MHC haplotype analysis by artificial neural networks

M.I. Bellgard; G.K. Tay; H.L. Hiew; C.S. Witt; N. Ketheesan; F.T. Christiansen; R.L. Dawkins

doi:10.1016/S0198-8859(97)00231-0

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MHC haplotype analysis by artificial neural networks

Journal article

Peer reviewed

MHC haplotype analysis by artificial neural networks

M.I. Bellgard, G.K. Tay, H.L. Hiew, C.S. Witt, N. Ketheesan, F.T. Christiansen and R.L. Dawkins

Human Immunology, Vol.59(1), pp.56-62

1998

DOI: https://doi.org/10.1016/S0198-8859(97)00231-0

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Abstract

Conventional matching is based on numbers of alleles shared between donor and recipient. This approach, however, ignores the degree of relationship between alleles and haplotypes, and therefore the actual degree of difference. To address this problem, we have compared family members using a block matching technique which reflects differences in genomic sequences. All parents and siblings had been genotyped using conventional MHC typing so that haplotypes could be assigned and relatives could be classified as sharing 0, 1 or 2 haplotypes. We trained an Artificial Neural Network (ANN) with subjects from 6 families (85 comparisons) to distinguish between relatives. Using the outputs of the ANN, we developed a score, the Histocompatibility Index (HI), as a measure of the degree of difference. Subjects from a further 3 families (106 profile comparisons) were tested. The HI score for each comparison was plotted. We show that the HI score is trimodal allowing the definition of three populations corresponding to approximately 0, 1 or 2 haplotype sharing. The means and standard deviations of the three populations were found. As expected, comparisons between family members sharing 2 haplotypes resulted in high HI scores with one exception. More interestingly, this approach distinguishes between the 1 and 0 haplotype groups, with some informative exceptions. This distinction was considered too difficult to attempt visually. The approach provides promise in the quantification of degrees of histo-compatibility.

Details

Title: MHC haplotype analysis by artificial neural networks
Authors/Creators: M.I. Bellgard (Author/Creator) - The University of Western Australia
G.K. Tay (Author/Creator) - The University of Western Australia
H.L. Hiew (Author/Creator) - Murdoch University
C.S. Witt (Author/Creator) - Royal Perth Hospital
N. Ketheesan (Author/Creator) - The University of Western Australia
F.T. Christiansen (Author/Creator) - Royal Perth Hospital
R.L. Dawkins (Author/Creator) - The University of Western Australia
Publication Details: Human Immunology, Vol.59(1), pp.56-62
Publisher: Elsevier BV
Identifiers: 991005542467807891
Copyright: 1998 American Society for Histocompatibility and Immunogenetics
Murdoch Affiliation: School of Information Technology
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration
Citation topics: 1 Clinical & Life Sciences; 1.6 Immunology; 1.6.607 MHC Diversity
Web Of Science research areas: Immunology
ESI research areas: Immunology