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Detecting genome wide haplotype sharing using SNP or microsatellite haplotype data

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Abstract

Genome wide association studies using high throughput technology are already being conducted despite the significant hurdles that need to be overcome (Nat Rev Genet 6:95–108, 2005; Nat Rev Genet 6:109–118, 2005). Methods for detecting haplotype association signals in genome wide haplotype datasets are as yet very limited. Much methodological research has already been devoted to linkage disequilibrium (LD) fine mapping where the focus is the identification of the disease locus rather than the detection of a disease signal. Applications of these approaches to genome wide scanning are limited by the strong model assumptions of the sharing process, which lead to computational complexity. We describe a new algorithm for the initial identification of disease susceptibility loci in genome wide haplotype association studies. Excess sharing of ancestral haplotypes, which indicates the presence of a disease locus, is detected with a simple, easy to interpret, χ 2 based statistic. The method allows genome wide scanning for qualitative traits within reasonable computational timeframes and can serve as a first pass analysis prior to the usage of likelihood based methods, providing candidate regions and inferred susceptibility haplotypes. Our method makes no assumptions regarding the population history or the pattern of background LD. Statistical significance is evaluated with permutation tests. The method is illustrated on simulated and real data where it is applied to simple (cystic fibrosis) and complex disease (multiple sclerosis) examples. The statistic has low type I error and greater power to map disease loci over conventional single marker tests for low to moderate levels of LD.

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Acknowledgements

We would like to thank the Tasmanian MS sufferers and their family members for participating in our study. We would also like to thank Prof. Trevor Kilpatrick, Dr. Helmut Butzkueven and Dr. Bruce Taylor who were the consulting neurologists. We would also like to acknowledge Ingileif Hallgrimsdottir, Department of Statistics, UC Berkeley, for useful discussions on the development of the algorithm. Dr. Melanie Bahlo is supported by the Australian National Health and Medical Research Council (NHMRC). Prof. Simon Foote, Prof. Terry Speed and Dr. Justin Rubio are fellows of the NHMRC. The MS study was funded by the NHMRC, MS Australia and the Co-operative Research Centre (CRC) for the Discovery of Genes for common Human Diseases.

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Authors and Affiliations

  1. The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, 3050, Parkville, VIC, Australia

    Melanie Bahlo, Jim Stankovich, Terence P. Speed & Simon J. Foote

  2. University of California, Berkeley, CA, USA

    Terence P. Speed

  3. The Howard Florey Institute, Parkville, VIC, Australia

    Justin P. Rubio & Rachel K. Burfoot

  4. The Menzies Research Institute, Hobart, TAS, Australia

    Simon J. Foote

Authors
  1. Melanie Bahlo
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  2. Jim Stankovich
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  3. Terence P. Speed
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  4. Justin P. Rubio
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  5. Rachel K. Burfoot
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  6. Simon J. Foote
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Correspondence to Melanie Bahlo.

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Bahlo, M., Stankovich, J., Speed, T.P. et al. Detecting genome wide haplotype sharing using SNP or microsatellite haplotype data. Hum Genet 119, 38–50 (2006). https://doi.org/10.1007/s00439-005-0114-9

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  • DOI: https://doi.org/10.1007/s00439-005-0114-9

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