Genome-wide association studies (GWAS) are used to identify relationships
between genetic variations and specific traits. When applied to
high-dimensional medical imaging data, a key step is to extract
lower-dimensional, yet informative representations of the data as traits.
Representation learning for imaging genetics is largely under-explored due to
the unique challenges posed by GWAS in comparison to typical visual
representation learning. In this study, we tackle this problem from the mutual
information (MI) perspective by identifying key limitations of existing
methods. We introduce a trans-modal learning framework Genetic InfoMax (GIM),
including a regularized MI estimator and a novel genetics-informed transformer
to address the specific challenges of GWAS. We evaluate GIM on human brain 3D
MRI data and establish standardized evaluation protocols to compare it to
existing approaches. Our results demonstrate the effectiveness of GIM and a
significantly improved performance on GWAS.

本研究从互信息的角度出发，通过识别现有方法的关键限制，引入了一种跨模态学习框架 Genetic InfoMax (GIM)，包括正则化的互信息估计器和一种新颖的遗传信息变换器，以解决 GWAS 所面临的特定挑战。我们在人类脑 3D MRI 数据上评估 GIM，并建立了标准化评估协议来与现有方法进行比较。我们的结果表明了 GIM 的有效性和在 GWAS 上显著提升的性能。

基因信息最大化：探索在高维成像遗传学研究中的互信息最大化

Genetic InfoMax: Exploring Mutual Information Maximization in  High-Dimensional Imaging Genetics Studies

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。