Abstract
The current availability of dense sets of marker SNPs for the human genome is having a large impact on genetic studies and offers new possibilities for clinical trials. This chapter offers a unified basis for the analysis of marker and response data, emphasizing the central importance of the correlation, or linkage disequilibrium, between SNP markers and the genes that affect response. It is convenient to phrase the development of association mapping in the language of quantitative genetics, using additive and non-additive components of variance. A novel feature of dense SNP data is that good estimates can be made of actual inbreeding and relatedness. These estimates are more relevant than values predicted from family pedigree, and are all that are available in the absence of family data.The dimensionality of SNP marker datasets has required the development of new methods that are appropriate for a large number of statistical comparisons, and the development of computational methods that allow high-dimensional regression. These methods are reviewed here, as is the use of biological annotation for both viewing the relevance of empirical associations, and to structure analysis in order to focus on those markers with the highest expectation for association with the outcomes under study.
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Acknowledgements
This work was supported in part by NIH grants GM 075091, HG 004464, HG 005157, HL 072966 and TR 000423.
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Weir, B.S., Heagerty, P.J. (2013). Genetic Markers in Clinical Trials. In: Fleming, T., Weir, B. (eds) Proceedings of the Fourth Seattle Symposium in Biostatistics: Clinical Trials. Lecture Notes in Statistics(), vol 1205. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5245-4_12
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DOI: https://doi.org/10.1007/978-1-4614-5245-4_12
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