An overview of the genetic loci implicated in the pathogenesis of PD. The position of each locus relative to the ideogram of each chromosome is depicted. The background color of each box indicates the method that was used to identify this locus. Abbreviation: GWAS, genome-wide association study.

A hypothetical systems based approach to identify aberrant networks of disease. Data (including biological, clinical, imaging) and samples are collected from a population. High-dimensional -omics data are acquired, integrated with clinical data, analyzed, and validated to identify networks involved in disease. Genomics will predict aberrant networks, whereas transcriptomics, proteomics, and metabolomics will report the outcomes of these networks. In turn, these networks and the aberrant nodes that are perturbed in disease can then be used to develop biomarkers, prognostic markers (e.g., markers that report disease progress or therapeutic efficacy), and rational therapeutics. The process is not inherently unidirectional nor is it intended to be single pass. Instead, as technologies improve, the process can be employed in an iterative fashion to refine nodes within aberrant disease networks and to generate better biomarkers, targets, and therapies. The approach is predicated on robust bioinformatics, and analytics that are critical to our abilities translate high-dimensional data to our understanding of disease and its treatment. (Image is from Wang et al. 2012; reprinted, with permission, from the author.)

Genomics will play an integral role in the development of personalized therapeutics. The availability of detailed phenotype data from large patient/control cohorts is an important prerequisite for high-throughput genetic screening studies, including GWAS and genomic sequencing. After genetic risk loci have been dissected, in silico, in vitro, and in vivo analyses establish the underlying functional pathways and help to posit targets for rational, personalized therapies.

Highlights of key genomic discoveries in PD over the past decade and a half.

Application of “-omics” based biomarker strategy to discover, validate, and apply molecular profiles to disease diagnosis, prognosis, and therapeutic development. Biomarker discovery efforts follow a predictable model. A discovery cohort of case (red) and control (green) subjects is amassed. Biological samples along with clinical, demographic, and other data are collected. High-dimensional genomic, transcriptomic, proteomic, and metabolomic data are generated and integrated with clinical data to elucidate the dynamic networks and their critical nodes that contribute to risk and evolution of disease. These pathways are then validated in a separate cohort of cases and controls. Robust, sensitive, and specific profiles can then be applied on a population scale to provide readouts for individuals' risk (pink) for disease. These profiles can be informative to disease diagnosis (pink to red), to prognosis and disease stratification (affected individuals with different temporal progression and severity), and in developing and monitoring therapies that slow, halt, or reverse disease progression. Additional historical (environment, lifestyle), clinical, and imaging data (e.g., PET, SPECT) will be integrated with molecular pathway data that will also be informative in disease diagnosis, stratification, and therapies. (PTMs, posttranslational modifications.) Images of myoglobin structure (http://en.wikipedia.org/wiki/File:Myoglobin.png) and ribosome/mRNA translation (http://en.wikipedia.org/wiki/File:Ribosome_mRNA_translation_en.svg) have been released to the public domain.