Center for Multimodal Imaging and Genetics, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
Hum Mol Genet. 2018 May 1;27(R1):R22-R28. doi: 10.1093/hmg/ddy082.
Structural neuroimaging measures based on magnetic resonance imaging have been at the forefront of imaging genetics. Global efforts to ensure homogeneity of measurements across study sites have enabled large-scale imaging genetic projects, accumulating nearly 50K samples for genome-wide association studies (GWAS). However, not many novel genetic variants have been identified by these GWAS, despite the high heritability of structural neuroimaging measures. Here, we discuss the limitations of using heritability as a guidance for assessing statistical power of GWAS, and highlight the importance of discoverability-which is the power to detect genetic variants for a given phenotype depending on its unique genomic architecture and GWAS sample size. Further, we present newly developed methods that boost genetic discovery in imaging genetics. By redefining imaging measures independent of traditional anatomical conventions, it is possible to improve discoverability, enabling identification of more genetic effects. Moreover, by leveraging enrichment priors from genomic annotations and independent GWAS of pleiotropic traits, we can better characterize effect size distributions, and identify reliable and replicable loci associated with structural neuroimaging measures. Statistical tools leveraging novel insights into the genetic discoverability of human traits, promises to accelerate the identification of genetic underpinnings underlying brain structural variation.
基于磁共振成像的结构神经影像学测量一直处于成像遗传学的前沿。全球范围内努力确保研究地点测量的同质性，使大规模成像遗传学项目得以实现，积累了近 50,000 个样本进行全基因组关联研究 (GWAS)。然而，尽管结构神经影像学测量具有很高的遗传性，但通过这些 GWAS 并未发现很多新的遗传变异。在这里，我们讨论了将遗传性用作评估 GWAS 统计功效的指导的局限性，并强调了可发现性的重要性——这是根据特定表型的独特基因组结构和 GWAS 样本量检测遗传变异的能力。此外，我们提出了新开发的方法来增强成像遗传学中的遗传发现。通过重新定义独立于传统解剖学惯例的成像测量，有可能提高可发现性，从而能够识别更多的遗传效应。此外，通过利用来自基因组注释和多效性性状独立 GWAS 的富集先验知识，我们可以更好地描述效应大小分布，并识别与结构神经影像学测量相关的可靠且可重复的位点。利用对人类特征遗传可发现性的新见解的统计工具，有望加速确定大脑结构变异的遗传基础。
