Tag Archives: QTL

Lior_RatVenn_sm

Video Tip of the Week: RGD’s OLGA tool, Object List Generator and Analyzer

Lior_RatVenn_smOne of the really persistent issues in genomics is how to either get a list of things, or handle a list of things. or the overlap among the things. I think that was one of the most popular topics we dealt with in the early days of OpenHelix, but it’s still a issue that people need to handle in various ways. Some of the most interesting solutions have been various organism Venn diagrams, and the Rat Genome one is a classic, modeled here by Lior Pachter. I’m certain the need to list and organize genome features won’t go away. So when I saw that the RGD folks had another tool to offer ways to do this, I put it right in my list of upcoming tips. And then the draft post got buried under a list of other things I had to do. But I wanted to get back to it–so here is their step-by-step guide to the OLGA tool they offer, as this week’s Video Tip of the Week.

OLGA stands for: Object List Generator and Analyzer tool. Their newsletter announcement describes it in more details.

OLGA is a straightforward list builder for rat, human and mouse genes or QTLs, or rat strains, using any (or all) of a variety of querying options.  The new tutorial video will walk you through the process of querying the RGD database using OLGA, including

  • how to perform a simple query in OLGA
  • how to further expand or filter your result set using additional criteria
  • how to change your query parameters on the fly to refine your result set
  • what options OLGA gives for analysis of your list once you have it.

You can get a list of items using various ontologies–maybe you want a specific type of receptor, for example, you can get a list of them. Or you can quickly create a list of genes in a certain genomic span. You can get the items that fall in a QTL. Or you can start with a list and get annotations. You can also look for overlaps among sets.

The video is a nice walk-through of how to construct your query and what you can access. One key feature is that it’s not just rat data as you might expect at RGD. Mouse and human data are also available.

You can create complex and clever queries, and link to all sorts of related data in very easy steps. Have a look at their resources, and their other videos for more help with different aspects of their collections.

Quick links:

RGD main site: http://rgd.mcw.edu/

OLGA directly: http://rgd.mcw.edu/rgdweb/generator/list.html

Reference:

Shimoyama, M., De Pons, J., Hayman, G., Laulederkind, S., Liu, W., Nigam, R., Petri, V., Smith, J., Tutaj, M., Wang, S., Worthey, E., Dwinell, M., & Jacob, H. (2014). The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease Nucleic Acids Research, 43 (D1) DOI: 10.1093/nar/gku1026

Linking phenotypes and genotypes.

Ok, that’s a really broad title for an important area. And it is a problem that we are starting to see addressed more and more with GWAS (genome wide association studies). ResearchBlogging.org If you came here hoping that I had solved this, I’m sorry to disappoint you. We are asked all the time for places to look for this kind of information. The relatively new “Phenotypes and Disease Association Tracks” on the human UCSC Genome Browser have been popular in the training sessions we have given (look at the human browser, and check the second group of tracks controls on the page). You can find OMIM data in Ensembl. You can add Morbid Map to your Map Viewer. Another resource that I just found out about is trying to get at the same types of data–but it is available from their interface and also on the HapMap browser.

MutaGeneSys is a tool with a very simple interface at their web site, but the data is also displayed as a track in the GBrowser at HapMap. The goal was to combine HapMap information + OMIM + whole-genome marker correlation data. The news page at HapMap that describes the addition of this track to HapMap says:

Predicted OMIM associations available in GBrowse

The OMIM associations track presents data from the MutaGeneSys database, which links genotype data from HapMap and whole genome association studies with the known disease variants reported by the OMIM database. Example of a region with multiple OMIM associations: Chr1:194923128..194933127

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You'd think they'd look slimmer….

small_sheep.jpgDark sheep, that is. A study in the new issue of Science (subscription required) reports that some dark sheep on a Scottish island are yielding a phenotype difference that is not what you might expect from the selection on the genotype. The darker sheep are larger in size, which was expected to correlate directly with increased fitness based on some previous studies. But it turns out to be more complicated than that. Dark sheep are decreasing in numbers.

The data in this report suggest that there are unexpected factors in play–TYRP1 (tyrosinase-related protein 1) is the gene they focused on, which is known to vary at a single allele in the population as GG and GT for dark coats and TT for light coats (Gratten et al, 2007). They find that there 2 are quantitative trait loci (QTLs) associated with their observations. (QTLs are essentially statistical suggestions that a trait appears to be associated with a genomic region.)

These QTLs appear to have antagonistic effects because the TYRP1 G allele (dominant for dark coat color) is associated with large body size (and hence with increased fitness) but also with decreased lifetime pt(i).

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