{
  "abstract": "Background A new approach to estimating gene carrier rates was introduced in 2019, relying on the frequency of pathogenic/likely pathogenic (P/LP) variants from ClinVar in a large, population sequencing database (Genome Aggregation Database; gnomAD). These carrier rates formed the basis for the 2021 carrier screening recommendations from the American College of Medical Genetics and Genomics. Though this method allows for objective, data-driven risk assessment, its reliance on ClinVar-reported variants can cause it to underestimate carrier rates for rare conditions. This paper describes a method for incorporating contributions from gnomAD to compensate for variants not present in ClinVar.Methods 924 autosomal recessive genes were selected based on their relevance for carrier screening. Variants in these genes were selected from gnomAD based on evidence of pathogenicity either from ClinVar or from algorithmic prediction with the Ensembl variant effect predictor (VEP). Population-specific carrier rates were calculated.Results Of 47 469 selected variants from gnomAD, 31% were classified as P/LP in ClinVar, while 69% were unique contributions from gnomAD due to high VEP scores. VEP scores had the largest impact on carrier rates for rare disorders and resulted in an additional 117 genes with carrier rates ≥1/200, consistent with genes that were included in American College of Medical Genetics and Genomics Tier 3 screening recommendations.Conclusion Incorporating high VEP score variants in carrier rate estimates may help offset the potential underestimation that can occur when relying solely on matches from ClinVar. More genes may be eligible for consideration on carrier screening panels, and pretest and post-test risk analysis may be improved from these data.",
  "authors": [
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Hui Zhu"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Qiandong Zeng"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Natalia T Leach"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Stanley Letovsky"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Jill M Rafalko"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Binbin Huang"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Shreegowri Mattighatta Bhojanna"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Lakshmanan K Iyer"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Angela Leo Kenyon"
    },
    {
      "affiliations": [
        "LabCorp, Burlington, North Carolina, USA"
      ],
      "name": "Ruth A Heim"
    }
  ],
  "title": "Optimising carrier frequency estimation using population sequencing data and variant effect predictions",
  "uid": "1f6b7a22-a722-5991-8963-a25a7dc53cef"
}