Trees Assembling Mann-Whitney approach for detecting genome-wide joint association among low-marginal-effect loci.
Trees Assembling Mann-Whitney approach for detecting genome-wide joint association among low-marginal-effect loci.
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DOI:
10.1002/gepi.21693
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发表时间:
2013-01
影响因子:
2.1
通讯作者:
Lu, Qing
中科院分区:
文献类型:
--
作者:
Wei, Changshuai;Schaid, Daniel J.;Lu, Qing
Common complex diseases are likely influenced by the interplay of hundreds, or even thousands, of genetic variants. Converging evidence shows that genetic variants with low-marginal-effects (LME) play an important role in disease development. Despite their potential significance, discovering LME genetic variants and assessing their joint association on high-dimensional data (e.g., genome-wide association studies) remain a great challenge. To facilitate joint association analysis among a large ensemble of LME genetic variants, we proposed a computationally efficient and powerful approach, which we call Trees Assembling Mann-Whitney (TAMW). Through simulation studies and an empirical data application, we found that TAMW outperformed multifactor dimensionality reduction (MDR) and the likelihood-ratio-based Mann-Whitney approach (LRMW) when the underlying complex disease involves multiple LME loci and their interactions. For instance, in a simulation with 20 interacting LME loci, TAMW attained a higher power (power=0.931) than both MDR (power=0.599) and LRMW (power=0.704). In an empirical study of 29 known Crohn’s disease (CD) loci, TAMW also identified a stronger joint association with CD than those detected by MDR and LRMW. Finally, we applied TAMW to Wellcome Trust CD GWAS to conduct a genome-wide analysis. The analysis of 459K single nucleotide polymorphisms was completed in 40 hours using parallel computing, and revealed a joint association predisposing to CD (p-value=2.763e-19). Further analysis of the newly discovered association suggested that 13 genes, such as ATG16L1 and LACC1, may play an important role in CD pathophysiological and etiological processes.
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DOI:
10.1038/nrg2579
发表时间:
2009-06
期刊:
Nature reviews. Genetics
影响因子:
--
作者:
Cordell HJ
通讯作者:
Cordell HJ
影响因子:
30.8
作者:
通讯作者:
--
影响因子:
2.1
作者:
Pattin, Kristine A.;White, Bill C.;Barney, Nate;Gui, Jiang;Nelson, Heather H.;Kelsey, Karl T.;Andrew, Angeline S.;Karagas, Margaret R.;Moore, Jason H.
通讯作者:
Moore, Jason H.
影响因子:
9.8
作者:
Ritchie, MD;Hahn, LW;Moore, JH
通讯作者:
Moore, JH
影响因子:
4.9
作者:
Hanauer, SB
通讯作者:
Hanauer, SB