Musings on genome medicine: genome wide association studies

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Genome-wide Association Studies

Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in par...

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Genome-wide Association Studies

Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in par...

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Genome-wide association studies.

Genome-wide association (GWA) studies are best understood as an extension of candidate gene association studies, scaled up to cover hundreds of thousands of markers across the genome in samples usually of several thousand cases and controls. The GWA approach allows the detection of much smaller effect sizes than with previous linkage-based genome-wide studies. However, this sensitivity makes th...

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Genome-Wide Association (GWA) Studies

A host of data on genetic variation from the Human Genome and International HapMap projects, and advances in high-throughput genotyping technologies, have made genome-wide association (GWA) studies technically feasible. GWA studies help in the discovery and quantification of the genetic components of disease risks, many of which have not been unveiled before, and have opened a new avenue to und...

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Statistical Methods for Genome-wide Association Studies and Personalized Medicine

In genome-wide association studies (GWAS), researchers analyze the genetic variation across the entire human genome, searching for variations that are associated with observable traits or certain diseases. There are several inference challenges in GWAS, including the huge number of genetic markers to test, the weak association between truly associated markers and the traits, and the correlation...

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ژورنال

عنوان ژورنال: Genome Medicine

سال: 2009

ISSN: 1756-994X

DOI: 10.1186/gm3