A novel statistical method for interpreting the pathogenicity of rare variants



To achieve the ultimate goal of personalized treatment of patients, accurate molecular diagnosis and precise interpretation of the impact of genetic variants on gene function is essential. With sequencing cost becoming increasingly affordable, the accurate distinguishing of benign from pathogenic variants becomes the major bottleneck. Although large normal population sequence databases have become a key resource in filtering benign variants, they are not effective at filtering extremely rare variants.


To address this challenge, we developed a novel statistical test by combining sequencing data from a patient cohort with a normal control population database. By comparing the expected and observed allele frequency in the patient cohort, variants that are likely benign can be identified.


The performance of this new method is evaluated on both simulated and real data sets coupled with experimental validation. As a result, we demonstrate this new test is well powered to identify benign variants, and is particularly effective for variants with low frequency in the normal population.


Overall, as a general test that can be applied to any type of variants in the context of all Mendelian diseases, our work provides a general framework for filtering benign variants with very low population allele frequency.

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Fig. 1: The simulation analysis of the test power.
Fig. 2: The simulation analysis of sampling bias.
Fig. 3: The distribution of population allele frequency (AF) of the Human Gene Mutation Database (HGMD) variants in three genes.
Fig. 4: The distribution of other variant prediction scores and ClinVar assignment for the Human Gene Mutation Database (HGMD) variants in ABCA4, LRP5, and USH2A.
Fig. 5: The luciferase reporter assay of LRP5 variants.

Code availability

Our code is available at https://github.com/fe4960/Binomial_test.


  1. 1.

    Bamshad MJ, Ng SB, Bigham AW, et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet. 2011;12:745–755.

    CAS  Article  Google Scholar 

  2. 2.

    Whiffin N, Minikel E, Walsh R, et al. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet Med. 2017;19:1151–1158.

    Article  Google Scholar 

  3. 3.

    Clarke GM, Anderson CA, Pettersson FH, et al. Basic statistical analysis in genetic case-control studies. Nat Protoc. 2011;6:121–133.

    CAS  Article  Google Scholar 

  4. 4.

    Michaelides M, Hunt DM, Moore AT. The genetics of inherited macular dystrophies. J Med Genet. 2003;40:641–650.

    CAS  Article  Google Scholar 

  5. 5.

    Zernant J, Xie YA, Ayuso C, et al. Analysis of the ABCA4 genomic locus in Stargardt disease. Hum Mol Genet. 2014;23:6797–6806.

    CAS  Article  Google Scholar 

  6. 6.

    Thiadens AA, Phan TM, Zekveld-Vroon RC, et al. Clinical course, genetic etiology, and visual outcome in cone and cone-rod dystrophy. Ophthalmology. 2012;119:819–826.

    Article  Google Scholar 

  7. 7.

    Downes SM, Packham E, Cranston T, et al. Detection rate of pathogenic mutations in ABCA4 using direct sequencing: clinical and research implications. Arch Ophthalmol. 2012;130:1486–1490.

    Article  Google Scholar 

  8. 8.

    Sun H, Smallwood PM, Nathans J. Biochemical defects in ABCR protein variants associated with human retinopathies. Nat Genet. 2000;26:242–246.

    CAS  Article  Google Scholar 

  9. 9.

    Downs K, Zacks DN, Caruso R, et al. Molecular testing for hereditary retinal disease as part of clinical care. Arch Ophthalmol. 2007;125:252–258.

    CAS  Article  Google Scholar 

  10. 10.

    Simonelli F, Testa F, de Crecchio G, et al. New ABCR mutations and clinical phenotype in Italian patients with Stargardt disease. Invest Ophthalmol Vis Sci. 2000;41:892–897.

    CAS  PubMed  Google Scholar 

  11. 11.

    Eisenberger T, Neuhaus C, Khan AO, et al. Increasing the yield in targeted next-generation sequencing by implicating CNV analysis, noncoding exons and the overall variant load: the example of retinal dystrophies. PLoS One. 2013;8:e78496.

    CAS  Article  Google Scholar 

  12. 12.

    Rosenberg T, Klie F, Garred P, Schwartz M. N965S is a common ABCA4 variant in Stargardt-related retinopathies in the Danish population. Mol Vis. 2007;13:1962–1969.

    CAS  PubMed  Google Scholar 

  13. 13.

    Wiszniewski W, Zaremba CM, Yatsenko AN, et al. ABCA4 mutations causing mislocalization are found frequently in patients with severe retinal dystrophies. Hum Mol Genet. 2005;14:2769–2778.

    CAS  Article  Google Scholar 

  14. 14.

    Burke TR, Tsang SH, Zernant J, et al. Familial discordance in Stargardt disease. Mol Vis. 2012;18:227–233.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Testa F, Rossi S, Sodi A, et al. Correlation between photoreceptor layer integrity and visual function in patients with Stargardt disease: implications for gene therapy. Invest Ophthalmol Vis Sci. 2012;53:4409–4415.

    CAS  Article  Google Scholar 

  16. 16.

    van Huet RA, Pierrache LH, Meester-Smoor MA, et al. The efficacy of microarray screening for autosomal recessive retinitis pigmentosa in routine clinical practice. Mol Vis. 2015;21:461–476.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Zernant J, Schubert C, Im KM, et al. Analysis of the ABCA4 gene by next-generation sequencing. Invest Ophthalmol Vis Sci. 2011;52:8479–8487.

    CAS  Article  Google Scholar 

  18. 18.

    Suarez T, Biswas SB, Biswas EE. Biochemical defects in retina-specific human ATP binding cassette transporter nucleotide binding domain 1 mutants associated with macular degeneration. J Biol Chem. 2002;277:21759–21767.

    CAS  Article  Google Scholar 

  19. 19.

    Biswas-Fiss EE, Affet S, Ha M, Biswas SB. Retinoid binding properties of nucleotide binding domain 1 of the Stargardt disease-associated ATP binding cassette (ABC) transporter, ABCA4. J Biol Chem. 2012;287:44097–44107.

    CAS  Article  Google Scholar 

  20. 20.

    Huang L, Mao Y, Yang J, et al. Mutation screening of the USH2A gene in retinitis pigmentosa and USHER patients in a Han Chinese population. Eye (Lond). 2018;32:1608–1614.

    CAS  Article  Google Scholar 

  21. 21.

    Sandberg MA, Rosner B, Weigel-DiFranco C, et al. Disease course in patients with autosomal recessive retinitis pigmentosa due to the USH2A gene. Invest Ophthalmol Vis Sci. 2008;49:5532–5539.

    Article  Google Scholar 

  22. 22.

    Glockle N, Kohl S, Mohr J, et al. Panel-based next generation sequencing as a reliable and efficient technique to detect mutations in unselected patients with retinal dystrophies. Eur J Hum Genet. 2014;22:99–104.

    Article  Google Scholar 

  23. 23.

    Shearer AE, Eppsteiner RW, Booth KT, et al. Utilizing ethnic-specific differences in minor allele frequency to recategorize reported pathogenic deafness variants. Am J Hum Genet. 2014;95:445–453.

    CAS  Article  Google Scholar 

  24. 24.

    Garcia-Garcia G, Aparisi MJ, Jaijo T, et al. Mutational screening of the USH2A gene in Spanish USH patients reveals 23 novel pathogenic mutations. Orphanet J Rare Dis. 2011;6:65.

    Article  Google Scholar 

  25. 25.

    Tajiguli A, Xu M, Fu Q, et al. Next-generation sequencing-based molecular diagnosis of 12 inherited retinal disease probands of Uyghur ethnicity. Sci Rep. 2016;6:21384.

    CAS  Article  Google Scholar 

  26. 26.

    Li LH, Li N, Zhao JY, et al. Findings of perinatal ocular examination performed on 3573, healthy full-term newborns. Br J Ophthalmol. 2013;97:588–591.

    Article  Google Scholar 

  27. 27.

    Salvo J, Lyubasyuk V, Xu M, et al. Next-generation sequencing and novel variant determination in a cohort of 92 familial exudative vitreoretinopathy patients. Invest Ophthalmol Vis Sci. 2015;56:1937–1946.

    CAS  Article  Google Scholar 

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We are grateful to the lab of David Moore at Baylor College of Medicine for providing L cells for the functional assays of LRP5 variants. We thank the computing cluster server in the Molecular and Human Genetics Department at Baylor College of Medicine for providing the computing resource. This work was supported by grants from the National Eye Institute (grant numbers R01EY022356, R01EY018571, EY002520 to R.C.); Retinal Research Foundation [to RC]; National Institutes of Health shared instrument grant (grant number S10OD023469 to R.C.); and the Competitive Renewal Grant of Knights Templar Eye Foundation (to J.W.).

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Correspondence to Rui Chen PhD.

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Wang, J., Liu, H., Bertrand, R.E. et al. A novel statistical method for interpreting the pathogenicity of rare variants. Genet Med (2020). https://doi.org/10.1038/s41436-020-00948-3

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  • variant interpretation
  • allele frequency
  • Mendelian diseases
  • statistical test
  • clinical genomics