Systems genetics: From GWAS to disease pathways

Biochimica et Biophysica Acta (Impact Factor: 4.66). 05/2014; 1842(10). DOI: 10.1016/j.bbadis.2014.04.025
Source: PubMed


Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.

Download full-text


Available from: Jingyuan Fu, May 19, 2015
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The completion of the human genome sequence in 2003 clearly marked the beginning of a new era for biomedical research. It spurred technological progress that was unprecedented in the life sciences, including the development of high-throughput technologies to detect genetic variation and gene expression. The study of genetics has become "big data science". One of the current goals of genetic research is to use genomic information to further our understanding of common complex diseases. An essential first step was made towards this goal by the identification of thousands of single nucleotide polymorphisms showing robust association with hundreds of different traits and diseases. As insight into common genetic variation has expanded enormously and the technology to identify more rare variation has become available, we can utilize these advances to gain a better understanding of disease etiology. This will lead to developments in personalized medicine and P4 healthcare. Here, we review some of the historical events and perspectives before and after the completion of the human genome sequence. We also describe the success of large-scale genetic association studies and how these are expected to yield more insight into complex disorders. We show how we can now combine gene-oriented research and systems-based approaches to develop more complex models to help explain the etiology of common diseases. This article is part of a Special Issue entitled: From Genome to Function.
    Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 05/2014; 1842(10). DOI:10.1016/j.bbadis.2014.05.002 · 4.88 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient's clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.
    Pharmacogenomics and Personalized Medicine 08/2014; 7(1):227-40. DOI:10.2147/PGPM.S48887
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Biological systems are exquisitely poised to respond and adjust to challenges, including damage. However, sustained damage can overcome the ability of the system to adjust and result in a disease phenotype, its underpinnings many times elusive. Unraveling the molecular mechanisms of systems biology, of how and why it falters, is essential for delineating the details of the path(s) leading to the diseased state and for designing strategies to revert its progression. An important aspect of this process is not only to define the function of a gene but to identify the context within which gene functions act. It is within the network, or pathway context, that the function of a gene fulfills its ultimate biological role. Resolving the extent to which defective function(s) affect the proceedings of pathway(s) and how altered pathways merge into overpowering the system¿s defense machinery are key to understanding the molecular aspects of disease and envisioning ways to counteract it. A network-centric approach to diseases is increasingly being considered in current research. It also underlies the deployment of disease pathways at the Rat Genome Database Pathway Portal. The portal is presented with an emphasis on disease and altered pathways, associated drug pathways, pathway suites, and suite networks.ResultsThe Pathway Portal at the Rat Genome Database (RGD) provides an ever-increasing collection of interactive pathway diagrams and associated annotations for metabolic, signaling, regulatory, and drug pathways, including disease and altered pathways. A disease pathway is viewed from the perspective of networks whose alterations are manifested in the affected phenotype. The Pathway Ontology (PW), built and maintained at RGD, facilitates the annotations of genes, the deployment of pathway diagrams, and provides an overall navigational tool. Pathways that revolve around a common concept and are globally connected are presented within pathway suites; a suite network combines two or more pathway suites.Conclusions The Pathway Portal is a rich resource that offers a range of pathway data and visualization, including disease pathways and related pathway suites. Viewing a disease pathway from the perspective of underlying altered pathways is an aid for dissecting the molecular mechanisms of disease.
    Human genomics 09/2014; 8(1):17. DOI:10.1186/PREACCEPT-2344511051385730 · 2.15 Impact Factor
Show more