Data/hora: 29/05/2024 05:41:18

Provedor de dados: | 17
País: | United Kingdom
Título: | Accurate modeling of confounding variation in eQTL studies leads to a great increase in power to detect trans-regulatory effects
Autores: | Nicolo Fusi; Oliver Stegle; Neil Lawrence.
Data: | 2011-06-02
Ano: | 2011
Palavras-chave: | Genetics &amp; Genomics; Bioinformatics.
Resumo: | Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown environmental influences.  These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals.  &amp;#xd;&amp;#xa;&amp;#xd;&amp;#xa;Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an&amp;#xd;&amp;#xa;eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators.  As a result, PANAMA can more accurately distinguish between true genetic association signals and confounding variation.   &amp;#xd;&amp;#xa;&amp;#xd;&amp;#xa;We applied our model and compared it to existing methods on a variety of datasets and biological systems.  PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators.  Importantly, PANAMA not only identified a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies.
Tipo: | Manuscript

Identificador:

http://precedings.nature.com/documents/5995/version/1

oai:nature.com:10101/npre.2011.5995.1

http://hdl.handle.net/10101/npre.2011.5995.1

Fonte: | Nature Precedings
Fonte: | Creative Commons Attribution 3.0 License