نتایج جستجو برای: wide association study
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Genome-wide association studies (GWAS) identify genetic variants that distinguish a control population from a population with a specific trait. Two challenges in GWAS are: (1) identification of the causal variant within a longer haplotype that is associated with the trait; (2) identification of causal variants for polygenic traits that are caused by variants in multiple genes within a pathway. ...
OBJECTIVE Predictive tests that capitalize on emerging genetic findings hold great promise for enhanced personalized healthcare. With the emergence of a large amount of data from genome-wide association studies (GWAS), interest has shifted towards high-dimensional risk prediction. METHODS To form predictive genetic tests on high-dimensional data, we propose a non-parametric method, called the...
Genetic epidemiologists strive to determine the genetic profile of diseases. Epistasis is the interaction between two or more genes to affect phenotype. Due to the often non-linearity of the interaction, it is difficult to detect statistical patterns of epistasis. Combinatorial methods for detecting epistasis investigate a subset of combinations of genes without employing a search strategy. The...
Apart from the specific findings of this analysis, the authors encourage the use of what they call a nutrientwide association study, analogous to a genome-wide association study (GWAS), to investigate dietary determinants of health outcomes.1 Similar to the agnostic approach used in a GWAS, the features of their approach include the following: (1) the inclusion of all available nutrient exposur...
In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and d...
2 Conducting GWAS with genphen 2 2.1 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2.1 runGenphenRf and runGenphenSvm . . . . . . . . . . 3 2.2.2 runGenphenBayes . . . . . . . . . . . . . . . . . . . . 5 2.3 Case studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3.1 SNP-phenot...
Most individuals throughout the Americas are admixed descendants of Native American, European, and African ancestors. Complex historical factors have resulted in varying proportions of ancestral contributions between individuals within and among ethnic groups. We developed a panel of 446 ancestry informative markers (AIMs) optimized to estimate ancestral proportions in individuals and populatio...
We consider the application of Efron's empirical Bayes classification method to risk prediction in a genome-wide association study using the Genetic Analysis Workshop 17 (GAW17) data. A major advantage of using this method is that the effect size distribution for the set of possible features is empirically estimated and that all subsequent parameter estimation and risk prediction is guided by t...
Multiple Sclerosis (MS) is the most common progressive and disabling neurological condition affecting young adults in the world today. From a genetic point of view, MS is a complex disorder resulting from the combination of genetic and non-genetic factors. We aimed to identify previously unidentified loci conducting a new GWAS of Multiple Sclerosis (MS) in a sample of 296 MS cases and 801 contr...
Genome-wide association studies usually involve several hundred thousand of single-nucleotide polymorphisms (SNPs). Conventional approaches face challenges when there are enormous number of SNPs but a relatively small number of samples and, in some cases, are not feasible. We introduce here an iterative Bayesian variable selection method that provides a unique tool for association studies with ...
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