Precision mapping of regulatory causal variants by expression CROPseq

ABSTRACT Genetic differences among individuals ultimately trace to single nucleotide polymorphisms, the majority of which affect traits, including disease susceptibility, by influencing the expression of nearby genes. Over the past decade, tens of thousands of loci of this type have been mapped to what are called credible intervals,

namely sets of anywhere from a handful to over one hundred polymorphisms that have similar statistical signals. The next step is to resolve the identities of the causal variant(s) within these credible intervals. Similarly, there is a parallel pressing need to validate that de novo and ultra-rare variants in the promoter of

a gene thought to contribute to a congenital abnormality actually disrupt expression of the gene. This project proposes a systematic effort to fine map causal variants for hundreds of genes that influence autoimmune and other immune conditions. It utilizes a combination of genome engineering and single cell genomics, in

a recently developed assay called expression CROP-seq, or eCROPseq. The idea is to knock out each of the variants in a credible interval with a pool of CRISPR-Cas9 guide RNAs, each targeting one SNP for microdeletion of mutation, and taken up by about 100 cells. The expression of the gene in those cells is then

compared with the expression in all other cells that receive different guide RNAs in the same experiment. Aim 1 is to use eCROPseq to identify causal variants in up to 600 credible intervals associated with 350 immune loci, measured in both a myeloid (HL60) and lymphoid (Jurkat) cell line. Aim 2 is to perform a similar

analysis of up to 500 rare variants in the promoter regions of these genes to evaluate whether new mutations can cause extreme levels of aberrant gene expression. With these results in hand, Aim 3 utilizes a more precise genome editing tool, search-and-replace CRISPR (also known as prime editing) to substitute one

allele for another instead of just evaluating mutations. Then Aim 4 asks whether the effects observed in the cell lines are also seen in primary T cells from eight different people, and whether the magnitude of effect differs among people. Collectively the proposed experiments will systematically map the causal regulatory

variants for hundreds of autoimmune genes, and establish a tool that should be readily adapted by even small labs to test the function of new genome-wide associations.