Sequential Quantitative Trait Locus Mapping in Experimental Crosses
Identifieur interne : 000158 ( Main/Exploration ); précédent : 000157; suivant : 000159Sequential Quantitative Trait Locus Mapping in Experimental Crosses
Auteurs : Jaya M. Satagopan ; Saunak Sen [États-Unis] ; Gary A. ChurchillSource :
- Statistical Applications in Genetics and Molecular Biology [ 1544-6115 ] ; 2007-04-17.
Abstract
The etiology of complex diseases is heterogeneous. The presence of risk alleles in one or more genetic loci affects the function of a variety of intermediate biological pathways, resulting in the overt expression of disease. Hence, there is an increasing focus on identifying the genetic basis of disease by systematically studying phenotypic traits pertaining to the underlying biological functions. In this paper we focus on identifying genetic loci linked to quantitative phenotypic traits in experimental crosses. Such genetic mapping methods often use a one stage design by genotyping all the markers of interest on the available subjects. A genome scan based on single locus or multi-locus models is used to identify the putative loci. Since the number of quantitative trait loci (QTLs) is very likely to be small relative to the number of markers genotyped, a one-stage selective genotyping approach is commonly used to reduce the genotyping burden, whereby markers are genotyped solely on individuals with extreme trait values. This approach is powerful in the presence of a single quantitative trait locus (QTL) but may result in substantial loss of information in the presence of multiple QTLs. Here we investigate the efficiency of sequential two stage designs to identify QTLs in experimental populations. Our investigations for backcross and F2 crosses suggest that genotyping all the markers on 60% of the subjects in Stage 1 and genotyping the chromosomes significant at 20% level using additional subjects in Stage 2 and testing using all the subjects provides an efficient approach to identify the QTLs and utilizes only 70% of the genotyping burden relative to a one stage design, regardless of the heritability and genotyping density. Complex traits are a consequence of multiple QTLs conferring main effects as well as epistatic interactions. We propose a two-stage analytic approach where a single-locus genome scan is conducted in Stage 1 to identify promising chromosomes, and interactions are examined using the loci on these chromosomes in Stage 2. We examine settings under which the two-stage analytic approach provides sufficient power to detect the putative QTLs.
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DOI: 10.2202/1544-6115.1264
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Churchill</name><affiliation><wicri:noCountry code="no comma">The Jackson Laboratory</wicri:noCountry></affiliation></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="2007-04-17">2007-04-17</date><biblScope unit="volume">6</biblScope><biblScope unit="issue">1</biblScope></imprint></series><idno type="istex">0BCCCD4152EB36C338C2B1C70614E3DAEFC03D66</idno><idno type="DOI">10.2202/1544-6115.1264</idno><idno type="ArticleID">1544-6115.1264</idno><idno type="Related-article-Href">sagmb.2007.6.1.1264.pdf</idno></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The etiology of complex diseases is heterogeneous. The presence of risk alleles in one or more genetic loci affects the function of a variety of intermediate biological pathways, resulting in the overt expression of disease. Hence, there is an increasing focus on identifying the genetic basis of disease by systematically studying phenotypic traits pertaining to the underlying biological functions. In this paper we focus on identifying genetic loci linked to quantitative phenotypic traits in experimental crosses. Such genetic mapping methods often use a one stage design by genotyping all the markers of interest on the available subjects. A genome scan based on single locus or multi-locus models is used to identify the putative loci. Since the number of quantitative trait loci (QTLs) is very likely to be small relative to the number of markers genotyped, a one-stage selective genotyping approach is commonly used to reduce the genotyping burden, whereby markers are genotyped solely on individuals with extreme trait values. This approach is powerful in the presence of a single quantitative trait locus (QTL) but may result in substantial loss of information in the presence of multiple QTLs. Here we investigate the efficiency of sequential two stage designs to identify QTLs in experimental populations. Our investigations for backcross and F2 crosses suggest that genotyping all the markers on 60% of the subjects in Stage 1 and genotyping the chromosomes significant at 20% level using additional subjects in Stage 2 and testing using all the subjects provides an efficient approach to identify the QTLs and utilizes only 70% of the genotyping burden relative to a one stage design, regardless of the heritability and genotyping density. Complex traits are a consequence of multiple QTLs conferring main effects as well as epistatic interactions. We propose a two-stage analytic approach where a single-locus genome scan is conducted in Stage 1 to identify promising chromosomes, and interactions are examined using the loci on these chromosomes in Stage 2. We examine settings under which the two-stage analytic approach provides sufficient power to detect the putative QTLs.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Californie</li></region><settlement><li>San Francisco</li></settlement></list><tree><noCountry><name sortKey="Churchill, Gary A" sort="Churchill, Gary A" uniqKey="Churchill G" first="Gary A." last="Churchill">Gary A. Churchill</name><name sortKey="Satagopan, Jaya M" sort="Satagopan, Jaya M" uniqKey="Satagopan J" first="Jaya M" last="Satagopan">Jaya M. Satagopan</name></noCountry><country name="États-Unis"><region name="Californie"><name sortKey="Sen, Saunak" sort="Sen, Saunak" uniqKey="Sen S" first="Saunak" last="Sen">Saunak Sen</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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