Human Genome Variation: Linking Genotypes to Clinical Phenotypes - Session Introduction

Now that major milestones have been reached by both the public and private sectors in the quest for the human genome sequence, the focus is shifting to the genetic variability of our species. Lying buried in human genetic variability is the source not only of all genetic disease, but the entire range of normal phenotypic variation, including susceptibilities to pathogens and environmental factors, and individual differences in response to drug treatment. Emerging high-throughput technologies like the DNA microarray are enabling for the first time large-scale genotyping 1 and gene expression profiling 2 of human populations. Databases comprising large number of polymorphisms 3 and gene expression profiles of normal and diseased tissues or from different clinical states 4 are now thriving. This is the second time a session devoted to human genome variation has been held at the Pacific Symposium on Biocomputing 5. The focus of the session has been broadened this year to include the computational challenges in elucidating connections between genotypes and phenotypes using high-throughput technologies. Submissions more than doubled as compared to the previous edition, making the selection of papers a difficult job for both reviewers and organizers. Six accepted manuscripts comprise this year's original work presented at the conference. The accepted papers demonstrate the increasing maturity of the science of linking the genotype to the phenotype. Rather than focusing on techniques that might come up with plausible associations, the submissions this year addressed "head on" many of the stumbling blocks involved in making robust the analytic techniques within formalized frameworks of the sources error and noise that are inherent to all genomic measurement systems. Furthermore, these manuscripts further the information theoretic foundations for many of the machine learning techniques developed for the investigation of functional genomics. Many large-scale genetic association studies have been proposed to tackle the genetic origin of common disease. Due to the low penetrance of complex traits, these approaches rely on the genotyping of large numbers of biallelic polymorphisms in hundreds or thousands of subjects seeking to find linkage disequilibrium between a dense map of markers and the disease loci. The contribution of Gordon and Ott provides valuable advise to the practitioners through an assessment of the effect of genotyping errors in the power of detecting association in case-control studies. Since the high-throughput technologies for SNP-genotyping are still in emergence 1 , a precise estimate of the impact of genotyping errors is crucial for …