Abstract: TH-PO0650
TOGA: Identification of Accurate Allele-Specific Expression in Human Kidneys by a Novel Method That Addresses Overlapping Genes
Session Information
- Genetic Diseases of the Kidneys: Complex Kidney Traits
November 06, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Genetic Diseases of the Kidneys
- 1202 Genetic Diseases of the Kidneys: Complex Kidney Traits
Authors
- Sung, Junmo, Brigham and Women's Hospital, Boston, Massachusetts, United States
- Onuchic-Whitford, Ana C., Brigham and Women's Hospital, Boston, Massachusetts, United States
- Sakkas, Erotokritos, Boston Children's Hospital, Boston, Massachusetts, United States
- McNulty, Michelle, Boston Children's Hospital, Boston, Massachusetts, United States
- Greenberg, Anya, Harvard Medical School, Boston, Massachusetts, United States
- Yoon, Jihoon, Yonsei University College of Medicine, Seodaemun-gu, Korea (the Republic of)
- Badina, Sowmya, The Apollo University, Chittoor, AP, India
- Sampson, Matt G., Boston Children's Hospital, Boston, Massachusetts, United States
- Lee, Dongwon, Boston Children's Hospital, Boston, Massachusetts, United States
Group or Team Name
- Nephrotic Syndrome Study Network (NEPTUNE).
Background
Allele-specific expression (ASE) analysis identifies imbalanced expression of two gene copies, and is a robust method to detect cis-regulation and genomic imprinting. ASE causes cancer and brain disease, and our preliminary study of Nephrotic Syndrome Study Network (NEPTUNE) biopsies shows correlation of ASE proportions with renal outcomes. However, quantifying ASE relies on precision of multiple factors, including mapping of reads to the correct gene. Notably, ~66% of autosomal human genes overlap with other genes, and in current ASE pipelines, RNA reads aligning to heterozygous (het) variants are assigned to all genes in that locus, leading to false-positive findings. We thus developed TOGA (Transcript reassignment for Overlapping Genes in ASE), a robust pipeline that separates ASE signals from overlapping genes, resulting in more accurate ASE quantification.
Methods
TOGA reassigns reads overlapping multiple genes to their most likely gene of origin. First, genes with no expression by RSEM (TPM=0) are excluded. Then, expressed genes with no overlapping het variants are deemed resolved. The remaining genes are grouped by shared het variants, and for each group the read count from overlapping variants is distributed to each overlap gene weighted by its TPM and the variant's exon/intron status per gene. The redistributed read counts are used to determine whether each gene’s original count was accurately assigned.
Results
We applied TOGA to genome-wide ASE data from 222 glomerular NEPTUNE RNA-seq samples paired with WGS. A mean of 7,080 genes were analyzed per individual. After TOGA, 2,772 (39.2%) genes were found to share overlapping regions with at least one other gene. Of those, we resolved 2,522 (91.0%) genes. PLCG2 and an overlapping lncRNA ENSG00000289733 were initially top ASE genes, but after TOGA, only PLCG2 was found to have true ASE.
Conclusion
TOGA reduced false-positive ASE and, in most cases, elucidated which gene was responsible for ASE in overlapping regions. Our method is generalizable to ASE pipelines and can be readily applied to existing data for increased accuracy of ASE identification.
Funding
- NIDDK Support