Honors & Awards


  • Stanford Science Fellow, Stanford University (2023)

Stanford Advisors


All Publications


  • An inexpensive semi-automated sample processing pipeline for cell-free RNA extraction. Nature protocols Moufarrej, M. N., Quake, S. R. 2023

    Abstract

    Despite advances in automated liquid handling and microfluidics, preparing samples for RNA sequencing at scale generally requires expensive equipment, which is beyond the reach of many academic laboratories. Manual sample preparation remains a slow, expensive and error-prone process. Here, we describe a low-cost, semi-automated pipeline to extract cell-free RNA using one of two commercially available, inexpensive and open-source robotic systems: the Opentrons OT1.0 or OT2.0. Like many RNA isolation protocols, ours can be decomposed into three subparts: RNA extraction, DNA digestion and RNA cleaning and concentration. RT-qPCR data using a synthetic spike-in confirms comparable RNA quality to the gold standard, manual sample processing. The semi-automated pipeline also shows improvement in sample throughput (+12×), time spent (-11×), cost (-3×) and biohazardous waste produced (-4×) compared with its manual counterpart. This protocol enables cell-free RNA extraction from 96 samples simultaneously in 4.5 h; in practice, this dramatically improves the time to results, as we recently demonstrated. Importantly, any laboratory already has most of the parts required (manual pipette and corresponding tips and kits for RNA isolation, cleaning and concentration) to build a semi-automated sample processing pipeline of their own and would only need to purchase or three-dimensionally print a few extra parts (US$5.5 K-12 K in total). This pipeline is also generalizable for many nucleic acid extraction applications, thereby increasing the scale of studies, which can be performed in small research laboratories.

    View details for DOI 10.1038/s41596-023-00855-2

    View details for PubMedID 37567931

    View details for PubMedCentralID 4034220

  • Noninvasive Prenatal Testing Using Circulating DNA and RNA: Advances, Challenges, and Possibilities. Annual review of biomedical data science Moufarrej, M. N., Bianchi, D. W., Shaw, G. M., Stevenson, D. K., Quake, S. R. 2023

    Abstract

    Prenatal screening using sequencing of circulating cell-free DNA has transformed obstetric care over the past decade and significantly reduced the number of invasive diagnostic procedures like amniocentesis for genetic disorders. Nonetheless, emergency care remains the only option for complications like preeclampsia and preterm birth, two of the most prevalent obstetrical syndromes. Advances in noninvasive prenatal testing expand the scope of precision medicine in obstetric care. In this review, we discuss advances, challenges, and possibilities toward the goal of providing proactive, personalized prenatal care. The highlighted advances focus mainly on cell-free nucleic acids; however, we also review research that uses signals from metabolomics, proteomics, intact cells, and the microbiome. We discuss ethical challenges in providing care. Finally, we look to future possibilities, including redefining disease taxonomy and moving from biomarker correlation to biological causation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 6 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

    View details for DOI 10.1146/annurev-biodatasci-020722-094144

    View details for PubMedID 37196360

  • Early prediction of preeclampsia in pregnancy with cell-free RNA. Nature Moufarrej, M. N., Vorperian, S. K., Wong, R. J., Campos, A. A., Quaintance, C. C., Sit, R. V., Tan, M., Detweiler, A. M., Mekonen, H., Neff, N. F., Baruch-Gravett, C., Litch, J. A., Druzin, M. L., Winn, V. D., Shaw, G. M., Stevenson, D. K., Quake, S. R. 2022

    Abstract

    Liquid biopsies that measure circulating cell-free RNA (cfRNA) offer an opportunity to study the development of pregnancy-related complications in a non-invasive manner and to bridge gaps in clinical care1-4. Here we used 404 blood samples from 199 pregnant mothers to identify and validate cfRNA transcriptomic changes that are associated with preeclampsia, a multi-organ syndrome that is the second largest cause of maternal death globally5. We find that changes in cfRNA gene expression between normotensive and preeclamptic mothers are marked and stable early in gestation, well before the onset of symptoms. These changes are enriched for genes specific to neuromuscular, endothelial and immune cell types and tissues that reflect key aspects of preeclampsia physiology6-9, suggest new hypotheses for disease progression and correlate with maternal organ health. This enabled the identification and independent validation of a panel of 18 genes that when measured between 5 and 16 weeks of gestation can form the basis of a liquid biopsy test that would identify mothers at risk of preeclampsia long before clinical symptoms manifest themselves. Tests based on these observations could help predict and manage who is at risk for preeclampsia-an important objective for obstetric care10,11.

    View details for DOI 10.1038/s41586-022-04410-z

    View details for PubMedID 35140405

  • Noninvasive blood tests for fetal development predict gestational age and preterm delivery SCIENCE Ngo, T. M., Moufarrej, M. N., Rasmussen, M. H., Camunas-Soler, J., Pan, W., Okamoto, J., Neff, N. F., Liu, K., Wong, R. J., Downes, K., Tibshirani, R., Shaw, G. M., Skotte, L., Stevenson, D. K., Biggio, J. R., Elovitz, M. A., Melbye, M., Quake, S. R. 2018; 360 (6393): 1133–36

    Abstract

    Noninvasive blood tests that provide information about fetal development and gestational age could potentially improve prenatal care. Ultrasound, the current gold standard, is not always affordable in low-resource settings and does not predict spontaneous preterm birth, a leading cause of infant death. In a pilot study of 31 healthy pregnant women, we found that measurement of nine cell-free RNA (cfRNA) transcripts in maternal blood predicted gestational age with comparable accuracy to ultrasound but at substantially lower cost. In a related study of 38 women (23 full-term and 15 preterm deliveries), all at elevated risk of delivering preterm, we identified seven cfRNA transcripts that accurately classified women who delivered preterm up to 2 months in advance of labor. These tests hold promise for prenatal care in both the developed and developing worlds, although they require validation in larger, blinded clinical trials.

    View details for PubMedID 29880692

  • Early prediction and longitudinal modeling of preeclampsia from multiomics. Patterns (New York, N.Y.) Maric, I., Contrepois, K., Moufarrej, M. N., Stelzer, I. A., Feyaerts, D., Han, X., Tang, A., Stanley, N., Wong, R. J., Traber, G. M., Ellenberger, M., Chang, A. L., Fallahzadeh, R., Nassar, H., Becker, M., Xenochristou, M., Espinosa, C., De Francesco, D., Ghaemi, M. S., Costello, E. K., Culos, A., Ling, X. B., Sylvester, K. G., Darmstadt, G. L., Winn, V. D., Shaw, G. M., Relman, D. A., Quake, S. R., Angst, M. S., Snyder, M. P., Stevenson, D. K., Gaudilliere, B., Aghaeepour, N. 2022; 3 (12): 100655

    Abstract

    Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC]= 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC= 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC= 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

    View details for DOI 10.1016/j.patter.2022.100655

    View details for PubMedID 36569558

  • Publisher Correction: Cell types of origin of the cell-free transcriptome. Nature biotechnology Vorperian, S. K., Moufarrej, M. N., Tabula Sapiens Consortium, Quake, S. R., Jones, R. C., Karkanias, J., Krasnow, M., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., Harper, W., Hemenez, M., Ponnusamy, R., Salehi, A., Sanagavarapu, B. A., Spallino, E., Aaron, K. A., Concepcion, W., Gardner, J. M., Kelly, B., Neidlinger, N., Wang, Z., Crasta, S., Kolluru, S., Morri, M., Tan, S. Y., Travaglini, K. J., Xu, C., Alcantara-Hernandez, M., Almanzar, N., Antony, J., Beyersdorf, B., Burhan, D., Calcuttawala, K., Carter, M. M., Chan, C. K., Chang, C. A., Chang, S., Colville, A., Culver, R. N., Cvijovic, I., D'Amato, G., Ezran, C., Galdos, F. X., Gillich, A., Goodyer, W. R., Hang, Y., Hayashi, A., Houshdaran, S., Huang, X., Irwin, J. C., Jang, S., Juanico, J. V., Kershner, A. M., Kim, S., Kiss, B., Kong, W., Kumar, M. E., Kuo, A. H., Leylek, R., Li, B., Loeb, G. B., Lu, W., Mantri, S., Markovic, M., McAlpine, P. L., de Morree, A., Mrouj, K., Mukherjee, S., Muser, T., Neuhofer, P., Nguyen, T. D., Perez, K., Phansalkar, R., Puluca, N., Qi, Z., Rao, P., Raquer-McKay, H., Schaum, N., Scott, B., Seddighzadeh, B., Segal, J., Sen, S., Sikandar, S., Spencer, S. P., Steffes, L., Subramaniam, V. R., Swarup, A., Swift, M., Van Treuren, W., Trimm, E., Veizades, S., Vijayakumar, S., Vo, K. C., Vorperian, S. K., Wang, W., Weinstein, H. N., Winkler, J., Wu, T. T., Xie, J., Yung, A. R., Zhang, Y., Detweiler, A. M., Mekonen, H., Neff, N. F., Sit, R. V., Tan, M., Yan, J., Bean, G. R., Charu, V., Forgo, E., Martin, B. A., Ozawa, M. G., Silva, O., Toland, A., Vemuri, V. N., Afik, S., Awayan, K., Bierman, R., Botvinnik, O. B., Byrne, A., Chen, M., Dehghannasiri, R., Gayoso, A., Granados, A. A., Li, Q., Mahmoudabadi, G., McGeever, A., Olivieri, J. E., Park, M., Ravikumar, N., Stanley, G., Tan, W., Tarashansky, A. J., Vanheusden, R., Wang, P., Wang, S., Xing, G., Xu, C., Yosef, N., Culver, R., Dethlefsen, L., Ho, P., Liu, S., Maltzman, J. S., Metzger, R. J., Sasagawa, K., Sinha, R., Song, H., Wang, B., Artandi, S. E., Beachy, P. A., Clarke, M. F., Giudice, L. C., Huang, F. W., Huang, K. C., Idoyaga, J., Kim, S. K., Kuo, C. S., Nguyen, P., Rando, T. A., Red-Horse, K., Reiter, J., Relman, D. A., Sonnenburg, J. L., Wu, A., Wu, S. M., Wyss-Coray, T. 2022

    View details for DOI 10.1038/s41587-022-01293-3

    View details for PubMedID 35347330

  • Cell types of origin of the cell-free transcriptome. Nature biotechnology Vorperian, S. K., Moufarrej, M. N., Tabula Sapiens Consortium, Quake, S. R., Jones, R. C., Karkanias, J., Krasnow, M., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., Harper, W., Hemenez, M., Ponnusamy, R., Salehi, A., Sanagavarapu, B. A., Spallino, E., Aaron, K. A., Concepcion, W., Gardner, J. M., Kelly, B., Neidlinger, N., Wang, Z., Crasta, S., Kolluru, S., Morri, M., Tan, S. Y., Travaglini, K. J., Xu, C., Alcantara-Hernandez, M., Almanzar, N., Antony, J., Beyersdorf, B., Burhan, D., Calcuttawala, K., Carter, M. M., Chan, C. K., Chang, C. A., Chang, S., Colville, A., Culver, R. N., Cvijovic, I., D'Amato, G., Ezran, C., Galdos, F. X., Gillich, A., Goodyer, W. R., Hang, Y., Hayashi, A., Houshdaran, S., Huang, X., Irwin, J. C., Jang, S., Juanico, J. V., Kershner, A. M., Kim, S., Kiss, B., Kong, W., Kumar, M. E., Kuo, A. H., Leylek, R., Li, B., Loeb, G. B., Lu, W., Mantri, S., Markovic, M., McAlpine, P. L., de Morree, A., Mrouj, K., Mukherjee, S., Muser, T., Neuhofer, P., Nguyen, T. D., Perez, K., Phansalkar, R., Puluca, N., Qi, Z., Rao, P., Raquer-McKay, H., Schaum, N., Scott, B., Seddighzadeh, B., Segal, J., Sen, S., Sikandar, S., Spencer, S. P., Steffes, L., Subramaniam, V. R., Swarup, A., Swift, M., Van Treuren, W., Trimm, E., Veizades, S., Vijayakumar, S., Vo, K. C., Vorperian, S. K., Wang, W., Weinstein, H. N., Winkler, J., Wu, T. T., Xie, J., Yung, A. R., Zhang, Y., Detweiler, A. M., Mekonen, H., Neff, N. F., Sit, R. V., Tan, M., Yan, J., Bean, G. R., Charu, V., Forgo, E., Martin, B. A., Ozawa, M. G., Silva, O., Toland, A., Vemuri, V. N., Afik, S., Awayan, K., Bierman, R., Botvinnik, O. B., Byrne, A., Chen, M., Dehghannasiri, R., Gayoso, A., Granados, A. A., Li, Q., Mahmoudabadi, G., McGeever, A., Olivieri, J. E., Park, M., Ravikumar, N., Stanley, G., Tan, W., Tarashansky, A. J., Vanheusden, R., Wang, P., Wang, S., Xing, G., Xu, C., Yosef, N., Culver, R., Dethlefsen, L., Ho, P., Liu, S., Maltzman, J. S., Metzger, R. J., Sasagawa, K., Sinha, R., Song, H., Wang, B., Artandi, S. E., Beachy, P. A., Clarke, M. F., Giudice, L. C., Huang, F. W., Huang, K. C., Idoyaga, J., Kim, S. K., Kuo, C. S., Nguyen, P., Rando, T. A., Red-Horse, K., Reiter, J., Relman, D. A., Sonnenburg, J. L., Wu, A., Wu, S. M., Wyss-Coray, T. 2022

    Abstract

    Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases.

    View details for DOI 10.1038/s41587-021-01188-9

    View details for PubMedID 35132263

  • Understanding how biologic and social determinants affect disparities in preterm birth and outcomes of preterm infants in the NICU. Seminars in perinatology Stevenson, D. K., Aghaeepour, N., Maric, I., Angst, M. S., Darmstadt, G. L., Druzin, M. L., Gaudilliere, B., Ling, X. B., Moufarrej, M. N., Peterson, L. S., Quake, S. R., Relman, D. A., Snyder, M. P., Sylvester, K. G., Shaw, G. M., Wong, R. J. 2021: 151408

    Abstract

    To understand the disparities in spontaneous preterm birth (sPTB) and/or its outcomes, biologic and social determinants as well as healthcare practice (such as those in neonatal intensive care units) should be considered. They have been largely intractable and remain obscure in most cases, despite a myriad of identified risk factors for and causes of sPTB. We still do not know how they might actually affect and lead to the different outcomes at different gestational ages and if they are independent of NICU practices. Here we describe an integrated approach to study the interplay between the genome and exposome, which may drive biochemistry and physiology, with health disparities.

    View details for DOI 10.1016/j.semperi.2021.151408

    View details for PubMedID 33875265

  • Towards personalized medicine in maternal and child health: integrating biologic and social determinants. Pediatric research Stevenson, D. K., Wong, R. J., Aghaeepour, N., Maric, I., Angst, M. S., Contrepois, K., Darmstadt, G. L., Druzin, M. L., Eisenberg, M. L., Gaudilliere, B., Gibbs, R. S., Gotlib, I. H., Gould, J. B., Lee, H. C., Ling, X. B., Mayo, J. A., Moufarrej, M. N., Quaintance, C. C., Quake, S. R., Relman, D. A., Sirota, M., Snyder, M. P., Sylvester, K. G., Hao, S., Wise, P. H., Shaw, G. M., Katz, M. 2020

    View details for DOI 10.1038/s41390-020-0981-8

    View details for PubMedID 32454518

  • Multiomic immune clockworks of pregnancy. Seminars in immunopathology Peterson, L. S., Stelzer, I. A., Tsai, A. S., Ghaemi, M. S., Han, X. n., Ando, K. n., Winn, V. D., Martinez, N. R., Contrepois, K. n., Moufarrej, M. N., Quake, S. n., Relman, D. A., Snyder, M. P., Shaw, G. M., Stevenson, D. K., Wong, R. J., Arck, P. n., Angst, M. S., Aghaeepour, N. n., Gaudilliere, B. n. 2020

    Abstract

    Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy "immune clock" is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth.

    View details for DOI 10.1007/s00281-019-00772-1

    View details for PubMedID 32020337

  • Investigating Pregnancy and Its Complications Using Circulating Cell-Free RNA in Women's Blood During Gestation. Frontiers in pediatrics Moufarrej, M. N., Wong, R. J., Shaw, G. M., Stevenson, D. K., Quake, S. R. 2020; 8: 605219

    Abstract

    In recent years, there have been major advances in the application of non-invasive techniques to predict pregnancy-related complications, for example by measuring cell-free RNA (cfRNA) in maternal blood. In contrast to cell-free DNA (cfDNA), which is already in clinical use to diagnose fetal aneuploidy, circulating RNA levels can correspond with tissue-specific gene expression and provide a snapshot of prenatal health across gestation. Here, we review the physiologic origins of cfRNA and its novel applications and corresponding challenges to monitor fetal and maternal health and predict pregnancy-related complications.

    View details for DOI 10.3389/fped.2020.605219

    View details for PubMedID 33381480

    View details for PubMedCentralID PMC7767905

  • Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries. JAMA network open Jehan, F. n., Sazawal, S. n., Baqui, A. H., Nisar, M. I., Dhingra, U. n., Khanam, R. n., Ilyas, M. n., Dutta, A. n., Mitra, D. K., Mehmood, U. n., Deb, S. n., Mahmud, A. n., Hotwani, A. n., Ali, S. M., Rahman, S. n., Nizar, A. n., Ame, S. M., Moin, M. I., Muhammad, S. n., Chauhan, A. n., Begum, N. n., Khan, W. n., Das, S. n., Ahmed, S. n., Hasan, T. n., Khalid, J. n., Rizvi, S. J., Juma, M. H., Chowdhury, N. H., Kabir, F. n., Aftab, F. n., Quaiyum, A. n., Manu, A. n., Yoshida, S. n., Bahl, R. n., Rahman, A. n., Pervin, J. n., Winston, J. n., Musonda, P. n., Stringer, J. S., Litch, J. A., Ghaemi, M. S., Moufarrej, M. N., Contrepois, K. n., Chen, S. n., Stelzer, I. A., Stanley, N. n., Chang, A. L., Hammad, G. B., Wong, R. J., Liu, C. n., Quaintance, C. C., Culos, A. n., Espinosa, C. n., Xenochristou, M. n., Becker, M. n., Fallahzadeh, R. n., Ganio, E. n., Tsai, A. S., Gaudilliere, D. n., Tsai, E. S., Han, X. n., Ando, K. n., Tingle, M. n., Maric, I. n., Wise, P. H., Winn, V. D., Druzin, M. L., Gibbs, R. S., Darmstadt, G. L., Murray, J. C., Shaw, G. M., Stevenson, D. K., Snyder, M. P., Quake, S. R., Angst, M. S., Gaudilliere, B. n., Aghaeepour, N. n. 2020; 3 (12): e2029655

    Abstract

    Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies.To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB.This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019.Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites.The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation.Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways.This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

    View details for DOI 10.1001/jamanetworkopen.2020.29655

    View details for PubMedID 33337494

  • Understanding health disparities. Journal of perinatology : official journal of the California Perinatal Association Stevenson, D. K., Wong, R. J., Aghaeepour, N., Angst, M. S., Darmstadt, G. L., DiGiulio, D. B., Druzin, M. L., Gaudilliere, B., Gibbs, R. S., B Gould, J., Katz, M., Li, J., Moufarrej, M. N., Quaintance, C. C., Quake, S. R., Relman, D. A., Shaw, G. M., Snyder, M. P., Wang, X., Wise, P. H. 2018

    Abstract

    Based upon our recent insights into the determinants of preterm birth, which is the leading cause of death in children under five years of age worldwide, we describe potential analytic frameworks that provides both a common understanding and, ultimately the basis for effective, ameliorative action. Our research on preterm birth serves as an example that the framing of any human health condition is a result of complex interactions between the genome and the exposome. New discoveries of the basic biology of pregnancy, such as the complex immunological and signaling processes that dictate the health and length of gestation, have revealed a complexity in the interactions (current and ancestral) between genetic and environmental forces. Understanding of these relationships may help reduce disparities in preterm birth and guide productive research endeavors and ultimately, effective clinical and public health interventions.

    View details for PubMedID 30560947