Mao S - Search Results

1

Surrogate Adiposity Markers and Mortality.

Khan I, Chong M, Le A, Mohammadi-Shemirani P, Morton R, Brinza C, Kiflen M, Narula S, Akhabir L, Mao S, Morrison K, Pigeyre M, Paré G. Khan I, et al. Among authors: mao s. JAMA Netw Open. 2023 Sep 5;6(9):e2334836. doi: 10.1001/jamanetworkopen.2023.34836. JAMA Netw Open. 2023. PMID: 37728925 Free PMC article.

2

A method to estimate the contribution of regional genetic associations to complex traits from summary association statistics.

Pare G, Mao S, Deng WQ. Pare G, et al. Among authors: mao s. Sci Rep. 2016 Jun 8;6:27644. doi: 10.1038/srep27644. Sci Rep. 2016. PMID: 27273519 Free PMC article.

3

A machine-learning heuristic to improve gene score prediction of polygenic traits.

Paré G, Mao S, Deng WQ. Paré G, et al. Among authors: mao s. Sci Rep. 2017 Oct 4;7(1):12665. doi: 10.1038/s41598-017-13056-1. Sci Rep. 2017. PMID: 28979001 Free PMC article.

4

A robust method to estimate regional polygenic correlation under misspecified linkage disequilibrium structure.

Paré G, Mao S, Deng WQ. Paré G, et al. Among authors: mao s. Genet Epidemiol. 2018 Oct;42(7):636-647. doi: 10.1002/gepi.22149. Epub 2018 Aug 29. Genet Epidemiol. 2018. PMID: 30156736

5

Identification of Novel Causal Blood Biomarkers Linking Metabolically Favorable Adiposity With Type 2 Diabetes Risk.

Pigeyre M, Sjaarda J, Mao S, Chong M, Hess S, Yusuf S, Gerstein H, Paré G. Pigeyre M, et al. Among authors: mao s. Diabetes Care. 2019 Sep;42(9):1800-1808. doi: 10.2337/dc18-2444. Epub 2019 Jun 24. Diabetes Care. 2019. PMID: 31235487

6

Analytical strategies to include the X-chromosome in variance heterogeneity analyses: Evidence for trait-specific polygenic variance structure.

Deng WQ, Mao S, Kalnapenkis A, Esko T, Mägi R, Paré G, Sun L. Deng WQ, et al. Among authors: mao s. Genet Epidemiol. 2019 Oct;43(7):815-830. doi: 10.1002/gepi.22247. Epub 2019 Jul 22. Genet Epidemiol. 2019. PMID: 31332826

7

Author Correction: A machine-learning heuristic to improve gene score prediction of polygenic traits.

Paré G, Mao S, Deng WQ. Paré G, et al. Among authors: mao s. Sci Rep. 2019 Nov 20;9(1):17507. doi: 10.1038/s41598-019-53666-5. Sci Rep. 2019. PMID: 31745232 Free PMC article.

8

Fine-tuning of Genome-Wide Polygenic Risk Scores and Prediction of Gestational Diabetes in South Asian Women.

Lamri A, Mao S, Desai D, Gupta M, Paré G, Anand SS. Lamri A, et al. Among authors: mao s. Sci Rep. 2020 Jun 2;10(1):8941. doi: 10.1038/s41598-020-65360-y. Sci Rep. 2020. PMID: 32488059 Free PMC article.

9

Cost-Effectiveness of Polygenic Risk Scores to Guide Statin Therapy for Cardiovascular Disease Prevention.

Kiflen M, Le A, Mao S, Lali R, Narula S, Xie F, Paré G. Kiflen M, et al. Among authors: mao s. Circ Genom Precis Med. 2022 Oct;15(5):e003423. doi: 10.1161/CIRCGEN.121.003423. Epub 2022 Jul 29. Circ Genom Precis Med. 2022. PMID: 35904973

10

A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets.

Di Scipio M, Khan M, Mao S, Chong M, Judge C, Pathan N, Perrot N, Nelson W, Lali R, Di S, Morton R, Petch J, Paré G. Di Scipio M, et al. Among authors: mao s. Nat Commun. 2023 Aug 25;14(1):5196. doi: 10.1038/s41467-023-40913-7. Nat Commun. 2023. PMID: 37626057 Free PMC article.

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