Background: Liquid biopsy provides a non-invasive alternative to tissue biopsy by profiling the genetic mutations and biomarker characteristics of cell-free DNA (cfDNA) derived from primary and metastatic tumors found in patients’ peripheral blood. Previous studies have demonstrated significant variation in cfDNA and circulating tumor DNA (ctDNA) yield based on patient age, sex, primary tumor type, disease stage, and treatment regimen. Somatic mutations are a primary determinant of tumor phenotype, characterizing therapeutic sensitivity and resistance, as well as growth and metastasis rates. However, there has not been extensive investigation as to how presence of somatic mutations affect downstream cfDNA yield and ctDNA fraction. Methods: We screened 140,000 samples analyzed using Guardant360 liquid biopsy test for patient age, diagnosis, treatment, mutations, and associated cfDNA yield (from assay input of 3-10ml peripheral blood, 1 streck tube). We selected and grouped somatic mutations based on prevalence across the cohort. Estimated ctDNA fractions of cfDNA yields were made using the maximum mutant allele frequency (MAF) from among somatic mutations. Samples were organized by phenotype and cfDNA yield and ctDNA fraction was assessed across groups using the Wilcoxon and Kruskal-Wallis for bi-variate and multivariate comparisons, respectively. To understand impact of cfDNA yield variation on assay performance, group comparisons were further evaluated on samples binned by input cfDNA (< 5ng, 5-15ng, 15-30ng, > 30ng) representing cfDNA input requirement ranges current commercial liquid biopsy tests advertise. Results: We found significant yield variation in sample cfDNA and estimated ctDNA fraction between primary disease types (M_{yield diff} = 16.35ng, p < 0.0001), therapy histories (M_{yield diff} = 1.49ng, p < 0.001), and patient age groups (M_{yield diff} = 2.24ng, p < 0.0001), although this finding did not extend to comparisons within input yield bins. We found significant differences in cfDNA and ctDNA fraction across several common somatic mutations, adjusting for test multiplicity, with the greatest difference between TERT splice + and - patients (M_{yield diff} = 8.57ng, p < 0.0001). Further, we found significant and strong correlations (𝝆> 0.8, p < 0.001) between EGFR exon 19 deletions and EGFR L858R MAF and estimated ctDNA fraction, and moderate correlation between MAF and cfDNA (𝝆> 0.25, p < 0.001). Conclusions: Our results indicate that input cfDNA and ctDNA fraction varies by patient age, diagnosis and treatment, as well as with the presence of several common somatic mutations. The demonstrated correlation between somatic mutation presence and cfDNA yield offers a potential explanation for the broad variation of cfDNA yields within tumor and patient phenotypes, and may help inform blood collection strategies for patients with known somatic variants or diagnoses.