基于全基因组变异的最大神经解剖覆盖范围的遗传效应研究。
Genome-wide variant-based study of genetic effects with the largest neuroanatomic coverage.
机构信息
College of Automation, Harbin Engineering University, NO. 145 Nantong Street, Nangang District, Harbin, 150001, China.
Computer and Information Science, IUPUI, 723 W Michigan St, Indianapolis, IN, 46202, USA.
出版信息
BMC Bioinformatics. 2021 Apr 30;22(1):223. doi: 10.1186/s12859-021-04145-0.
BACKGROUND
Brain image genetics provides enormous opportunities for examining the effects of genetic variations on the brain. Many studies have shown that the structure, function, and abnormality (e.g., those related to Alzheimer's disease) of the brain are heritable. However, which genetic variations contribute to these phenotypic changes is not completely clear. Advances in neuroimaging and genetics have led us to obtain detailed brain anatomy and genome-wide information. These data offer us new opportunities to identify genetic variations such as single nucleotide polymorphisms (SNPs) that affect brain structure. In this paper, we perform a genome-wide variant-based study, and aim to identify top SNPs or SNP sets which have genetic effects with the largest neuroanotomic coverage at both voxel and region-of-interest (ROI) levels. Based on the voxelwise genome-wide association study (GWAS) results, we used the exhaustive search to find the top SNPs or SNP sets that have the largest voxel-based or ROI-based neuroanatomic coverage. For SNP sets with >2 SNPs, we proposed an efficient genetic algorithm to identify top SNP sets that can cover all ROIs or a specific ROI.
RESULTS
We identified an ensemble of top SNPs, SNP-pairs and SNP-sets, whose effects have the largest neuroanatomic coverage. Experimental results on real imaging genetics data show that the proposed genetic algorithm is superior to the exhaustive search in terms of computational time for identifying top SNP-sets.
CONCLUSIONS
We proposed and applied an informatics strategy to identify top SNPs, SNP-pairs and SNP-sets that have genetic effects with the largest neuroanatomic coverage. The proposed genetic algorithm offers an efficient solution to accomplish the task, especially for identifying top SNP-sets.
背景
脑影像遗传学为研究遗传变异对大脑的影响提供了巨大的机会。许多研究表明，大脑的结构、功能和异常（例如与阿尔茨海默病相关的异常）是可遗传的。然而，哪些遗传变异导致了这些表型变化尚不完全清楚。神经影像学和遗传学的进步使我们能够获得详细的大脑解剖结构和全基因组信息。这些数据为我们提供了新的机会来识别遗传变异，如单核苷酸多态性（SNP），这些变异会影响大脑结构。在本文中，我们进行了一项全基因组变异的研究，旨在识别具有最大神经解剖覆盖范围的基因效应的顶级 SNP 或 SNP 集合，无论是在体素还是感兴趣区（ROI）水平上。基于体素全基因组关联研究（GWAS）结果，我们使用穷举搜索找到了具有最大体素或 ROI 神经解剖覆盖范围的顶级 SNP 或 SNP 集合。对于具有 >2 个 SNP 的 SNP 集合，我们提出了一种有效的遗传算法来识别可以覆盖所有 ROI 或特定 ROI 的顶级 SNP 集合。
结果
我们确定了一组具有最大神经解剖覆盖范围的顶级 SNP、SNP 对和 SNP 集合，其效应具有最大的神经解剖覆盖范围。在真实的影像遗传学数据上的实验结果表明，提出的遗传算法在识别顶级 SNP 集合的计算时间方面优于穷举搜索。
结论
我们提出并应用了一种信息学策略来识别具有最大神经解剖覆盖范围的遗传效应的顶级 SNP、SNP 对和 SNP 集合。提出的遗传算法为完成任务提供了一种有效的解决方案，特别是在识别顶级 SNP 集合方面。
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