Comparison of empirical strategies to maximize GENEHUNTER lod scores
Identifieur interne : 00AA32 ( Main/Exploration ); précédent : 00AA31; suivant : 00AA33Comparison of empirical strategies to maximize GENEHUNTER lod scores
Auteurs : Chien H. Chen [États-Unis] ; Stephen J. Finch [États-Unis] ; Nancy R. Mendell [États-Unis] ; Derek Gordon [États-Unis]Source :
- Genetic Epidemiology [ 0741-0395 ] ; 1999.
English descriptors
- Teeft :
- Additional time, Allele, Center point, Chromosome, Collaborative study, Computational, Computer experiments, Disease allele, Disease allele frequency, Dominant models, Evolutionary operation, Evop, Factorial, Factorial design, Gene frequency, Genehunter, Genetic model, Genetic parameters, Hypercube, Initial iteration, Initial phase, Initial settings, Initial values, Input variables, Iterated, Iteration, Local maxima, Numerical optimization, Numerical optimization techniques, Numerical search, Optimization, Orthogonal, Orthogonal latin hypercube, Penetrance, Pentium, Phenocopy, Phenocopy rate, Range space, Recessive, Recessive model, Response surface, Small numbers, Software, Stony brook, Transmission model parameters.
Abstract
We compare four strategies for finding the settings of genetic parameters that maximize the lod scores reported in GENEHUNTER 1.2. The four strategies are iterated complete factorial designs, iterated orthogonal Latin hypercubes, evolutionary operation, and numerical optimization. The genetic parameters that are set are the phenocopy rate, penetrance, and disease allele frequency; both recessive and dominant models are considered. We selected the optimization of a recessive model on the Collaborative Study on the Genetics of Alcoholism (COGA) data of chromosome 1 for complete analysis. Convergence to a setting producing a local maximum required the evaluation of over 100 settings (for a time budget of 800 minutes on a Pentium II 300 MHz PC). Two notable local maxima were detected, suggesting the need for a more extensive search before claiming that a global maximum had been found. The orthogonal Latin hypercube design was the best strategy for finding areas that produced high lod scores with small numbers of evaluations. Numerical optimization starting from a region producing high lod scores was the strategy that found the highest maximum observed.
Url:
DOI: 10.1002/gepi.1370170718
Affiliations:
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Mendell</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><placeName><region type="state">État de New York</region></placeName><wicri:cityArea>SUNY at Stony Brook, Stony Brook</wicri:cityArea></affiliation><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><placeName><region type="state">État de New York</region></placeName><wicri:cityArea>Correspondence address: Dept. of Applied Mathematics and Statistics, SUNY at Stony Brook, Stony Brook</wicri:cityArea></affiliation></author><author><name sortKey="Gordon, Derek" sort="Gordon, Derek" uniqKey="Gordon D" first="Derek" last="Gordon">Derek Gordon</name><affiliation wicri:level="2"><country xml:lang="fr">États-Unis</country><placeName><region type="state">État de New York</region></placeName><wicri:cityArea>Rockefeller University, New York City</wicri:cityArea></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Genetic Epidemiology</title><title level="j" type="alt">GENETIC EPIDEMIOLOGY</title><idno type="ISSN">0741-0395</idno><idno type="eISSN">1098-2272</idno><imprint><biblScope unit="vol">17</biblScope><biblScope unit="issue">S1</biblScope><biblScope unit="page" from="S103">S103</biblScope><biblScope unit="page" to="S108">S108</biblScope><biblScope unit="page-count">6</biblScope><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><pubPlace>New York</pubPlace><date type="published" when="1999">1999</date></imprint><idno type="ISSN">0741-0395</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0741-0395</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="Teeft" xml:lang="en"><term>Additional time</term><term>Allele</term><term>Center point</term><term>Chromosome</term><term>Collaborative study</term><term>Computational</term><term>Computer experiments</term><term>Disease allele</term><term>Disease allele frequency</term><term>Dominant models</term><term>Evolutionary operation</term><term>Evop</term><term>Factorial</term><term>Factorial design</term><term>Gene frequency</term><term>Genehunter</term><term>Genetic model</term><term>Genetic parameters</term><term>Hypercube</term><term>Initial iteration</term><term>Initial phase</term><term>Initial settings</term><term>Initial values</term><term>Input variables</term><term>Iterated</term><term>Iteration</term><term>Local maxima</term><term>Numerical optimization</term><term>Numerical optimization techniques</term><term>Numerical search</term><term>Optimization</term><term>Orthogonal</term><term>Orthogonal latin hypercube</term><term>Penetrance</term><term>Pentium</term><term>Phenocopy</term><term>Phenocopy rate</term><term>Range space</term><term>Recessive</term><term>Recessive model</term><term>Response surface</term><term>Small numbers</term><term>Software</term><term>Stony brook</term><term>Transmission model parameters</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We compare four strategies for finding the settings of genetic parameters that maximize the lod scores reported in GENEHUNTER 1.2. The four strategies are iterated complete factorial designs, iterated orthogonal Latin hypercubes, evolutionary operation, and numerical optimization. The genetic parameters that are set are the phenocopy rate, penetrance, and disease allele frequency; both recessive and dominant models are considered. We selected the optimization of a recessive model on the Collaborative Study on the Genetics of Alcoholism (COGA) data of chromosome 1 for complete analysis. Convergence to a setting producing a local maximum required the evaluation of over 100 settings (for a time budget of 800 minutes on a Pentium II 300 MHz PC). Two notable local maxima were detected, suggesting the need for a more extensive search before claiming that a global maximum had been found. The orthogonal Latin hypercube design was the best strategy for finding areas that produced high lod scores with small numbers of evaluations. Numerical optimization starting from a region producing high lod scores was the strategy that found the highest maximum observed.</div></front></TEI><affiliations><list><country><li>États-Unis</li></country><region><li>Ohio</li><li>État de New York</li></region></list><tree><country name="États-Unis"><region name="Ohio"><name sortKey="Chen, Chien H" sort="Chen, Chien H" uniqKey="Chen C" first="Chien H." last="Chen">Chien H. Chen</name></region><name sortKey="Finch, Stephen J" sort="Finch, Stephen J" uniqKey="Finch S" first="Stephen J." last="Finch">Stephen J. Finch</name><name sortKey="Gordon, Derek" sort="Gordon, Derek" uniqKey="Gordon D" first="Derek" last="Gordon">Derek Gordon</name><name sortKey="Mendell, Nancy R" sort="Mendell, Nancy R" uniqKey="Mendell N" first="Nancy R." last="Mendell">Nancy R. Mendell</name><name sortKey="Mendell, Nancy R" sort="Mendell, Nancy R" uniqKey="Mendell N" first="Nancy R." last="Mendell">Nancy R. 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