ID
45166
Beschreibung
Principal Investigator: Xiaobin Wang, MD, MPH, ScD, Zanvyl Krieger Professor, Director, Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, and Professor of Pediatrics, John Hopkins University School of Medicine, Baltimore, MD, USA MeSH: Infant, Premature,Premature Birth,Gestational Age https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000332 Preterm birth (PTB, born before 37 weeks of gestation) is a leading cause of neonatal mortality and post-natal morbidity. PTB affects one in nine all live births in the U.S. Notably, the highest rate of PTB occurs among African Americans (one in six). PTB is a complex trait, likely determined by multiple environmental and genetic factors and their interactions. We demonstrated strong familial aggregation of preterm and low birthweight in the US Blacks and Whites (Wang et al, NEJM, 1995) and conducted the largest candidate gene study of preterm birth at that time (Hao et al, HMG, 2004). We showed that a subset of mothers with certain metabolic gene variants are particularly vulnerable to the adverse effects of cigarette smoking on low birthweight and preterm births (Wang et al, JAMA, 2002). We also published a number of papers that examined the effect of maternal pre-pregnancy BMI, micronutrient status, stress and environmental toxins on the risk of preterm birth and related conditions. This project, supported by a grant from the NICHD (2R01HD41702, PI, Xiaobin Wang), aimed to conduct a genome-wide association study (GWAS) and apply advanced statistical methods to identify susceptibility loci of PTB in a predominantly urban low-income African American sample, a subset of the Boston Birth Cohort. PUBLIC HEALTH REVELANCE: We anticipate that this study will lead to the identification of novel genetic loci of PTB and gene-environment interactions. Such findings not only will provide important insights into mechanisms leading to PTB, but also may help identify women at high-risk of PTB, which in turn, may lead to the development of early and targeted interventions that can prevent PTB or mitigate the severity and consequences of PTB.
Link
Stichworte
Versionen (2)
- 25.08.22 25.08.22 - Chiara Middel
- 12.10.22 12.10.22 - Adrian Schulz
Rechteinhaber
Xiaobin Wang, MD, MPH, ScD, Zanvyl Krieger Professor, Director, Center on the Early Life Origins of Disease, Department of Population, Family and Reproductive Health, Johns Hopkins University Bloomberg School of Public Health, and Professor of Pediatrics, John Hopkins University School of Medicine, Baltimore, MD, USA
Hochgeladen am
12. Oktober 2022
DOI
Für eine Beantragung loggen Sie sich ein.
Lizenz
Creative Commons BY 4.0
Modell Kommentare :
Hier können Sie das Modell kommentieren. Über die Sprechblasen an den Itemgruppen und Items können Sie diese spezifisch kommentieren.
Itemgroup Kommentare für :
Item Kommentare für :
Um Formulare herunterzuladen müssen Sie angemeldet sein. Bitte loggen Sie sich ein oder registrieren Sie sich kostenlos.
dbGaP phs000332 GWAS of Preterm Birth
The dataset provides medical background data related to the pregnancies of participating mothers (affection status: mothers who gave birth to a preterm [cases] or term [controls] infant): e.g. presence/absence of hypertension, diabetes, preeclampsia, number of previous pregnancies, type of delivery, presence/absence of placenta abruptio or, amount of amniotic fluid present; in addition, data about mother's lifestyle (smoking, alcohol and other drug consumption) and general socio-demographic data (age, educational level, ethnicity) are provided. General data about the infants are also included, e.g. gestational age, birthweight, and gender.
- StudyEvent: SEV1
- Eligibility Criteria
- Subject - Consent Information
- Subject - Sample Mapping
- The dataset provides medical background data related to the pregnancies of participating mothers (affection status: mothers who gave birth to a preterm [cases] or term [controls] infant): e.g. presence/absence of hypertension, diabetes, preeclampsia, number of previous pregnancies, type of delivery, presence/absence of placenta abruptio or, amount of amniotic fluid present; in addition, data about mother's lifestyle (smoking, alcohol and other drug consumption) and general socio-demographic data (age, educational level, ethnicity) are provided. General data about the infants are also included, e.g. gestational age, birthweight, and gender.
- Sample Attribute Information
Ähnliche Modelle
The dataset provides medical background data related to the pregnancies of participating mothers (affection status: mothers who gave birth to a preterm [cases] or term [controls] infant): e.g. presence/absence of hypertension, diabetes, preeclampsia, number of previous pregnancies, type of delivery, presence/absence of placenta abruptio or, amount of amniotic fluid present; in addition, data about mother's lifestyle (smoking, alcohol and other drug consumption) and general socio-demographic data (age, educational level, ethnicity) are provided. General data about the infants are also included, e.g. gestational age, birthweight, and gender.
- StudyEvent: SEV1
- Eligibility Criteria
- Subject - Consent Information
- Subject - Sample Mapping
- The dataset provides medical background data related to the pregnancies of participating mothers (affection status: mothers who gave birth to a preterm [cases] or term [controls] infant): e.g. presence/absence of hypertension, diabetes, preeclampsia, number of previous pregnancies, type of delivery, presence/absence of placenta abruptio or, amount of amniotic fluid present; in addition, data about mother's lifestyle (smoking, alcohol and other drug consumption) and general socio-demographic data (age, educational level, ethnicity) are provided. General data about the infants are also included, e.g. gestational age, birthweight, and gender.
- Sample Attribute Information
C4684638 (UMLS CUI [1,2])
C0332152 (UMLS CUI [1,2])
C1148523 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0085207 (UMLS CUI [1,2])
C0347984 (UMLS CUI [1,3])
C0032961 (UMLS CUI [1,4])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C2114320 (UMLS CUI [1,2])
C0347984 (UMLS CUI [1,3])
C0032961 (UMLS CUI [1,4])
C0347984 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C0011008 (UMLS CUI [1,2])
C1148523 (UMLS CUI [1,3])
C0332152 (UMLS CUI [1,2])
C0032961 (UMLS CUI [1,3])
C1442461 (UMLS CUI [1,2])
C0332152 (UMLS CUI [1,3])
C0032961 (UMLS CUI [1,4])
C3475470 (UMLS CUI [2,1])
C0347984 (UMLS CUI [2,2])
C0032961 (UMLS CUI [2,3])
C0347984 (UMLS CUI [1,2])
C0032980 (UMLS CUI [1,3])
C0032981 (UMLS CUI [1,4])
C0543414 (UMLS CUI [1,2])
C0032979 (UMLS CUI [1,3])