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Studies of liver tissue identify functional gene regulatory elements associated to gene expression, type 2 diabetes, and other metabolic 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, 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, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Medicinsk genetik och genomik.
Vise andre og tillknytning
2019 (engelsk)Inngår i: HUMAN GENOMICS, ISSN 1473-9542, Vol. 13, artikkel-id 20Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Background:

Genome-wide association studies (GWAS) of diseases and traits have found associations to gene regions but not the functional SNP or the gene mediating the effect. Difference in gene regulatory signals can be detected using chromatin immunoprecipitation and next-gen sequencing (ChIP-seq) of transcription factors or histone modifications by aligning reads to known polymorphisms in individual genomes. The aim was to identify such regulatory elements in the human liver to understand the genetics behind type 2 diabetes and metabolic diseases.

Methods:

The genome of liver tissue was sequenced using 10X Genomics technology to call polymorphic positions. Using ChIP-seq for two histone modifications, H3K4me3 and H3K27ac, and the transcription factor CTCF, and our established bioinformatics pipeline, we detected sites with significant difference in signal between the alleles.

Results:

We detected 2329 allele-specific SNPs (AS-SNPs) including 25 associated to GWAS SNPs linked to liver biology, e.g., 4 AS-SNPs at two type 2 diabetes loci. Two hundred ninety-two AS-SNPs were associated to liver gene expression in GTEx, and 134 AS-SNPs were located on 166 candidate functional motifs and most of them in EGR1-binding sites.

Conclusions:

This study provides a valuable collection of candidate liver regulatory elements for further experimental validation.

sted, utgiver, år, opplag, sider
2019. Vol. 13, artikkel-id 20
Emneord [en]
ChIP-seq, T2D, Regulatory SNPs
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-383513DOI: 10.1186/s40246-019-0204-8ISI: 000466335200001PubMedID: 31036066OAI: oai:DiVA.org:uu-383513DiVA, id: diva2:1316229
Tilgjengelig fra: 2019-05-16 Laget: 2019-05-16 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: 2019-11-06

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