Morton, Newton, Maniatis, Nikolas, Zhang, Weihua, Ennis, Sarah and Collins, Andrew
Genome scanning by composite likelihood.
American Journal of Human Genetics, 80, (1), . (doi:10.1086/510401).
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.
|Digital Object Identifier (DOI):
||research, models, dna, research support, meta-analysis, genes, meta-analysis as topic, phenotype, genome, databases, humans, chromosome mapping, theoretical, human, genetics, genome, genetic markers, human, analysis
||Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Q Science > QH Natural history > QH426 Genetics
||University Structure - Pre August 2011 > School of Medicine > Human Genetics
|Accepted Date and Publication Date:
||26 Feb 2007
||31 Mar 2016 12:18
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