uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik. Uppsala University, Science for Life Laboratory, SciLifeLab.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.ORCID iD: 0000-0001-8505-403x
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
Martin Luther Univ Halle Wittenberg, Inst Comp Sci, Halle, Germany.
Show others and affiliations
2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 2695Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2019. Vol. 9, article id 2695
National Category
Medical Genetics
Identifiers
URN: urn:nbn:se:uu:diva-379258DOI: 10.1038/s41598-019-39633-0ISI: 000459571100059PubMedID: 30804403OAI: oai:DiVA.org:uu-379258DiVA, id: diva2:1296414
Funder
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-3526/2Available from: 2019-03-15 Created: 2019-03-15 Last updated: 2019-10-07Bibliographically approved
In thesis
1. Predictive Healthcare: Cervical Cancer Screening Risk Stratification and Genetic Disease Markers
Open this publication in new window or tab >>Predictive Healthcare: Cervical Cancer Screening Risk Stratification and Genetic Disease Markers
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
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

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 62
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1862
Keywords
Bioinformatics, Cervical Cancer, Screening, Computer Science, Algorithmics, Machine Learning, Genetics, SNPs, Rough Sets
National Category
Bioinformatics and Systems Biology
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-394293 (URN)978-91-513-0768-8 (ISBN)
Public defence
2019-11-28, Room A1:111, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2019-11-06 Created: 2019-10-07 Last updated: 2019-11-27

Open Access in DiVA

fulltext(3019 kB)92 downloads
File information
File name FULLTEXT01.pdfFile size 3019 kBChecksum SHA-512
c2161a0ce1a00953b6cc8882d2fe8a41269664a6742e9e377eec932cde204b51dc2aacc016f9fbdb0441ab7e0459dad7155b8569dafe6b83286b404f0f8605ed
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Authority records BETA

Cavalli, MarcoBaltzer, NicholasUmer, Husen MuhammadPan, GangWallerman, OlaKomorowski, JanWadelius, Claes

Search in DiVA

By author/editor
Cavalli, MarcoBaltzer, NicholasUmer, Husen MuhammadPan, GangWallerman, OlaSpalinskas, RapolasKomorowski, JanWadelius, Claes
By organisation
Medicinsk genetik och genomikScience for Life Laboratory, SciLifeLabComputational Biology and Bioinformatics
In the same journal
Scientific Reports
Medical Genetics

Search outside of DiVA

GoogleGoogle Scholar
Total: 92 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 204 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf