Jiang Libo, Liu Jingyuan, Zhu Xuli, Ye Meixia, Sun Lidan, Lacaze Xavier, Wu Rongling
Brief Bioinform. 2015 Nov;16(6):905-11. doi: 10.1093/bib/bbv002. Epub 2015 Feb 19.
Whole-genome search of genes is an essential approach to dissecting complex traits, but a marginal one-single-nucleotide polymorphism (SNP)/one-phenotype regression analysis widely used in current genome-wide association studies fails to estimate the net and cumulative effects of SNPs and reveal the developmental pattern of interplay between genes and traits. Here we describe a computational framework, which we refer to as two-side high-dimensional genome-wide association studies (2HiGWAS), to associate an ultrahigh dimension of SNPs with a high dimension of developmental trajectories measured across time and space. The model is implemented with a dual dimension-reduction procedure for both predictors and responses to select a sparse but full set of significant loci from an extremely large pool of SNPs and estimate their net time-varying effects on trait development. The model can not only help geneticists to precisely identify an entire set of genes underlying complex traits but also allow them to elucidate a global picture of how genes control developmental and dynamic processes of trait formation. We investigated the statistical properties of the model via extensive simulation studies. With the increasing availability of GWAS in various organisms, 2HiGWAS will have important implications for genetic studies of developmental compelx traits.
全基因组基因搜索是剖析复杂性状的重要方法，但目前全基因组关联研究中广泛使用的边际单核苷酸多态性（SNP）/单表型回归分析无法估计SNP的净效应和累积效应，也无法揭示基因与性状之间相互作用的发育模式。在此，我们描述了一个计算框架，我们称之为双侧高维全基因组关联研究（2HiGWAS），用于将超高维度的SNP与跨时间和空间测量的高维发育轨迹相关联。该模型通过对预测变量和响应变量的双重降维程序来实现，以便从极大量的SNP中选择一组稀疏但完整的显著位点，并估计它们对性状发育的净时变效应。该模型不仅可以帮助遗传学家精确识别复杂性状背后的整套基因，还能让他们阐明基因如何控制性状形成的发育和动态过程的全局图景。我们通过广泛的模拟研究调查了该模型的统计特性。随着各种生物体中GWAS数据的日益丰富，2HiGWAS将对发育复杂性状的遗传研究产生重要影响。
