My name is Layla Ahmed, and I am from Canada. I completed my HBSc in Neuroscience and Molecular Genetics at the University of Toronto, where I also pursued my MSc through the Institute of Medical Science. My Master’s research focused on developing a phenotype-driven genome analysis pipeline, integrating clinical phenotyping with advanced genomic interpretation to improve diagnostic yield. I am eager to expand on this work during my PhD, further exploring the intersection of computational genomics and rare disease biology. I look forward to contributing my expertise in genomic analysis and variant interpretation to this network.

 Outside of my research, I enjoy reading scifi and fantasy, archery, and jewlery making!

Interpreting non-coding and structural genomic variation in renal ciliopathies by ML approaches

Renal ciliopathies pose an important health burden. Short-read genome sequencing (srGS) is the upcoming standard in genetic diagnostics of rare disease. However, because the current interpretation is limited to the exome, 30-40% of patients with suspected genetic kidney disease, including renal ciliopathies, remain unsolved. Innovative approaches are required to interpret non-coding and structural genomic variation. We hypothesize that GS will increase diagnostic yield by 10-15% and improve treatment opportunities for patients with genetic kidney disease. We aim to: (1) Identify non-coding variation using srGS data from 30 patient-parent trios complemented by RNAseq; (2) Detect structural variation by performing long-read GS in 10 patients unsolved by srGS (3) Uncover novel therapeutic targets by ML-approaches that incorporate pathway and GO-term analyses for identified loci.