A Multinomial Ordinal Probit Model with Singular Value Decomposition Method for a Multinomial Trait.
A Multinomial Ordinal Probit Model with Singular Value Decomposition Method for a Multinomial Trait.
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多项性状的奇异值分解方法的多项序概率模型。
DOI：
10.1155/2012/419832
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发表时间：
2012
影响因子：
1.1
通讯作者：
Guo,Xiuqing
中科院分区：
文献类型：
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作者：
Kwon,Soonil;Goodarzi,MarkO;Taylor,KentD;Cui,Jinrui;Chen,Y-DIda;Rotter,JeromeI;Hsueh,Willa;Guo,Xiuqing
我们开发了一个多项有序概率单位模型与奇异值分解测试了大量的单核苷酸多态性（SNP）同时与多疾病状态的关联时，样本量远小于SNP的数量。通过仿真验证了该方法的有效性和性能。我们将该方法应用于通过墨西哥-美国冠状动脉疾病研究招募的真实的研究样本。我们发现3个基因（SORCS 1、AMPD 1和PPARα）与IGT和IFG的发生相关，5个基因（AMPD 2、PRKAA 2、C5、TCF 7 L2和ITR）仅与IGT机制相关，6个基因（CAPN 10、IL 4、NOS 3、CD 14、GCG和SORT 1）仅与IFG机制相关。这些数据表明IGT和IFG可能通过不同的遗传决定因素指示不同的生理机制。
We developed a multinomial ordinal probit model with singular value decomposition for testing a large number of single nucleotide polymorphisms (SNPs) simultaneously for association with multidisease status when sample size is much smaller than the number of SNPs. The validity and performance of the method was evaluated via simulation. We applied the method to our real study sample recruited through the Mexican‐American Coronary Artery Disease study. We found 3 genes (SORCS1, AMPD1, and PPARα) to be associated with the development of both IGT and IFG, while 5 genes (AMPD2, PRKAA2, C5, TCF7L2, and ITR) with the IGT mechanism only and 6 genes (CAPN10, IL4, NOS3, CD14, GCG, and SORT1) with the IFG mechanism only. These data suggest that IGT and IFG may indicate different physiological mechanism to prediabetes, via different genetic determinants.