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The technology to simultaneously genotype hundreds of thousands of single nucleotide polymorphisms in a single assay has only recently been developed. These advances have the potential to revolutionize our ability to identify disease-associated proteins and their corresponding pathways as drugable targets. Several strategies that can take advantage of extremely high-density, genome-wide single nucleotide polymorphism genotyping to hone in on pathogenic genetic variants will be discussed. In familial linkage studies, high-density single nucleotide polymorphism genotyping has already been proven to speed up mutation identification of Mendelian traits several fold. Many studies now report examining loss of heterozygosity and genomic amplifications on a whole-genome level. Genotyping hundreds of thousands of single nucleotide polymorphisms in a single set of assays now also allows for whole-genome association studies in complex, multigenic diseases. The technology of high-density single nucleotide polymorphism genotyping has emerged rapidly, leaving data analysis and bioinformatic challenges only partially met. In this review, the immediate applications and implications of the rapidly changing high-density, whole-genome single nucleotide polymorphism genotyping field on translational research will be described.

Identification of biomarkers in neurological disease remains impeded by many obstacles. Among them are the availability of tissue at the site of pathology, poor clinical diagnostics, the complexity of the brain and a general dearth of functional end points and models for validation. However, advances in technology have helped to overcome these challenges. Some of these advances include standardization and increased efficiency in brain banking, novel techniques for brain imaging, improved methods for reducing tissue heterogeneity including laser capture microdissection, high-throughput genomics, new functional validation techniques such as RNA interference, and the development of new animal models of neurologic disease. In order to efficiently handle the wealth of information that will be gleaned from these new technologies, new integrated databasing protocols will be necessary. Access to these databases by researchers and clinicians is critical to the continued progress being made in biomarker identification in neurological disease. These challenges and ways to overcome them are presented here in the context of a disease known to be a robust model for biomarker identification, Alzheimer's disease.

Positional cloning represents one of the most successful paradigm shifts in identifying the underlying patho-mechanisms in human disease. While traditional discovery tools focused on identifying defects at the tissue or cellular level, positional cloning identifies the damaged region of the genome as the preliminary step. While a large number of inherited single gene disorders have been mapped using this approach, a bottleneck still exists in combing through the genomic interval, often millions of nucleotides in length, to identify the nucleotide changes which result in a defective protein and subsequent disease. Along with the recent unravelling of the human genetic code, the development of massively parallel tools, such as microarrays, represent an equally important step forward in unraveling pathogenic genome dysfunctions. There are many emerging variants on microarray technology, such as expression arrays, exon arrays, array-based comparative genomic hybridization and sequencing arrays. Several of these platforms, if used properly, can accelerate the positional cloning process. The proper use of the platform is driven by knowledge of the underlying molecular defect being searched for and the operating characteristics of the array. The resultant insight forms the basis for improved molecular diagnostics and novel therapeutic targets.