A Preliminary Discussion of the Kinematics of BHB and RR Lyrae Stars near the North Galactic Pole

The radial velocity dispersion of 67 RR Lyrae variable and blue horizontal branch (BHB) stars that are more than 4 kpc above the galactic plane at the North Galactic Pole is 110 km/sec and shows no trend with Z (the height above the galactic plane). Nine stars with Z < 4 kpc show a smaller velocity dispersion (40 +/-9 km/sec) as is to be expected if they mostly belong to a population with a flatter distribution. Both RR Lyrae stars and BHB stars show evidence of stream motion; the most significant is in fields RR2 and RR3 where 24 stars in the range 4.0 < Z < 11.0 kpc have a mean radial velocity of -59 +/- 16 km/sec. Three halo stars in field RR 2 appear to be part of a moving group with a common radial velocity of -90 km/sec. The streaming phenomenon therefore occurs over a range of spatial scales. The BHB and RR Lyrae stars in our sample both have a similar range of metallicity (-1.2 < [Fe/H] < -2.2). Proper motions of BHB stars in fields SA 57 (NGP) and the Anticenter field (RR 7) (both of which lie close to the meridional plane of the Galaxy) show that the stars that have Z 4 kpc have a Galactic V motion that is < -200 km/sec and which is characteristic of the halo. Thus the stars that have a flatter distribution are really halo stars and not members of the metal-weak thick-disk.Comment: Accepted for publication in the March 1996 AJ. 15 pages, AASTeX V4.0 latex format (including figures), 2 eps figures, 2 separate AASTeX V4.0 latex table

Towards Prioritizing Documentation Effort

Programmers need documentation to comprehend software, but they often lack the time to write it. Thus, programmers must prioritize their documentation effort to ensure that sections of code important to program comprehension are thoroughly explained. In this paper, we explore the possibility of automatically prioritizing documentation effort. We performed two user studies to evaluate the effectiveness of static source code attributes and textual analysis of source code towards prioritizing documentation effort. The first study used open-source API Libraries while the second study was conducted using closed-source industrial software from ABB. Our findings suggest that static source code attributes are poor predictors of documentation effort priority, whereas textual analysis of source code consistently performed well as a predictor of documentation effort priority

Which Method-Stereotype Changes are Indicators of Code Smells?

A study of how method roles evolve during the lifetime of a software system is presented. Evolution is examined by analyzing when the stereotype of a method changes. Stereotypes provide a high-level categorization of a method\u27s behavior and role, and also provide insight into how a method interacts with its environment and carries out tasks. The study covers 50 open-source systems and 6 closed-source systems. Results show that method behavior with respect to stereotype is highly stable and constant over time. Overall, out of all the history examined, only about 10% of changes to methods result in a change in their stereotype. Examples of methods that change stereotype are further examined. A select number of these types of changes are indicators of code smells

Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use.

Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders1. They are heritable2,3 and etiologically related4,5 behaviors that have been resistant to gene discovery efforts6-11. In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures

Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.

Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition

Hierarchical Generalized Linear Models for Multiple Groups of Rare and Common Variants: Jointly Estimating Group and Individual-Variant Effects

Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/)