A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles
Identifieur interne : 000418 ( Main/Exploration ); précédent : 000417; suivant : 000419A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles
Auteurs : Steven J. SchrodiSource :
- Statistical Applications in Genetics and Molecular Biology [ 1544-6115 ] ; 2005-11-01.
Abstract
Recent analytic and technological breakthroughs have set the stage for genome-wide linkage disequilibrium studies to map disease-susceptibility variants. This paper discusses a probabilistic methodology for making disease-mapping inferences in large-scale case-control genetic studies. The semi-Bayesian approach promoted compares the probability of the observed data under disease hypotheses to the probability of the data under a null hypothesis defined by data at all the markers interrogated in a large study. This method automatically adjusts for the effects of diffuse population stratification. It is claimed that this characterization of the evidence for or against disease models may facilitate more appropriate inductions for large-scale genetic studies. Results include (i) an analytic solution for the population stratification-adjusted Bayes factor, (ii) the relationship between sample size and Bayes factors, (iii) an extension to an approximate Bayes factor calculated across closely-linked sites, and (iv) an extension across multiple studies. Although this paper deals exclusively with genetic studies, it is possible to generalize the approach to treat many different large-scale experiments including studies of gene expression and proteomics.
Url:
DOI: 10.2202/1544-6115.1168
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles</title>
<author wicri:is="90%"><name sortKey="Schrodi, Steven J" sort="Schrodi, Steven J" uniqKey="Schrodi S" first="Steven J" last="Schrodi">Steven J. Schrodi</name>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">ISTEX</idno>
<idno type="RBID">ISTEX:779ABB621E5FD8EA6A24F9865CBADB881E95F7B2</idno>
<date when="2011" year="2011">2011</date>
<idno type="doi">10.2202/1544-6115.1168</idno>
<idno type="url">https://api.istex.fr/document/779ABB621E5FD8EA6A24F9865CBADB881E95F7B2/fulltext/pdf</idno>
<idno type="wicri:Area/Main/Corpus">002020</idno>
<idno type="wicri:Area/Main/Curation">001D21</idno>
<idno type="wicri:Area/Main/Exploration">000418</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles</title>
<author wicri:is="90%"><name sortKey="Schrodi, Steven J" sort="Schrodi, Steven J" uniqKey="Schrodi S" first="Steven J" last="Schrodi">Steven J. Schrodi</name>
</author>
</analytic>
<monogr></monogr>
<series><title level="j">Statistical Applications in Genetics and Molecular Biology</title>
<title level="j" type="abbrev">Stat Appl Genet Mol Biol.</title>
<idno type="eISSN">1544-6115</idno>
<imprint><publisher>De Gruyter</publisher>
<date type="published" when="2005-11-01">2005-11-01</date>
<biblScope unit="volume">4</biblScope>
<biblScope unit="issue">1</biblScope>
</imprint>
</series>
<idno type="istex">779ABB621E5FD8EA6A24F9865CBADB881E95F7B2</idno>
<idno type="DOI">10.2202/1544-6115.1168</idno>
<idno type="ArticleID">1544-6115.1168</idno>
<idno type="Related-article-Href">sagmb.2005.4.1.1168.pdf</idno>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass></textClass>
<langUsage><language ident="en">en</language>
</langUsage>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Recent analytic and technological breakthroughs have set the stage for genome-wide linkage disequilibrium studies to map disease-susceptibility variants. This paper discusses a probabilistic methodology for making disease-mapping inferences in large-scale case-control genetic studies. The semi-Bayesian approach promoted compares the probability of the observed data under disease hypotheses to the probability of the data under a null hypothesis defined by data at all the markers interrogated in a large study. This method automatically adjusts for the effects of diffuse population stratification. It is claimed that this characterization of the evidence for or against disease models may facilitate more appropriate inductions for large-scale genetic studies. Results include (i) an analytic solution for the population stratification-adjusted Bayes factor, (ii) the relationship between sample size and Bayes factors, (iii) an extension to an approximate Bayes factor calculated across closely-linked sites, and (iv) an extension across multiple studies. Although this paper deals exclusively with genetic studies, it is possible to generalize the approach to treat many different large-scale experiments including studies of gene expression and proteomics.</div>
</front>
</TEI>
<affiliations><list></list>
<tree><noCountry><name sortKey="Schrodi, Steven J" sort="Schrodi, Steven J" uniqKey="Schrodi S" first="Steven J" last="Schrodi">Steven J. Schrodi</name>
</noCountry>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Sante/explor/ParkinsonV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000418 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000418 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Wicri/Sante |area= ParkinsonV1 |flux= Main |étape= Exploration |type= RBID |clé= ISTEX:779ABB621E5FD8EA6A24F9865CBADB881E95F7B2 |texte= A Probabilistic Approach to Large-Scale Association Scans: A Semi-Bayesian Method to Detect Disease-Predisposing Alleles }}
This area was generated with Dilib version V0.6.23. |