Citations

Abstract Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their cis-predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of P < 2.2 × 10−6, we identified 35 genes, including FZD4 at 11q14.2 (Z = 5.08, P = 3.83 × 10−7, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained (P < 1.47 × 10−3). These data identify one novel locus (FZD4) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis. Significance: Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. Cancer Res; 78(18); 5419–30. ©2018 AACR.

Highly purified human Factor V was used for the development of a competitive double antibody radioimmunoassay (RIA) using 125I-human Factor V, burro anti-human Factor V antisera as the primary antibody and goat anti-burro antisera as the precipitating antibody. The standard curve allows the detection of as little as 20 ng of Factor V per ml of plasma. With this specific RIA for human Factor V, we have measured the level of Factor V in the plasma and platelets of normal individuals. The normal level of Factor V in plasma ranges from 4 to 14 μg per ml, with the average value equal to 7.0 ± 2.0 μg/ml (n = 64; 33 females; 31 males; 22 to 61 years of age). There appeared to be no correlation between antigen levels and age or sex. Factor V clotting assays were consistent with the RIA data for any given plasma preparation providing freshly drawn plasma was used in the bioassay. The bioassay data were quantitated based upon the specific activity of purified plasma Factor V; 1.7 units of Factor V equals 1 μg of protein. Plasma Factor V antigen levels were not affected by lyophilization of the plasma, prolonged storage of the plasma at -20°C or intentional conversion of the plasma to serum. The levels of Factor V present in washed human platelets were also determined using the RIA. Assay of washed platelets lysed in 0.2% Triton X-100 indicated that 0.6 to 0.85 μg of Factor V was present per 2.5 × 108 platelets (4400 to 6200 molecules of Factor V per platelet). This result is in marked contrast to our observations for the bovine system, where we found that bovine platelets possess approximately 400 to 800 molecules of Factor V per platelet, and plasma Factor V levels range from 30 to 50 μg per ml. In the bovine system, the platelets possess approximately 1% of the total Factor V present, while in human blood, the platelets possess as much as 10 to 15% of the total Factor V present.

Abstract Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.

Background and Purpose— White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods— In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results— At 17q25, we confirmed the association of multiple common variants in TRIM65 , FBF1 , and ACOX1 ( P <6×10 −7 ). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; P EA =4.5×10 −8 ) partially independent of known common signal ( P EA(conditional) =1.4×10 −3 ). We further identified a locus at 2q33 containing common variants in NBEAL1 , CARF , and WDR12 (lead, rs2351524; P all =1.9×10 −10 ). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( P rs34136221 =2.8×10 −8 ). Conclusions— Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.

Objectives: Dizziness, vertigo, and unsteadiness are common complaints of patients who present to primary care providers. These patients often are referred to otology for assessment and management. Unfortunately, there are a small number of specialists to manage these patients. However, there are several dizziness disorders that can be successfully managed by primary care providers if the disorder is properly identified. To assist in the identification of several of the most common dizziness disorders, we developed the dizziness symptom profile (DSP). The DSP is a self-report questionnaire designed to generate one or more differential diagnoses that can be combined with the patient’s case history and physical examination. Design: This report describes three investigations. Investigations 1 and 2 (i.e., exploratory and confirmatory investigations, N = 514) describe the development of the DSP. Investigation 3 (N = 195) is a validation study that describes the level of agreement between the DSP completed by the patient, and, the differential diagnosis of the otologist. Results: The final version of the DSP consists of 31 items. Preliminary findings suggest that the DSP is in agreement with the differential diagnoses of ear specialists for Meniere’s disease (100% agreement), vestibular migraine (95% agreement), and benign paroxysmal positional vertigo (82% agreement). Conclusions: Early results suggest that DSP may be useful in the creation of differential diagnoses for dizzy patients that can be evaluated and managed locally. This has the potential to reduce the burden on primary care providers and reduce delays in the diagnosis of common dizziness and vertigo disorders.