Citations

To identify novel genes influencing blood pressure response to thiazide diuretic therapy for hypertension, we conducted genome-wide association meta-analyses of ≈1.1 million single-nucleotide polymorphisms in a combined sample of 424 European Americans with primary hypertension treated with hydrochlorothiazide from the Pharmacogenomic Evaluation of Antihypertensive Responses study (n=228) and the Genetic Epidemiology of Responses to Antihypertensive study (n=196). Polymorphisms associated with blood pressure response at P <10 –5 were tested for replication of the associations in independent samples of hydrochlorothiazide-treated European hypertensives. The rs16960228 polymorphism in protein kinase C, α replicated for same-direction association with diastolic blood pressure response in the Nordic Diltiazem study (n=420) and the Genetics of Drug Responsiveness in Essential Hypertension study (n=206), and the combined 4-study meta-analysis P value achieved genome-wide significance ( P =3.3×10 −8 ). Systolic or diastolic blood pressure responses were consistently greater in carriers of the rs16960228 A allele than in GG homozygotes (>4/4 mm Hg) across study samples. The rs2273359 polymorphism in the GNAS-EDN3 region also replicated for same-direction association with systolic blood pressure response in the Nordic Diltiazem study, and the combined 3-study meta-analysis P value approached genome-wide significance ( P =5.5×10 −8 ). The findings document clinically important effects of genetic variation at novel loci on blood pressure response to a thiazide diuretic, which may be a basis for individualization of antihypertensive drug therapy and identification of new drug targets.

Background Clinical classification of rare sequence changes identified in the breast cancer susceptibility genes BRCA1 and BRCA2 is essential for appropriate genetic counselling of individuals carrying these variants. We previously showed that variant BRCA1 c.5096G>A p.Arg1699Gln in the BRCA1 transcriptional transactivation domain demonstrated equivocal results from a series of functional assays, and proposed that this variant may confer low to moderate risk of cancer. Methods Measures of genetic risk (report of family history, segregation) were assessed for 68 BRCA1 c.5096G>A p.Arg1699Gln (R1699Q) families recruited through family cancer clinics, comparing results with 34 families carrying the previously classified pathogenic BRCA1 c.5095C>T p.Arg1699Trp (R1699W) mutation at the same residue, and to 243 breast cancer families with no BRCA1 pathogenic mutation (BRCA-X). Results Comparison of BRCA1 carrier prediction scores of probands using the BOADICEA risk prediction tool revealed that BRCA1 c.5096G>A p.Arg1699Gln variant carriers had family histories that were less ‘ BRCA1 -like’ than BRCA1 c.5095C>T p.Arg1699Trp mutation carriers (p<0.00001), but more ‘ BRCA1 -like’ than BRCA-X families (p=0.0004). Further, modified segregation analysis of the subset of 30 families with additional genotyping showed that BRCA1 c.5096G >A p.Arg1699Gln had reduced penetrance compared with the average truncating BRCA1 mutation penetrance (p=0.0002), with estimated cumulative risks to age 70 of breast or ovarian cancer of 24%. Conclusions Our results provide substantial evidence that the BRCA1 c.5096G>A p.Arg1699Gln (R1699Q) variant, demonstrating ambiguous functional deficiency across multiple assays, is associated with intermediate risk of breast and ovarian cancer, highlighting challenges for risk modelling and clinical management of patients of this and other potential moderate-risk variants.

With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a Bayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the Bayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types. Genet. Epidemiol. 36:352–359, 2012. © 2012 Wiley Periodicals, Inc.

Abstract Background: Genome-wide association studies (GWAS) identified variants at 19p13.1 and ZNF365 (10q21.2) as risk factors for breast cancer among BRCA1 and BRCA2 mutation carriers, respectively. We explored associations with ovarian cancer and with breast cancer by tumor histopathology for these variants in mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Methods: Genotyping data for 12,599 BRCA1 and 7,132 BRCA2 mutation carriers from 40 studies were combined. Results: We confirmed associations between rs8170 at 19p13.1 and breast cancer risk for BRCA1 mutation carriers [HR, 1.17; 95% confidence interval (CI), 1.07–1.27; P = 7.42 × 10−4] and between rs16917302 at ZNF365 (HR, 0.84; 95% CI, 0.73–0.97; P = 0.017) but not rs311499 at 20q13.3 (HR, 1.11; 95% CI, 0.94–1.31; P = 0.22) and breast cancer risk for BRCA2 mutation carriers. Analyses based on tumor histopathology showed that 19p13 variants were predominantly associated with estrogen receptor (ER)-negative breast cancer for both BRCA1 and BRCA2 mutation carriers, whereas rs16917302 at ZNF365 was mainly associated with ER-positive breast cancer for both BRCA1 and BRCA2 mutation carriers. We also found for the first time that rs67397200 at 19p13.1 was associated with an increased risk of ovarian cancer for BRCA1 (HR, 1.16; 95% CI, 1.05–1.29; P = 3.8 × 10−4) and BRCA2 mutation carriers (HR, 1.30; 95% CI, 1.10–1.52; P = 1.8 × 10−3). Conclusions: 19p13.1 and ZNF365 are susceptibility loci for ovarian cancer and ER subtypes of breast cancer among BRCA1 and BRCA2 mutation carriers. Impact: These findings can lead to an improved understanding of tumor development and may prove useful for breast and ovarian cancer risk prediction for BRCA1 and BRCA2 mutation carriers. Cancer Epidemiol Biomarkers Prev; 21(4); 645–57. ©2012 AACR.

BackgroundThe cerebrospinal fluid (CSF) biomarkers amyloid β (Aβ)‐42, total‐tau (T‐tau), and phosphorylated‐tau (P‐tau) demonstrate good diagnostic accuracy for Alzheimer's disease (AD). However, there are large variations in biomarker measurements between studies, and between and within laboratories. The Alzheimer's Association has initiated a global quality control program to estimate and monitor variability of measurements, quantify batch‐to‐batch assay variations, and identify sources of variability. In this article, we present the results from the first two rounds of the program.MethodsThe program is open for laboratories using commercially available kits for Aβ, T‐tau, or P‐tau. CSF samples (aliquots of pooled CSF) are sent for analysis several times a year from the Clinical Neurochemistry Laboratory at the Mölndal campus of the University of Gothenburg, Sweden. Each round consists of three quality control samples.ResultsForty laboratories participated. Twenty‐six used INNOTEST enzyme‐linked immunosorbent assay kits, 14 used Luminex xMAP with the INNO‐BIA AlzBio3 kit (both measure Aβ‐(1‐42), P‐tau(181P), and T‐tau), and 5 used Meso Scale Discovery with the Aβ triplex (AβN‐42, AβN‐40, and AβN‐38) or T‐tau kits. The total coefficients of variation between the laboratories were 13% to 36%. Five laboratories analyzed the samples six times on different occasions. Within‐laboratory precisions differed considerably between biomarkers within individual laboratories.ConclusionsMeasurements of CSF AD biomarkers show large between‐laboratory variability, likely caused by factors related to analytical procedures and the analytical kits. Standardization of laboratory procedures and efforts by kit vendors to increase kit performance might lower variability, and will likely increase the usefulness of CSF AD biomarkers.