MAGIC - the Meta-Analyses of Glucose and Insulin-related traits Consortium

The final human genetics consortium (for now) is MAGIC - the Meta-Analyses of Glucose and Insulin-related traits Consortium, another co-author of the DIAGRAM consortium. This is probably the most contrived of the lot, in that it failed to use all its words but did include “Consortium”. At the same time, being part of MAGIC has a certain appeal above CHARGE, DIAGRAM or GIANT.

“MAGIC (the Meta-Analyses of Glucose and Insulin-related traits Consortium) represents a collaborative effort to combine data from multiple GWAS to identify additional loci that impact on glycemic and metabolic traits.

MAGIC investigators have initially studied fasting glucose, fasting insulin, 2h glucose and HBA1c, as well as performed meta-analysis of more sophisticated measures of insulin secretion and sensitivity. Through these efforts, dozens of loci influencing these traits have been idenified, a subset of which also influence risk of type 2 diabetes.”

There is a certain degree of magic involved in a good genome-wide association study (GWAS) but I still rate this one ad hoc.

If contrived consortium acronyms are your thing, you can probably do a lot worse than sign up to the Table of Contents alerts for the journal Nature Genetics.

GIANT - Genetic Investigation of ANthropometric Traits

Human genetics consortium number three is the GIANT - Genetic Investigation of ANthropometric Traits - consortium, a co-author of one of the recent DIAGRAM papers in Nature Genetics.

“The Genetic Investigation of ANthropometric Traits (GIANT) consortium is an international collaboration that seeks to identify genetic loci that modulate human body size and shape, including height and measures of obesity. The GIANT consortium is a collaboration between investigators from many different groups, institutions, countries, and studies, and the results represent their combined efforts. The primary approach has been meta-analysis of genome-wide association data and other large-scale genetic data sets. Anthropometric traits that have been studied by GIANT include body mass index (BMI), height, and traits related to waist circumference (such as waist-hip ratio adjusted for BMI, or WHRadjBMI). Thus far, the GIANT consortium has identified common genetic variants at hundreds of loci that are associated with anthropometric traits.”

Genome-wide association studies are big by nature, and GIANT does have a lot of participating cohorts and groups on their webpage, so I think that GIANT can be given a post hoc rating.

DIAGRAM - DIAbetes Genetics Replication And Meta-analysis

The DIAGRAM - DIAbetes Genetics Replication And Meta-analysis - consortium is the second ORCA entry this month for human genetics consortia, which seem to be almost as productive a source of contrived acronyms as bioinformatics.

“The DIAGRAM (DIAbetes Genetics Replication And Meta-analysis) consortium is a grouping of researchers with shared interests in performing large-scale studies to characterise the genetic basis of type 2 diabetes, and a principal focus on samples of European descent.”

You can read more on their website, which features an array of additional acronyms (including some future ORCA entries).

CHARGE - Cohorts for Heart and Aging Research in Genomic Epidemiology

The CHARGE - Cohorts for Heart and Aging Research in Genomic Epidemiology - consortium is the first of four human genetics consortia to hit ORCA this month.

I’m not really sure what tigers have to do with hearts or aging, or even charging for that matter, but it’s a nice logo. Coordinating research with that many participants must be challenging enough - I am not sure how you contrive an acronym and logo that everyone agrees with!

According to the CHARGE consortium website:

"The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium was formed to facilitate genome-wide association study meta-analyses and replication opportunities among multiple large and well-phenotyped longitudinal cohort studies."

• Psaty BM, O’Donnell CJ, Gudnason V, Lunetta KL, Folsom AR, Rotter JI, Uitterlinden AG, Harris TB, Witteman JCM, Boerwinkle E, on behalf of the CHARGE Consortium (2009) Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from five cohorts. Circ Cardiovasc Genet. 2:73-80.

PEPPER - Protein complex Expansion using Protein-Protein intERactions

Another offering from the ever-fruitful world of bioinformatics today - with a fruit, fittingly enough.

PEPPER - Protein complex Expansion using Protein-Protein intERactions - pulls out all the stops when it comes to contriving a functional word, skipping words and using internal letters.

For the curious, PEPPER is available from the Cytoscape App Store and is:

“designed to identify protein complexes as densely connected subnetworks from seed lists of proteins derived from proteomic studies. Pepper identifies connected subgraph by using multi-objective optimization involving two functions: (i) the coverage, a solution must contain as many proteins from the seed as possible, (ii) the density, the proteins of a solution must be as connected as possible, using only interactions from a proteome-wide interaction network.”

Despite its use of “seed lists”, I don’t think there is a direct connection with peppers, so PEPPER is being classified as an ad hoc intranym.

I’m not sure if there’s a bigger version of the logo available but if you look carefully, you can see the little chilli peppers.

Winterhalter C et al. (2014) PEPPER: cytoscape app for protein complex expansion using protein-protein interaction networks. Bioinformatics. Aug 18. pii: btu517. [Epub ahead of print]