Genetics and molecular biology: Edited by Robert HegeleGene–gene and gene–environment interactions defining lipid-related traitsOrdovás, José Ma,b,c; Robertson, Ruairia; Cléirigh, Ellen NíaAuthor Information aJean Mayer US Department of Agriculture Human Nutrition Research Centre on Aging at Tufts University, Boston, Massachusetts, USA bCentro Nacional de Investigaciones Cardiovasculares (CNIC), Spain cInstituto Madrileño de Estudios Avanzados en Alimentación (IMDEA), Madrid, Spain Correspondence to José M. Ordovás, PhD, Nutrition and Genomics Laboratory, Jean Mayer USDA HNRCA at Tufts University, 711 Washington St, Boston, MA 02111-1524, USA E-mail: [email protected] Current Opinion in Lipidology: April 2011 - Volume 22 - Issue 2 - p 129-136 doi: 10.1097/MOL.0b013e32834477a9 Buy Metrics Abstract Purpose of review Steps towards reducing chronic disease progression are continuously being taken through the form of genomic research. Studies over the last year have highlighted more and more polymorphisms, pathways and interactions responsible for metabolic disorders such as cardiovascular disease, obesity and dyslipidemia. Recent findings Many of these chronic illnesses can be partially blamed by altered lipid metabolism, combined with individual genetic components. Critical evaluation and comparison of these recent studies is essential in order to comprehend the results, conclusions and future prospects in the field of genomics as a whole. Recent literature elucidates significant gene–diet and gene–environment interactions resulting in altered lipid metabolism, inflammation and other metabolic imbalances leading to cardiovascular disease and obesity. Summary Epigenetic and epistatic interactions are now becoming more significantly associated with such disorders, as genomic research digs deeper into the complex nature of genetic individuality and heritability. The vast array of data collected from genome-wide association studies must now be empowered and explored through more complex interaction studies, using standardized methods and larger sample sizes. In doing so the etiology of chronic disease progression will be further understood. © 2011 Lippincott Williams & Wilkins, Inc.