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Improved power and precision with whole genome sequencing data in genome-wide association studies of inflammatory biomarkers
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.ORCID iD: 0000-0001-8061-3947
Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
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, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik.ORCID iD: 0000-0002-5056-9137
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 16844Article in journal (Refereed) Published
Abstract [en]

Genome-wide association studies (GWAS) have identified associations between thousands of common genetic variants and human traits. However, common variants usually explain a limited fraction of the heritability of a trait. A powerful resource for identifying trait-associated variants is whole genome sequencing (WGS) data in cohorts comprised of families or individuals from a limited geographical area. To evaluate the power of WGS compared to imputations, we performed GWAS on WGS data for 72 inflammatory biomarkers, in a kinship-structured cohort. When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of <5%. Our results suggest that, even when applying a GWAS approach, we gain power and precision using WGS data, presumably due to more accurate determination of genotypes. The lack of a comparable dataset for replication of our results is a limitation in our study. However, this further highlights that there is a need for more genetic epidemiological studies based on WGS data.

Place, publisher, year, edition, pages
2019. Vol. 9, article id 16844
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Medical Genetics
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URN: urn:nbn:se:uu:diva-397948DOI: 10.1038/s41598-019-53111-7ISI: 000496416000054PubMedID: 31727947OAI: oai:DiVA.org:uu-397948DiVA, id: diva2:1382311
Funder
Swedish National Infrastructure for Computing (SNIC), sens2016007Swedish Heart Lung FoundationAvailable from: 2020-01-02 Created: 2020-01-02 Last updated: 2020-01-02Bibliographically approved

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Höglund, JuliaRafati, NimaRask-Andersen, MathiasEnroth, StefanKarlsson, TorgnyEk, Weronica EJohansson, Åsa
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