Journal
SCIENTIFIC REPORTS
Volume 6, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/srep29085
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Funding
- Jane and Aatos Erkko Foundation
- Paivikki and Sakari Sohlberg Foundation
- Research Funds of the University of Helsinki
- Government Special state subsidy for Health Sciences (EVO funding) at Helsinki and Uusimaa Hospital District
- Novo Nordisk Foundation
- Finnish Foundation for Pediatric Research
- Emil Aaltonen Foundation
- Sigrid Juselius Foundation
- Biocentrum Helsinki
- Doctoral Programme in Biomedicine (DPBM)
- Doctoral Programme in Clinical Research (KLTO)
- Research Foundation of the University of Helsinki
- Biomedicum Helsinki Foundation
- Swedish Research Council [K-2013-52X-22198-01-3]
- Academy of Finland
- Novo Nordisk Fonden [NNF12OC1016374, NNF15OC0016362] Funding Source: researchfish
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Pre-eclampsia is a common pregnancy disorder that is a major cause for maternal and perinatal mortality and morbidity. Variants predisposing to pre-eclampsia might be under negative evolutionary selection that is likely to keep their population frequencies low. We exome sequenced samples from a hundred Finnish pre-eclamptic women in pools of ten to screen for low-frequency, large-effect risk variants for pre-eclampsia. After filtering and additional genotyping steps, we selected 28 low-frequency missense, nonsense and splice site variants that were enriched in the pre-eclampsia pools compared to reference data, and genotyped the variants in 1353 pre-eclamptic and 699 non-pre-eclamptic women to test the association of them with pre-eclampsia and quantitative traits relevant for the disease. Genotypes from the SISu project (n = 6118 exome sequenced Finnish samples) were included in the binary trait association analysis as a population reference to increase statistical power. In these analyses, none of the variants tested reached genome-wide significance. In conclusion, the genetic risk for pre-eclampsia is likely complex even in a population isolate like Finland, and larger sample sizes will be necessary to detect risk variants.
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