious mutation in BRCA1/2 at the University of Michigan Comprehensive Cancer Center. We genotyped 102 members from 90 families for CHEK2*1100delC. Most of these families had several cases of breast cancer or ovarian cancer (or both), as well as multiple members with other cancer types in a single lineage. No CHEK2*1100delC mutations were detected in any of the 102 individuals, including 51 women diagnosed with breast cancer at an early age (<45 years), 8 women with bilateral breast cancer, 3 men with breast cancer, and 8 women with ovarian cancer. Our data are consistent with the reported very low frequency of CHEK2*1100delC mutations in North American populations (compared with Northern Europe), rendering CHEK2*1100delC such an unlikely culprit in BRCA1/2 negative families that routine testing of these families appears unwarranted.

Computerized image analysis: texture-field orientation method for pectoral muscle identification on MLO-view mammograms.

PURPOSE: To develop a new texture-field orientation (TFO) method that combines a priori knowledge, local and global information for the automated identification of pectoral muscle on mammograms. METHODS: The authors designed a gradient-based directional kernel (GDK) filter to enhance the linear texture structures, and a gradient-based texture analysis to extract a texture orientation image that represented the dominant texture orientation at each pixel. The texture orientation image was enhanced by a second GDK filter for ridge point extraction. The extracted ridge points were validated and the ridges that were less likely to lie on the pectoral boundary were removed automatically. A shortest-path finding method was used to generate a probability image that represented the likelihood that each remaining ridge point lay on the true pectoral boundary. Finally, the pectoral boundary was tracked by searching for the ridge points with the highest probability lying on the pectoral boundary. A data set of 130 MLO-view digitized film mammograms (DFMs) from 65 patients was used to train the TFO algorithm. An independent data set of 637 MLO-view DFMs from 562 patients was used to evaluate its performance. Another independent data set of 92 MLO-view full field digital mammograms (FFDMs) from 92 patients was used to assess the adaptability of the TFO algorithm to FFDMs. The pectoral boundary detection accuracy of the TFO method was quantified by comparison with an experienced radiologist's manually drawn pectoral boundary using three performance metrics: The percent overlap area (POA), the Hausdorff distance (Hdist), and the average distance (AvgDist). RESULTS: The mean and standard deviation of POA, Hdist, and AvgDist were 95.0 +/- 3.6%, 3.45 +/- 2.16 mm, and 1.12 +/- 0.82 mm, respectively. For the POA measure, 91.5%, 97.3%, and 98.9% of the computer detected pectoral muscles had POA larger than 90%, 85%, and 80%, respectively. For the distance measures, 85.4% and 98.0% of the computer detected pectoral boundaries had Hdist within 5 and 10 mm, respectively, and 99.4% of computer detected pectoral muscle boundaries had AvgDist within 5 mm from the radiologist's manually drawn boundaries. CONCLUSIONS: The pectoral muscle on DFMs can be detected accurately by the automated TFO method. The preliminary study of applying the same pectoral muscle identification algorithm to FFDMs without retraining demonstrates that the TFO method is reasonably robust against the differences in the image properties between the digitized and digital mammograms.

Dynamic multiple thresholding breast boundary detection algorithm for mammograms.

PURPOSE: Automated detection of breast boundary is one of the fundamental steps for computer-aided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms. METHODS: A large data set of 716 screen-film mammograms (442 CC view and 274 MLO view) obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary (MTBB-Initial) was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary (MTBB-Final). The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap measure (AOM). RESULTS: In comparison with the authors' previously developed gradient-based breast boundary (GBB) algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels (4.8 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels (1.2 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. The improvement by the MTBB-Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test (p < 0.0001). CONCLUSIONS: The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary detection algorithm that mainly used gradient information.

Extent and distribution of linkage disequilibrium in the Old Order Amish.

Knowledge of the extent and distribution of linkage disequilibrium (LD) is critical to the design and interpretation of gene mapping studies. Because the demographic history of each population varies and is often not accurately known, it is necessary to empirically evaluate LD on a population-specific basis. Here we present the first genome-wide survey of LD in the Old Order Amish (OOA) of Lancaster County Pennsylvania, a closed population derived from a modest number of founders. Specifically, we present a comparison of LD between OOA individuals and US Utah participants in the International HapMap project (abbreviated CEU) using a high-density single nucleotide polymorphism (SNP) map. Overall, the allele (and haplotype) frequency distributions and LD profiles were remarkably similar between these two populations. For example, the median absolute allele frequency difference for autosomal SNPs was 0.05, with an inter-quartile range of 0.02-0.09, and for autosomal SNPs 10-20 kb apart with common alleles (minor allele frequency > or =0.05), the LD measure r(2) was at least 0.8 for 15 and 14% of SNP pairs in the OOA and CEU, respectively. Moreover, tag SNPs selected from the HapMap CEU sample captured a substantial portion of the common variation in the OOA ( approximately 88%) at r(2) > or =0.8. These results suggest that the OOA and CEU may share similar LD profiles for other common but untyped SNPs. Thus, in the context of the common variant-common disease hypothesis, genetic variants discovered in gene mapping studies in the OOA may generalize to other populations.

Sequence variation in the mitochondrial gene cytochrome c oxidase subunit I and prostate cancer in African American men.

BACKGROUND: Previous studies have found associations between mitochondrial DNA (mtDNA) mutations and several cancer types. Recently, we found that mutations in the mtDNA gene cytochrome c oxidase subunit 1 (COI) were both linked to and associated with prostate cancer (PCa) in Caucasian men. Here we examine the association between COI mutations and PCa in African American men. METHODS: The entire COI gene was directly sequenced in 132 PCa cases and 135 controls from the Flint Men's Health Study, a community-based sample of African American men with and without PCa. Associations between all variants and PCa were evaluated. RESULTS: We identified 102 COI single nucleotide polymorphisms (SNPs), including 15 missense variants. Overall, the presence of one or more COI missense variants was not significantly associated with PCa. Individually, two SNPs (T6221C and T7389C) were significantly associated with prostate cancer (P < 0.05) and in strong linkage disequilibrium with each other (r(2) > 0.6). CONCLUSIONS: Of the two significantly associated SNPs, one is a synonymous substitution and the other is part of the African-specific mitochondrial haplogroup (L). Additional research will be needed to determine the clinical relevance of these associations in African populations.

Analysis of RhoC expression and lymphovascular emboli in inflammatory vs non-inflammatory breast cancers in Egyptian patients.

Understanding the molecular factors that distinguish inflammatory breast cancer (IBC) from non-IBC is important for IBC diagnosis. We reviewed the records of 48 IBC patients and 64 non-IBC patients from Egypt. We determined RhoC expression and tumor emboli and their relationship to demographic and reproductive characteristics. Compared with non-IBC patients, IBC patients had significantly lower parity (P=0.018) and fewer palpable tumors (P<0.0001). IBC tumors showed RhoC overexpression more frequently than non-IBC tumors (87% vs. 17%, respectively) (P<0.0001). Tumor emboli were significantly more frequent in IBC tumors than non-IBC tumors (Mean+/- SD: 14.1+/-14.0 vs. 7.0+/-12.9, respectively) (P<0.0001). This study illustrates that RhoC overexpression and tumor emboli are more frequent in tumors of IBC relative to non-IBC from Egypt. Future studies should focus on relating epidemiologic factors to molecular features of IBC in this population.

Mammographic breast density--evidence for genetic correlations with established breast cancer risk factors.

Previous twin and family studies indicate that the familial aggregation of breast density is due (in part) to genetic factors. Whether these genetic influences are shared with other breast cancer risk factors, however, is not known. Using standard film-screen mammography, we screened 550 women, including 611 pairs of sisters, from the Old Order Amish population of Lancaster County, Pennsylvania. We digitized mammograms and quantified the dense and nondense areas of the breast using a computer-assisted method. Information about other breast cancer risk factors was collected via questionnaires and a physical exam. Using pedigree-based variance component methods, we estimated the genetic contributions to several breast cancer risk factors, including breast density, and evaluated the evidence for shared genetic influences between them. After adjusting for covariates, genetic effects accounted for >33% of the total variance of each risk factor (P < 0.001), including breast density, and the dense and nondense areas of the breast were significantly genetically correlated with parity [genetic correlation (rho(G)) = -0.47; P = 0.013] and age at menarche (rho(G) = -0.38; P = 0.008), respectively. The nondense area of the breast and, in turn, breast density, expressed as a ratio of dense area to total area, were also genetically correlated with most measures of adiposity but in opposite directions (rho(G) > or = 0.75; P < 10(-7) for nondense area). We conclude that the genetic components that influence breast density are not independent of the genetic components that influence other breast cancer risk factors. This shared genetic architecture should be considered in future genetic studies of breast density.

Chromosome 17q12 variants contribute to risk of early-onset prostate cancer.

In a recent genome-wide association study by Gudmundsson and colleagues, two prostate cancer susceptibility loci were identified on chromosome 17q. The first locus, at 17q12, was distinguished by two intronic single-nucleotide polymorphisms (SNPs) in the TCF2 gene (rs4430796 and rs7501939). The second locus was in a gene-poor region of 17q24, where the strongest evidence of association was for SNP rs1859962. To determine if these loci were also associated with hereditary prostate cancer, we genotyped them in a family-based association sample of 403 non-Hispanic white families, including 1,015 men with and without prostate cancer. SNPs rs4430796 and rs7501939, which were in strong linkage disequilibrium (r(2) = 0.68), showed the strongest evidence of prostate cancer association. Using a family-based association test, the A allele of SNP rs4430796 was overtransmitted to affected men (P = 0.006), with an odds ratio of 1.40 (95% confidence interval, 1.09-1.81) under an additive genetic model. Notably, rs4430796 was significantly associated with prostate cancer among men diagnosed at an early (<50 years) but not later age (P = 0.006 versus P = 0.118). Our results confirm the prostate cancer association with SNPs on chromosome 17q12 initially reported by Gudmundsson and colleagues. In addition, our results suggest that the increased risk associated with these SNPs is approximately doubled in individuals predisposed to develop early-onset disease. Importantly, these SNPs do not account for a significant portion of our prior prostate cancer linkage evidence on chromosome 17. Thus, there likely exist one or more additional independent prostate cancer susceptibility loci in this region.

The genetic response to short-term interventions affecting cardiovascular function: rationale and design of the Heredity and Phenotype Intervention (HAPI) Heart Study.

BACKGROUND: The etiology of cardiovascular disease (CVD) is multifactorial. Efforts to identify genes influencing CVD risk have met with limited success to date, likely because of the small effect sizes of common CVD risk alleles and the presence of gene by gene and gene by environment interactions. METHODS: The HAPI Heart Study was initiated in 2002 to measure the cardiovascular response to 4 short-term interventions affecting cardiovascular risk factors and to identify the genetic and environmental determinants of these responses. The measurements included blood pressure responses to the cold pressor stress test and to a high salt diet, triglyceride excursion in response to a high-fat challenge, and response in platelet aggregation to aspirin therapy. RESULTS: The interventions were carried out in 868 relatively healthy Amish adults from large families. The heritabilities of selected response traits for each intervention ranged from 8% to 38%, suggesting that some of the variation associated with response to each intervention can be attributed to the additive effects of genes. CONCLUSIONS: Identifying these response genes may identify new mechanisms influencing CVD and may lead to individualized preventive strategies and improved early detection of high-risk individuals.