Good Gwas Papers / Resources

Question

[brentp]

There have been varied levels of success in genome-wide association studies (GWAS).

What do you recommend as exemplar papers that have well-documented methods? Any other online resources to check out? Any research groups to keep an eye on?

Answer 1

[Khader Shameer]

The following articles were really useful for me to understand the concepts around GWAS.

I would recommend the following reviews to understand the concept and methods. Most of these reviews refers the major studies and specific details can be obtained from individual papers. But you can get an overall idea about the concept, statistical methods and expected results from a GWAS studies from these review articles.

How to Interpret a Genome-wide Association Study

An easy to ready review article that start with basic concepts and discuss future prospects of GWAS Genome-wide association studies and beyond.

A detailed introduction to basic concepts of GWAS from the perspective of vascular disease : Genome-wide Association Studies for Atherosclerotic Vascular Disease and Its Risk Factors

Great overview of the current state of GWAS studies: Genomewide Association Studies and Assessment of the Risk of Disease

Detailed overview of statistical methods: Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

For a bioinformatics perspective Jason Moore et.al's review will be a good start : Bioinformatics Challenges for Genome-Wide Association Studies

Soumya Raychaudhuri's review provides overview of various approaches for interpretations of variants from GWAS Mapping rare and common causal alleles for complex human diseases.

A tutorial on statistical methods for population association studies

Online Resources: I would recommend to start from GWAS page at Wikipedia followed by NIH FAQ on GWAS, NHGRI Catalog of GWAS, dbGAP, GWAS integrator and a related question at BioStar.

[brentp]

@Khader thanks for a very thorough answer. I will check these out. On quick look, the Moore et al paper looks like what i was seeking out.

Answer 2

[Neilfws]

For introductory material, the new blog Genomes Unzipped has a couple of great posts:

• How to read a genome-wide association study
• How well can a screening test predict disease risk?

The recent paper from 23andMe, Web-Based, Participant-Driven Studies Yield Novel Genetic Associations for Common Traits, is a good example of a publication where methods were clearly explained.

[Khader Shameer]

Nice pick from Genomes Unzipped. I think I have missed the second post from GenomesUnzipped. Thanks for sharing this.

Answer 3

[Andrew Su]

A Catalog of Published Genome-Wide Association Studies, as far as I can tell the best online database of GWAS results.

See also some of the resources mentioned in this previous thread.

[brentp]

thanks, i had seen that genome.gov site. i am more interested in the methods of particular papers, rather than drowning in the growing GWAS sea, but that's a good place to start.

Answer 4

[brentp]

I just saw this which seems to be a very good summary of the situation, and with links to informative discussions. With one of the punch-lines being:

This study demonstrates that combining studies using meta-analysis, achieving massive sample sizes to detect extremely small effects can result in both clinically and biologically meaningful discoveries using GWAS.

Answer 5

[Ian]

The SNP/WGA analysis tools within Galaxy might be of help. Galaxy (http://main.g2.bx.psu.edu/) is a really useful tool for handling large data sets based on genome coordinates.

Answer 6

[Larry_Parnell]

The papers that Khader mentions are ones I would also suggest you read. There was a recent paper on GWAS for personality traits which failed to show anything significant. This is also relevant because it shows one aspect of trying to take GWAS too far into the realm of culture and learned behavior. One review on this is here. Last week's article on Cloninger's Temperament scales is here.

You may also be interested in Eric Schadt's pathway approach and the open access database of GWAS results by Johnson and O'Donnell of NHLBI. We use the latter db a lot!

Added in edit on 14 Oct 2011: The paper entitled "Pathways of distinction analysis: a new technique for multi-SNP analysis of GWAS data" is highly relevant.

Certainly two very important emerging trends in GWAS work are:[?]

1. Assignment of function to SNPs with top association values. This has been demonstrated recently for SORT1. (added in edit on 6 Dec 2011) The 2011 papers by Suhre et al on GWAS of metabolite values from serum and urine often do well to point toward function, as in associations of substrate and product ratios to the gene encoding that enzyme or transporter.[?]
2. Looking at the environment - diet, sleep, exercise, alcohol consumption, tobacco smoking, altitude and latitude of residence (oxygen tension and seasonality/day length, respecitvely) in order to assess gene-environment interactions. This is now taking place for Framingham Heart Study and GOLDN. We're doing the GOLDN GWAS and are involved in FHS.

Answer 7

[Chl]

You may also have a look at the special issue published in the Lancet in 2005. Although it may seem a little bit old, it includes a very interesting review from David Clayton. Also, it is worth reading the method used by the Wellcome Trust Consortium, e.g.

The Wellcome Trust Case Control Consortium. (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661-678.

The 1000 Genomes project includes useful ressources as well, especially real data that you can use to better understand issues surrounding GWAS analyses. But you can also check the Bioconductor project (snpMatrix package and the like). There is also an interesting map of published GWAS on Genome.gov.

Answer 8

[Allpowerde]

You might be interested in the ICSB special topics workshop "The successes, challenges and prospects for GWAS mega-analyses for complex diseases such as schizophrenia", which is on the 15. October 2010 in Edinburgh:

Especially the last session would be of interest to you:

Session 4: The road ahead 1. Introduction to novel computational paradigms (Eleazar Eskin) 2. Few samples with many features – lessons already learned from micro-array analyses (Geoff McLachlan) 3. Multi-instance learning and the benefit of sophisticated and customized machine learning approaches (Karsten Borgwardt)

Discussion: Future directions for GWAS analysis with customized machine learning approaches and data integration

• Signal to noise ratio in public databases
• Selection bias with filter- and wrapper-based feature selection
• Robustness and over-fitting control
• Risks of using "black-box" approaches
• How to verify the accuracy of our findings