Literature DB >> 9433582

Detection of major genes underlying several quantitative traits associated with a common disease using different ascertainment schemes.

S Iyengar1, F Calafell, K K Kidd.   

Abstract

Using the Problem 2A data sets of GAW10, we assessed the power of four ascertainment schemes to localize major genes underlying a disease trait; the schemes were based on disease or quantitative trait status of nuclear families. MAPMAKER/SIBS was used to perform sib-pair analysis for all four data sets using marker data from three chromosomes, 4, 5 and 8. Each scheme varied in power to identify major genes underlying the quantitative traits depending on the genetic architecture of the data set. Three different methods, Haseman-Elston quantitative trait locus (QTL) regression analysis, maximum likelihood variance estimation and a non-parametric method, were used to assess the strength of linkage in all four data sets. False positive mappings localizing to the same region of the genome, verifiable across all three methods did not occur. Two major genes, MG1 and MG2, were successfully assigned to chromosomes 5 and 8, respectively, by at least one of the ascertainment schemes. MG1 was localized under two schemes, selection of families with exactly two affected sibs and selection of families with two sibs who had extremely discordant values for Q1. Additional weak evidence of the location of MG1 was also obtained under the other two ascertainment schemes. MG2 could not be detected by analyzing data sets ascertained either by affected sib pairs or by sib pairs with extremely discordant values for Q1. In the data set ascertained by a third strategy, selection of families with sib pairs extremely discordant for Q2, MG2 could be mapped to chromosome 8. A random ascertainment scheme yielded a data set in which we could find weak evidence for MG1 and no evidence for MG2. Thus our ability to detect major genes underlying the QTL depended on several factors which included the ascertainment scheme, the population allele frequencies, linkage and epistasis.

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Year:  1997        PMID: 9433582     DOI: 10.1002/(SICI)1098-2272(1997)14:6<809::AID-GEPI41>3.0.CO;2-R

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  5 in total

1.  Imprinting detection by extending a regression-based QTL analysis method.

Authors:  Olga Y Gorlova; Lei Lei; Dakai Zhu; Shih-Feng Weng; Sanjay Shete; Yiqun Zhang; Wei-Dong Li; R Arlen Price; Christopher I Amos
Journal:  Hum Genet       Date:  2007-06-12       Impact factor: 4.132

2.  Identification of an acute ethanol response quantitative trait locus on mouse chromosome 2.

Authors:  K Demarest; J McCaughran; E Mahjubi; L Cipp; R Hitzemann
Journal:  J Neurosci       Date:  1999-01-15       Impact factor: 6.167

3.  The Consortium on the Genetics of Endophenotypes in Schizophrenia: model recruitment, assessment, and endophenotyping methods for a multisite collaboration.

Authors:  Monica E Calkins; Dorcas J Dobie; Kristin S Cadenhead; Ann Olincy; Robert Freedman; Michael F Green; Tiffany A Greenwood; Raquel E Gur; Ruben C Gur; Gregory A Light; Jim Mintz; Keith H Nuechterlein; Allen D Radant; Nicholas J Schork; Larry J Seidman; Larry J Siever; Jeremy M Silverman; William S Stone; Neal R Swerdlow; Debby W Tsuang; Ming T Tsuang; Bruce I Turetsky; David L Braff
Journal:  Schizophr Bull       Date:  2006-10-11       Impact factor: 9.306

Review 4.  Deconstructing schizophrenia: an overview of the use of endophenotypes in order to understand a complex disorder.

Authors:  David L Braff; Robert Freedman; Nicholas J Schork; Irving I Gottesman
Journal:  Schizophr Bull       Date:  2006-11-06       Impact factor: 9.306

5.  A quantitative-trait locus on chromosome 6p influences different aspects of developmental dyslexia.

Authors:  S E Fisher; A J Marlow; J Lamb; E Maestrini; D F Williams; A J Richardson; D E Weeks; J F Stein; A P Monaco
Journal:  Am J Hum Genet       Date:  1999-01       Impact factor: 11.025

  5 in total

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