Division of Biology and Biomedical Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
Department of Genetics, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri.
Biol Psychiatry. 2021 Jan 1;89(1):76-89. doi: 10.1016/j.biopsych.2020.06.011. Epub 2020 Jun 18.
Neuropsychiatric phenotypes have long been known to be influenced by heritable risk factors, directly confirmed by the past decade of genetic studies that have revealed specific genetic variants enriched in disease cohorts. However, the initial hope that a small set of genes would be responsible for a given disorder proved false. The more complex reality is that a given disorder may be influenced by myriad small-effect noncoding variants and/or by rare but severe coding variants, many de novo. Noncoding genomic sequences-for which molecular functions cannot usually be inferred-harbor a large portion of these variants, creating a substantial barrier to understanding higher-order molecular and biological systems of disease. Fortunately, novel genetic technologies-scalable oligonucleotide synthesis, RNA sequencing, and CRISPR (clustered regularly interspaced short palindromic repeats)-have opened novel avenues to experimentally identify biologically significant variants en masse. Massively parallel reporter assays (MPRAs) are an especially versatile technique resulting from such innovations. MPRAs are powerful molecular genetics tools that can be used to screen thousands of untranscribed or untranslated sequences and their variants for functional effects in a single experiment. This approach, though underutilized in psychiatric genetics, has several useful features for the field. We review methods for assaying putatively functional genetic variants and regions, emphasizing MPRAs and the opportunities they hold for dissection of psychiatric polygenicity. We discuss literature applying functional assays in neurogenetics, highlighting strengths, caveats, and design considerations-especially regarding disease-relevant variables (cell type, neurodevelopment, and sex), and we ultimately propose applications of MPRA to both computational and experimental neurogenetics of polygenic disease risk.
神经精神表型早已被证实受遗传风险因素的影响,过去十年的遗传学研究直接证实了这一点,这些研究揭示了在疾病队列中富集的特定遗传变异。然而,最初认为一小部分基因将负责特定疾病的希望被证明是错误的。更复杂的现实情况是,特定疾病可能受到无数微小效应的非编码变异和/或罕见但严重的编码变异的影响,其中许多是新生的。非编码基因组序列——通常无法推断其分子功能——包含了这些变异的很大一部分,这给理解疾病的更高阶分子和生物学系统造成了巨大障碍。幸运的是,新的遗传技术——可扩展的寡核苷酸合成、RNA 测序和 CRISPR(成簇规律间隔短回文重复序列)——为大规模实验鉴定具有生物学意义的变异开辟了新途径。大规模平行报告基因分析(MPRA)就是源于此类创新的一种特别多功能的技术。MPRA 是一种强大的分子遗传学工具,可用于在单个实验中筛选数千个未转录或未翻译的序列及其变体的功能效应。这种方法虽然在精神遗传学中未得到充分利用,但对该领域有几个有用的特点。我们回顾了用于检测假定的功能遗传变异和区域的方法,重点介绍了 MPRA 及其在精神多基因分析中的应用机会。我们讨论了在神经遗传学中应用功能分析的文献,强调了其优势、注意事项和设计考虑因素——特别是与疾病相关的变量(细胞类型、神经发育和性别),最终我们提出将 MPRA 应用于多基因疾病风险的计算和实验神经遗传学。