Pairing GWAS with in-depth metabolomics: assigning functions to genetic variants

In the September 1^st issue of Nature (reference), scientists from the Helmholtz Zentrum Munchen Institute in Munich, Germany, the Wellcome Trust/Sanger Centre, King’s College, and Metabolon, Inc. present the most comprehensive Genome Wide Association Study (GWAS) aimed at identifying relationship between individual genetic variations and specific metabolic pathways.  Using ultra-high performance LC-MS and GC-MS, the levels of over 250 metabolites, representing over 60 metabolic pathways, were analyzed in serum samples from volunteers enrolled in the German KORA F4 study (n= 1768) and in the British TwinsUK study (n= 1052).  From these measures, over 37,000 metabolic traits (concentrations or ratios of metabolite pairs) were derived and their association with about 600,000 SNPs was assessed.  The team identified 37 independent genetic loci with genome-wide significant associations with metabolic traits, 23 of which represented novel associations.  Moreover, among these 37 genetic loci, 15 overlapped with known disease-associated genetic loci, shedding new light on possible new pathobiological mechanisms of diseases such as diabetes, kidney failure, venous thromboembolism, and coronary artery disease. 

This remarkable work represents a major evolution in the field of GWAS by providing a means to place genetic information within a functional biological context.  Indeed, despite identifying thousands of disease risk loci, most GWAS are cataloging exercises offering little or no information about the biological processes potentially associated with the identified genetic variants.  

Thierry Sornasse for Integrated Biomarker Strategy

Genome Wide Association Studies: Beyond the Sample Size Barrier

Genome Wide Association (GWA) studies (GWAS) are hypothesis-free experiments aimed at identifying possible associations between subtle genetic variations and disease risk and/or disease state (see overview in Nature Review Drug Discovery 7, 221).  Over the last few years, the number of GWAS has exploded thanks to the shrinking cost and improved performance of all-genome analysis tools.  Despite their power to decipher the genetic susceptibility of many diseases, GWAS suffer from a major limitation: the sample size required to identify credible associations.  Because GWAS are hypothesis-free, all possible genetic variations are tested for possible association with the phenotype of interest, requiring much more stringent thresholds for statistical significance: depending on the penetrance of the genetic variation the type I error threshold (aka alpha) is usually set between 10^-7 and 10^-5.  Thus, sample sizes in GWAS tend to be well above a 1,000 cases and often exceed the 10,000 mark.  When you are considering a relatively rare disease or condition, these numbers can become a limiting factor.  Furthermore, these large sample sizes only permit testing of direct association hypothesis and not more complex hypotheses such as interactions between variants; the latter would require even larger sample size.  So, if sample size constitutes an inherent limit in GWAS, how can this field progress beyond this barrier?

A recent paper published by Hicks and colleagues in Cancer Informatics (Cancer Inform 2011; 10: 285-204) offers a possible solution.  Focusing on breast cancer, the authors combined GWAS information with gene expression data to determine the combined contribution of multiple genetic variants acting within genes and putative biological.  In addition, thanks to this approach, the authors were able to identify novel genes and biological pathways that could not be identified using traditional GWAS.

Thierry Sornasse for Integrated Biomarker Strategy

Missing the forest for the trees: Genome structure vs SNPs

In the July 24 issue of Nature Biotechnology, Jun Wang from the Beijing Genomics Institute reports that large scale variations in the structure (e.g. deletion, duplication) of the human genome may contain more information about an individual than the collection of Single Nucleotide Polymorphisms (SNPs) usually reported in genomic biomarker studies such as Genome Wide Association Studies (GWAS). If confirmed, this report would force the field of genomics to rethink the way genomic data is assembled and reported. Indeed, even the most advanced DNA sequencing methods available today rely on chopping the DNA into small fragments that are easy to read but extremely difficult to reassemble into a full, structurally correct full genome.

Read also the piece in Wired magazine about this article: Your Genome Structure, Not Genetic Mutations, Makes You Different | Wired Science | Wired.com

Thierry Sornasse for Integrated Biomarker Strategy