CRISPR/Cas9 systems are designed to make site- and sequence-specific alterations to the genomes of a wide variety of organisms by targeting with user-defined guide RNAs (gRNAs) and Cas9 nucleases. Their high degree of efficiency and ease of construction in performing genome editing have led to a revolution in life sciences and medicine. However, challenges in quantifying genome editing outcomes need to be addressed. In this work, we first utilized CRISPR/Cas9 mediated genome editing to introduce small indel (insertion and deletion) mutations in the nearby regions of pre-selected single nucleotide polymorphisms (SNPs) and evaluated the potential of this approach for fine mapping causal variants to identify expression quantitative trait loci (eQTL) associated with inflammatory bowel disease. Our approach, expression CROP-seq, consists of multiplexed CRISPR/Cas9 genome editing followed by single-cell RNA-seq to measure perturbations in transcript abundance. We found that two SNPs out of 67 total target loci significantly altered the expressions of CISD1 and PARK7 genes, respectively. The expression CROP-seq approach is relatively inexpensive and has the capability of large scale fine-mapping. We further addressed two unmet needs that are critical for therapeutic genome editing: the profiling of on-target mutagenesis patterns and the analysis of genome-wide off-target effects. CRISPR/Cas9 induced DNA double-strand breaks result in large deletions and complex genomic rearrangements, which cannot be detected by short-read Next Generation Sequencing (NGS). Therefore, we developed long-range PCR based assays to profile on-target CRISPR/Cas9 mutagenesis patterns. Our results confirmed that the current gold standard of short-range PCR based assessment is unable to detect alleles containing large deletions. We also found that the profiles of large deletions are gRNA- and locus-specific. We further demonstrated that individual-specific genome variants could change the target-site profiling of gRNA designs. Our evaluation of current off-target prediction algorithms demonstrated the lack of capability to identify novel individual-specific off-target sites created by SNPs. To address this issue, we developed a bioinformatics tool that accommodates personal genomes for off-target identification to facilitate personalized therapeutic genome editing. In summary, we have developed quantitative tools for profiling genome editing outcomes that can be used widely for biological studies and disease treatment.