Rapid testing of SNPs and gene-environment interactions in case-parent trio data based on exact analytic parameter estimation.
Rapid testing of SNPs and gene-environment interactions in case-parent trio data based on exact analytic parameter estimation.
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DOI：
10.1111/j.1541-0420.2011.01713.x
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发表时间：
2012-09
期刊：
影响因子：
1.9
通讯作者：
Ruczinski I
中科院分区：
文献类型：
--
作者：
Schwender H;Taub MA;Beaty TH;Marazita ML;Ruczinski I
Case–parent trio studies concerned with children affected by a disease and their parents aim to detect single nucleotide polymorphisms (SNPs) showing a preferential transmission of alleles from the parents to their affected offspring. A popular statistical test for detecting such SNPs associated with disease in this study design is the genotypic transmission/disequilibrium test (gTDT) based on a conditional logistic regression model, which usually needs to be fitted by an iterative procedure. In this article, we derive exact closed-form solutions for the parameter estimates of the conditional logistic regression models when testing for an additive, a dominant, or a recessive effect of a SNP, and show that such analytic parameter estimates also exist when considering gene–environment interactions with binary environmental variables. Because the genetic model underlying the association between a SNP and a disease is typically unknown, it might further be beneficial to use the maximum over the gTDT statistics for the possible effects of a SNP as test statistic. We therefore propose a procedure enabling a fast computation of the test statistic and the permutation-based p-value of this MAX gTDT. All these methods are applied to whole-genome scans of the case–parent trios from the International Cleft Consortium. These applications show our procedures dramatically reduce the required computing time compared to the conventional iterative methods allowing, for example, the analysis of hundreds of thousands of SNPs in a few minutes instead of several hours.
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影响因子：
1.7
作者：
Louis, Thomas A.;Ruczinski, Ingo
通讯作者：
Ruczinski, Ingo
影响因子：
2.1
作者：
Wakefield, Jon
通讯作者：
Wakefield, Jon
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作者：
Zheng, G;Freidlin, B;Gastwirth, JL
通讯作者：
Gastwirth, JL
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McNemar, Quinn
通讯作者：
McNemar, Quinn