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Genome scanning by composite likelihood

Genome scanning by composite likelihood
Genome scanning by composite likelihood
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.
research, models, dna, research support, meta-analysis, genes, meta-analysis as topic, phenotype, genome, databases, humans, chromosome mapping, theoretical, human, genetics, genetic markers, analysis
0002-9297
19-28
Morton, Newton
c668e2be-074a-4a0a-a2ca-e8f51830ebb7
Maniatis, Nikolas
369fb005-aae0-4243-807b-b42af088debd
Zhang, Weihua
1a759991-f2d4-4324-b8e2-c5b4c2b527d6
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Morton, Newton
c668e2be-074a-4a0a-a2ca-e8f51830ebb7
Maniatis, Nikolas
369fb005-aae0-4243-807b-b42af088debd
Zhang, Weihua
1a759991-f2d4-4324-b8e2-c5b4c2b527d6
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64

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

Record type: Article

Abstract

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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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 Sarah Ennis: ORCID iD orcid.org/0000-0003-2648-0869
ORCID for Andrew Collins: ORCID iD orcid.org/0000-0001-7108-0771

Catalogue record

Date deposited: 26 Feb 2007
Last modified: 16 Mar 2024 03:07

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Contributors

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

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