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Int J Epidemiol. 2012 Dec;41(6):1764-75. doi: 10.1093/ije/dys162. Epub 2012 Nov 5.

Genetic association studies in pre-eclampsia: systematic meta-analyses and field synopsis.

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1
Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, UK.

Abstract

BACKGROUND:

Pre-eclampsia is thought to have a polygenic basis, but the identification of susceptibility genes and the quantification of associated risks have been elusive owing to lack of replication from published genetic association studies.

OBJECTIVE:

To perform a systematic review and meta-analysis of genetic association studies to evaluate the evidence for the associations of various candidate genes with pre-eclampsia.

METHODS:

For inclusion, studies had to involve unrelated subjects and examine the associations between pre-eclampsia (excluding publications without a measurement of proteinuria) and any candidate variant. Authors were contacted to obtain unpublished data when necessary. A meta-analysis was conducted for all variants with three or more independent samples available. Summary odds ratios (ORs), 99% confidence intervals (CIs) and P-values were calculated using random effects models.

RESULTS:

Data from 192 genetic association studies met the selection criteria and were included in 25 independent meta-analyses. There was some evidence of association for F5 rs6025 (OR = 1.74; 99% CI 1.43-2.12), F2 rs1799963 (OR = 1.72; 99% CI 1.31-2.26), ACE rs4646994 (OR = 1.17; 99% CI 0.99-1.40), AGT rs699 (OR = 1.26; 99% CI 1.00-1.59) and AGTR1 rs5186 (OR = 1.22; 99% CI 0.96-1.56), but only the first two associations reached moderate epidemiological credibility. Possible bias resulting from small study size and poor reporting of individual studies were the most important factors affecting the reported associations.

CONCLUSION:

To date, candidate gene studies in pre-eclampsia have not robustly documented any associations with strong epidemiological credibility. Large-scale replication of the most promising associations, exhibited by two genetic variants, and incorporation of agnostic high-throughput data may improve our genetic knowledge base for this complex phenotype.

PMID:
23132613
DOI:
10.1093/ije/dys162
[Indexed for MEDLINE]
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