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Our Research Findings

Our Research Findings

Asthma and Mental Health Disorders. European Respiratory Journal 2019; 54: 1901507

  • This study was based on UK Biobank application 45052. GWAS single SNP summary statistics generated from this study. Citation: Zhu Z, Zhu X, Liu CL, Shi H, Shen S, Yang Y, Hasegawa K, Camargo CA Jr, Liang L. (2019) Shared genetics of asthma and mental health disorders: a large-scale genome-wide cross-trait analysis. Eur Respir J. 2019 Dec 19;54(6). pii: 1901507. doi: 10.1183/13993003.01507-2019. Print 2019 Dec.

Asthma and allergy cross-trait heritability. Nature Genetics. 2018; 50(6): 857-864.

  • Cross-trait heritability for asthma and allergy. We used UK Biobank (UKBB) data field 6152 as our doctor diagnosed asthma and allergic disease (hay fever, allergic rhinitis or eczema) phenotypes. This data is generated from UKBB application #16549.
  • Data field 6152 contains a question from touchscreen for the participants to answer: “has a doctor ever told you that you have had any of the following conditions? (You can select more than one answer): Blood clot in the leg (DVT); blood clot in the lung; emphysema/chronic bronchitis; asthma; hayfever, allergic rhinitis or eczema; none of the above; prefer not to answer.
  • Citation: Zhaozhong Zhu, Phil H. Lee, Mark D. Chaffin, Wonil Chung, Po-Ru Loh, Quan Lu, David C. Christiani, Liming Liang (2018) A genome-wide cross trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases. Nature Genetics. 2018 June ; 50(6): 857–864.
    GWAS single SNP summary statistics for asthma
    GWAS single SNP summary statistics for allergic diseases

Obesity and asthma cross-trait heritability. Journal of Allergy and Clinical Immunology. 2020; 145(2):537-549.

Cross-trait polygenic risk model. Nature Communications. 10(1), 569

  • All Predictors for human height by aid of BMI with 437K training samples from UK Biobank using PRS, LDpred, MCP, Lasso, PRS+MTAG, LDpred+MTAG, MCP+CTPR and Lasso+CTPR are available from the following link: Link
  • Citation: Wonil Chung, Jun Chen, Constance Chen, Sara Lindstroem, Zhaozhong Zhu, Po-Ru Loh, Peter Kraft and Liming Liang (2019), Efficient Cross-Trait Penalized Regression Increases Prediction Accuracy in Large Cohorts using Secondary Phenotypes, Nature Communications, 10(1), 569: Link
  • Wonil Chung at Harvard T.H. Chan School of Public Health

Cell type specific effect for EWAS

DNA methylation patterns across cord blood and placenta. Epigenetics. 2019 Apr;14(4):405-420.

  • Citation: Baoshan Ma, Catherine Allard, Luigi Bouchard, Patrice Perron, Murray Mittleman, Marie-France Hivert, Liming Liang. Locus-specific Methylation Prediction in Cord Blood and Placenta (Epigenetics, 2019).
  • DNA methylation is known to be responsive to prenatal exposures, which may be a part of the mechanism linking early developmental exposures to future chronic diseases. Many studies use blood to measure DNA methylation, yet we know that DNA methylation is tissue specific. Placenta is central to fetal growth and development, but it is rarely feasible to collect this tissue in large epidemiological studies; on the other hand, cord blood samples are more accessible. Our previous research suggested that large scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve the understanding of epigenetics in hard-to-access tissues (e.g. atrium and artery) and might enable non-invasive disease screening using epigenetic profiles. In this study, based on paired samples of both placenta and cord blood tissues from 169 individuals, we investigated the methylation concordance between placenta and cord blood. We then employed a machine-learning-based model to predict locus-specific DNA methylation levels in placenta using DNA methylation levels in cord blood. We found that methylation correlation between placenta and cord blood is lower than other tissue pairs, consistent with existing observations that placenta methylation has a distinct pattern. Nonetheless, there are still a number of CpG sites showing robust association between the two tissues. We built prediction models for placenta methylation based on cord blood data and documented a subset of 1,012 CpG sites with high correlation between measured and predicted placenta methylation levels. The resulting list of CpG sites and prediction models could help to reveal the loci where internal or external influences may affect DNA methylation in both placenta and cord blood, and provide a reference data to predict the effects on placenta in future study even when the tissue is not available in an epidemiological study.
  • This method is easily applicable to other samples and tissues. In order for the investigators to use our method, we have developed an R package which can be used to build the prediction model based on a training dataset with paired surrogate and target tissues, and generate predicted target tissue methylation with only surrogate tissue methylation. The program, detailed instruction and example datasets, as well as supplementary files can be found in the below links. If you find our method and program useful, please cite the above reference.
  • Download: Package with R function and instructions prediction-software-package-20190218.zip.
  • Supplementary figures:
  • Supplementary tables:

Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies. Gut. 2022;71(6):1095-1105.

The Mediterranean diet, plasma metabolome, and cardiovascular disease risk. Eur Heart J. 2020;41(28):2645-2656.

  • Summary statistics here are the GWAS for the Medi-Diet metabolic signature (n=1,925) SNPs only
  • Li J, Guasch-Ferré M, Chung W, Ruiz-Canela M, Toledo E, Corella D, Bhupathiraju SN, Tobias DK, Tabung FK, Hu J, Zhao T, Turman C, Feng YA, Clish CB, Mucci L, Eliassen AH, Costenbader KH, Karlson EW, Wolpin BM, Ascherio A, Rimm EB, Manson JE, Qi L, Martínez-González MÁ, Salas-Salvadó J, Hu FB, Liang L. The Mediterranean diet, plasma metabolome, and cardiovascular disease risk. Eur Heart J. 2020;41(28):2645-2656. PMID: 32406924.

Epigenome-wide association analysis of infant bronchiolitis severity: a multicenter prospective cohort study. Nature Communications. 2023;14(1):5495.

Cross omics risk scores of inflammation markers are associated with all-cause mortality: The Canadian Longitudinal Study on Aging.

  • R example code for 1- 2- and 3-way CRP risk scores
  • Citation: Anat Yaskolka Meir, Huan Yun, Jie Hu, Jun Li, Jiaxuan Liu, Alaina Bever, Andrew Ratanatharathorn, Mingyang Song, A. Heather Eliassen, Lori Chibnik, Karestan Koenen, Guillaume Pare, Meir J Stampfer, Liming Liang. Cross omics risk scores of inflammation markers are associated with all-cause mortality: The Canadian Longitudinal Study on Aging. PMID: XXXXXXXX (the issue number and PMID are to be determined)
    • Contact: Please contact Dr. Liming Liang for any questions related to this study

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