计算生物学
生物
选择(遗传算法)
背景(考古学)
否定选择
遗传学
基因组编辑
基因组
基因
计算机科学
机器学习
古生物学
作者
Michael Herger,Christina M. Kajba,Megan Buckley,Ana Cunha,Molly Strom,Gregory M. Findlay
标识
DOI:10.1101/2024.04.01.587366
摘要
ABSTRACT Understanding the effects of rare genetic variants remains challenging, both in coding and non-coding regions. While multiplexed assays of variant effect (MAVEs) have enabled scalable functional assessment of variants, established MAVEs are limited by either exogenous expression of variants or constraints of genome editing. Here, we introduce a pooled prime editing (PE) platform in haploid human cells to scalably assay variants in their endogenous context. We first optimized delivery of variants to HAP1 cells, defining optimal pegRNA designs and establishing a co-selection strategy for improved efficiency. We characterize our platform in the context of negative selection by testing over 7,500 pegRNAs targeting SMARCB1 for editing activity and observing depletion of highly active pegRNAs installing loss-of-function variants. We next assess variants in MLH1 via 6-thioguanine selection, assaying 65.3% of all possible SNVs in a 200-bp region spanning exon 10 and distinguishing LoF variants with high accuracy. Lastly, we assay 362 non-coding MLH1 variants across a 60 kb region in a single experiment, identifying pathogenic variants acting via multiple mechanisms with high specificity. Our analyses detail how filtering for highly active pegRNAs can facilitate both positive and negative selection screens. Accordingly, our platform promises to enable highly scalable functional assessment of human variants.
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