Time-varying effects are common in genetic control of gestational duration

Abstract Preterm birth is a major burden to neonatal health worldwide, determined in part by genetics. Recently, studies discovered several genes associated with this trait or its continuous equivalent—gestational duration. However, their effect timing, and thus clinical importance, is still unclear. Here, we use genotyping data of 31 000 births from the Norwegian Mother, Father and Child cohort (MoBa) to investigate different models of the genetic pregnancy ‘clock’. We conduct genome-wide association studies using gestational duration or preterm birth, replicating known maternal associations and finding one new fetal variant. We illustrate how the interpretation of these results is complicated by the loss of power when dichotomizing. Using flexible survival models, we resolve this complexity and find that many of the known loci have time-varying effects, often stronger early in pregnancy. The overall polygenic control of birth timing appears to be shared in the term and preterm, but not very preterm, periods and exploratory results suggest involvement of the major histocompatibility complex genes in the latter. These findings show that the known gestational duration loci are clinically relevant and should help design further experimental studies.

[1]  T. Werge,et al.  ADuLT: An efficient and robust time-to-event GWAS , 2022, medRxiv.

[2]  D. Charnock-Jones,et al.  The human placenta exhibits a unique transcriptomic void , 2022, bioRxiv.

[3]  P. Royston,et al.  A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors. , 2022, Biostatistics.

[4]  Scott M. Williams,et al.  Genetic effects on the timing of parturition and links to fetal birth weight , 2022, Nature Genetics.

[5]  P. Sham,et al.  On the Transformation of Genetic Effect Size from Logit to Liability Scale , 2021, Behavior Genetics.

[6]  L. Muglia,et al.  Developing a theoretical evolutionary framework to solve the mystery of parturition initiation , 2020, eLife.

[7]  Jun Zhang,et al.  Incidence and trend of preterm birth in China, 1990–2016: a systematic review and meta-analysis , 2020, BMJ Open.

[8]  G. McVean,et al.  The impact of age on genetic risk for common diseases , 2021, PLoS genetics.

[9]  T. Bianco-Miotto,et al.  Temporal placental genome wide expression profiles reflect three phases of utero-placental blood flow during early to mid human gestation , 2020, medRxiv.

[10]  Hein Putter,et al.  A tutorial on frailty models , 2020, Statistical methods in medical research.

[11]  Liang He,et al.  Fast Algorithms for Conducting Large-Scale GWAS of Age-at-Onset Traits Using Cox Mixed-Effects Models , 2020, Genetics.

[12]  M. O’Donovan,et al.  Recent advances in the genetics of preterm birth , 2019, Annals of human genetics.

[13]  M. Rämet,et al.  Risk of spontaneous preterm birth and fetal growth associates with fetal SLIT2 , 2019, PLoS genetics.

[14]  J. Vilo,et al.  g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update) , 2019, Nucleic Acids Res..

[15]  Daniel E. Miller,et al.  Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 are associated with gestational duration , 2018, bioRxiv.

[16]  P. Lumbiganon,et al.  The global epidemiology of preterm birth. , 2018, Best practice & research. Clinical obstetrics & gynaecology.

[17]  Andreas Bender,et al.  A generalized additive model approach to time-to-event analysis , 2018 .

[18]  Daniel E. Miller,et al.  Genetic Associations With Gestational Duration and Spontaneous Preterm Birth , 2018, Obstetric Anesthesia Digest.

[19]  Michael P Snyder,et al.  A genome-wide association study identifies only two ancestry specific variants associated with spontaneous preterm birth , 2018, Scientific Reports.

[20]  B. Jacobsson,et al.  Time-Variant Genetic Effects as a Cause for Preterm Birth: Insights from a Population of Maternal Cousins in Sweden , 2017, G3: Genes, Genomes, Genetics.

[21]  P. Magnus,et al.  Cohort Profile Update: The Norwegian Mother and Child Cohort Study (MoBa). , 2016, International journal of epidemiology.

[22]  Carson C Chow,et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.

[23]  S. Fisher,et al.  Preterm labor: One syndrome, many causes , 2014, Science.

[24]  Ross M. Fraser,et al.  A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness , 2014, PLoS genetics.

[25]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[26]  Ann-Beth Moller,et al.  National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications , 2012, The Lancet.

[27]  N. Auger,et al.  Association between maternal comorbidity and preterm birth by severity and clinical subtype: retrospective cohort study , 2011, BMC pregnancy and childbirth.

[28]  Michael Boehnke,et al.  LocusZoom: regional visualization of genome-wide association scan results , 2010, Bioinform..

[29]  D. Baird The gestational timing of pregnancy loss: Adaptive strategy? , 2009, American journal of human biology : the official journal of the Human Biology Council.

[30]  D. Ragland,et al.  Dichotomizing Continuous Outcome Variables: Dependence of the Magnitude of Association and Statistical Power on the Cutpoint , 1992, Epidemiology.

[31]  A. Dreher Modeling Survival Data Extending The Cox Model , 2016 .