Konopka Genevieve
Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390-9111, USA.
Netw Neurosci. 2017 Feb 1;1(1):3-13. doi: 10.1162/NETN_a_00003. eCollection 2017 Winter.
Correlations of genetic variation in DNA with functional brain activity have already provided a starting point for delving into human cognitive mechanisms. However, these analyses do not provide the specific genes driving the associations, which are complicated by intergenic localization as well as tissue-specific epigenetics and expression. The use of brain-derived expression datasets could build upon the foundation of these initial genetic insights and yield genes and molecular pathways for testing new hypotheses regarding the molecular bases of human brain development, cognition, and disease. Thus, coupling these human brain gene expression data with measurements of brain activity may provide genes with critical roles in brain function. However, these brain gene expression datasets have their own set of caveats, most notably a reliance on postmortem tissue. In this perspective, I summarize and examine the progress that has been made in this realm to date, and discuss the various frontiers remaining, such as the inclusion of cell-type-specific information, additional physiological measurements, and genomic data from patient cohorts.
DNA中的基因变异与大脑功能活动之间的相关性已经为深入研究人类认知机制提供了一个起点。然而,这些分析并未提供驱动这些关联的具体基因,这些关联因基因间定位以及组织特异性表观遗传学和表达而变得复杂。使用源自大脑的表达数据集可以建立在这些初步基因见解的基础上,并产生用于测试有关人类大脑发育、认知和疾病分子基础的新假设的基因和分子途径。因此,将这些人类大脑基因表达数据与大脑活动测量相结合可能会揭示在大脑功能中起关键作用的基因。然而,这些大脑基因表达数据集有其自身的一系列问题,最明显的是依赖死后组织。从这个角度来看,我总结并审视了迄今为止在这个领域取得的进展,并讨论了仍然存在的各种前沿问题,例如纳入细胞类型特异性信息、额外的生理测量以及来自患者队列的基因组数据。