Identifying rare, medically relevant variation via population-based genomic screening in Alabama: opportunities and pitfalls



To evaluate the effectiveness and specificity of population-based genomic screening in Alabama.


The Alabama Genomic Health Initiative (AGHI) has enrolled and evaluated 5369 participants for the presence of pathogenic/likely pathogenic (P/LP) variants using the Illumina Global Screening Array (GSA), with validation of all P/LP variants via Sanger sequencing in a CLIA-certified laboratory before return of results.


Among 131 variants identified by the GSA that were evaluated by Sanger sequencing, 67 (51%) were false positives (FP). For 39 of the 67 FP variants, a benign/likely benign variant was present at or near the targeted P/LP variant. Variants detected within African American individuals were significantly enriched for FPs, likely due to a higher rate of nontargeted alternative alleles close to array-targeted P/LP variants.


In AGHI, we have implemented an array-based process to screen for highly penetrant genetic variants in actionable disease genes. We demonstrate the need for clinical validation of array-identified variants in direct-to-consumer or population testing, especially for diverse populations.

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Fig. 1
Fig. 2: Differences in genetic variation types between true and false positive variants.
Fig. 3: False positive (FP) findings in the context of race/ethnicity.


  1. 1.

    Clayton EW. Be ready to talk with parents about direct-to-consumer genetic testing. JAMA Pediatr. 2020;174:117–118.

    PubMed  Google Scholar 

  2. 2.

    Regalado A. More than 26 million people have taken an at-home ancestry test. 2019. Accessed 15 June 2020.

  3. 3.

    Jonas MC, et al. Physician experience with direct-to-consumer genetic testing in Kaiser Permanente. J Pers Med. 2019;9:47.

    PubMed Central  Google Scholar 

  4. 4.

    Moscarello T, et al. Direct-to-consumer raw genetic data and third-party interpretation services: more burden than bargain? Genet Med. 2019;21:539–541.

    CAS  PubMed  Google Scholar 

  5. 5.

    Wang C, et al. Consumer use and response to online third-party raw DNA interpretation services. Mol Genet Genomic Med. 2018;6:35–43.

    PubMed  Google Scholar 

  6. 6.

    All of Us Research Program Investigators, et al. The “All of Us” research program. N Engl J Med. 2019;381:668–676.

    Google Scholar 

  7. 7.

    Steemers FJ, et al. Whole-genome genotyping with the single-base extension assay. Nat Methods. 2006;3:31–33.

    CAS  PubMed  Google Scholar 

  8. 8.

    Tandy-Connor S, et al. False-positive results released by direct-to-consumer genetic tests highlight the importance of clinical confirmation testing for appropriate patient care. Genet Med. 2018;20:1515–1521.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Tennessen JA, et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science. 2012;337:64–69.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Manly KF, Nettleton D, Hwang JT. Genomics, prior probability, and statistical tests of multiple hypotheses. Genome Res. 2004;14:997–1001.

    CAS  PubMed  Google Scholar 

  11. 11.

    Lippi G, Favaloro EJ, Plebani M. Direct-to-consumer testing: more risks than opportunities. Int J Clin Pract. 2011;65:1221–1229.

    CAS  PubMed  Google Scholar 

  12. 12.

    Landrum MJ, et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016;44:D862–D868.

    CAS  PubMed  Google Scholar 

  13. 13.

    Landrum MJ, et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46:D1062–D1067.

    CAS  PubMed  Google Scholar 

  14. 14.

    Kalia SS, et al. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2.0): a policy statement of the American College of Medical Genetics and Genomics. Genet Med. 2017;19:249–255.

    PubMed  Google Scholar 

  15. 15.

    Manichaikul A, et al. Robust relationship inference in genome-wide association studies. Bioinformatics. 2010;26:2867–2873.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Lek M, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285–291.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    1000 Genomes Project Consortium, et al. An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65.

    Google Scholar 

  18. 18.

    Kircher M, et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–315.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Richards S, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405–424.

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    100 Genomes Project Consortium, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.

    Article  Google Scholar 

  21. 21.

    Health Resources and Services Administration. Data. 2020.

  22. 22.

    US Census. Alabama census data. 2019.

  23. 23.

    Hensley Alford S, et al. Participation in genetic testing research varies by social group. Public Health Genomics. 2011;14:85–93.

    Article  Google Scholar 

  24. 24.

    “All of Us” Research Hub. Data browser. 2020.

  25. 25.

    Amendola LM, et al. Actionable exomic incidental findings in 6503 participants: challenges of variant classification. Genome Res. 2015;25:305–315.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Thompson ML, et al. Genomic sequencing identifies secondary findings in a cohort of parent study participants. Genet Med. 2018;20:1635–1643.

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Green RC, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med. 2013;15:565–574.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Bien SA, et al. The future of genomic studies must be globally representative: perspectives from PAGE. Annu Rev Genomics Hum Genet. 2019;20:181–200.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Manrai AK, et al. Genetic misdiagnoses and the potential for health disparities. N Engl J Med. 2016;375:655–665.

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Hoffmann TJ, et al. Next generation genome-wide association tool: design and coverage of a high-throughput European-optimized SNP array. Genomics. 2011;98:79–89.

    CAS  PubMed  PubMed Central  Google Scholar 

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We thank all AGHI participants for their contributions to this study. This study was conducted at the University of Alabama at Birmingham and the HudsonAlpha Institute for Biotechnology and funded by the state of Alabama.

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Correspondence to Gregory M. Cooper PhD.

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B.R.K. discloses a potential conflict of interest as a member of the medical advisory boards of AstraZeneca, SpringWorks, and Genome Medical. The other authors declare no conflicts of interest.

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Bowling, K.M., Thompson, M.L., Gray, D.E. et al. Identifying rare, medically relevant variation via population-based genomic screening in Alabama: opportunities and pitfalls. Genet Med (2020).

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  • population screening
  • genotyping array
  • false positive
  • clinically actionable
  • diverse population