New paper on steroid-regulated gene expression

Recent paper in Molecular Endocrinology 7:1194-206. doi: 10.1210/me.2014-1069:

Research Resource: Modulators of glucocorticoid receptor activity identified by a new high-throughput screening assay

John A. Blackford, Jr., Kyle R. Brimacombe, Edward J. Dougherty , Madhumita Pradhan, Min Shen, Zhuyin Li, Douglas S. Auld, Carson C. Chow, Christopher P. Austin, and S. Stoney Simons, Jr.

Abstract: Glucocorticoid steroids affect almost every tissue-type and thus are widely used to treat a variety of human pathologies. However, the severity of numerous side-effects limits the frequency and duration of glucocorticoid treatments. Of the numerous approaches to control off-target responses to glucocorticoids, small molecules and pharmaceuticals offer several advantages. Here we describe a new, extended high throughput screen in intact cells to identify small molecule modulators of dexamethasone-induced glucocorticoid receptor (GR) transcriptional activity. The novelty of this assay is that it monitors changes in both GR maximal activity (Amax) and EC50, or the position of the dexamethasone dose-response curve. Upon screening 1280 chemicals, ten with the greatest change in the absolute value of Amax or EC50 were selected for further examination. Qualitatively identical behaviors for 60 –90% of the chemicals were observed in a completely different system, suggesting that other systems will be similarly affected by these chemicals. Additional analysis of the ten chemicals in a recently described competition assay determined their kinetically-defined mechanism and site of action. Some chemicals had similar mechanisms of action despite divergent effects on the level of GR-induced product. These combined assays offer a straightforward method of identifying numerous new pharmaceuticals that can alter GR transactivation in ways that could be clinically useful.

Paper on new version of Plink

The paper describing the updated version of the genome analysis software tool Plink has just been published.

Second-generation PLINK: rising to the challenge of larger and richer datasets
Christopher C Chang, Carson C Chow, Laurent CAM Tellier, Shashaank Vattikuti, Shaun M Purcell, and James J Lee

GigaScience 2015, 4:7  doi:10.1186/s13742-015-0047-8

Abstract
Background
PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1’s primary data format.

Findings
To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, View MathML-time/constant-space Hardy-Weinberg equilibrium and Fisher’s exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0).

Conclusions
The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

Keywords: GWAS; Population genetics; Whole-genome sequencing; High-density SNP genotyping; Computational statistics

 

This project started out with us trying to do some genomic analysis that involved computing various distance metrics on sequence space. Programming virtuoso Chris Chang stepped in and decided to write some code to speed up the computations. His program, originally called wdist, was so good and fast that we kept asking him to put in more capabilities. Eventually,  he had basically replicated the suite of functions that Plink performed so he contacted Shaun Purcell, the author of Plink, if he could just call his code Plink too and Shaun agreed. We then ran a series of tests on various machines to check the speed-ups compared to the original Plink and gcta. If you do any GWAS analysis at all, I highly recommend you check out Plink 1.9.

Why science is hard to believe

Here is an excerpt from a well written opinion piece by Washington Post columnist Joel Achenbach:

Washington Post: We live in an age when all manner of scientific knowledge — from the safety of fluoride and vaccines to the reality of climate change — faces organized and often furious opposition. Empowered by their own sources of information and their own interpretations of research, doubters have declared war on the consensus of experts. There are so many of these controversies these days, you’d think a diabolical agency had put something in the water to make people argumentative.

Science doubt has become a pop-culture meme. In the recent movie “Interstellar,” set in a futuristic, downtrodden America where NASA has been forced into hiding, school textbooks say the Apollo moon landings were faked.

I recommend reading the whole piece.