Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.
Random field modeling of multi-trait multi-locus association for detecting methylation quantitative trait loci.
复制标题
用于检测甲基化数量性状基因座的多性状多基因座关联的随机场建模。
DOI:
10.1093/bioinformatics/btac443
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发表时间:
2022
期刊:
影响因子:
--
通讯作者:
Li,Ming
中科院分区:
文献类型:
--
作者:
Lyu,Chen;Huang,Manyan;Liu,Nianjun;Chen,Zhongxue;Lupo,PhilipJ;Tycko,Benjamin;Witte,JohnS;Hobbs,CharlotteA;Li,Ming
MotivationCpG sites within the same genomic region often share similar methylation patterns and tend to be co-regulated by multiple genetic variants that may interact with one another.ResultsWe propose a multi-trait methylation random field (multi-MRF) method to evaluate the joint association between a set of CpG sites and a set of genetic variants. The proposed method has several advantages. First, it is a multi-trait method that allows flexible correlation structures between neighboring CpG sites (e.g. distance-based correlation). Second, it is also a multi-locus method that integrates the effect of multiple common and rare genetic variants. Third, it models the methylation traits with a beta distribution to characterize their bimodal and interval properties. Through simulations, we demonstrated that the proposed method had improved power over some existing methods under various disease scenarios. We further illustrated the proposed method via an application to a study of congenital heart defects (CHDs) with 83 cardiac tissue samples. Our results suggested that geneBACE2, a methylation quantitative trait locus (QTL) candidate, colocalized with expression QTLs in artery tibial and harbored genetic variants with nominal significant associations in two genome-wide association studies of CHD.Availability and implementationhttps://github.com/chenlyu2656/Multi-MRF.Supplementary informationSupplementary data are available atBioinformaticsonline.
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影响因子:
56.9
作者:
MCDOUGAL, JS;KENNEDY, MS;NICHOLSON, JKA
通讯作者:
NICHOLSON, JKA
影响因子:
64.8
作者:
LAYNE, SP;MERGES, MJ;NARA, PL
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NARA, PL
影响因子:
64.5
作者:
ARTHOS, J;DEEN, KC;SWEET, RW
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SWEET, RW
影响因子:
39.2
作者:
R. Schooley;T. Merigan;P. Gaut;M. Hirsch;M. Holodniy;T. Flynn;S. Liu;R. Byington;S. Henochowicz;E. Gubish
通讯作者:
R. Schooley;T. Merigan;P. Gaut;M. Hirsch;M. Holodniy;T. Flynn;S. Liu;R. Byington;S. Henochowicz;E. Gubish
影响因子:
3.7
作者:
Schawaller,M;Smith,GE;Skehel,JJ;Wiley,DC
通讯作者:
Wiley,DC