Racial Disparities in Genomic Data Quality in Cancer: Impact and Potential Mitigation

C.E. Credits: P.A.C.E. CE Florida CE
Speaker
  • Daniel P. Wickland, PhD

    Associate Consultant, Department of Quantitative Health Sciences, Assistant Professor of Biomedical Informatics, Mayo Clinic College of Medicine and Science
    BIOGRAPHY

Abstract

In the United States, cancer disproportionately impacts Black and African American individuals. Identifying genetic factors underlying cancer disparities has been an important research focus and requires data that are equitable in both quantity and quality across racial groups. It is widely recognized that DNA databases quantitatively underrepresent minorities. However, the differences in data quality between racial groups have not been well studied. We compared the qualities of germline and tumor exomes between ancestrally African and European patients in The Cancer Genome Atlas of 7 cancers with at least 50 self-reported Black patients in the context of sequencing depth, tumor purity, and qualities of germline variants and somatic mutations. Germline and tumor exomes from ancestrally African patients were sequenced at statistically significantly lower depth in 6 out of the 7 cancers. For 3 cancers, most ancestrally European exomes were sequenced in early sample batches at higher depth, whereas ancestrally African exomes were concentrated in later batches and sequenced at much lower depth. For the other 3 cancers, the reasons for lower sequencing coverage of ancestrally African exomes remain unknown. Furthermore, even when the sequencing depths were comparable, African exomes had disproportionally higher percentages of positions with insufficient coverage, likely because of the known European bias in the human reference genome that impacted exome capture kit design. Overall and positional lower sequencing depths of ancestrally African exomes in The Cancer Genome Atlas led to underdetection and lower quality of variants, highlighting the need to consider epidemiological factors for future genomics studies.

Learning Objectives:

1. Summarize the primary explanation for higher total read depth among the TCGA patients with European ancestry compared to those with African ancestry.

2. Define how lower coverage impact germline variants discovery, particularly among patients with African ancestry.

3. Explain how besides lower total read depth, what could have contributed to the enrichment of low-coverage positions among African exomes.