High-throughput genome engineering

遗传学的一个主要目标是利用遗传信息来预测表型，这可能会对医学和我们对生物学的一般理解产生巨大影响。我们正在通过使用高通量基因组工程方法来产生和研究数千种遗传变异，从而为实现这一目标做出贡献。这将使我们能够理解我们研究的特定变异的后果，并了解变异后果背后的一般原理，以便扩展到更广阔的未经测试的变异空间。 为了完成高通量基因组工程，我们使用大规模并行寡核苷酸合成来生成数千个独特的酵母质粒池，每个质粒库携带一个引导RNA(GRNA)基因和一个成对的修复模板，编码感兴趣的特定突变。在Cas9使用gRNA直接切割DNA之后，修复模板在酵母基因组中引入突变。然后对编辑后的酵母细胞池进行感兴趣的表型选择。因此，所选择的池中独特的质粒的丰度报告了每个遗传变异对所研究的表型的影响。 我们还在开发新的高通量基因组工程方法。这些包括CRISPR技术的进步，通过扩大CRISPR可以针对的遗传结果的空间，以及高通量编辑的新应用，例如仔细部署CRISPR指示的缺失。 在过去的一年里，我们参与了一项关于犹太家庭身高遗传学的研究。这项研究旨在测试一种假设，即尽管人类身高的大部分遗传性来自常见的基因变异，但罕见的变异往往会产生巨大的表型后果，这种对遗传力的总体贡献被此类变异的稀有性掩盖了。通过专门研究家族内身高的遗传学，罕见的变异将以与常见变异相同的频率分离，因此在这些家族中发现的数量性状基因座(QTL)可能比对无关个体的全基因组关联研究产生更大的影响大小。事实上，这项研究发现了大效应QTL在被研究家庭中分离的证据，支持了最初的假设。

A major goal in genetics is to use genetic information to predict phenotype, which could have enormous impact in medicine and in our general understanding of biology. We are contributing towards this goal by using high-throughput genome engineering methods to generate and study thousands of genetic variants. This will allow us to understand the consequences of the specific variants we study, and also learn about the general principles underlying variant consequences for extension to the even broader space of untested variants. To accomplish high-throughput genome engineering, we use large-scale parallelized oligonucleotide synthesis to generate pools of thousands of unique yeast plasmids, each of which carries a guide RNA (gRNA) gene and a paired repair template encoding a specific mutation of interest. Following directed DNA cleavage by Cas9 using the gRNA, the repair template introduces the mutation in the yeast genome. The edited pool of yeast cells is then subjected to selection for a phenotype of interest. The abundances of the unique plasmids in the selected pool thus reports on the effect each genetic variant had on the phenotype under study. We are also developing new high-throughput genome engineering approaches. These include advancements to CRISPR technology, by expanding the space of genetic outcomes that CRISPR can target, as well as novel applications of high-throughput editing, such as careful deployment of CRISPR-directed deletions. In the past year, we contributed to a study of the genetics of human height in Jewish families. This study was designed to test the hypothesis that despite the majority of heritability in human height coming from common genetic variants, rare variants nonetheless might often have large phenotypic consequences, with this overall contribution to heritability being masked by such variants' rarity. By specifically looking at the genetics of height within families, rare variants will segregate at the same frequency as common variants, and thus quantitative trait loci (QTLs) uncovered in these families could have larger effect sizes than seen through genome-wide association studies on unrelated individuals. Indeed, this study found evidence of large-effect QTLs segregating in the families under study, supporting the initial hypothesis.