Methodology for estimating detectable change in water quality due to prescribed fire in northern Colorado, A

2001 Spring.Includes bibliographical references.Increases in nutrients and metals in receiving waters have been documented after wildfire. However, water quality impacts from prescribed fire are not well known. This research investigated the design of a post-fire water quality monitoring program using a pre-fire dataset to detect water quality changes from prescribed fire. Since water quality changes due to land use practices are often difficult to detect due to high natural variability, a paired watershed approach was implemented. Two small watersheds were selected in the Cache la Poudre watershed in Northern Colorado and monitored for one year, resulting in 14 pre-fire water quality samples. A single station and paired approach, which consider statistical power are presented and the minimum detectable change is calculated for a range of post-fire sample sizes. Samples from the Bobcat Fire in the Big Thompson Watershed near Drake, Colorado are used to evaluate the results. These results show that with 16 post-fire samples a change of less than 1% of the difference between pre-firewater quality samples and samples from the Bobcat Fire can be detected for most parameters with a statistical power of 80%. The paired watershed approach is shown to reduce the minimum detectable change by half for parameters that are correlated between the two watersheds

The MESA accelerometer for space application

An electrostatically suspended proof mass in the Miniature Electrostatic Accelerometer (MESA) is used to measure acceleration in the submicro-g range. Since no fixed mechanical suspension (such as springs or strings) is used, the constrainment scaling can be changed electrically after being placed in orbit. A single proof mass can sense accelerations in three axes simultaneously. It can survive high-g pyrotechnic-generated shocks and launch environments while unpowered

Genomic alterations indicate tumor origin and varied metastatic potential of disseminated cells from prostate-cancer patients

Disseminated epithelial cells can be isolated from the bone marrow of a far greater fraction of prostate-cancer patients than the fraction of patients who progress to metastatic disease. To provide a better understanding of these cells, we have characterized their genomic alterations. We first present an array comparative genomic hybridization method capable of detecting genomic changes in the small number of disseminated cells (10-20) that can typically be obtained from bone-marrow aspirates of prostate-cancer patients. We show multiple regions of copy-number change, including alterations common in prostate cancer, such as 8p loss, 8q gain, and gain encompassing the androgen-receptor gene on Xq, in the disseminated cell pools from 11 metastatic patients. We found fewer and less striking genomic alterations in the 48 pools of disseminated cells from patients with organ-confined disease. However, we identify changes shared by these samples with their corresponding primary tumors and prostate-cancer alterations reported in the literature, evidence that these cells, like those in advanced disease, are disseminated tumor cells (DTCs). We also demonstrate that DTCs from patients with advanced and localized disease share several abnormalities, including losses containing cell-adhesion genes and alterations reported to associate with progressive disease. These shared alterations might confer the capability to disseminate or establish secondary disease. Overall, the spectrum of genomic deviations is evidence for metastatic capacity in advanced-disease DTCs and variation in that capacity in DTCs from localized disease. Our analysis lays the foundation for elucidation of the relationship between DTC genomic alterations and progressive prostate cancer

Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work

LSST: from Science Drivers to Reference Design and Anticipated Data Products

(Abridged) We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). A vast array of science will be enabled by a single wide-deep-fast sky survey, and LSST will have unique survey capability in the faint time domain. The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the Solar System, exploring the transient optical sky, and mapping the Milky Way. LSST will be a wide-field ground-based system sited at Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel camera. The standard observing sequence will consist of pairs of 15-second exposures in a given field, with two such visits in each pointing in a given night. With these repeats, the LSST system is capable of imaging about 10,000 square degrees of sky in a single filter in three nights. The typical 5$\sigma$ point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The project is in the construction phase and will begin regular survey operations by 2022. The survey area will be contained within 30,000 deg$^2$ with $\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$, covering the wavelength range 320--1050 nm. About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18,000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The remaining 10\% of the observing time will be allocated to projects such as a Very Deep and Fast time domain survey. The goal is to make LSST data products, including a relational database of about 32 trillion observations of 40 billion objects, available to the public and scientists around the world.Comment: 57 pages, 32 color figures, version with high-resolution figures available from https://www.lsst.org/overvie

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

Supplemental Data Supplemental Data include one figure and five tables and can be found with this article online at http://dx.doi.org/10.1016/j.ajhg.2016.08.016. Supplemental Data Document S1. Figure S1 and Tables S1–S5 Download Document S2. Article plus Supplemental Data Download Web Resources ClinVar, https://www.ncbi.nlm.nih.gov/clinvar/ dbNSFP, https://sites.google.com/site/jpopgen/dbNSFP Human Gene Mutation Database, http://www.hgmd.cf.ac.uk/ REVEL, https://sites.google.com/site/revelgenomics/ SwissVar, http://swissvar.expasy.org/ The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10−12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046–0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027–0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale

ADIPOQ, ADIPOR1, and ADIPOR2 Polymorphisms in Relation to Serum Adiponectin Levels and BMI in Black and White Women

Adiponectin is an adipose-secreted protein with influence on several physiologic pathways including those related to insulin sensitivity, inflammation, and atherogenesis. Adiponectin levels are highly heritable and several single nucleotide polymorphisms (SNPs) in adiponectin-related genes (ADIPOQ, ADIPOR1, ADIPOR2) have been examined in relation to circulating adiponectin levels and obesity phenotypes, but despite differences in adiponectin levels and obesity prevalence by race, few studies have included black participants. Using cross-sectional interview data and blood samples collected from 990 black and 977 white women enrolled in the Southern Community Cohort Study from 2002 to 2006, we examined 25 SNPs in ADIPOQ, 19 in ADIPOR1, and 27 in ADIPOR2 in relation to serum adiponectin levels and body mass index (BMI) using race-stratified linear regression models adjusted for age and percentage African ancestry. SNP rs17366568 in ADIPOQ was significantly associated with serum adiponectin levels in white women only (adjusted mean adiponectin levels = 15.9 for G/G genotype, 13.7 for A/G, and 9.3 for A/A, p=0.00036). No other SNPs were associated with adiponectin or BMI among blacks or whites. Because adiponectin levels as well as obesity are highly heritable and vary by race but associations with polymorphisms in the ADIPOQ, ADIPOR1, and ADIPOR2 genes have been few in this and other studies, future work including large populations from diverse racial groups is needed to detect additional genetic variants that influence adiponectin and BMI

Evaluation of polygenic risk scores for breast and ovarian cancer risk prediction in BRCA1 and BRCA2 mutation carriers

Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]-positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2 x 10(53)). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2 x 10(-20)). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management

Genome-wide association of familial prostate cancer cases identifies evidence for a rare segregating haplotype at 8q24.21

Previous genome-wide association studies (GWAS) of prostate cancer risk focused on cases unselected for family history and have reported over 100 significant associations. The International Consortium for Prostate Cancer Genetics (ICPCG) has now performed a GWAS of 2511 (unrelated) familial prostate cancer cases and 1382 unaffected controls from 12 member sites. All samples were genotyped on the Illumina 5M+exome single nucleotide polymorphism (SNP) platform. The GWAS identified a significant evidence for association for SNPs in six regions previously associated with prostate cancer in population-based cohorts, including 3q26.2, 6q25.3, 8q24.21, 10q11.23, 11q13.3, and 17q12. Of note, SNP rs138042437 (p = 1.7e−8) at 8q24.21 achieved a large estimated effect size in this cohort (odds ratio = 13.3). 116 previously sampled affected relatives of 62 risk-allele carriers from the GWAS cohort were genotyped for this SNP, identifying 78 additional affected carriers in 62 pedigrees. A test for an excess number of affected carriers among relatives exhibited strong evidence for co-segregation of the variant with disease (p = 8.5e−11). The majority (92 %) of risk-allele carriers at rs138042437 had a consistent estimated haplotype spanning approximately 100 kb of 8q24.21 that contained the minor alleles of three rare SNPs (dosage minor allele frequencies <1.7 %), rs183373024 (PRNCR1), previously associated SNP rs188140481, and rs138042437 (CASC19). Strong evidence for co-segregation of a SNP on the haplotype further characterizes the haplotype as a prostate cancer pre-disposition locus