Combination of linkage evidence in complex inheritance
Combination of linkage evidence in complex inheritance
The central problem of complex inheritance is to combine evidence from data that typically differ in markers, phenotypes, ascertainment, and other factors, without sacrificing the reliability that lods have given to linkage mapping for major loci. Here we evaluate 5 possible solutions on 200 replicates simulated in Genetic Analysis Workshop 10. Two methods differ from less efficient ones by distinguishing the tails of a normal distribution. Maximum likelihood scores (currently implemented only for the BETA model) and the approach of Self and Liang perform about as well as pooling samples, which is not feasible with heterogeneous data. With moderately heterogeneous data the Self and Liang method appears to be more efficient than maximum likelihood scores. Although improvements are being made in sample design and statistical analysis, the problem of combining linkage evidence from multiple data sets appears to have been solved. Allelic association presents different problems not yet addressed.
meta-analysis, retrospective collaboration, self and liang test
132-135
Zhang, W.
1c80d4f2-4ba8-41f6-85a6-a76a4d65dc9b
Collins, A.
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Morton, N.E.
c668e2be-074a-4a0a-a2ca-e8f51830ebb7
September 2001
Zhang, W.
1c80d4f2-4ba8-41f6-85a6-a76a4d65dc9b
Collins, A.
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Morton, N.E.
c668e2be-074a-4a0a-a2ca-e8f51830ebb7
Zhang, W., Collins, A. and Morton, N.E.
(2001)
Combination of linkage evidence in complex inheritance.
Human Heredity, 52 (3), .
(doi:10.1159/000053367).
Abstract
The central problem of complex inheritance is to combine evidence from data that typically differ in markers, phenotypes, ascertainment, and other factors, without sacrificing the reliability that lods have given to linkage mapping for major loci. Here we evaluate 5 possible solutions on 200 replicates simulated in Genetic Analysis Workshop 10. Two methods differ from less efficient ones by distinguishing the tails of a normal distribution. Maximum likelihood scores (currently implemented only for the BETA model) and the approach of Self and Liang perform about as well as pooling samples, which is not feasible with heterogeneous data. With moderately heterogeneous data the Self and Liang method appears to be more efficient than maximum likelihood scores. Although improvements are being made in sample design and statistical analysis, the problem of combining linkage evidence from multiple data sets appears to have been solved. Allelic association presents different problems not yet addressed.
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Published date: September 2001
Keywords:
meta-analysis, retrospective collaboration, self and liang test
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Local EPrints ID: 25064
URI: http://eprints.soton.ac.uk/id/eprint/25064
ISSN: 0001-5652
PURE UUID: 8c8eb621-2245-4752-8bb7-f975a2ef43ad
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Date deposited: 06 Apr 2006
Last modified: 16 Mar 2024 02:42
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Author:
W. Zhang
Author:
N.E. Morton
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