[pub]: Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases

http://onlinelibrary.wiley.com/doi/10.1002/gepi.21675/abstract;jsessionid=0E67E391238867DA8CC7EDD1FAABCE88.d03t01

Genet Epidemiol. 2012 Sep 5. doi: 10.1002/gepi.21675. [Epub ahead of print]

Genome-Wide Association Analysis of Imputed Rare Variants: Application to Seven Common Complex Diseases.

Mägi R, Asimit JL, Day-Williams AG, Zeggini E, Morris AP.

Source

Estonian Genome Centre, University of Tartu, Tartu, Estonia.

Abstract

Genome-wide association studies have been successful in identifying loci contributing effects to a range of complex human traits. The majority of reproducible associations within these loci are with common variants, each of modest effect, which together explain only a small proportion of heritability. It has been suggested that much of the unexplained genetic component of complex traits can thus be attributed to rare variation. However, genome-wide association study genotyping chips have been designed primarily to capture common variation, and thus are underpowered to detect the effects of rare variants. Nevertheless, we demonstrate here, by simulation, that imputation from an existing scaffold of genome-wide genotype data up to high-density reference panels has the potential to identify rare variant associations with complex traits, without the need for costly re-sequencing experiments. By application of this approach to genome-wide association studies of seven common complex diseases, imputed up to publicly available reference panels, we identify genome-wide significant evidence of rare variant association in PRDM10 with coronary artery disease and multiple genes in the major histocompatibility complex (MHC) with type 1 diabetes. The results of our analyses highlight that genome-wide association studies have the potential to offer an exciting opportunity for gene discovery through association with rare variants, conceivably leading to substantial advancements in our understanding of the genetic architecture underlying complex human traits.

© 2012 Wiley Periodicals, Inc.

PMID:

 

22951892

 

[PubMed - as supplied by publisher]

Genegames.org: who said online games are COMPLETE time wasters?

tweet from Open Helix .. Found at #ISMB poster session http://genegames.org  (along with a lots blogging fodder) http://ow.ly/i/M3yc 

Down with fever .. so am burning time with http://sulab.scripps.edu/dizeez/index.html

The Rules

1. You are shown one gene name
2. You are also shown five diseases
3. Pick the disease that is linked to the gene to get points
4. Get as many points as you can in one minute

Exome sequencing reveals mutations in previously unconsidered candidate genes

Excerpted from Bio-IT World 

December 20, 2010 | Doctors at the Medical College in Wisconsin have published in the journal Genetics and Medicine the results of exome sequencing in a seriously ill boy with undiagnosed bowel disease. The study (using 454 sequencing) revealed mutations in a gene called XIAP, which interestingly was not previously considered among more than 2,000 candidate genes before the DNA sequencing was performed. ....read full article

Throwing the baby out with the bathwater:Non-Synonymous and Synonymous Coding SNPs Show Similar Likelihood and Effect Size of Human Disease Association

I was literally having a 'oh shoot' moment when i saw this news in GenomeWeb

Synonymous SNPs Shouldn't Be Discounted in Disease, Study Finds

NEW YORK (GenomeWeb News) – Synonymous SNPs that don't change the amino acid sequence encoded by a gene appear just as likely to influence human disease as non-synonymous SNPs that do, according to a paper appearing online recently in PLoS ONE by researchers from Stanford University and the Lucile Packard Children's Hospital.

from the abstract of the paper

The enrichment of disease-associated SNPs around the 80^th base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3′untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies.

Hmmmm how is this going to affect your carefully crafted pipeline now?