Genomic Predictors of Recurrent Pregnancy Loss
Purpose
The overall goals of this proposal are to determine the genetic architecture of recurrent pregnancy loss (RPL) and to discover genomic predictors of RPL.
Condition
- Recurrent Pregnancy Loss
Eligibility
- Eligible Ages
- Between 18 Years and 50 Years
- Eligible Genders
- All
- Accepts Healthy Volunteers
- Yes
Criteria
1. Women with loss of a current singleton pregnancy at < 20 0/7 weeks gestation
(documented by ultrasonography or histopathological examination) and one or more prior
pregnancy losses.
2. Euploid current pregnancy by karyotype or microarray (a limited number of aneuploid
losses will be included as part of the pilot)
3. No history of parental karyotype abnormalities
4. No history of antiphospholipid antibody syndrome
5. No evidence of uncontrolled diabetes
6. No evidence of uncontrolled thyroid disease
7. No history of autoimmune disease (SLE, RA)
8. No history of uterine anomalies
9. No history of cervical insufficiency
Study Design
- Phase
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Recruiting Locations
San Antonio, Texas 78299
More Details
- Status
- Recruiting
- Sponsor
- Yale University
Detailed Description
The following specific aims are proposed: Aim 1: Collect clinically well-characterized samples from trios (product of conception (POC), biological mother, and biological father) with unexplained RPL. Specifically, a cohort of 1,000 trios that are rigorously-phenotyped will be recruited, and for which couples' RPL is not attributable to known causes. The POC and parental DNA samples will be collected. If it is necessary for the purpose of determining the pathogenicity of sequence variants from the trio, collecting DNA samples from other family members after consent will also be considered. The study team may also request DNA or POC tissues from a prior pregnancy loss(es) if available. Aim 2: A whole genome sequencing (WGS) at the Yale Center for Genome Analysis (YCGA) will be performed and bioinformatic analyses to identify pathogenic variants in included trios will performed as well. Pathogenic variants will be comprehensively defined and fully annotated variant maps in all included trios to provide the substrate for subsequent novel gene discovery, and ultimately, the development of clinical diagnostic tests will be generated.