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Abstract: PO1669

In Silico Prediction Performance for Type IV Collagen Variants

Session Information

Category: Genetic Diseases of the Kidneys

  • 1002 Genetic Diseases of the Kidneys: Non-Cystic

Authors

  • Shulman, Cole, Toronto General Hospital, Toronto, Ontario, Canada
  • Liang, Emerald, Toronto General Hospital, Toronto, Ontario, Canada
  • Kamura, Misato, Kumamoto Daigaku, Kumamoto, Kumamoto, Japan
  • Udwan, Khalil, Toronto General Hospital, Toronto, Ontario, Canada
  • Yao, Tony, Toronto General Hospital, Toronto, Ontario, Canada
  • Cattran, Daniel C., Toronto General Hospital, Toronto, Ontario, Canada
  • Reich, Heather N., Toronto General Hospital, Toronto, Ontario, Canada
  • Hladunewich, Michelle A., Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
  • Pei, York P., Toronto General Hospital, Toronto, Ontario, Canada
  • Savige, Judith A., The University of Melbourne, Melbourne, Victoria, Australia
  • Paterson, Andrew, Hospital for Sick Children, Toronto, Ontario, Canada
  • Suico, Mary Ann, Kumamoto Daigaku, Kumamoto, Kumamoto, Japan
  • Kai, Hirofumi, Kumamoto Daigaku, Kumamoto, Kumamoto, Japan
  • Barua, Moumita, Toronto General Hospital, Toronto, Ontario, Canada
Background

Advances in genomics technology and knowledge has led to increased sequencing for diagnosis, including in kidney disease. However, sequencing can reveal rare missense variants for which the relationship to disease is unclear. To address this need, in silico programs have been developed to assign variant categorization. Recently, pathogenic variants in COL4A3/A4/A5 have been reported to account for a significant minority of chronic kidney disease. Here we evaluate the performance of in silico programs for type IV collagen variants.

Methods

Rare COL4A3/A4/A5 missense variants were identified in an FSGS cohort, unscreened controls (gnomAD) and disease databases (ClinVAR, ARUP, LOVD). Comparisons between in silico predictions, disease database classifications and functional characterization were performed.

Results

In silico predictions and functional characterization classified all 9 definitely pathogenic COL4A3/A4/A5 variants in the FSGS cohort correctly. In silico predictions correctly classified the majority (93-97%) of definitely pathogenic COL4A3/A4/A5 variants in ClinVAR, ARUP and LOVD. However, a significant proportion of benign variants were predicted as pathogenic. Thirty-five percent of COL4A3/A4/A5 missense variants obtained from gnomAD were also predicted deleterious. In silico predictions tended to overestimate the effects of COL4A variants of uncertain significance (VUS) when compared to functional characterization.

Conclusion

Our results demonstrate that in silico programs are sensitive but not specific to assign COL4A3/A4/A5 variant pathogenicity, with misclassification of benign variants. Limitations of our computational work include overestimation of in silico program sensitivity given that they have likely been used in the categorization of variants labelled as pathogenic in disease databases; and the lack of clinical data to correlate rare variants in gnomAD controls.

Funding

  • Private Foundation Support