Sufficient direction factor model and its application to gene expression quantitative trait loci discovery.
Sufficient direction factor model and its application to gene expression quantitative trait loci discovery.
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DOI：
10.1093/biomet/asz010
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发表时间：
2019-04
期刊：
影响因子：
2.7
通讯作者：
Fei Jiang;Yanyuan Ma;Ying Wei
中科院分区：
文献类型：
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作者：
Fei Jiang;Yanyuan Ma;Ying Wei
技术的快速进步使得收集基因数据的成本相对较低，但对现有数据进行统计分析的成本仍然要低得多。因此，单核苷酸多态性SNP数据的二次分析，即，重新分析现有数据以提取更多信息，是收集新数据的一个有吸引力和成本效益高的替代办法。我们通过因子分析和降维估计相结合的方法研究基因表达与SNPs之间的关系。为了利用传统因子模型的灵活性，其中潜在因子不需要是正态的，我们建议使用半参数充分降维方法的联合估计的组合模型。由此产生的估计是灵活的，并具有上级性能相对于现有的估计，这依赖于额外的假设的潜在因素。我们量化的渐近性能的参数估计和估计变异性进行评估，并通过构建置信区间进行推断。新的结果使我们能够识别，第一次，统计学上显着的单核苷酸多态性的基因-单核苷酸多态性关系在肺组织中的基因型-组织表达数据。
Rapid improvement in technology has made it relatively cheap to collect genetic data, however statistical analysis of existing data is still much cheaper. Thus, secondary analysis of single-nucleotide polymorphism, SNP, data, i.e., reanalysing existing data in an effort to extract more information, is an attractive and cost-effective alternative to collecting new data. We study the relationship between gene expression and SNPs through a combination of factor analysis and dimension reduction estimation. To take advantage of the flexibility in traditional factor models where the latent factors are not required to be normal, we recommend using semiparametric sufficient dimension reduction methods in the joint estimation of the combined model. The resulting estimator is flexible and has superior performance relative to the existing estimator, which relies on additional assumptions on the latent factors. We quantify the asymptotic performance of the proposed parameter estimator and perform inference by assessing the estimation variability and by constructing confidence intervals. The new results enable us to identify, for the first time, statistically significant SNPs concerning gene-SNP relations in lung tissue from genotype-tissue expression data.