Browsing by Author "Yan, Qi"

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    A Comprehensive and Bias-Free Machine Learning Approach for Risk Prediction of Preeclampsia with Severe Features in a Nulliparous Study Cohort
    (Research Square, 2023-04-10) Lin, Yun; Mallia, Daniel; Clark-Sevilla, Andrea; Catto, Adam; Leshchenko, Alisa; Yan, Qi; Haas, David; Wapner, Ronald; Pe'er, Itsik; Raja, Anita; Salleb-Aouissi, Ansaf; Obstetrics and Gynecology, School of Medicine
    Objective: Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort. Materials and methods: The prospective study cohort to which we applied machine learning is the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b) study, which contains information from eight clinical sites across the US. Maternal serum samples were collected for 1,857 individuals between the first and second trimesters. These patients with serum samples collected are selected as the final cohort. Results: Our prediction models achieved an AUROC of 0.72 (95% CI, 0.69-0.76), 0.75 (95% CI, 0.71-0.79), and 0.77 (95% CI, 0.74-0.80), respectively, for the three visits. Our initial models were biased toward non-Hispanic black participants with a high predictive equality ratio of 1.31. We corrected this bias and reduced this ratio to 1.14. The top features stress the importance of using several tests, particularly for biomarkers and ultrasound measurements. Placental analytes were strong predictors for screening for the early onset of preeclampsia with severe features in the first two trimesters. Conclusion: Experiments suggest that it is possible to create racial bias-free early screening models to predict the patients at risk of developing preeclampsia with severe features or eclampsia nulliparous pregnant study cohort.
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    A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort
    (Springer Nature, 2024-12-24) Lin, Yun C.; Mallia, Daniel; Clark‑Sevilla, Andrea O.; Catto, Adam; Leshchenko, Alisa; Yan, Qi; Haas, David M.; Wapner, Ronald; Pe’er, Itsik; Raja, Anita; Salleb‑Aouissi, Ansaf; Obstetrics and Gynecology, School of Medicine
    Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed racial bias-free machine learning models that predict the onset of preeclampsia with severe features or eclampsia at discrete time points in a nulliparous pregnant study cohort. To focus on those most at risk, we selected probands with severe PE (sPE). Those with mild preeclampsia, superimposed preeclampsia, and new onset hypertension were excluded.The prospective study cohort to which we applied machine learning is the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b) study, which contains information from eight clinical sites across the US. Maternal serum samples were collected for 1,857 individuals between the first and second trimesters. These patients with serum samples collected are selected as the final cohort.Our prediction models achieved an AUROC of 0.72 (95% CI, 0.69-0.76), 0.75 (95% CI, 0.71-0.79), and 0.77 (95% CI, 0.74-0.80), respectively, for the three visits. Our initial models were biased toward non-Hispanic black participants with a high predictive equality ratio of 1.31. We corrected this bias and reduced this ratio to 1.14. This lowers the rate of false positives in our predictive model for the non-Hispanic black participants. The exact cause of the bias is still under investigation, but previous studies have recognized PLGF as a potential bias-inducing factor. However, since our model includes various factors that exhibit a positive correlation with PLGF, such as blood pressure measurements and BMI, we have employed an algorithmic approach to disentangle this bias from the model.The top features of our built model stress the importance of using several tests, particularly for biomarkers (BMI and blood pressure measurements) and ultrasound measurements. Placental analytes (PLGF and Endoglin) were strong predictors for screening for the early onset of preeclampsia with severe features in the first two trimesters.
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    Genome-wide association study of brain amyloid deposition as measured by Pittsburgh Compound-B (PiB)-PET imaging
    (Springer Nature, 2018-10-25) Yan, Qi; Nho, Kwangsik; Del-Aguila, Jorge L.; Wang, Xingbin; Risacher, Shannon L.; Fan, Kang-Hsien; Snitz, Beth E.; Aizenstein, Howard J.; Mathis, Chester A.; Lopez, Oscar L.; Demirci, F. Yesim; Feingold, Eleanor; Klunk, William E.; Saykin, Andrew J.; Cruchaga, Carlos; Kamboh, M. Ilyas; Radiology and Imaging Sciences, School of Medicine
    Deposition of amyloid plaques in the brain is one of the two main pathological hallmarks of Alzheimer's disease (AD). Amyloid positron emission tomography (PET) is a neuroimaging tool that selectively detects in vivo amyloid deposition in the brain and is a reliable endophenotype for AD that complements cerebrospinal fluid biomarkers with regional information. We measured in vivo amyloid deposition in the brains of ~1000 subjects from three collaborative AD centers and ADNI using 11C-labeled Pittsburgh Compound-B (PiB)-PET imaging followed by meta-analysis of genome-wide association studies, first to our knowledge for PiB-PET, to identify novel genetic loci for this endophenotype. The APOE region showed the most significant association where several SNPs surpassed the genome-wide significant threshold, with APOE*4 being most significant (P-meta = 9.09E-30; β = 0.18). Interestingly, after conditioning on APOE*4, 14 SNPs remained significant at P < 0.05 in the APOE region that were not in linkage disequilibrium with APOE*4. Outside the APOE region, the meta-analysis revealed 15 non-APOE loci with P < 1E-05 on nine chromosomes, with two most significant SNPs on chromosomes 8 (P-meta = 4.87E-07) and 3 (P-meta = 9.69E-07). Functional analyses of these SNPs indicate their potential relevance with AD pathogenesis. Top 15 non-APOE SNPs along with APOE*4 explained 25-35% of the amyloid variance in different datasets, of which 14-17% was explained by APOE*4 alone. In conclusion, we have identified novel signals in APOE and non-APOE regions that affect amyloid deposition in the brain. Our data also highlights the presence of yet to be discovered variants that may be responsible for the unexplained genetic variance of amyloid deposition.
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    Polygenic prediction of preeclampsia and gestational hypertension
    (Springer Nature, 2023) Honigberg, Michael C.; Truong, Buu; Khan, Raiyan R.; Xiao, Brenda; Bhatta, Laxmi; Vy, Ha My T.; Guerrero, Rafael F.; Schuermans, Art; Selvaraj, Margaret Sunitha; Patel, Aniruddh P.; Koyama, Satoshi; Cho, So Mi Jemma; Vellarikkal, Shamsudheen Karuthedath; Trinder, Mark; Urbut, Sarah M.; Gray, Kathryn J.; Brumpton, Ben M.; Patil, Snehal; Zöllner, Sebastian; Antopia, Mariah C.; Saxena, Richa; Nadkarni, Girish N.; Do, Ron; Yan, Qi; Pe’er, Itsik; Verma, Shefali Setia; Gupta, Rajat M.; Haas, David M.; Martin, Hilary C.; van Heel, David A.; Laisk, Triin; Natarajan, Pradeep; Obstetrics and Gynecology, School of Medicine
    Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR-CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification.
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    Searching and visualizing genetic associations of pregnancy traits by using GnuMoM2b
    (Oxford University Press, 2023) Yan, Qi; Guerrero, Rafael F.; Khan, Raiyan R.; Surujnarine, Andy A.; Wapner, Ronald J.; Hahn, Matthew W.; Raja, Anita; Salleb-Aouissi, Ansaf; Grobman, William A.; Simhan, Hyagriv; Blue, Nathan R.; Silver, Robert; Chung, Judith H.; Reddy, Uma M.; Radivojac, Predrag; Pe’er, Itsik; Haas, David M.; Obstetrics and Gynecology, School of Medicine
    Adverse pregnancy outcomes (APOs) are major risk factors for women's health during pregnancy and even in the years after pregnancy. Due to the heterogeneity of APOs, only few genetic associations have been identified. In this report, we conducted genome-wide association studies (GWASs) of 479 traits that are possibly related to APOs using a large and racially diverse study, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). To display extensive results, we developed a web-based tool GnuMoM2b (https://gnumom2b.cumcobgyn.org/) for searching, visualizing, and sharing results from a GWAS of 479 pregnancy traits as well as phenome-wide association studies of more than 17 million single nucleotide polymorphisms. The genetic results from 3 ancestries (Europeans, Africans, and Admixed Americans) and meta-analyses are populated in GnuMoM2b. In conclusion, GnuMoM2b is a valuable resource for extraction of pregnancy-related genetic results and shows the potential to facilitate meaningful discoveries.