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Genome scanning by composite likelihood

Record type: Article

Ambitious programs have recently been advocated or launched to create genomewide databases for meta-analysis of association between DNA markers and phenotypes of medical and/or social concern. A necessary but not sufficient condition for success in association mapping is that the data give accurate estimates of both genomic location and its standard error, which are provided for multifactorial phenotypes by composite likelihood. That class includes the Malecot model, which we here apply with an illustrative example. This preliminary analysis leads to five inferences: permutation of cases and controls provides a test of association free of autocorrelation; two hypotheses give similar estimates, but one is consistently more accurate; estimation of the false-discovery rate is extended to causal genes in a small proportion of regions; the minimal data for successful meta-analysis are inferred; and power is robust for all genomic factors except minor-allele frequency. An extension to meta-analysis is proposed. Other approaches to genome scanning and meta-analysis should, if possible, be similarly extended so that their operating characteristics can be compared.

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Citation

Morton, Newton, Maniatis, Nikolas, Zhang, Weihua, Ennis, Sarah and Collins, Andrew (2007) Genome scanning by composite likelihood The American Journal of Human Genetics, 80, (1), pp. 19-28. (doi:10.1086/510401).

More information

Published date: January 2007
Keywords: research, models, dna, research support, meta-analysis, genes, meta-analysis as topic, phenotype, genome, databases, humans, chromosome mapping, theoretical, human, genetics, genetic markers, analysis

Identifiers

Local EPrints ID: 44324
URI: http://eprints.soton.ac.uk/id/eprint/44324
ISSN: 0002-9297
PURE UUID: 8f084e70-8575-48e0-b64b-d32795dcf437
ORCID for Andrew Collins: ORCID iD orcid.org/0000-0001-7108-0771

Catalogue record

Date deposited: 26 Feb 2007
Last modified: 17 Jul 2017 15:15

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Contributors

Author: Newton Morton
Author: Nikolas Maniatis
Author: Weihua Zhang
Author: Sarah Ennis
Author: Andrew Collins ORCID iD

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