Individual SNP Allele Reconstruction from Informative Markers Selected by a Non-Linear Gauss-Type Algorithm

Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483 and Schmidt, Karl Michael ORCID: https://orcid.org/0000-0002-0227-3024 2006. Individual SNP Allele Reconstruction from Informative Markers Selected by a Non-Linear Gauss-Type Algorithm. Human Heredity 62 (2) , pp. 97-106. 10.1159/000096097

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Abstract

OBJECTIVES: In view of the linkage disequilibrium structure of the genome, the selection of maximally informative SNP markers is a fundamental issue in the design of association studies. Currently used selection methods rely on pairwise marker correlation or informativity measures for subsets of markers. Nevertheless, the selected markers do not provide a completely satisfactory description of the individual remaining markers. The number of tag markers can be further reduced by using haplotypic information, but then the results of association analysis are difficult to interpret. METHODS AND RESULTS: We propose a non-linear Gauss-type algorithm selecting a subset of markers which is optimal with respect to the informativity measures and allows an explicit reconstruction of all other known markers, thus permitting direct inference of allelic association. The selection is based on the haplotype distribution in the population, but can be adapted to work with unphased genotypes directly. CONCLUSIONS: The proposed algorithm provides a rational methodology of informative marker selection, allowing for control and optimisation of information content and full marker reconstruction. Moreover, the reconstruction step can also be applied to tag markers selected using a different method at the stage of study design, identifying those markers which cannot be uniquely recovered from the chosen tags.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics Medicine MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Subjects:	Q Science > QA Mathematics R Medicine > R Medicine (General)
Publisher:	Karger
ISSN:	1423-0062
Last Modified:	03 Dec 2022 11:36
URI:	https://orca.cardiff.ac.uk/id/eprint/13825

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