{
  "abstract": "Objectives Whole-exome sequencing (WES) is increasingly used to investigate patients with suspected monogenic forms of Systemic Lupus Erythematosus and other systemic autoimmune rheumatic diseases (SARDs). With the increasing use of AI-assisted variant interpretation tools, there is a need to evaluate their real-world utility against expert-guided approaches. In a SARD cohort, we compared the performance of AI-assisted and expert-guided WES analysis strategies in identifying disease-relevant variants within lupus-causing genes.Methods We analysed WES data, generated with Illumina NovaSeq6000, from 120 patients with SARD disease and 20 healthy controls. Two analytic strategies were applied using the same platform, Emedgene: (1) an AI-based approach and (2) a semi-automated approach. The platform offers a fully automated phenotype-based variant analysis using AI, resulting in a list of prioritised variants for each case that are more likely to be causative, labelled as ‘most likely’. The ‘most likely’ variants were extracted in a spreadsheet for further analysis ( table 1). For the expert-guided approach, the Emedgene platform was used for the initial part of the analysis, as it allows the application of a preset of filters followed by the extraction of data for additional analysis (table 1).Results Before the analysis outside the platform, a total of 2,906 variants likely to be causative were identified with the Emedgene AI tool across all cases (mean 20.8 variants per case) compared to 8,391 variants (mean 59.93 variants per case) with the expert-guided method ( table 1). The majority of variants with both methods were loss-of-function variants of frameshift: 1,623 variants (77.4%) with the AI-guided variant analysis and 8,044 (95.9%) with the semi-automated approach. Following ClinVar curation, both analytic strategies identified a comparable number of variants (78 vs 56) in lupus-causing genes, including two likely monogenic cases (table 1). Notably, a known pathogenic variant in PEPD (c.819-1G>A, homozygous), previously identified in a patient with SLE, was detected using both approaches.Abstract PO:06:173 Table 1Flowchart summarising the steps and outcomes of Al-guided and expert-guided variant analysis in a Systemic Autoimmune Rheumatic Disease cohort (n = 140)Conclusions AI-assisted WES analysis offers efficiency and standardisation, which could enable the more widespread use of WES, yet expert review remains essential to ensure clinically meaningful interpretation in autoimmune genomics. Further studies in well-characterised monogenic cohorts will delineate the optimal integration of AI into autoimmune genomics.",
  "authors": [
    {
      "affiliations": [
        "Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK"
      ],
      "name": "Anastasia Madenidou"
    },
    {
      "affiliations": [
        "Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK"
      ],
      "name": "Sarah Dyball"
    },
    {
      "affiliations": [
        "Rheumatology Department, Royal Manchester Children’s Hospital, Manchester, UK"
      ],
      "name": "Alice Chieng"
    },
    {
      "affiliations": [
        "The Kellgren Centre for Rheumatology, Manchester Royal Infirmary, Manchester, UK"
      ],
      "name": "Ben Parker"
    },
    {
      "affiliations": [
        "Manchester Centre for Genomic Medicine, Saint Mary’s Hospital, Manchester, UK"
      ],
      "name": "Gillian I Rice"
    },
    {
      "affiliations": [
        "Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK"
      ],
      "name": "Ian N Bruce"
    }
  ],
  "title": "PO:06:173 Comparative analysis of AI-assisted and expert-guided whole-exome sequencing in lupus-causing genes",
  "uid": "95cd2d9f-fda4-5196-9c37-9654c22999d3"
}