Please use this identifier to cite or link to this item:
|Title:||Association mapping via regularized regression analysis of single-nucleotide-polymorphism haplotypes in variable-sized sliding windows|
|Source:||Li, Y., Sung, W.-K., Liu, J.J. (2007). Association mapping via regularized regression analysis of single-nucleotide-polymorphism haplotypes in variable-sized sliding windows. American Journal of Human Genetics 80 (4) : 705-715. ScholarBank@NUS Repository. https://doi.org/10.1086/513205|
|Abstract:||Large-scale haplotype association analysis, especially at the whole-genome level, is still a very challenging task without an optimal solution. In this study, we propose a new approach for haplotype association analysis that is based on a variable-sized sliding-window framework and employs regularized regression analysis to tackle the problem of multiple degrees of freedom in the haplotype test. Our method can handle a large number of haplotypes in association analyses more efficiently and effectively than do currently available approaches. We implement a procedure in which the maximum size of a sliding window is determined by local haplotype diversity and sample size, an attractive feature for large-scale haplotype analyses, such as a whole-genome scan, in which linkage disequilibrium patterns are expected to vary widely. We compare the performance of our method with that of three other methods-a test based on a single-nucleotide polymorphism, a cladistic analysis of haplotypes, and variable-length Markov chains-with use of both simulated and experimental data. By analyzing data sets simulated under different disease models, we demonstrate that our method consistently outperforms the other three methods, especially when the region under study has high haplotype diversity. Built on the regression analysis framework, our method can incorporate other risk-factor information into haplotype-based association analysis, which is becoming an increasingly necessary step for studying common disorders to which both genetic and environmental risk factors contribute. © 2007 by The American Society of Human Genetics. All rights reserved.|
|Source Title:||American Journal of Human Genetics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 15, 2018
WEB OF SCIENCETM
checked on Jan 30, 2018
checked on Feb 19, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.