Connecting the Human Variome Project to nutrigenomics

Kaput, Jim; Evelo, Chris T.; Perozzi, Giuditta; Ommen, Ben van; Cotton, Richard G.H.

doi:10.1007/s12263-010-0186-6

Cited by 4 publications

(2 citation statements)

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“…In this context, the Nutrigenomics Organization (NuGO) and the Human Variome Project (HVP) can play a central and important role [115,116].…”

Section: Conclusion and Future Aspectsmentioning

confidence: 99%

Past, Present and Future of Nutrigenomics and its Influence on Drug Development

Lundström¹

2013

Current Drug Discovery Technologies

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The importance of nutrition in disease prevention and treatment has gained much attention with the emergence of next generation sequence technologies allowing full-genome sequencing at reduced cost in weeks rather than months. The vast genetic information needs to be efficiently channeled into a useful format to provide applicability for improved health and treatment of disease. Recently, it also led to the birth of nutrigenomics, which facilitates the investigation of the effects of nutrition on gene expression and beyond. At present, a number of studies have showed the effect of nutrition on gene expression in health and disease. For instance, weight loss and as importantly weight keeping has been demonstrated to be efficiently achieved in obesity treatment through personalized diet planning. Likewise, intensive dietary interventions have showed a significant effect on the expression pattern on cancer-related genes in prostate cancer patients. Epigenetic modifications such as DNA methylation, histone modifications, and microRNA-based gene silencing are strongly affected by nutritional intake. Better understanding of the human genome will further accelerate nutrigenomics applications and the development of nutritional modifications including personalized nutrition for our well-being and will also present a strong influence on future drug discovery.

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“…In this context, the Nutrigenomics Organization (NuGO) and the Human Variome Project (HVP) can play a central and important role [115,116].…”

Section: Conclusion and Future Aspectsmentioning

confidence: 99%

Past, Present and Future of Nutrigenomics and its Influence on Drug Development

Lundström¹

2013

Current Drug Discovery Technologies

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“…Nutrigenomics investigates molecular relationships between dietary components and genes, proteins and/or metabolites on a large scale. It addresses the question of how nutrition influences gene transcription, protein expression and/or metabolism, with the aim of understanding how dietary factors can affect an individual’s health on a systems level (Müller and Kersten 2003 ; Afman and Müller 2006 ; Baccini et al 2008 ; Kaput et al 2010 ; Evelo et al 2011 ). The data used in the present work originate from a large-scale nutritional intervention survey performed in Apolipoprotein E3-Leiden (ApoE3Leiden) transgenic mice (Radonjic et al 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study

et al. 2012

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Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods.Electronic supplementary materialThe online version of this article (doi:10.1007/s12263-012-0288-4) contains supplementary material, which is available to authorized users.

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