%0 Journal Article
%T New strategies and emerging technologies for massively parallel sequencing: applications in medical research
%A Elaine R Mardis
%J Genome Medicine
%D 2009
%I BioMed Central
%R 10.1186/gm40
%X The human genome lies at the core of research into human disease. New technologies for obtaining genome sequence data are being combined with novel bioinformatics analyses to characterize disease samples of many types, in the hope of enhancing our fundamental understanding of susceptibility and onset for inherited diseases, of the somatic changes that take place to initiate cancers and cause metastatic disease, and of the identity and allelic spectra of pathogenic and commensal microbes that infect humans. These sequencing-based discoveries will have a major impact on medical practice, including the development of diagnostic and prognostic assays, the identification of altered proteins to which targeted therapies may be developed, the ability to predict onset and severity of disease, and an improved capability to predict our range of responses to pathogenic agents. They will also create large datasets that effectively identify each patient by their sequence information, establishing the potential of linking a patient to a disease and heightening the need to safeguard the privacy of these data through legislation against genetic discrimination.Inherited complex diseases have proved the most pervasive yet recalcitrant examples of human disease to reveal their genomic secrets. From a standpoint of statistical significance, studying inherited disease at the genomic level requires large numbers (ideally thousands) of cases (affected) versus controls (unaffected) to uncover initial findings, as well as the replication of any primary discoveries in other case-control cohorts to solidify the association of a given genomic variant(s) with disease. Although genome-wide association studies (GWAS) have been broadly applied across the spectrum of hypertension, diabetes, autism and other diseases, the identification of disease-associated genes by GWAS has so far identified mainly genes of low effect size or within regions of the genome that do not contain annotated genes, hence m
%U http://genomemedicine.com/content/1/4/40