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Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption. Agency for Science, Technology and Research (A*STAR) Ministry of Health (MOH) National Medical Research Council (NMRC) National Research Foundation (NRF) We thank all investigators, staf members and study participants of the contributing cohorts and studies: (1) the HELIOS study at the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; (2) the GUSTO study jointly hosted by the National University Hospital, KK Women’s and Children’s Hospital, the National University of Singapore and the Singapore Institute for Clinical Sciences, the Agency for Science Technology and Research (A*STAR); (3) the SEED cohort at the Singapore Eye Research Institute; (4) the MEC, National University of Singapore; (5) the PRISM cohort; and (6) the TTSH Personalised Medicine Normal Controls cohort. We also thank the National Supercomputing Centre, Singapore (https://www.ncss.sg) for computation resources. The SG10K_Health project is funded by the Industry Alignment Fund (Pre-Positioning) (IAF-PP, H17/01/a0/007); the project made use of participating study cohorts supported by the following funding sources: (1) the HELIOS study by grants from a Strategic Initiative at Lee Kong Chian School of Medicine, the Singapore MOH under its Singapore Translational Research Investigator Award (NMRC/STaR/0028/2017) and the IAF-PP (H18/01/a0/016); (2) the GUSTO study by the Singapore National Research Foundation under its Translational and Clinical Research Flagship Program and administered by the Singapore MOH’s National Medical Research Council Singapore (NMRC/TCR/004-NUS/2008, NMRC/ TCR/012-NUHS/2014) with additional funding support available through the A*STAR and the IAF-PP (H17/01/a0/005); (3) the SEED study by NMRC/CIRG/1417/2015, NMRC/CIRG/1488/2018 and NMRC/OFLCG/004/2018; (4) the MEC by individual research and clinical scientist award schemes from the Singapore National Medical Research Council (including MOH-000271-00) and the Singapore Biomedical Research Council, the Singapore MOH, the National University of Singapore and the Singapore National University Health System; (5) the PRISM cohort study by NMRC/CG/ M006/2017_NHCS, NMRC/STaR/0011/2012, NMRC/STaR/0026/2015, the Lee Foundation and the Tanoto Foundation; and (6) the TTSH cohort study by NMRC/CG12AUG2017 and CGAug16M012. This research is also supported by the National Research Foundation Singapore under its NPM program Phase II funding (MOH-000588) and administered by the Singapore MOH’s National Medical Research Council.

Additional file 1: Fig. S1. Flowchart of sample selection and analysis steps in this study. Fig. S2. Pairwise Pearson correlation coefficient heat map of fasting glucose, 2-h post-load glucose, fasting insulin, HOMA-IR and HbA1c. Fig. S3. Histograms and pair-wise scatter plots of fasting glucose and 2-h post-load glucose concentrations at preconception, pregnancy and postpartum using trio subjects. Fig. S4. Histograms and pair-wise scatter plots of fasting insulin concentration and HOMA-IR at preconception, pregnancy and postpartum using trio subjects. Fig. S5. Histograms and pair-wise scatter plots of glycated haemoglobin (HbA1c, %) at preconception, pregnancy and postpartum using trio subjects. Fig. S6. Forest plots of fasting glucose concentration at postpartum, 2-h post-load glucose concentration at preconception, and HbA1c level at preconception, pregnancy and postpartum. Fig. S7. Scatter plots of effect sizes at preconception, pregnancy and postpartum in the fasting glucose, 2-h post-load glucose, fasting insulin, HOMA-IR association studies. Fig. S8. Venn diagrams of significant lipid species at preconception, pregnancy and postpartum for fasting glucose concentration, 2-h post-load glucose concentration and GDM status based on nominal p-value cut-off and the profiles of six selected lipid species from the 37 preconception signatures of GDM. Fig. S9. Association results of plasma fasting insulin concentration with plasma lipidomic profiles at preconception, pregnancy and postpartum. Fig. S10. Association results of fasting glucose, 2-h post-load glucose, impaired glucose tolerance status, fasting insulin, HOMA-IR and HbA1c levels at preconception. Fig. S11. Scatter plots of effect sizes in the association results of fasting glucose, 2-h post-load glucose, fasting insulin and HOMA-IR at preconception using trio and all subjects. Fig. S12. Percentage of individual lipid species within phosphatidylcholine, phosphatidylethanolamine and phosphatidylinositol classes. Fig. S13. Lipid ratios for enzyme indices of phosphatidylethanolamine n-methyltransferase (PEMT), lecithin-cholesterol acyltransferase (LCAT) and phospholipase A2 (PLA2).

Additional file 1: Fig. S1. Outlier detection by principal component analysis of the lipidomics data at four time points. Fig. S2. Number of pairs between the participants in antenatal, postnatal, cord blood and child lipidomes. Fig. S3. Comparison of the results of linear regression and paired t-test. Fig. S4. Ratios of PC to PE levels and lysophospholipids (lyso-PL). Fig. S5. Scatter plots of effect sizes and adjusted p-values of plasma lipids associated with mother and child adiposity. Fig. S6. Correlation of pre-pregnancy, antenatal and postnatal maternal body mass indices (BMIs). Fig. S7. Scatter plot of effect sizes in the pre-pregnancy and antenatal BMI studies. Fig. S8. Overlapping plasma lipid signatures associated with mother and child adiposity. Fig. S9. Sex difference in the birth weight and child BMI studies. Fig. S10. Forest plots and volcano plots of the meta-analysis results in GUSTO and BIS for ppBMI and BW studies. Table S1A. Comparison of lipid classes across four sample groups (antenatal, postnatal, cord blood and 6-year-old child) using linear regression model. Table S1B. Comparison of lipids species across four sample groups (antenatal, postnatal, cord blood and 6-year-old child) using linear regression model. Table S1C. Comparison of lipids species across four sample groups (antenatal, postnatal, cord blood and 6-year-old child) using paired t-test. Table S2A. Association of lipid classes with BMI or BW in four sample groups (antenatal, postnatal, cord blood and 6-year-old child) in terms of levels. Table S2B. Association of lipid species with BMI or BW in four sample groups (antenatal, postnatal, cord blood and 6-year-old child) in terms of levels. Table S2C. Association of antenatal lipidomic profiles with antenatal BMI. Table S3A. The overlapping lipid species between the ppBMI and postnatal BMI studies. Table S3B. The overlapping lipid species between the ppBMI and birth weight studies. Table S3C. The overlapping lipid species between the birth weight and 6-year-old child BMI studies. Table S3D. The overlapping lipid species between the 6-year-old child BMI and adult (postnatal mother) BMI studies. Table S3E. Overlapping lipid species in the four lipid-adiposity association studies. Table S4A. Sex difference in the birth weight and child BMI studies in terms of lipid class levels. Table S4B. Sex difference in the birth weight and child BMI in terms of species levels. Table S5. Association studies of lipids with pre-pregnancy BMI and birth weight in Barwon Infant Study (BIS), and the meta-analysis results of GUSTO and BIS.

Additional file 1: Figure S1. DNA quality analysis by agarose gel electrophoresis. Figure S2. Flowchart of sample selection and analysis steps. Figure S3. Trans-ethnic genome-wide association studies on telomere length. Figure S4. Boxplots of the top six genetic variants. Figure S5. Effect allele frequencies of the top six genetic variants. Figure S6. Heat map of pairwise Pearson correlation coefficients between clinical variables. Figure S7. Significant sex-specific effects of the selected factors on newborn telomere length. Figure S8. Association between maternal telomere length and antenatal maternal factors. Figure S9. The variance percentage explained by each factor. Figure S10. Scatter plot of average relative telomere length of cord blood and cord tissue. Figure S11. Mediation analysis of maternal telomere length. Table S1. Intra-class correlation coefficient of intra-assay and inter-assay for telomere length measurements. Table S2. Comparison of the basic characteristics of 950 subjects and the full cohort. Table S3. Clinical characteristics of maternal-offspring subjects in this study and linear regression results for newborn TL. Table S4. The association of SNPs at 3q26.2 in the GWAS results of newborn and maternal telomere lengths and the meta-analysis results. Table S5. Pairwise Linkage Disequilibrium measures between the top six genetic variants. Table S6. Linear regression results between maternal telomere length and antenatal maternal factors. Table S7. The results of sensitivity analysis after adding DNA storage time and sample collection month in the best multivariate models of newborn telomere length. Table S8. The results of sensitivity analysis after further adjustment for DNA storage time and sample collection month in the association studies between maternal telomere length and antenatal maternal factors. Table S9. The genetic variants in a strong Linkage Disequilibrium with rs10936600. Table S10. The association of candidate genes in the GWAS results of newborn and maternal telomere lengths and the meta-analysis results.

Supplementary figures. (PDF 6638 kb)

Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.

Cell type-specific DNA methylation in neonatal cord tissue and cord blood: A 850K-reference panel and comparison of cell-types

This study included 168 and 85 mother-infant dyads from Asian and United States of America cohorts to examine whether a genomic profile risk score formajor depressive disorder (GPRSMDD) moderates the association between antenatal maternal depressive symptoms (or socio-economic status, SES) and fetal neurodevelopment, and to identify candidate biological processes underlying such association. Both cohorts showed a significant interaction between antenatal maternal depressive symptoms and infant GPRSMDD on the right amygdala volume. The Asian cohort also showed such interaction on the right hippocampal volume and shape, thickness of the orbitofrontal and ventromedial prefrontal cortex. Likewise, a significant interaction between SES and infant GPRSMDD was on the right amygdala and hippocampal volumes and shapes. After controlling for each other, the interaction effect of antenatal maternal depressive symptoms and GPRSMDD was mainly shown on the right amygdala, while the interaction effect of SES and GPRSMDD was mainly shown on the right hippocampus. Bioinformatic analyses suggested neurotransmitter/neurotrophic signaling, SNAp REceptor complex, and glutamate receptor activity as common biological processes underlying the influence of antenatal maternal depressive symptoms on fetal cortico-limbic development. These findings suggest gene-environment interdependence in the fetal development of brain regions implicated in cognitive-emotional function. Candidate biological mechanisms involve a range of brain region-specific signaling pathways that converge on common processes of synaptic development.

Supplementary tables and figures. (PDF 2610 kb)

Information on using methylation data in Additional file 2. (PDF 42 kb)

Interindividual variability in the epigenome has gained tremendous attention for its potential in pathophysiological investigation, disease diagnosis, and evaluation of clinical intervention. DNA methylation is the most studied epigenetic mark in epigenome-wide association studies (EWAS) as it can be detected from limited starting material. Infinium HumanMethylation450 array (Infinium 450K) is the most popular platform for high-throughput profiling of this mark in clinical samples, as it is cost-effective and requires small amounts of DNA. However, this method suffers from low genome coverage and errors introduced by probe cross-hybridization. Whole-genome bisulfite sequencing can overcome these limitations but elevates the costs tremendously. Methyl-Capture Sequencing (MC Seq) is an attractive intermediate solution to increase the methylome coverage in large sample sets. Here we first demonstrate that MC Seq can be employed using DNA amounts comparable to the amounts used for Infinium 450K. Second, to provide guidance when choosing between the two platforms for EWAS, we evaluate and compare MC Seq and Infinium 450K in terms of coverage, technical variation, and concordance of methylation calls in clinical samples. Last, since the focus in EWAS is to study interindividual variation, we demonstrate the utility of MC Seq in studying interindividual variation in subjects from different ethnicities.

Information on using DNA methylation data in Additional files 2 and 3. (PDF 36 kb)