Recent advances in the genetics of preterm birth

Wadon, Megan E.; Modi, Neena; Wong, Hilary; Thapar, Anita; O’Donovan, Michael

doi:10.1111/ahg.12373

Cited by 34 publications

(26 citation statements)

References 55 publications

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“…Thus, the effect size is expected to be weak for common alleles that influence liability to a phenotype. 82 In addition, GWAS studies on preterm birth have so far been underpowered. However, the finding of preterm birth-associated alleles in these GWAS studies suggests that further increasing the sample size in GWAS will reveal new loci and define the genetic pathways for birth timing and sPTB.…”

Section: Genome-wide Association Studiesmentioning

confidence: 99%

“…The association of preterm birth with neonatal morbidity and mortality makes sPTB likely subjected to negative selection. Thus, the effect size is expected to be weak for common alleles that influence liability to a phenotype 82 . In addition, GWAS studies on preterm birth have so far been underpowered.…”

Section: Family‐based and Epidemiological Evidence For Preterm Birthmentioning

confidence: 99%

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Genetics, epigenetics, and transcriptomics of preterm birth

Jain

Monangi

Zhang

et al. 2022

American J Rep Immunol

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Preterm birth contributes significantly to neonatal mortality and morbidity. Despite its global significance, there has only been limited progress in preventing preterm birth. Spontaneous preterm birth (sPTB) results from a wide variety of pathological processes. Although many non‐genetic risk factors influence the timing of gestation and labor, compelling evidence supports the role of substantial genetic and epigenetic influences and their interactions with the environment contributing to sPTB. To investigate a common and complex disease such as sPTB, various approaches such as genome‐wide association studies, whole‐exome sequencing, transcriptomics, and integrative approaches combining these with other ‘omics studies have been used. However, many of these studies were typically small or focused on a single ethnicity or geographic region with limited data, particularly in populations at high risk for sPTB, or lacked a robust replication. These studies found many genes involved in the inflammation and immunity‐related pathways that may affect sPTB. Recent studies also suggest the role of epigenetic modifications of gene expression by the environmental signals as a potential contributor to the risk of sPTB. Future genetic studies of sPTB should continue to consider the contributions of both maternal and fetal genomes as well as their interaction with the environment.

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Section: Genome-wide Association Studiesmentioning

confidence: 99%

Section: Family‐based and Epidemiological Evidence For Preterm Birthmentioning

confidence: 99%

Genetics, epigenetics, and transcriptomics of preterm birth

Jain

Monangi

Zhang

et al. 2022

American J Rep Immunol

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“…The copyright holder for this preprint this version posted July 5, 2022. ; https://doi.org/10.1101/2022.06. 15.22276464 doi: medRxiv preprint underlying variation of gestational durations remain unclear, primarily because insufficient maternal or fetal genotypes with widespread gestational ages have been collected (13). Recently, Preimplantation Genetic Testing (PGT) with trophectoderm biopsy for embryo aneuploidy screening has become a common practice in in vitro fertilization (14,15), and poses as an expectant source of genotypes for GWAS.…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Ultra-low coverage genome-wide association study – insights into gestational age using 17,844 embryo samples with preimplantation genetic testing

Yan

et al. 2022

Preprint

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Background: Very low coverage (0.1 to 1x) whole genome sequencing (WGS) has become a promising and affordable approach to discover genomic variants of human populations for Genome-Wide Association Study (GWAS). To support genetic screening using Preimplantation Genetic Testing (PGT) in a large population, the sequencing coverage goes below 0.1x to an ultra-low level. However, its feasibility and effectiveness for GWAS remains undetermined. Methods: We devised a pipeline to process ultra-low coverage WGS data and benchmarked the accuracy of genotype imputation at the combination of different coverages below 0.1x and sample sizes from 2,000 to 16,000, using 17,844 embryo PGT with approximately 0.04x average coverage and the standard Chinese sample HG005 with known genotypes. We then applied the imputed genotypes of 1,744 transferred embryos who have gestational ages and complete follow-up records to GWAS. Results: The accuracy of genotype imputation under ultra-low coverage can be improved by increasing the sample size and applying a set of filters. From 1,744 born embryos, we identified 11 genomic risk loci associated with gestational ages and 166 genes mapped to these loci according to positional, expression quantitative trait locus and chromatin interaction strategies. Among these mapped genes, CRHBP, ICAM1 and OXTR were more frequently reported as preterm birth related. By joint analysis of gene expression data from previous studies, we constructed interrelationships of mainly CRHBP, ICAM1, PLAGL1, DNMT1, CNTLN, DKK1 and EGR2 with preterm birth, infant disease and breast cancer. Conclusions: This study not only demonstrates that ultra-low coverage WGS could achieve relatively high accuracy of adequate genotype imputation and is capable of GWAS, but also provides insights into uncovering genetic associations of gestational age trait existed in the fetal embryo samples from Chinese or Eastern Asian populations.

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“…The indications for ICU admission are multifactorial and vary according to age, with preterm birth being a significant reason. Genetic disorders seem to be one of the many reasons for prematurity [9, 10]. The clinical presentation of monogenic diseases is highly heterogeneous and often unspecific.…”

Section: Introductionmentioning

confidence: 99%

Whole-Exome Sequencing in Critically Ill Neonates and Infants: Diagnostic Yield and Predictability of Monogenic Diagnosis

Scholz¹,

Blohm²,

Kortüm³

et al. 2021

Neonatology

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Introduction: Monogenic diseases play an important role in critically ill neonates and infants treated in the intensive care unit. This study aimed to determine the diagnostic yield of whole-exome sequencing (WES) for monogenic diseases and identify phenotypes more likely associated with a genetic etiology. Methods: From March 2017 to 2020, a comprehensive diagnostic workup including WES in a single academic center was performed in 61 unrelated, critically ill neonates and infants with an unknown underlying disease within the first year of life. We conducted 59 trio-WES, 1 duo-WES, and 1 single-WES analyses. Symptoms were classified according to the Human Phenotype Ontology. Results: The overall molecular genetic diagnostic rate within our cohort was 46% (28/61) and 50% (15/30) in the subgroup of preterm neonates. Identifying the genetic cause of disease facilitates individualized management in the majority of patients. A positive or negative predictive power of specific clinical features for a genetic diagnosis could not be observed. Conclusion: WES is a powerful noninvasive diagnostic tool in critically ill neonates and infants with a high diagnostic rate. We recommend initiating WES as early as possible due to the impact on management and family counseling. Recommendations regarding the clinical utility of WES in critically ill neonates and infants should not be based on the phenotype alone. Here, we present a clinical workflow for the application of WES for critically ill neonates and infants in an interdisciplinary setting.

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Recent advances in the genetics of preterm birth

Cited by 34 publications

References 55 publications

Genetics, epigenetics, and transcriptomics of preterm birth

Genetics, epigenetics, and transcriptomics of preterm birth

Ultra-low coverage genome-wide association study – insights into gestational age using 17,844 embryo samples with preimplantation genetic testing

Whole-Exome Sequencing in Critically Ill Neonates and Infants: Diagnostic Yield and Predictability of Monogenic Diagnosis

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