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.

中文翻译：

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充分方向因子模型及其在基因表达数量性状位点发现中的应用

技术的快速进步使得收集遗传数据相对便宜，但现有数据的统计分析仍然便宜得多。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努力提取更多信息，是收集新数据的一种有吸引力且具有成本效益的替代方案。我们通过因子分析和降维估计相结合来研究基因表达和 SNP 之间的关系。为了利用传统因子模型的灵活性（不需要潜在因子为正态），我们建议在组合模型的联合估计中使用半参数充分降维方法。与现有的估计器相比，所得的估计器非常灵活并且具有优越的性能，现有的估计器依赖于对潜在因素的额外假设。我们量化所提出的参数估计器的渐近性能，并通过评估估计变异性和构建置信区间来进行推理。新结果使我们首次能够从基因型-组织表达数据中识别出与肺组织中基因-SNP 关系相关的具有统计学意义的 SNP。