Bioinformatics challenges for genome-wide association studies

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Bioinformatics challenges for genome-wide association studies

MOTIVATION The sequencing of the human genome has made it possible to identify an informative set of >1 million single nucleotide polymorphisms (SNPs) across the genome that can be used to carry out genome-wide association studies (GWASs). The availability of massive amounts of GWAS data has necessitated the development of new biostatistical methods for quality control, imputation and analysis ...

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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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Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies--challenges and opportunities.

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2010

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/btp713