Gene-environment interactions for complex traits: Definitions, methodological requirements and challenges
Astrid Dempfle,André Scherag,André Scherag,Rebecca Hein,Lars Beckmann,Jenny Chang-Claude,Helmut Schäfer +6 more
TLDR
This review discusses methodological issues involved in investigating gene–environment (G × E) interactions in genetic–epidemiological studies of complex diseases and their potential relevance for clinical application and attempts to clarify conceptual differences of the term ‘interaction’ in the statistical and biological sciences.Abstract:
Genetic and environmental risk factors and their interactions contribute to the development of complex diseases In this review, we discuss methodological issues involved in investigating gene-environment (G x E) interactions in genetic-epidemiological studies of complex diseases and their potential relevance for clinical application Although there are some important examples of interactions and applications, the widespread use of the knowledge about G x E interaction for targeted intervention or personalized treatment (pharmacogenetics) is still beyond current means This is due to the fact that convincing evidence and high predictive or discriminative power are necessary conditions for usefulness in clinical practice We attempt to clarify conceptual differences of the term 'interaction' in the statistical and biological sciences, since precise definitions are important for the interpretation of results We argue that the investigation of G x E interactions is more rewarding for the detailed characterization of identified disease genes (ie at advanced stages of genetic research) and the stratified analysis of environmental effects by genotype or vice versa Advantages and disadvantages of different epidemiological study designs are given and sample size requirements are exemplified These issues as well as a critical appraisal of common methodological concerns are finally discussedread more
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Journal ArticleDOI
Genotype imputation in case-only studies of gene-environment interaction: validity and power
Milda Aleknonytė-Resch,Silke Szymczak,Silke Szymczak,Sandra Freitag-Wolf,Astrid Dempfle,Michael Krawczak +5 more
TL;DR: The statistical power was found to be reduced by imputation, particularly for SNPs with low MAF, and a gradual loss of statistical power resulted when the level of LD to the SNPs driving the imputation decreased, which highlights that genotypes should be employed with great care in CO studies of G×E interaction.
Dissertation
Interaction between dietary factors and genetic risk for lipoprotein traits and cardiovascular disease
TL;DR: Results showed that intake of long-chain omega-3 (ω-3) PUFAs can modify the associated effects of FADS1 genetic variations on LDL-C concentrations, and suggested that a high quality diet that reflects the Swedish nutrition recommendations might attenuate the association between genetic risk for high HDL-C and increased risk of ischemic stroke compared to a low quality diet.
Genetics of Two Mendelian Traits and Validation of Induced Pluripotent Stem Cell (iPSC) Technology for Disease Modeling
TL;DR: Novel technologies for genome analysis have provided almost unlimited opportunities to uncover structural gene variants behind human disorders and Whole exome sequencing (WES) is especially useful for this purpose.
Posted ContentDOI
FALCON systematically interrogates free fatty acid biology and identifies a novel mediator of lipotoxicity
Nicolas Wolfgang Wieder,Juliana Coraor Fried,Choah Kim,Eriene Sidhom,Matthew R. Brown,Jamie L. Marshall,Carlos Arevalo,Moran Dvela-Levitt,Maria Kost-Alimova,Jonas Sieber,Katlyn R. Gabriel,Julian A. Pacheco,Clary B. Clish,Hamdah S. Abbasi,Shantanu Singh,Justine C. Rutter,Martine Therrien,Haejin Yoon,Zon Weng Lai,Aaron Baublis,Renuka Subramanian,Ranjan Devkota,Jonnell Candice Small,Vedagopuram Sreekanth,Myeonghoon Han,Donghyun Lim,Anne E. Carpenter,Jason Flannick,Hilary K. Finucane,Marcia C. Haigis,Melina Claussnitzer,Eric G. Sheu,Beth Stevens,Bridget K. Wagner,Amit Choudhary,Jillian L. Shaw,Juan Pablo,Anna Greka +37 more
TL;DR: FALCON (Fatty Acid Library for Comprehensive Ontologies) enables multimodal profiling of 61 free fatty acids (FFAs) to reveal 5 FFA clusters with distinct biological effects as mentioned in this paper .
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