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Potpourri: An Epistasis Test Prioritization Algorithm via Diverse SNP Selection
KTH, School of Electrical Engineering and Computer Science (EECS).ORCID iD: 0000-0001-9703-6912
2020 (English)In: 24TH INTERNATIONAL CONFERENCE ON RESEARCH IN COMPUTATIONAL MOLECULAR BIOLOGY, Padova (Italy), May 10-13, 2020., 2020, Vol. 12074, p. 243-244Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Genome-wide association studies explain a fraction of the underlying heritability of genetic diseases. Investigating epistatic interactions between two or more loci help closing this gap. Unfortunately, sheer number of loci combinations to process and hypotheses to test prohibit the process both computationally and statistically. Epistasis test prioritization algorithms rank likely-epistatic SNP pairs to limit the number of tests. Yet, they still suffer from very low precision. It was shown in the literature that selecting SNPs that are individually correlated with the phenotype and also diverse with respect to genomic location, leads to better phenotype prediction due to genetic complementation. Here, we propose that an algorithm that pairs SNPs from such diverse regions and ranks them can improve prediction power. We propose an epistasis test prioritization algorithm which optimizes a submodular set function to select a diverse and complementary set of genomic regions that span the underlying genome. SNP pairs from these regions are then further ranked w.r.t. their co-coverage of the case cohort. We compare our algorithm with the state-of-the-art on three GWAS and show that (i) we substantially improve precision (from 0.003 to 0.652) while maintaining the significance of selected pairs, (ii) decrease the number of tests by 25 folds, and (iii) decrease the runtime by 4 folds. We also show that promoting SNPs from regulatory/coding regions improves the performance (up to 0.8). Potpourri is available at http:/ciceklab.cs.bilkent.edu.tr/potpourri.

Place, publisher, year, edition, pages
2020. Vol. 12074, p. 243-244
Keywords [en]
Artificial intelligence, Computer science, Computers, Molecular biology
National Category
Engineering and Technology
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-267332DOI: 10.1007/978-3-030-45257-5_22Scopus ID: 2-s2.0-85084249742ISBN: 978-3-030-45256-8 (print)ISBN: 978-3-030-45257-5 (electronic)OAI: oai:DiVA.org:kth-267332DiVA, id: diva2:1392265
Conference
RECOMB
Note

QC 20200518

Available from: 2020-02-06 Created: 2020-02-06 Last updated: 2020-05-18Bibliographically approved

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Çaylak, GizemÇiçek, A. Ercüment
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