Summary
Advancements in our understanding of variation in the human genome and rapid improvements in high-throughput genotyping technology have made it feasible to study most of the human genetic diversity that is due to common variations in relation to observable phenotypes. Over the past few years, public SNP databases have matured and empirical genome-wide SNP data, such as that generated by the International HapMap Project, have shown the utility and efficiency of selecting and testing informative markers (“tag SNPs”) that exploit redundancies among nearby polymorphisms due to linkage disequilibrium (LD). In this chapter, we will demonstrate how to use the HapMap resource and the Haploview program to process and analyze genetic data from HapMap, to evaluate LD relations between SNPs, and to select tagging SNPs to be examined in disease association studies.
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Abbreviations
- D′:
-
dprime
- HWE:
-
Hardy Weinberg equilibrium
- kb:
-
kilobase
- LD:
-
linkage disequilibrium
- MAF:
-
minor allele frequency
populations mapped in the HapMap project include: Utah residents with Northern and Western European ancestry(CEU)
Han Chinese (CHB)
Japanese(JPT)
Yorubans from Nigeria(YRI)
- Mb:
-
megabase
- SNP:
-
single nucleotide polymorphism
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Acknowledgments
We would like to thank the investigators of the HapMap Project and the developers of the HapMap website for allowing us to use screen shots of their website. We would also like to acknowledge Jeffery Barrett, Mark Daly, and others in the Daly Lab who developed and maintain the Haploview program.
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© 2008 Humana Press, a part of Springer Science+Business Media, LLC
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Haiman, C.A., Stram, D.O. (2008). Utilizing HapMap and Tagging SNPs. In: Trent, R.J. (eds) Clinical Bioinformatics. Methods in Molecular Medicine™, vol 141. Humana Press. https://doi.org/10.1007/978-1-60327-148-6_3
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DOI: https://doi.org/10.1007/978-1-60327-148-6_3
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