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Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.ORCID-id: 0000-0001-8505-403x
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik.
Martin Luther Univ Halle Wittenberg, Inst Comp Sci, Halle, Germany.
Vise andre og tillknytning
2019 (engelsk)Inngår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, artikkel-id 2695Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Several Genome Wide Association Studies (GWAS) have reported variants associated to immune diseases. However, the identified variants are rarely the drivers of the associations and the molecular mechanisms behind the genetic contributions remain poorly understood. ChIP-seq data for TFs and histone modifications provide snapshots of protein-DNA interactions allowing the identification of heterozygous SNPs showing significant allele specific signals (AS-SNPs). AS-SNPs can change a TF binding site resulting in altered gene regulation and are primary candidates to explain associations observed in GWAS and expression studies. We identified 17,293 unique AS-SNPs across 7 lymphoblastoid cell lines. In this set of cell lines we interrogated 85% of common genetic variants in the population for potential regulatory effect and we identified 237 AS-SNPs associated to immune GWAS traits and 714 to gene expression in B cells. To elucidate possible regulatory mechanisms we integrated long-range 3D interactions data to identify putative target genes and motif predictions to identify TFs whose binding may be affected by AS-SNPs yielding a collection of 173 AS-SNPs associated to gene expression and 60 to B cell related traits. We present a systems strategy to find functional gene regulatory variants, the TFs that bind differentially between alleles and novel strategies to detect the regulated genes.

sted, utgiver, år, opplag, sider
NATURE PUBLISHING GROUP , 2019. Vol. 9, artikkel-id 2695
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-379258DOI: 10.1038/s41598-019-39633-0ISI: 000459571100059PubMedID: 30804403OAI: oai:DiVA.org:uu-379258DiVA, id: diva2:1296414
Forskningsfinansiär
Swedish Research Council, 78081Swedish National Infrastructure for Computing (SNIC)EXODIAB - Excellence of Diabetes Research in SwedenSwedish Diabetes AssociationErnfors FoundationSwedish Cancer Society, 160518German Research Foundation (DFG), GR-3526/1German Research Foundation (DFG), GR-3526/2Tilgjengelig fra: 2019-03-15 Laget: 2019-03-15 Sist oppdatert: 2019-10-07bibliografisk kontrollert
Inngår i avhandling
1. Predictive Healthcare: Cervical Cancer Screening Risk Stratification and Genetic Disease Markers
Åpne denne publikasjonen i ny fane eller vindu >>Predictive Healthcare: Cervical Cancer Screening Risk Stratification and Genetic Disease Markers
2019 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The use of Machine Learning is rapidly expanding into previously uncharted waters. In the medicine fields there are vast troves of data available from hospitals, biobanks and registries that now are being explored due to the tremendous advancement in computer science and its related hardware. The progress in genomic extraction and analysis has made it possible for any individual to know their own genetic code. Genetic testing has become affordable and can be used as a tool in treatment, discovery, and prognosis of individuals in a wide variety of healthcare settings. This thesis addresses three different approaches to-wards predictive healthcare and disease exploration; first, the exploita-tion of diagnostic data in Nordic screening programmes for the purpose of identifying individuals at high risk of developing cervical cancer so that their screening schedules can be intensified in search of new dis-ease developments. Second, the search for genomic markers that can be used either as additions to diagnostic data for risk predictions or as can-didates for further functional analysis. Third, the development of a Ma-chine Learning pipeline called ||-ROSETTA that can effectively process large datasets in the search for common patterns. Together, this provides a functional approach to predictive healthcare that allows intervention at early stages of disease development resulting in treatments with reduced health consequences at a lower financial burden

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2019. s. 62
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1862
Emneord
Bioinformatics, Cervical Cancer, Screening, Computer Science, Algorithmics, Machine Learning, Genetics, SNPs, Rough Sets
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
urn:nbn:se:uu:diva-394293 (URN)978-91-513-0768-8 (ISBN)
Disputas
2019-11-28, Room A1:111, BMC, Husargatan 3, Uppsala, 09:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-11-06 Laget: 2019-10-07 Sist oppdatert: 2019-11-27

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