A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold.

在本研究中，我们介绍了一种基于偏最小二乘（PLS）的框架，称为Cluster-Bootstrap PLS（CLUB-PLS），它可与高维度的遗传和脑成像数据以及大规模样本一起工作，通过使用聚类自助法为两个领域的单个输入特征提供稳健的统计数据。我们将CLUB-PLS应用于英国生物库的33,000个受试者的表面积和皮层厚度的遗传基础研究中，发现了107个与386个不同基因相关的基因组范围显著性基因座-表型对。我们进一步经典GWAS或基因组范围内推断统计（GWIS）的结果验证了其中大多数基因座-表型对，其中85个基因座-表型对超过了基因组范围建议性（P<1e-05）的阈值。

应用CLUB-PLS解决图像遗传学中的维度问题