Glacial cycles promote greater dispersal, which can help explain larger clutch sizes, in north temperate birds

Earth’s glacial history and patterns in the life history traits of the planet’s avifauna suggest the following interpretations of how recent geological history has affected these key characteristics of the biota: 1) Increased colonizing ability has been an important advantage of increased dispersal, and life history strategies are better categorized by dispersive colonizing ability than by their intrinsic growth rates; 2) Birds of the North Temperate Zone show a greater tendency to disperse, and they disperse farther, than tropical or south temperate birds; 3) Habitat changes associated with glacial advance and retreat selected for high dispersal ability, particularly in the North; and 4) Selection for greater dispersal throughout the unstable Pleistocene has also resulted in other well-recognized life history contrasts, especially larger clutch sizes in birds of North Temperate areas

EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data

Summary: The R package EasyStrata facilitates the evaluation and visualization of stratified genome-wide association meta-analyses (GWAMAs) results. It provides (i) statistical methods to test and account for between-strata difference as a means to tackle gene-strata interaction effects and (ii) extended graphical features tailored for stratified GWAMA results. The software provides further features also suitable for general GWAMAs including functions to annotate, exclude or highlight specific loci in plots or to extract independent subsets of loci from genome-wide datasets. It is freely available and includes a user-friendly scripting interface that simplifies data handling and allows for combining statistical and graphical functions in a flexible fashion. Availability: EasyStrata is available for free (under the GNU General Public License v3) from our Web site www.genepi-regensburg.de/easystrata and from the CRAN R package repository cran.r-project.org/web/packages/EasyStrata/. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation.

Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (>175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

Identification of genes associated with brain aging should markedly improve our understanding of the biological processes that govern normal age-related decline. However, challenges to identifying genes that facilitate successful brain aging are considerable, including a lack of established phenotypes and difficulties in modeling the effects of aging per se, rather than genes that influence the underlying trait. In a large cohort of randomly selected pedigrees (n = 1,129 subjects), we documented profound aging effects from young adulthood to old age (18-83 y) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for pleiotropy between these classes of phenotypes was observed. Applying an advanced quantitative gene-by-environment interaction analysis where age is treated as an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration as a function of age. Furthermore, by decomposing gene-by-aging (G × A) interactions, we infer that different genes influence some neurocognitive traits as a function of age, whereas other neurocognitive traits are influenced by the same genes, but to differential levels, from young adulthood to old age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. These results clearly demonstrate that traits sensitive to the genetic influences on brain aging can be identified, a critical first step in delineating the biological mechanisms of successful aging

Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution

The Fungicide Chlorothalonil Is Nonlinearly Associated with Corticosterone Levels, Immunity, and Mortality in Amphibians

Background: Contaminants have been implicated in declines of amphibians, a taxon with vital systems similar to those of humans. However, many chemicals have not been thoroughly tested on amphibians or do not directly kill them

Chances and challenges of machine learning based disease classification in genetic association studies illustrated on age-related macular degeneration

Imaging technology and machine learning algorithms for disease classification set the stage for high-throughput phenotyping and promising new avenues for genome-wide association studies (GWAS). Despite emerging algorithms, there has been no successful application in GWAS so far. We establish machine learning-based phenotyping in genetic association analysis as misclassification problem. To evaluate chances and challenges, we performed a GWAS based on automatically classified age-related macular degeneration (AMD) in UK Biobank (images from 135,500 eyes; 68,400 persons). We quantified misclassification of automatically derived AMD in internal validation data (4,001 eyes; 2,013 persons) and developed a maximum likelihood approach (MLA) to account for it when estimating genetic association. We demonstrate that our MLA guards against bias and artifacts in simulation studies. By combining a GWAS on automatically derived AMD and our MLA in UK Biobank data, we were able to dissect true association (ARMS2/HTRA1,CFH) from artifacts (nearHERC2) and identified eye color as associated with the misclassification. On this example, we provide a proof-of-concept that a GWAS using machine learning-derived disease classification yields relevant results and that misclassification needs to be considered in analysis. These findings generalize to other phenotypes and emphasize the utility of genetic data for understanding misclassification structure of machine learning algorithms

Chandra ACIS Survey of M33 (ChASeM33): A First Look

We present an overview of the Chandra ACIS Survey of M33 (ChASeM33): A Deep Survey of the Nearest Face-on Spiral Galaxy. The 1.4 Ms survey covers the galaxy out to R \approx 18\arcmin (\approx 4 kpc). These data provide the most intensive, high spatial resolution assessment of the X-ray source populations available for the confused inner regions of M33. Mosaic images of the ChASeM33 observations show several hundred individual X-ray sources as well as soft diffuse emission from the hot interstellar medium. Bright, extended emission surrounds the nucleus and is also seen from the giant \hii regions NGC 604 and IC 131. Fainter extended emission and numerous individual sources appear to trace the inner spiral structure. The initial source catalog, arising from $\sim$~2/3 of the expected survey data, includes 394 sources significant at the $3\sigma$ confidence level or greater, down to a limiting luminosity (absorbed) of $\sim$1.6\ergs{35} (0.35 -- 8.0 keV). The hardness ratios of the sources separate those with soft, thermal spectra such as supernova remnants from those with hard, non-thermal spectra such as X-ray binaries and background active galactic nuclei. Emission extended beyond the Chandra point spread function is evident in 23 of the 394 sources. Cross-correlation of the ChASeM33 sources against previous catalogs of X-ray sources in M33 results in matches for the vast majority of the brighter sources and shows 28 ChASeM33 sources within 10\arcsec of supernova remnants identified by prior optical and radio searches. This brings the total number of such associations to 31 out of 100 known supernova remnants in M33.Comment: accepted for publication ApJS, full resolution images and complete tables available at http://hea-www.harvard.edu/vlp_m33_public