Genomewide association studies and human disease.

Institute of Neurology, University College London, London, United Kingdom. at
New England Journal of Medicine (Impact Factor: 54.42). 05/2009; 360(17):1759-68. DOI: 10.1056/NEJMra0808700
Source: PubMed
  • Neurotherapeutics 10/2014; 11(4):732-737. · 3.88 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Biodemography became one of the most innovative and fastest growing areas in demography. This progress is fueled by the growing variability and amount of relevant data available for analyses as well as by methodological developments allowing for addressing new research questions using new approaches that can better utilize the potential of these data. In this review paper, we summarize recent methodological advances in biodemography and their diverse practical applications. Three major topics are covered: (1) computational approaches to reconstruction of age patterns of incidence of geriatric diseases and other characteristics such as recovery rates at the population level using Medicare claims data; (2) methodological advances in genetic and genomic biodemography and applications to research on genetic determinants of longevity and health; and (3) biodemographic models for joint analyses of time-to-event data and longitudinal measurements of biomarkers collected in longitudinal studies on aging. We discuss how such data and methodology can be used in a comprehensive prediction model for joint analyses of incomplete datasets that take into account the wide spectrum of factors affecting health and mortality transitions including genetic factors and hidden mechanisms of aging-related changes in physiological variables in their dynamic connection with health and survival.
    Advances in geriatrics. 2014.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Identifying genetic interactions in data obtained from genome-wide association studies (GWASs) can help in understanding the genetic basis of complex diseases. The large number of single nucleotide polymorphisms (SNPs) in GWASs however makes the identification of genetic interactions computationally challenging. We developed the Bayesian Combinatorial Method (BCM) that can identify pairs of SNPs that in combination have high statistical association with disease.
    BioData Mining 01/2014; 7(1-1). · 1.54 Impact Factor


Available from