{"entity": "researcher", "timestamp": "2026-08-20T20:43:21.202Z", "family": "Ganapathy", "given": "Kaushik Ram", "initials": "KR", "orcid": "0000-0002-2439-9373", "affiliations": ["Department of Integrative Structural and Computational Biology, Scripps Research Institute, La Jolla, California 92037, USA."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/a3cbe7b6c96a4a11bcc196a4a6541fe7.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/a3cbe7b6c96a4a11bcc196a4a6541fe7"}}, "publications": [{"entity": "publication", "iuid": "5470ece56a8c4dbc9d952ed9283ec96a", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/5470ece56a8c4dbc9d952ed9283ec96a.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/5470ece56a8c4dbc9d952ed9283ec96a"}}, "title": "Improved Identification of Large-effect Rare Genetic Variants using Haplotype Aggregated Allele-specific Expression Data.", "authors": [{"family": "Ganapathy", "given": "Kaushik Ram", "initials": "KR", "orcid": "0000-0002-2439-9373", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a3cbe7b6c96a4a11bcc196a4a6541fe7.json"}}, {"family": "Broly", "given": "Martin", "initials": "M", "orcid": "0009-0005-4685-7063", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4b30cd1040b44cd1b66c34990b8c5c22.json"}}, {"family": "Silverstein", "given": "Sarah", "initials": "S", "orcid": "0000-0003-3428-3734", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2a1ba93ff73044b0ba5ea8ba189d4b32.json"}}, {"family": "Mendoza", "given": "Marcela", "initials": "M"}, {"family": "Song", "given": "Eric", "initials": "E"}, {"family": "Kotis", "given": "Bence", "initials": "B"}, {"family": "Hoffman", "given": "Paul", "initials": "P", "orcid": "0000-0002-7693-8957", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/d896695f142645a5883ecfa727c11982.json"}}, {"family": "PCGC Consortium", "given": "", "initials": ""}, {"family": "Torkamani", "given": "Ali", "initials": "A", "orcid": "0000-0003-0232-8053", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3ae21020e98743268624f071d67ff59e.json"}}, {"family": "Adams", "given": "David R", "initials": "DR", "orcid": "0000-0002-6660-1242", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/5d21d5473e6b4d479919dee26040fed5.json"}}, {"family": "Bonnemann", "given": "Carsten", "initials": "C", "orcid": "0000-0002-5930-2324", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a7c497515eaf440eb5ffa0e64b3b7c58.json"}}, {"family": "Lappalainen", "given": "Tuuli", "initials": "T", "orcid": "0000-0002-7746-8109", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e8401dbf3ffb4f35bcc8e16f4a04cd7c.json"}}, {"family": "Mohammadi", "given": "Pejman", "initials": "P", "orcid": "0000-0003-2291-9272", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/01b7fe26479a4dd0b9be2ae8fe615daf.json"}}], "type": "journal article", "published": "2025-12-18", "journal": {"title": "medRxiv", "issn-l": null}, "abstract": "Allele-specific expression (ASE) outlier detection is a powerful tool for identifying genes affected by large effect rare genetic regulatory variants but suffers from data sparsity and noisy signal in low-count genes. Genome phasing can be utilized to aggregate ASE signal along haplotypes to alleviate both sparsity and noise. Yet statistical tools for utilizing haplotype-level ASE data for rare variant interpretation are lacking. Here, we present ANEVA-h, to quantify the amount of genetic variation in gene expression from haplotype-level ASE data in a population, enabling more accurate and comprehensive detection of regulatory effects. We apply ANEVA-h to GTEx project data, along with a compatible dosage outlier test, to show an over 2-fold increase in the number of testable genes, reduction of spurious outlier calls, and improved enrichment for rare high-impact variants. In clinical cohorts of neuromuscular and congenital heart disease, it enhances gene prioritization and identifies candidate diagnoses missed by DROP-MAE and ANEVA. Finally, we analyze globally diverse populations to characterize the impact of ancestry background in reference and the test population. We provide tools and data necessary to facilitate integration of haplotype level ASE outlier testing in rare variant interpretation pipelines.", "doi": "10.64898/2025.12.16.25341855", "pmid": "41445643", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12723776"}, {"db": "pii", "key": "2025.12.16.25341855"}], "notes": [], "created": "2026-08-20T13:48:39.712Z", "modified": "2026-08-20T13:49:38.482Z"}, {"entity": "publication", "iuid": "0f8e9d629d52449692e50f2a940813cd", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/0f8e9d629d52449692e50f2a940813cd.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/0f8e9d629d52449692e50f2a940813cd"}}, "title": "Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes.", "authors": [{"family": "Rentzsch", "given": "Philipp", "initials": "P", "orcid": "0000-0002-0413-7974", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/768e0f165868444baa175d4958bec15c.json"}}, {"family": "Kollotzek", "given": "Aaron", "initials": "A", "orcid": "0009-0009-7142-4015", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/8e3e7078a9844e20a99d7e6fdaea697a.json"}}, {"family": "Ganapathy", "given": "Kaushik Ram", "initials": "KR", "orcid": "0000-0002-2439-9373", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a3cbe7b6c96a4a11bcc196a4a6541fe7.json"}}, {"family": "Mohammadi", "given": "Pejman", "initials": "P", "orcid": "0000-0003-4457-8552", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9f1472cdecfd46e6bde50cdacfa3cd0b.json"}}, {"family": "Lappalainen", "given": "Tuuli", "initials": "T", "orcid": "0000-0002-7746-8109", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e8401dbf3ffb4f35bcc8e16f4a04cd7c.json"}}], "type": "journal article", "published": "2025-10-01", "journal": {"title": "Genome Res", "issn": "1549-5469", "volume": "35", "issue": "10", "pages": "2316-2325", "issn-l": "1088-9051"}, "abstract": "Differential expression (DE) analysis is a widely used method for identifying genes that are functionally relevant for an observed phenotype or biological response. However, typical DE analysis includes selection of genes based on a threshold of fold change in expression under the implicit assumption that all genes are equally sensitive to dosage changes of their transcripts. This tends to favor highly variable genes over more constrained genes where even small changes in expression may be biologically relevant. To address this limitation, we have developed a method to recalibrate each gene's DE fold change based on genetic expression variance observed in the human population. The newly established metric ranks statistically differentially expressed genes, not by nominal change of expression, but by relative change in comparison to natural dosage variation for each gene. We apply our method to RNA sequencing data sets from in vitro stimulus response and neuropsychiatric disease experiments. Compared to the standard approach, our method adjusts the bias in discovery toward highly variable genes and enriches for pathways and biological processes related to metabolic and regulatory activity, indicating a prioritization of functionally relevant driver genes. Tissue-specific recalibration increases detection of known disease-relevant processes. Altogether, our method provides a novel view on DE and contributes toward bridging the existing gap between statistical and biological significance. We believe that this approach will simplify the identification of disease-causing molecular processes and enhance the discovery of therapeutic targets.", "doi": "10.1101/gr.280360.124", "pmid": "40962677", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12487816"}, {"db": "pii", "key": "gr.280360.124"}, {"db": "medline", "key": "9509184"}], "notes": [], "created": "2026-08-20T11:15:39.056Z", "modified": "2026-08-20T11:15:39.185Z"}]}