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Human Genetics

, Volume 131, Issue 3, pp 341–351 | Cite as

Associations between gene polymorphisms in fatty acid metabolism pathway and preterm delivery in a US urban black population

  • Xin Liu
  • Guoying Wang
  • Xiumei Hong
  • Hui-Ju Tsai
  • Rong Liu
  • Shanchun Zhang
  • Hongjian Wang
  • Colleen Pearson
  • Katherin Ortiz
  • Deli Wang
  • Emmet Hirsch
  • Barry Zuckerman
  • Xiaobin Wang
Original Investigation

Abstract

There is increasing evidence suggesting that higher intakes of fish or n-3 polyunsaturated fatty acids supplements may decrease the risk of preterm delivery (PTD). We hypothesized that genetic variants of the enzymes critical to fatty acids biosynthesis and metabolism may be associated with PTD. We genotyped 231 potentially functional single nucleotide polymorphisms (SNPs) and tagSNPs in 9 genes (FADS1, FADS2, PTGS1, PTGS2, ALOX5, ALOX5AP, PTGES, PTGES2, and PTGES3) among 1,110 black mothers, including 542 mothers who delivered preterm (<37 weeks gestation) and 568 mothers who delivered full-term babies (≥37 weeks gestation) at Boston Medical Center. After excluding SNPs that are in complete linkage disequilibrium or have lower minor allele frequency (<1%) or call rate (<90%), we examined the association of 206 SNPs with PTD using multiple logistic regression models. We also imputed 190 HapMap SNPs via program MACH and examined their associations with PTD. Finally, we explored gene-level and pathway-level associations with PTD using the adaptive rank truncated product (ARTP) methods. A total of 21 SNPs were associated with PTD (p value ranging from 0.003 to 0.05), including 3 imputed SNPs. Gene-level ARTP statistics indicated that the gene PTGES2 was significantly associated with PTD with a gene-based p value equal to 0.01. No pathway-based association was found. In this large and comprehensive candidate gene study, we found a modest association of genes in fatty acid metabolism pathway with PTD. Further investigation of these gene polymorphisms jointly with fatty acid measures and other genetic factors would help better understand the pathogenesis of PTD.

Keywords

Complete Linkage Disequilibrium Black Mother Impute SNPs Infant Gender Fatty Acid Metabolism Pathway 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank all of the participating mothers and their families enrolled in the Boston Medical Center Cohort study; their efforts have helped further our understanding of the causes of preterm delivery. We thank the nursing staff of Labor and Delivery at Boston Medical Center for their continuous support and assistance to the study, as well as Lingling Fu for data management and Ann Ramsay for administrative support. We thank Dr. Kai Yu from the NCI for his helpful instruction on ARTP methods, and also Dr. Fuhong He from the Beijing Institute of Genomics, Chinese Academy of Sciences for her help on bioinformatics analyses. We thank Tami R. Bartell for English editing. The study was supported in part by grants from the National Institutes of Health (R01 HD041702, R01ES11682, R21ES11666, and R21HD066471), and March of Dimes Birth Defects Foundation (20-FY98-0701, 20-FY02-56).

Conflict of interest

None of the authors have a conflict of interest pertaining to this work.

Supplementary material

439_2011_1079_MOESM1_ESM.doc (1.3 mb)
Supplementary material 1 (DOC 1361 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Xin Liu
    • 1
  • Guoying Wang
    • 1
  • Xiumei Hong
    • 1
  • Hui-Ju Tsai
    • 1
    • 2
  • Rong Liu
    • 1
  • Shanchun Zhang
    • 1
    • 3
  • Hongjian Wang
    • 1
  • Colleen Pearson
    • 4
  • Katherin Ortiz
    • 4
  • Deli Wang
    • 5
  • Emmet Hirsch
    • 6
    • 7
  • Barry Zuckerman
    • 4
  • Xiaobin Wang
    • 1
  1. 1.Mary Ann and J. Milburn Smith Child Health Research Program, Children’s Memorial Hospital, Children’s Memorial Research Center, Department of Pediatrics, Feinberg School of Medicine Northwestern UniversityChicagoUSA
  2. 2.Division of Biostatistics and BioinformaticsInstitute of Population Health Sciences, National Health Research InstitutesZhunanTaiwan
  3. 3.Department of Epidemiology and Health Statistics, School of MedicineZhejiang UniversityHangzhouChina
  4. 4.Department of Pediatrics, Boston Medical CenterBoston University School of Medicine BostonUSA
  5. 5.Biostatistics Research Core, Children’s Memorial Research CenterFeinberg School of Medicine, Northwestern UniversityChicagoUSA
  6. 6.Department of Obstetrics and GynecologyNorthShore University HealthSystemEvanstonUSA
  7. 7.Department of Obstetrics and Gynecology, Pritzker School of MedicineUniversity of ChicagoChicagoUSA

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