Literature DB >> 10323184

Exploring the dense mapping of a region of potential linkage in complex disease: an example in multiple sclerosis.

R Feakes1, S Sawcer, J Chataway, F Coraddu, S Broadley, J Gray, H B Jones, D Clayton, P N Goodfellow, A Compston.   

Abstract

In 1996 we reported the results of a genome screen in multiple sclerosis, in which potential linkage was identified in a total of twenty regions, including the centromeric region of chromosome 5. In order to investigate the efficiency of typing dense arrays of markers in regions of potential linkage, we have typed an additional nineteen microsatellite markers from this chromosome 5 region (D5S623 - D5S428) in the same sibling pair families. The mean additional information extracted per marker typed declined with increasing map density, while inaccuracies in the mapping and the density of genotyping errors increased. Our empirical results suggest that, in linkage-based experiments, there is a limit to the benefits that are gained from typing additional markers in the same families. Increasing map density up to the 2.5-5 cM level efficiently extracts valuable extra information; however, beyond this level efficiency declines while the confounding effects of mapping and genotyping errors accumulate. We, therefore, recommend that extra markers typed in linkage studies be limited to this level of resolution. Mapping regions beyond this density should only be initiated when searching for linkage disequilibrium.

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Year:  1999        PMID: 10323184     DOI: 10.1002/(SICI)1098-2272(1999)17:1<51::AID-GEPI4>3.0.CO;2-V

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


  10 in total

Review 1.  The genetic epidemiology of multiple sclerosis.

Authors:  A Compston
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  1999-10-29       Impact factor: 6.237

2.  A multipoint method for detecting genotyping errors and mutations in sibling-pair linkage data.

Authors:  J A Douglas; M Boehnke; K Lange
Journal:  Am J Hum Genet       Date:  2000-03-28       Impact factor: 11.025

3.  Genetic maps of microsatellite and single-nucleotide polymorphism markers: are the distances accurate?

Authors:  Suzanne M Leal
Journal:  Genet Epidemiol       Date:  2003-05       Impact factor: 2.135

4.  Map error reduction: using genetic and sequence-based physical maps to order closely linked markers.

Authors:  Andrew T DeWan; Antonio R Parrado; Tara C Matise; Suzanne M Leal
Journal:  Hum Hered       Date:  2002       Impact factor: 0.444

5.  Maximum-likelihood estimation of allelic dropout and false allele error rates from microsatellite genotypes in the absence of reference data.

Authors:  Paul C D Johnson; Daniel T Haydon
Journal:  Genetics       Date:  2006-12-18       Impact factor: 4.562

6.  Fine mapping of the chromosome 12 late-onset Alzheimer disease locus: potential genetic and phenotypic heterogeneity.

Authors:  W K Scott; J M Grubber; P M Conneally; G W Small; C M Hulette; C K Rosenberg; A M Saunders; A D Roses; J L Haines; M A Pericak-Vance
Journal:  Am J Hum Genet       Date:  2000-03       Impact factor: 11.025

7.  The map problem: a comparison of genetic and sequence-based physical maps.

Authors:  Andrew T DeWan; Antonio R Parrado; Tara C Matise; Suzanne M Leal
Journal:  Am J Hum Genet       Date:  2001-11-09       Impact factor: 11.025

8.  Software for quantifying and simulating microsatellite genotyping error.

Authors:  Paul C D Johnson; Daniel T Haydon
Journal:  Bioinform Biol Insights       Date:  2009-11-24

Review 9.  Searching for osteoporosis genes in the post-genome era: progress and challenges.

Authors:  Qing-Yang Huang; Robert R Recker; Hong-Wen Deng
Journal:  Osteoporos Int       Date:  2003-08-05       Impact factor: 4.507

10.  Analysis of IMGSAC autism susceptibility loci: evidence for sex limited and parent of origin specific effects.

Authors:  J A Lamb; G Barnby; E Bonora; N Sykes; E Bacchelli; F Blasi; E Maestrini; J Broxholme; J Tzenova; D Weeks; A J Bailey; A P Monaco
Journal:  J Med Genet       Date:  2005-02       Impact factor: 6.318

  10 in total

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