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In genetic disease assessment centers, DNA sequencing can produce results irrelevant to the genetic examination’s purpose. The American College of Medical Genetics and Genomics (ACMG) recommends evaluating and reporting 81 genes discovered using clinical genomic sequencing. While population studies on large cohorts can provide statistics on the prevalence of secondary findings (SFs), no studies have been published yet on large cohorts in Turkiye. We investigated ACMG SF by evaluating clinical exome sequencing data in 1600 individuals from different regions in Turkiye. We detected SF variants reported in ClinVar in 86 individuals (5.375%). Of the SFs, 30% were cardiovascular, 26% were cancer, 16% were neonatal metabolic disorders, and 28% were variants associated with various genetic diseases. In addition, we identified 212 different variants in 226 individuals and 45 different genes, which were not reported in ClinVar. When our results are compared with the Turkish National Genome and Bioinformatics Project database and studies in the literature, the studies vary in terms of participant characteristics, sequencing techniques, and versions of the ACMG SF list. Our findings highlight the importance of expanding and tailoring SF reporting guidelines in populations with high consanguinity and limited cohort-based data.

Structural information holds immense potential for pathogenicity prediction of missense variations, albeit structure-based pathogenicity classifiers are limited compared to their sequence-based counterparts due to the well-known gap between sequence and structure data. Leveraging the highly accurate protein structure prediction method, AlphaFold2 (AF2), we introduce AFFIPred, an ensemble machine learning classifier that combines established sequence and AF2-based structural characteristics to predict disease-causing missense variant pathogenicity. Based on the assessments on unseen datasets, AFFIPred reached a comparable level of performance with the state-of-the-art predictors such as AlphaMissense and Rhapsody. We also showed that the recruitment of AF2 structures that are full-length and represent the unbound states ensures more precise SASA calculations compared to the recruitment of experimental structures. Second, in line with the the completeness of the AF2 structures, their use provide a more comprehensive view of the structural characteristics of the missense variation datasets by capturing all variants. AFFIPred maintains high-level accuracy without the well-known limitations of structure-based pathogenicity classifiers, paving the way for the development of more sophisticated structure-based methods without PDB dependence. AFFIPred has predicted over 210 million variations of the human proteome, which are accessible at https://affipred.timucinlab.com/.