Voxel-level summary statistics of hippocampus shape, white matter microstructure, and cortical surface curvature in UK Biobank (n=33,324)

This deposit hosts GWAS summary statistics of hippocampus shape (n=33,324), white matter microstructure (n=33,324), and cortical surface curvature (n=15,752) using UKB unrelated white subjects. The data was generated by using the highly efficient imaging genetics (HEIG v1.1.0) framework where only the triplets - summary statistics of low-dimensional representations (LDRs), the functional bases, and the variance-covariance matrix LDRs - are shared, which is sufficient to recover all voxel-variant pairs as well as to conduct voxel-level heritability and (cross-trait) genetic correlation analysis. Check the tutorial and the example data used in the tutorial. The shared data includes: 1. Triplets for hippocampus shape measured by the radial distance from the medial model for each vertex. The original images contain 30,000 vertices while the shared data contains 49 LDRs. Left and right hemispheres were analyzed separately, each with 15,000 vertices. 2. Triplets for 21 white matter tracts measured by fractional anisotropy. The original images contain 32,217 voxels and each tract contains 88 ~ 3503 voxels while the shared data contains 1,034 LDRs. Tracts were analyzed separately. 3. Triplets for cortical surface curvature. The original images contain 59,412 vertices while the shared data contains 1,750 LDRs. The entire brain was analyzed as a whole. 4. LD matrix and its inverse for 22 chromosomes including 460k genotyped SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 8.4k white unrelated subjects in UKB. Two regularization levels are provided: {85%, 80%} for heritability and genetic correlations within images and {75%, 70%} for cross-trait genetic correlations. 5. LD matrix and its inverse for 22 chromosomes including 1.2 million imputed HapMap3 SNPs. LD matrix and its inverse were estimated by using two separate datasets each containing 42k white unrelated subjects in UKB. Two regularization levels are provided: {98%, 95%} for heritability and genetic correlations within images and {90%, 85%} for cross-trait genetic correlations.本数据集包含基于英国生物库（UKB, UK Biobank）无关白人受试者的三项影像学表型的全基因组关联研究（GWAS, Genome-Wide Association Study）汇总统计量：海马形态（样本量n=33,324）、白质微结构（样本量n=33,324）以及皮层表面曲率（样本量n=15,752）。 本数据集采用高效影像遗传学框架（HEIG v1.1.0, Highly Efficient Imaging Genetics）生成，仅共享三类三联体数据：低维表征（LDRs, Low-dimensional Representations）的汇总统计量、功能基以及方差-协方差矩阵LDRs。该三联体数据足以还原所有体素-变异位点对，同时支持开展体素级遗传力及（跨性状）遗传相关性分析。请参阅教程及教程中使用的示例数据。 共享数据包含： 1. 基于每个顶点至内侧模型的径向距离测算的海马形态三联体数据。原始影像包含30,000个顶点，共享数据则包含49个LDRs。左右大脑半球分别进行分析，每个半球各含15,000个顶点。 2. 基于各向异性分数（FA, Fractional Anisotropy）测算的21条白质纤维束三联体数据。原始影像包含32,217个体素，每条纤维束包含88~3503个体素，共享数据包含1,034个LDRs。各纤维束分别进行分析。 3. 皮层表面曲率三联体数据。原始影像包含59,412个顶点，共享数据包含1,750个LDRs，以全脑整体为分析单位。 4. 覆盖22条染色体、包含46万个基因型单核苷酸多态性（SNPs, Single Nucleotide Polymorphism）的连锁不平衡（LD, Linkage Disequilibrium）矩阵及其逆矩阵。该LD矩阵及其逆矩阵基于英国生物库中两个独立的、各包含8.4k名无关白人受试者的数据集估算得到，提供两种正则化水平：用于影像表型内部遗传力及遗传相关性分析的{85%, 80%}，以及用于跨性状遗传相关性分析的{75%, 70%}。 5. 覆盖22条染色体、包含120万个经基因型填充的HapMap3 SNPs的连锁不平衡矩阵及其逆矩阵。该LD矩阵及其逆矩阵基于英国生物库中两个独立的、各包含42k名无关白人受试者的数据集估算得到，提供两种正则化水平：用于影像表型内部遗传力及遗传相关性分析的{98%, 95%}，以及用于跨性状遗传相关性分析的{90%, 85%}。