Genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) associated with various traits and diseases, yet understanding the functional consequences of these variants remains challenging. In this study, we focused on Chromosome X, selecting 1,852 variants (577 sentinel variants and the rest as proxies) associated with red blood cell traits, primarily located in non-coding regions, along with 55 clinically relevant variants catalogued in ClinVar, all associated with haematological disorders. We employed a near-PAMless adenine base editor (ABE8e-SpRY), and D10A-SpRY without deaminase as a control, in a proliferation and differentiation screen within erythroid precursor HUDEP-2 cells. By including ~5 guide RNAs (gRNAs) installing or perturbing each variant positioned along the gRNA editing window, we designed a total of 11,036 gRNAs. We are also developing a comprehensive computational pipeline tailored for PAM-less CRISPR base editing screens to analyze the large datasets generated from these screens. For instance, our current pipeline is capable of handling gRNA mapping despite self-editing of the gRNA cassette along with characterization of per-gRNA editing outcomes by processing the gRNA sensor construct containing a target-mimicking “surrogate” sequence. Our screen successfully identified a set of GWAS variants on Chromosome X that significantly impacted erythroid precursor differentiation. By comparing ABE8e and D10A using MAGeCK and BEAN, we identified 90 deletion variants and 69 enrichment variants, as well as 300 depletion gRNAs and 282 enrichment gRNAs with FDR ≤0.01. Ongoing work seeks to experimentally validate causal variants and determine their molecular mechanisms, including based on single cell perturbation analysis. This study aims to enhance our understanding of how genetic variants influence erythroid differentiation cellular phenotypes. Additionally, the development of a robust computational pipeline for base editing screens will provide a valuable resource for future studies aimed at exploring the functional genomics of trait-associated variants.