Genetics and patient outcome after cardiac surgery: unravelling translational findings.

In the current issue of the British Journal of Anaesthesia, Frey and colleagues present a study entitled ‘Genetic interactions in the b-adrenoceptor/G protein signal transduction pathway and survival after coronary artery bypass grafting: a pilot study’. This report is an extension of a previously published study designed to translate genetics from the laboratory bench into better understanding of human clinical cardiovascular outcomes after cardiac surgery. The goal of this editorial is to walk the clinical anaesthetist through this manuscript in ways that illuminate understanding overall, and also highlight strengths and weakness of the study. To do so, I start with a brief review of recent human genetic breakthroughs since they provide context for the study. The year 2000 will be remembered as the year of the human genome. Indeed, the DNA sequence of the first human genome was submitted by two competing public and private groups in 2000, then officially published simultaneously in Science and Nature in February 2001. – 5 Genetic variation between individuals was immediately noticed, including missing or added sequences, and also the most frequent variation called single-nucleotide DNA base pair alterations or polymorphisms (SNPs). Over 1.42 million SNPs have been identified in 25 000 human genes and intervening sequences in the human genome. Furthermore, it was quickly noticed that genetic variants often travel together during naturally occurring crossovers between maternal and paternal DNA strands which make up the DNA double-helix in every human. Such ‘chunks’ of DNA travelling together are called haplotypes and the statistical probability that two SNPs will travel together is described as linkage (the more precise mathematical term for this concept is linkage disequilibrium). Owing to initial expense of sequencing all DNA in a given individual, as soon as the first human genome sequence was officially completed, the haplotype mapping (HapMap) project began with analysis of 270 individuals across four geographic populations. The goal of this project, completed in 2005, was to identify which human SNPs travel together and use this as a way to map fragments of DNA associated with human traits, diseases, or the ability to modify disease. The ability to deduce haplotypes has become so commonplace in the intervening years that many commercial and university computational computer programs, now widely available, are capable of analysing human DNA sequences and inferring haplotypes based on variation in a given data set of human DNA. Initially, clinical genetic studies focused on individual SNPs as they could be easily identified. These studies often targeted SNPs in ‘known’ biological pathways and then ‘associated’ them with the presence of disease, altered drug metabolism, or other traits; hence these were described as association studies. Since biological processes contributing to diseases are incompletely understood, and only a few SNPs were chosen for analysis, these studies were often not reproducible across populations. Whether this was due to genetic variation across populations themselves, small study size, inconsistent tracking of co-morbid diseases/ drugs, or lack of robust sensitivity and specificity of defined outcomes, was unknown. Genome scientists argued that such studies were ‘biased’ since they assumed that important pathways were known, so ‘unbiased’ studies were proposed (sometimes called fishing expeditions by more traditional hypothesis-driven researchers) examining thousands of RNAs or proteins present after specific perturbations such as heat, glucose deprivation, specific diseases or drug Volume 107, Number 6, December 2011