Wan Jing, Kim Sungeun, Inlow Mark, Nho Kwangsik, Swaminathan Shanker, Risacher Shannon L, Fang Shiaofen, Weiner Michael W, Beg M Faisal, Wang Lei, Saykin Andrew J, Shen Li
Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):376-83. doi: 10.1007/978-3-642-23629-7_46.
Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.
海马形状的基因图谱是一个尚未充分探索的领域，作为阿尔茨海默病（AD）和轻度认知障碍（MCI）的神经退行性变生物标志物具有巨大潜力。本研究在一个大型队列中，研究了顶级候选单核苷酸多态性（SNP）对海马形状特征作为数量性状（QT）的遗传效应。使用FS+LDDMM从MRI扫描中分割海马表面，并在表面配准后提取形状特征。提出了弹性网络（EN）和稀疏典型相关分析（SCCA）来检验SNP-QT关联，并与多元回归（MR）进行比较。尽管功效相似，但EN产生的预测因子比MR少得多。通过MR和EN确定了全局和局部遗传效应的详细表面图谱，以揭示多SNP-单QT关系，并通过SCCA发现多SNP-多QT关联。形状分析比体积分析确定了更强的SNP-QT相关性。稀疏多变量模型具有更大的能力来揭示复杂的SNP-QT关系。对定量形状特征的遗传分析对于增强对AD等复杂疾病的机制理解具有相当大的潜力。
