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Multiple Genetic Variants (multiple + genetic_variants)
Selected AbstractsJoint Identification of Multiple Genetic Variants via Elastic-Net Variable Selection in a Genome-Wide Association AnalysisANNALS OF HUMAN GENETICS, Issue 5 2010Seoae Cho Summary Unraveling the genetic background of common complex traits is a major goal in modern genetics. In recent years, genome-wide association (GWA) studies have been conducted with large-scale data sets of genetic variants. Most of those studies have relied on single-marker approaches that identify single genetic factors individually and can be limited in considering fully the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and would provide better prediction on complex traits since it utilizes combined information across variants. Here we propose a multi-stage approach for GWA analysis: (1) prescreening, (2) joint identification of putative SNPs based on elastic-net variable selection, and (3) empirical replication using bootstrap samples. Our approach enables an efficient joint search for genetic associations in GWA analysis. The suggested empirical replication method can be beneficial in GWA studies because one can avoid a costly, independent replication study while eliminating false-positive associations and focusing on a smaller number of replicable variants. We applied the proposed approach to a GWA analysis, and jointly identified 129 genetic variants having an association with adult height in a Korean population. [source] Independent predictive roles of eotaxin Ala23Thr, paraoxonase 2 Ser311Cys and ,3 -adrenergic receptor Trp64Arg polymorphisms on cardiac disease in Type 2 Diabetes,an 8-year prospective cohort analysis of 1297 patientsDIABETIC MEDICINE, Issue 4 2010Y. Wang Diabet. Med. 27, 376,383 (2010) Abstract Aims, To examine the independent and joint effects of multiple genetic variants on a cardiac end-point in an 8-year prospective study of a Chinese diabetic cohort. Methods, Seventy-seven single nucleotide polymorphisms (SNPs) of 53 candidate genes for inflammation, thrombosis, vascular tone regulation and lipid metabolism were genotyped in 1297 Chinese patients with no prior history of coronary heart disease (CHD) or heart failure at baseline. Cardiac end-point was defined by the occurrence of CHD and/or heart failure. Results, In Cox regression model, after adjustment for baseline confounding variables including age, sex, smoking status, duration of diabetes, glycaemic control, lipid levels, waist circumference, blood pressure, albuminuria and estimated glomerular filtration rate, genetic variants, including Ala/Ala of SCYA11 (eotaxin) Ala23Thr, Cys/Cys or Cys/Ser of PON2 (paraoxonase 2) Ser311Cys and Arg/Arg of ADRB3 (,3 -adrenergic receptor) Trp64Arg, were independently associated with incident cardiac end-point, with respective hazard ratios (95% confidence interval) of 1.70 (1.10,2.61, P = 0.037), 1.42 (1.08,1.88, P = 0.013) and 3.84 (1.18,12.50, P = 0.025). Analysis of the joint effect of the risk alleles showed significant increased risk of the cardiac end-point with increasing number of risk alleles (P < 0.001). The adjusted risk for the cardiac end-point was 4.11 (P = 0.002) for patients carrying four risk alleles compared with those carrying one or no risk allele. Conclusions, The independent risk conferred by genetic variants encoding pathways such as inflammation and lipid metabolism, not adequately reflected by conventional biomarkers, may identify high-risk individuals for intensified control of modifiable risk factors. [source] Analysis of multilocus models of associationGENETIC EPIDEMIOLOGY, Issue 1 2003B. Devlin Abstract It is increasingly recognized that multiple genetic variants, within the same or different genes, combine to affect liability for many common diseases. Indeed, the variants may interact among themselves and with environmental factors. Thus realistic genetic/statistical models can include an extremely large number of parameters, and it is by no means obvious how to find the variants contributing to liability. For models of multiple candidate genes and their interactions, we prove that statistical inference can be based on controlling the false discovery rate (FDR), which is defined as the expected number of false rejections divided by the number of rejections. Controlling the FDR automatically controls the overall error rate in the special case that all the null hypotheses are true. So do more standard methods such as Bonferroni correction. However, when some null hypotheses are false, the goals of Bonferroni and FDR differ, and FDR will have better power. Model selection procedures, such as forward stepwise regression, are often used to choose important predictors for complex models. By analysis of simulations of such models, we compare a computationally efficient form of forward stepwise regression against the FDR methods. We show that model selection includes numerous genetic variants having no impact on the trait, whereas FDR maintains a false-positive rate very close to the nominal rate. With good control over false positives and better power than Bonferroni, the FDR-based methods we introduce present a viable means of evaluating complex, multivariate genetic models. Naturally, as for any method seeking to explore complex genetic models, the power of the methods is limited by sample size and model complexity. Genet Epidemiol 25:36,47, 2003. © 2003 Wiley-Liss, Inc. [source] Nutrigenomics,new approaches for human nutrition researchJOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, Issue 8 2006Helen M Roche Abstract Food intake and nutrient exposure are key environmental factors involved in the pathogenesis and progression of the common polygenic, diet-related diseases. An individual's phenotype represents a complex interaction between the human genome and environmental factors during an individual's lifetime. This review explores the concept that there is a dynamic, two-way interaction between nutrition and the human genome which determines gene expression, the metabolic response and an individual's health status. It addresses the relevance of new high-throughput genomic, transcriptomic, proteomic and metabolomic technologies within human nutrition research. Common, polygenic, diet-related diseases (CVD, obesity, T2DM, etc.) reflect multiple genetic variants interacting with numerous environmental factors, each combination making a relatively small contribution to overall cellular homeostasis, whole body metabolism and health. This review highlights the value of a nutrigenomics-based systems biology approach to understanding human nutrition and identifying new biomarkers of nutrition and health. The challenge will be to develop and apply robust nutritional genomics research initiatives that are sensitive enough to take account of both human genetic heterogeneity and diverse nutrient exposure. If nutrigenomic approaches enhance our understanding of human nutrition at the molecular level, then it may be possible to apply a more targeted and effective personalized nutrition approach to attenuate the effect of risk factors associated with diet-related diseases. Indeed it could be proposed that a personalized nutrition approach may assist in improving the effectiveness of dietary guidelines/recommendations in general. Copyright © 2006 Society of Chemical Industry [source] Using the Optimal Robust Receiver Operating Characteristic (ROC) Curve for Predictive Genetic TestsBIOMETRICS, Issue 2 2010Qing Lu Summary Current ongoing genome-wide association (GWA) studies represent a powerful approach to uncover common unknown genetic variants causing common complex diseases. The discovery of these genetic variants offers an important opportunity for early disease prediction, prevention, and individualized treatment. We describe here a method of combining multiple genetic variants for early disease prediction, based on the optimality theory of the likelihood ratio (LR). Such theory simply shows that the receiver operating characteristic (ROC) curve based on the LR has maximum performance at each cutoff point and that the area under the ROC curve so obtained is highest among that of all approaches. Through simulations and a real data application, we compared it with the commonly used logistic regression and classification tree approaches. The three approaches show similar performance if we know the underlying disease model. However, for most common diseases we have little prior knowledge of the disease model and in this situation the new method has an advantage over logistic regression and classification tree approaches. We applied the new method to the type 1 diabetes GWA data from the Wellcome Trust Case Control Consortium. Based on five single nucleotide polymorphisms, the test reaches medium level classification accuracy. With more genetic findings to be discovered in the future, we believe a predictive genetic test for type 1 diabetes can be successfully constructed and eventually implemented for clinical use. [source] Tag SNP selection using particle swarm optimizationBIOTECHNOLOGY PROGRESS, Issue 2 2010Li-Yeh Chuang Abstract Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations amongst species. With the genome-wide SNP discovery, many genome-wide association studies are likely to identify multiple genetic variants that are associated with complex diseases. However, genotyping all existing SNPs for a large number of samples is still challenging even though SNP arrays have been developed to facilitate the task. Therefore, it is essential to select only informative SNPs representing the original SNP distributions in the genome (tag SNP selection) for genome-wide association studies. These SNPs are usually chosen from haplotypes and called haplotype tag SNPs (htSNPs). Accordingly, the scale and cost of genotyping are expected to be largely reduced. We introduce binary particle swarm optimization (BPSO) with local search capability to improve the prediction accuracy of STAMPA. The proposed method does not rely on block partitioning of the genomic region, and consistently identified tag SNPs with higher prediction accuracy than either STAMPA or SVM/STSA. We compared the prediction accuracy and time complexity of BPSO to STAMPA and an SVM-based (SVM/STSA) method using publicly available data sets. For STAMPA and SVM/STSA, BPSO effective improved prediction accuracy for smaller and larger scale data sets. These results demonstrate that the BPSO method selects tag SNP with higher accuracy no matter the scale of data sets is used. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010 [source] | ||||||||||