Tag Archives: hapmap


Friday SNPpets

This week’s SNPpets have lentils. I love lentils. But also zombies. And a baronet established by DNA sequencing. A need for some CRISPR edting. And the sudden retirement of HapMap. There’s also an attempt to save the model organisms from retirement or zombie-ness. Wormbase/Flybase/ZFIN et al need your signature. I wish signaures could save science from the Brexit as well. But I suspect that’s unlikely.

SNPpets_2Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…


Video Tip of the Week: Population Genetics Introduction

We are on the road this week at a workshop in Southern California, so I am going to hand off my tip responsibilities to Lynn Jorde.

Another session in the Current Topics in Genome Analysis 2012 course that has been organized by the NHGRI featured Lynn Jorde. Lynn delivered a lecture (about 1.5 hours long in total–but he makes you stand up at 1 hour to stretch :) ) that provides a nice and gentle introduction to population genetics.

Jorde starts with a list of applications of human genetic variation, such as:

  • deciphering human history
  • inferring individual ancestry
  • forensics (I had no idea that there were 25,000 criminal cases a year with DNA issues)
  • finding and understanding disease causing genes

He does some very clever and helpful comparisons to make his points. At one point he compares humans and broccoli. And he uses an item from the Weekly World News to illustrate a point–this made me laugh because I’ve done the same thing.

Touching carefully on the issue of “race”, he acknowledges that human genetics discussions on that can generate more heat than light. So he doesn’t use that term in his writing. And there are a number of cases where social concepts of race vs. medical treatment are not cohering. He finds “ancestry” the more useful way to think about predictions for responses to drugs or treatments.

He also notes though that there is need for caution at this point on reliance on the data we are seeing from next-gen sequencing platforms. Specifically he calls out this paper in Genetics in Medicine as a key awareness (emphasis mine):

CONCLUSIONS: Our analyses demonstrate that clinical prognoses are complicated by sequencing platform-specific errors and ethnicity. We show that disease-causing alleles are globally distributed along ethnic lines, with alleles known to be disease causing in Eurasians being significantly more likely to be homozygous in Africans.

[By the way: that paper is interesting on a couple of other fronts too: it tries to figure out what a “healthy genome” would look like, and heavily uses OMIM to assess that.]

Another clever example to illustrate relationships among people used an analysis of the Supreme Court decisions to describe neighbor-joining networks. And he used profiles of political candidates to explain distance matrix. It seemed pretty approachable to me.

This talk isn’t specific about any particular software tools, but he does reference important population genetics data sets that you should be familiar with if you use tools that have that data. He speaks about the HapMap project, the 1000 Genomes data, and VAAST (the Variant Annotation, Analysis & Search Tool) software.

So check out this talk for a nice overview of population genetics, and important and current factors around this field today.

Quick links:

Lecture on YouTube: http://youtu.be/Ng6vKcGkzZs

Current Topics in Genome Analysis course: http://www.genome.gov/12514288


Moore, B., Hu, H., Singleton, M., De La Vega, F., Reese, M., & Yandell, M. (2011). Global analysis of disease-related DNA sequence variation in 10 healthy individuals: Implications for whole genome-based clinical diagnostics Genetics in Medicine, 13 (3), 210-217 DOI: 10.1097/GIM.0b013e31820ed321

Yandell, M., Huff, C., Hu, H., Singleton, M., Moore, B., Xing, J., Jorde, L., & Reese, M. (2011). A probabilistic disease-gene finder for personal genomes Genome Research, 21 (9), 1529-1542 DOI: 10.1101/gr.123158.111

Tip of the Week: Human SNP-coexpression associations, SNPxGE2

Today’s tip is on a new database based on data from a single interesting paper, SNPxGE2. With a  large scale association study from HapMap data (269 individuals, 4 populations, over 500k SNPs and 15k expression profiles), the research reported:

the computationally predicted human SNP-coexpression associations, that is, the differential co-expression between 2 genes is associated with the genotype of an SNP.

This data is organized in an easily searchable database called SNPxGE2. As the paper only came out 2 months ago, it’s a promising database. It’s interesting and helpful as is, but I can see more data being added over time.

Related Links:

Tip of the Week on SNPexp (correlation between SNPs and expression)
False Discovery Rate article

Wang, Y., Joseph, S., Liu, X., Kelley, M., & Rekaya, R. (2011). SNPxGE2: a database for human SNP-coexpression associations Bioinformatics, 28 (3), 403-410 DOI: 10.1093/bioinformatics/btr663

Tip of the Week: SNPexp, correlation between SNPs & gene expression

SNPexp is a nice simple tool that uses PLINK to  calculate the correlation (p-value) between SNPs in a given range of locations in the genome, or alternatively a list of specific SNP rsIDs, and the expression of a gene of interest. It combines the data from these two datasets: the HapMap project and GENEVAR*. It provides a simple web-based interface to allow you to make those calculations and to either download the results in a series of files, or to view the results as a custom track in the UCSC Genome Browser. Today’s tip gives you a quick introduction to using the tool.

*GENEVAR is both a database of  “analysis of gene expression variation in the HapMap samples using genome-wide expression arrays (47294 transcripts) from EBV-transformed lymphoblastoid cell lines from the same 270 HapMap individuals AND a downloadable software tool to allow you to “perform analysis and visualization of associations between sequence variation and gene expression in eQTL studies.” It’s an additional tool that might be of interest and providing for more in-depth analysis.

Quick link to SNPexp: http://app3.titan.uio.no/biotools/tool.php?app=snpexp

News bits: WikiGenes opportunity; HapMap data issue

Ok, I’m back from Thanksgiving and catching up on some emails and found a couple of news items I wanted to pass along.

WikiGenes invitation to edit a Nature Genetics paper

Here’s an interesting “experiment” I got notified about. You could potentially get authorship on this paper if you contribute to the development of this article.  Here’s the email I got from the WikiGenes mailing list–but click over for more details and you can see the article over there. I haven’t had time to check it all out yet, but as there is a deadline I wanted to mention it now.

Dear Mary,
The editor of Nature Genetics has commissioned a collaborative standards paper on Genome Wide Association Studies. An editable draft of this paper is now online at WikiGenes, http://www.wikigenes.org/GWAS.html?wpc=12

I hope this is an interesting opportunity for you, because significant contributions to this draft might get you a co-authorship on the final paper in Nature Genetics.

I would also like to use this occasion to ask you a favor.

If you like WikiGenes, please tell your friends about it. We do not have the budget of big publishers, so we depend fully on word-of-mouth publicity.

Or you could also help us by linking to WikiGenes from your website. Thank you!

Best of science,

Robert Hoffmann, PhD
Branco Weiss Fellow
WikiGenes – Evolutionary Knowledge

HapMap data in the HaploView tool

This came across from the HapMap mailing list. We tell people about using HapMap + HaploView all the time, so I wanted to mention this possible issue with some of the data:

Dear HapMap users,

Recently, there are several questions about Haploview data format errors when users tried to analyze HapMap release 28 data.  The current Haploview version (4.2) does not recognize the new individuals in release 28 and the software will generate an error similar to “Hapmap data format error: NA18876″ when trying to open the data.

Haploview is developed and maintained by an organization different from HapMap.  Please contact Haploview help desk (haploview@broadinstitute.org) for questions specific to this software.


Hua Zhang, Ph.D.
dbSNP Group

Friday SNPpets

Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…

Friday SNPpets

Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…

Guest Post: SNAP — Andrew Johnson

This next post in our continuing semi-regular Guest Post series is from Andrew Johnson, one of the developers and the concept designer of SNAP, SNP Annotation and Proxy Search which is hosted at the Broad Institute. If you are a provider of a free, publicly available genomics tool, database or resource and would like to convey something to users on our guest post feature, please feel free to contact us at wlathe AT openhelix DOT com or the contact form (write ‘guest post’ as subject heading). We welcome introductions to your resource, information on updates, highlights of little known gems or opinion pieces on the state of genomic research and databases.

SNAP (http://www.broadinstitute.org/mpg/snap/, Johnson et al. (2008) Bioinformatics 24(24): 2938), “SNP Annotation and Proxy search”, is a flexible, web-based tool that allows anyone in the world to quickly accomplish a range of SNP-related genetics and bioinformatics tasks. This post highlights some common questions andfeatures of SNAP, some more obscure uses, and recent and planned developments.

How did SNAP come about?

The idea for SNAP was originally sparked by GWAS analysts within a large collaborative group (the Framingham Heart Study SHARe project). This was in the pre-imputation era when GWAS investigators from different groups using different SNP arrays often wanted to find best proxy SNPs based on HapMap for comparison when they didn’t have common genotyped SNPs across groups. We initially implemented local programs to lookup upHapMap LD and also consider the presence of query and proxy SNPs on different commercial genotyping arrays. We quickly realized this was a community-wide problem as we received requests from outside collaborators so we decided it was worth developing a public tool and approached investigators at the Broad Institute. Through collaboration with Paul de Bakker, Bob Handsaker and others at the Broad Institute we were able to add more features like plotting and build a nice, quick and accessible interface. Many people have contributed ideas, testingand improvements to SNAP, and Bob Handsaker and Pei Lin in particular continue to maintain and update SNAP.

What do you use SNAP for the most?

The two major features of SNAP widely used 1) SNP LD queries, and 2) plotting of LD and association data. There are a number of flexible options for these functions. Beyond these, as a SNP bioinformatics specialist, I often use SNAP to rapidly retrieve information about a list of SNPs for other uses (see specialized queries below).

What are some commonly asked questions from users of SNAP?

Continue reading

Tip of the Week: HapMap data in Haploview

HapMap has had a few minor updates to their browser, and importantly, new phase 3 data was released early last year (drafts of that data were released in 2008). Haploview, the downloaded software that allows the user to perform in depth LD and haplotype analysis, has been recently updated from version 4.1 to version 4.2. Haploview can be used with user data or data downloaded from the HapMap project. Though, version 4.1 did not work for phase III HapMap project data, so the user had to use phase I and II data if they wanted to use version 4.1. Haploview has now been updated to version 4.2, allowing the user to use HapMap phase III data.

That’s a lot of versions and phases :). The short of it is, if you use Haploview 4.2, you can view and analyze data from any phase of the HapMap project.

Today’s tip briefly shows you how to download data from the HapMap project and view it in Haploview.

Top SNPs of the year

Interesting post from SNPedia blog (we mentioned being able to view SNPedia SNPS HapMap last year in a post) of the top 10 SNPs of the year.

Of course, as they mention, it’s very subjective.

Because they have chosen SNPs with serious health interest, I’ll semi-frivolously (because hey, no knowledge is necessarily “frivolous” :) nominate either:

The “ear wax” SNP which determines whether you have ‘wet or dry’ earwax, only because (yes, TMI) I have both, one in each ear so now I’m curious as to why.


The “Perfect Musical Pitch” SNP, only because my daughter and I seem to have that particular variation, and we know a few people who don’t ;-).