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Gene-based bin analysis of genome-wide association studies.

Omont N, Forner K, Lamarine M, Martin G, Képès F, Wojcik J - BMC Proc (2008)

Bottom Line: With the improvement of genotyping technologies and the exponentially growing number of available markers, case-control genome-wide association studies promise to be a key tool for investigation of complex diseases.The algorithm is based on a Bayesian model that integrates genotyping errors and genomic structure dependencies. p-values are assigned to genomic regions termed bins, which are defined from a gene-biased partitioning of the genome, and the false-discovery rate is estimated.We have applied this algorithm to data coming from three genome-wide association studies of Multiple Sclerosis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Merck Serono International S,A, 9 chemin des Mines, 1202 Geneva, Switzerland.

ABSTRACT

Background: With the improvement of genotyping technologies and the exponentially growing number of available markers, case-control genome-wide association studies promise to be a key tool for investigation of complex diseases. However new analytical methods have to be developed to face the problems induced by this data scale-up, such as statistical multiple testing, data quality control and computational tractability.

Results: We present a novel method to analyze genome-wide association studies results. The algorithm is based on a Bayesian model that integrates genotyping errors and genomic structure dependencies. p-values are assigned to genomic regions termed bins, which are defined from a gene-biased partitioning of the genome, and the false-discovery rate is estimated. We have applied this algorithm to data coming from three genome-wide association studies of Multiple Sclerosis.

Conclusion: The method practically overcomes the scale-up problems and permits to identify new putative regions statistically associated with the disease.

No MeSH data available.


Related in: MedlinePlus

Error and LD model of bin b.
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Figure 2: Error and LD model of bin b.

Mentions: An error model (Figure 2) is introduced linking observed genotypes with real ones ( ∈ {aa, Aa, AA, ∅}, where ∅ means that the observed genotype is missing):


Gene-based bin analysis of genome-wide association studies.

Omont N, Forner K, Lamarine M, Martin G, Képès F, Wojcik J - BMC Proc (2008)

Error and LD model of bin b.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654974&req=5

Figure 2: Error and LD model of bin b.
Mentions: An error model (Figure 2) is introduced linking observed genotypes with real ones ( ∈ {aa, Aa, AA, ∅}, where ∅ means that the observed genotype is missing):

Bottom Line: With the improvement of genotyping technologies and the exponentially growing number of available markers, case-control genome-wide association studies promise to be a key tool for investigation of complex diseases.The algorithm is based on a Bayesian model that integrates genotyping errors and genomic structure dependencies. p-values are assigned to genomic regions termed bins, which are defined from a gene-biased partitioning of the genome, and the false-discovery rate is estimated.We have applied this algorithm to data coming from three genome-wide association studies of Multiple Sclerosis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Merck Serono International S,A, 9 chemin des Mines, 1202 Geneva, Switzerland.

ABSTRACT

Background: With the improvement of genotyping technologies and the exponentially growing number of available markers, case-control genome-wide association studies promise to be a key tool for investigation of complex diseases. However new analytical methods have to be developed to face the problems induced by this data scale-up, such as statistical multiple testing, data quality control and computational tractability.

Results: We present a novel method to analyze genome-wide association studies results. The algorithm is based on a Bayesian model that integrates genotyping errors and genomic structure dependencies. p-values are assigned to genomic regions termed bins, which are defined from a gene-biased partitioning of the genome, and the false-discovery rate is estimated. We have applied this algorithm to data coming from three genome-wide association studies of Multiple Sclerosis.

Conclusion: The method practically overcomes the scale-up problems and permits to identify new putative regions statistically associated with the disease.

No MeSH data available.


Related in: MedlinePlus