Contact
schliessen

Filtern

 

Bibliotheken

Logo der Bibliothek

Siegel TU Braunschweig Universitätsbibliothek Braunschweig
You do not seem to be within the network of Braunschweig University.
As student, researcher or staff member of Braunschweig University you can use the VPN service to gain access to electronic publications.
Alternatively, you can use your university username and password via Shibboleth to gain access to electronic publications with certain publishers. You can find more details in our Blog (in German).

Sampling genotypes in large pedigrees with loops

Abstract Markov chain Monte Carlo (MCMC) methods have been proposed to overcome computational problems in linkage and segregation analyses. This approach involves sampling genotypes at the marker and trait loci. Scalar-Gibbs is easy to implement, and it is widely used in genetics. However, the Marko... Full description

Main Author: Guldbrandtsen Bernt
Contributors: Fernando Rohan L | Author
Fernández Soledad A | Author
Totir Liviu R | Author
Carriquiry Alicia L | Author
Contained in: Genetics Selection Evolution (01.07.2001)
Journal Title: Genetics Selection Evolution
Fulltext access: Fulltext access (direct link - free access) 10.1186/1297-9686-33-4-337
Availability is being checked...
Interlibrary loan: Check possibility for interlibrary loan
Links: Additional Link (dx.doi.org)
Additional Link (doaj.org)
Additional Link (www.gsejournal.org)
Fulltext access (doaj.org)
Fulltext access (doaj.org)
ISSN: 0999-193X
DOI: 10.1186/1297-9686-33-4-337
Language: German
English
French
Physical Description: Online-Ressource
ID (e.g. DOI, URN): 10.1186/1297-9686-33-4-337
PPN (Catalogue-ID): DOAJ017854326
more publication details ...

Associated Publications/Volumes

  • Associated records are being queried...
more (+)
Internes Format
LEADER 03167nma a2200337 c 4500
001 DOAJ017854326
003 DE-601
005 20190329101531.0
007 cr uuu---uuuuu
008 171226s2001 000 0 ger d
024 7 |a 10.1186/1297-9686-33-4-337  |2 doi 
035 |a (DE-599)DOAJ758fc130829d4c2e97bfce8c5086dd53 
040 |b ger  |c GBVCP 
041 0 |a ger  |a eng  |a fre 
100 0 |a Guldbrandtsen Bernt  |e verfasserin  |4 aut 
245 1 0 |a Sampling genotypes in large pedigrees with loops  |h Elektronische Ressource 
300 |a Online-Ressource 
520 |a Abstract Markov chain Monte Carlo (MCMC) methods have been proposed to overcome computational problems in linkage and segregation analyses. This approach involves sampling genotypes at the marker and trait loci. Scalar-Gibbs is easy to implement, and it is widely used in genetics. However, the Markov chain that corresponds to scalar-Gibbs may not be irreducible when the marker locus has more than two alleles, and even when the chain is irreducible, mixing has been observed to be slow. These problems do not arise if the genotypes are sampled jointly from the entire pedigree. This paper proposes a method to jointly sample genotypes. The method combines the Elston-Stewart algorithm and iterative peeling, and is called the ESIP sampler. For a hypothetical pedigree, genotype probabilities are estimated from samples obtained using ESIP and also scalar-Gibbs. Approximate probabilities were also obtained by iterative peeling. Comparisons of these with exact genotypic probabilities obtained by the Elston-Stewart algorithm showed that ESIP and iterative peeling yielded genotypic probabilities that were very close to the exact values. Nevertheless, estimated probabilities from scalar-Gibbs with a chain of length 235 000, including a burn-in of 200 000 steps, were less accurate than probabilities estimated using ESIP with a chain of length 10 000, with a burn-in of 5 000 steps. The effective chain size (ECS) was estimated from the last 25 000 elements of the chain of length 125 000. For one of the ESIP samplers, the ECS ranged from 21 579 to 22 741, while for the scalar-Gibbs sampler, the ECS ranged from 64 to 671. Genotype probabilities were also estimated for a large real pedigree consisting of 3 223 individuals. For this pedigree, it is not feasible to obtain exact genotype probabilities by the Elston-Stewart algorithm. ESIP and iterative peeling yielded very similar results. However, results from scalar-Gibbs were less accurate. 
700 0 |a Fernando Rohan L  |e verfasserin  |4 aut 
700 0 |a Fernández Soledad A  |e verfasserin  |4 aut 
700 0 |a Totir Liviu R  |e verfasserin  |4 aut 
700 0 |a Carriquiry Alicia L  |e verfasserin  |4 aut 
773 0 8 |i In  |t Genetics Selection Evolution  |g  (01.07.2001)  |w (DE-601)DOAJ000011487  |x 0999-193X 
856 4 0 |u http://dx.doi.org/10.1186/1297-9686-33-4-337 
856 4 0 |y DOAJ  |u https://doaj.org/article/758fc130829d4c2e97bfce8c5086dd53 
856 4 0 |u http://www.gsejournal.org/content/33/4/337 
856 4 0 |u https://doaj.org/toc/0999-193X 
856 4 0 |u https://doaj.org/toc/1297-9686 
912 |a GBV_DOAJ 
951 |a AR 
952 |j 2001  |b 01  |c 07