A Polygenic Approach to the Study of Polygenic Diseases

Scientific Center of Russian Federation Research Institute for Genetics and Selection of Industrial Microorganisms "Genetika", 1-st Dorozny proezd, 1, Moscow, Russia, 113545.
Acta Naturae (Impact Factor: 1). 11/2012; 4(3):59-71.
Source: PubMed


Polygenic diseases are caused by the joint contribution of a number of independently acting or interacting polymorphic genes; the individual contribution of each gene may be small or even unnoticeable. The carriage of certain combinations of genes can determine the occurrence of clinically heterogeneous forms of the disease and treatment efficacy. This review describes the approaches used in a polygenic analysis of data in medical genomics, in particular, pharmacogenomics, aimed at identifying the cumulative effect of genes. This effect may result from the summation of gains of different genes or be caused by the epistatic interaction between the genes. Both cases are undoubtedly of great interest in investigating the nature of polygenic diseases. The means that allow one to discriminate between these two possibilities are discussed. The methods for searching for combinations of alleles of different genes associated with the polygenic phenotypic traits of the disease, as well as the methods for presenting and validating the results, are described and compared. An attempt is made to evaluate the applicability of the existing methods to an epistasis analysis. The results obtained by the authors using the APSampler software are described and summarized.

Download full-text


Available from: Alexander V Favorov
    • "It should also be noted that the GWAS design is based on the CDCV hypothesis and omits a possible effect of rare alleles, while these alleles may contribute substantially to the disease[48]. The bioinformatics analysis currently employed in GWASs does not report the risk factors that are determined by nonlinear (epii static) interactions between alleles (including both rare and common ones) in an individual allele set[49], as well as interactions with nongenetic factors. Another aspect of the problem became clear in 2012, after completion of the ENCODE project, which was aimed at deciphering the functional part of the genome. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Genome-wide association study (GWAS) provides a powerful tool for investigating the genetic architecture of human polygenic diseases and is generally used to identify the genetic factors of disease susceptibility, clinical phenotypes, and treatment response. The differences in allele frequencies of single nucleotide polymorphisms (SNPs) distributed throughout the genome are analyzed with a microarray technique or other technologies that allow simultaneous genotyping at several tens of thousands to several millions of SNPs per sample. Owing to its power to find out highly reliable differences between patients and controls, GWAS became a common approach to identification of the genetic susceptibility factors in complex diseases of a polygenic nature. Using multiple sclerosis (MS) as a prototype complex disease, the review considers the main achievements and challenges of using GWAS to identify the genes involved in the disease and, therefore, to better understand the pathogenetic molecular mechanisms and genetic risk factors.
    No preview · Article · Jul 2014 · Molecular Biology
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a unified Bayesian framework for detecting genetic variants associated with a disease while exploiting image-based features as an intermediate phenotype. Traditionally, imaging genetics methods comprise two separate steps. First, image features are selected based on their relevance to the disease phenotype. Second, a set of genetic variants are identified to explain the selected features. In contrast, our method performs these tasks simultaneously to ultimately assign probabilistic measures of relevance to both genetic and imaging markers. We derive an efficient approximate inference algorithm that handles high dimensionality of imaging genetic data. We evaluate the algorithm on synthetic data and show that it outperforms traditional models. We also illustrate the application of the method on ADNI data.
    Full-text · Article · Jun 2013 · Information processing in medical imaging: proceedings of the ... conference
  • [Show abstract] [Hide abstract]
    ABSTRACT: Nonalcoholic fatty liver disease (NAFLD) is a complex disease. The considerable variability in the natural history of the disease suggests an important role for genetic variants in the disease development and progression. There is evidence based on genome-wide association studies and/or candidate gene studies that genetic polymorphisms underlying insulin signaling, lipid metabolism, oxidative stress, fibrogenesis, and inflammation can predispose individuals to NAFLD. This review highlights some of the genetic variants in NAFLD.
    No preview · Article · Feb 2014 · Clinics in liver disease
Show more