Driver Projects for PRECISE SG100K

• Lead PI: Li Jingmei
  A*STAR Genome Institute of Singapore

• Co-Lead PI: Rajkumar s/o Dorajoo
  A*STAR Genome Institute of Singapore

• Co-Lead PI: Khor Chiea Chuen
  A*STAR Genome Institute of Singapore

Taking reference from similar work performed by the Pan-UK Biobank, the team will compute in-sample dosage-based LD matrices and scores for each of the three major ancestry groups in SG100K:

• LD score regression analysis to estimate heritabilities

• Fine-mapping analysis to identify causal variants of well-powered complex traits

The plan is to make the LD matrices available in Hail's BlockMatrix format or similar. LD scores are also made available in LDSC-compatible flat files (.l2.ldscore.gz and .M_5_50).

• Lead PI: Mark Chan
  National University Hospital
  Cardiology

• Co-Lead PI: Dr Nicholas Chew
  National University Hospital
  Cardiology

1. Investigate associations between established non-invasive Chronic Liver Disease (CLD) biomarkers and cardiometabolic outcomes.

2. Evaluate how these associations relate to major adverse cardiac events.

3. Examine whether these associations with CLD are independent from associated metabolic disease.

• Lead PI: Liu Dianbo
  National University of Singapore
  Ophthamology

• Co-Lead PI: A/P Wee Hwee Lin
  National University of Singapore

• Co-Lead PI: Dr Nicolas Bertin
  A*STAR Genome Institute of Singapore

1. Identify non-linear genetic associations contributing to \ the susceptibility and manifestation of diverse eye diseases.

2. Explore epistatic interactions and allelic heterogeneity within the genomic data to unravel the complex relationships between multiple genetic variants.

3. Investigate how non-linear responses to environmental variables contribute to the phenotypic variation, with a focus on refining our understanding of gene- environment interactions in the context of ocular health.

4. Investigate and interpret the biological relevance of non-linear genetic associations. Aim to gain insights into the underlying mechanisms linking identified genetic variants to specific eye diseases and contribute to a more comprehensive understanding of the biology involved.

5. Evaluate the public health implications of the identified non-linear genetic associations, considering their potential impact on disease prevention, intervention, and personalised treatment strategies. Assess the translational potential of the research findings to inform clinical practice, public health policies, and contribute to advancements in precision medicine for ocular health.

• Lead PI: Yew Yik Weng
  National Skin Centre

• Co-Lead PI: Steven Thng Tien Guan
  National Skin Centre

• Co-Lead PI: Marie Loh
  Lee Kong Chian School of Medicine

1. Identify host genetic factors associated with chronic inflammatory skin diseases, specifically AD, psoriasis and chronic urticaria using genome wide association and rare variant analyses among SG100K study participants, taking advantage of whole genome sequence data and linkage to disease information from national electronic health records (NEHR).

2. Examine the relationship between genetic variants and polygenic risk scores (PRS) associated with skin phenotypes and real-world health data for skin diseases (including diagnosis, onset, severity and treatment outcomes) to identify genetic predictors of disease trajectories, complications and co-morbidities and treatment outcomes using the TRUST dataset.

• Lead PI: Theresia Mina
  Lee Kong Chian School of Medicine

• Co-Lead PI: Jojn Chambers
  Lee Kong Chian School of Medicine

• Co-Lead PI: Sim Xueling
  National University of Singapore

1. Evaluate the dietary pattern and characteristics of patients with IBS in Singapore.

2. Identify patterns of mood disorders in patients with IBS in Singapore.

3. Identify genetic variants associated with IBS in the multi-ethnic Singaporean Population. Explore and characterise common genetic polymorphisms IBS within the diverse Singaporean population. This comprehensive genetic investigation aims to unravel the unique genetic landscape of IBS, considering the multi-ethnic composition of the population.

4. Investigate the effects of lifestyle factors on IBS Risk and progression. Systematically examine the impact of lifestyle factors, including diet, physical activity, sleep, stress, and mental health, on the risk and progression of IBS. This multifaceted investigation seeks to discern the intricate relationships between lifestyle choices and IBS, contributing valuable insights for developing targeted interventions and improving patient outcomes.

5. Elucidate potential interactions between genetic and environmental influences on IBS. Uncover and elucidate potential interactions between genetic factors and environmental influences in the development and progression of IBS. This integrated approach aims to provide a nuanced understanding of how genetic predispositions and environmental exposures collaboratively contribute to the manifestation of IBS, offering a foundation for personalised and precision medicine strategies.

• Lead PI: Theresia Mina
  Lee Kong Chian School of Medicine

• Co-Lead PI: Jojn Chambers
  Lee Kong Chian School of Medicine

• Co-Lead PI: Sim Xueling
  National University of Singapore

1. Conduct phenotypic associations of macronutrients with visceral adiposity as primary outcome, and the visceral fat linked cardiometabolic traits and diseases as secondary outcomes in multiethnic Asian population.

2. Perform GWAS of macronutrients in multiethnic Asian population using the SG100K dataset and a GWAS meta-analysis using the UK Biobank macronutrient intake data.

3. Perform functional annotation of significant loci and estimate the genetic correlations of macronutrient intake with visceral fat linked cardiometabolic traits and diseases as secondary outcomes.

4. Conduct one-sample and two-sample Mendelian Randomisation (MR) with macronutrient intake as exposure variables and visceral adiposity as outcome variables, with relevant sensitivity analyses.

• Lead PI: Joanna Tan Hui Juan
  A*STAR Genome Institute of Singapore

• Co-Lead PI: Shyam Prabhakar
  A*STAR Genome Institute of Singapore

• Co-Lead PI: Patrick Tan Boon Ooi
  A*STAR Genome Institute of Singapore

1. Build a catalogue of SVs (deletions, insertions, duplications, inversions, translocations, and tandem repeats) from the PRECISE-SG100K dataset.

2. Investigate the identified SVs to uncover population-specific trends.

3. Examine the functional consequences of SVs in different genomic regions as well as predict the impact of SVs in medically relevant genes.

4. Identify SVs that are associated with phenotypic traits within the PRECISE-SG100K dataset.

5. Elucidate the impact of SVs on variation in cell type-specific gene expression (SV-eQTLs) and validate SVs through copy number variation inferences from scRNA-seq data.

• Lead PI: Joseph Lo
  Woodlands Health

• Co-Lead PI: Kavita Venkataraman
  National University of Singapore

• Co-Lead PI: Yusuf Ali
  Lee Kong Chian School of Medicine

Primary aim:
Identify genetic loci associated with diabetic foot ulcers in Asian patients with diabetes mellitus.

Secondary aims:

• Identify differences in genetic loci within Malay/Indian ethnicities.

• Identify genetic loci associated with diabetic peripheral neuropathy.

• Identify potential gene-environment interactions (for example, tobacco smoking) associated with the risk ofdiabetic foot ulcers.

• Identify socio-economical and other risk factors associated with diabetic foot ulcers.

• Identify correlations between macro-angiopathy, micro-vascular reactivity nephropathy and retinopathy and diabetic foot ulcers.

• Develop multi-polygenic risk score for developing diabetic foot ulcers.

• Lead PI: Joanne Ngeow
  Lee Kong Chian School of Medicine

• Co-Lead PI: Rajkumar s/o Dorajoo
  A*STAR Genome Institute of Singapore

• Co-Lead PI: Neerja Karnani
  A*STAR Genome Institute of Singapore

1. Generate common variant polygenic risk scores for common cancers (breast, colorectal, liver, lung, and prostate cancers) and identify potential functional rare coding genetic mutations in strong cancer related genes in the SG100K dataset.

2. Generate additional age-related biomarkers (i.e as telomere length estimates) related to cancer risk from the SG100K WGS data and identify genetic predispositions associated with these biomarkers.

3. Linkage of genetic datasets with TRUST to derive clinical data and determine common cancer status (breast, colorectal, liver, lung, and prostate cancers).

• Lead PI: Liu Jianjun
  A*STAR Genome Institute of Singapore

• Co-Lead PI: Nicolas Bertin
  A*STAR Genome Institute of Singapore

• Co-Lead PI: Lim Weng Khong
  Duke-NUS Medical School

1. Generate a SG100K genome wide TR variation catalogue and characterisation their respective prevalence in Asian populations.

2. Characterise the contributions of TR variations to the aetiology of complex neurological and neurocognitive disorders.

• Lead PI: Huang Jian
  A*STAR Singapore Institute for Clinical Sciences and Bioinformatics Institute

• Co-Lead PI: Dennis Wan
  A*STAR Singapore Institute for Clinical Sciences and Bioinformatics Institute

1. Investigate the effects of GLP-1 receptor agonist on various domains of health outcomes using an observational study design

2. Identify the non-synonymous single nucleotide polymorphisms (SNPs) of GLP-1 receptor agonist prescription and predict nsSNPs responsible for the differential response to GLP-1 receptor agonist.

3. Provide genetic evidence for the therapeutic potentials and adverse effects of GLP-1 receptor agonists by adopting a drug target Mendelian randomisation design.

• Lead PI: Sunny Wong
  Lee Kong Chian School of Medicine

• Co-Lead PI: Anselm Mak
  National University of Singapore

• Co-Lead PI: Bernett Lee
  Lee Kong Chian School of Medicine

1. Identify Genetic Variants Associated with IBD and Related Immune-Mediated Disorders in the Multi-Ethnic Singaporean Population. Explore and characterise both common and rare genetic variants linked to IBD, and spondyloarthropathies, uveitis, Behcet's disease, psoriasis, and other related immune-mediated conditions within the diverse Singaporean population. This comprehensive genetic investigation aims to unravel the unique genetic landscape of this disease cluster, considering the multi- ethnic composition of the population.

2. Investigate the Effects of Lifestyle Factors on IBD and Associated Diseases Risk and Progression. Systematically examine the impact of lifestyle factors, including diet, physical activity, sleep, stress, and mental health, on the risk and progression of IBD and associated immune-mediated diseases. This multifaceted investigation seeks to discern the intricate relationships between lifestyle choices and disease outcomes, contributing valuable insights for developing targeted interventions and improving patient wellbeing.

3. Delineate shared and distinct mechanisms underlying IBD, spondyloarthropathy, uveitis, psoriasis, Behcet's and other related conditions. Elucidate the shared and unique genetic and biological pathways driving IBD, spondyloarthropathy, uveitis, psoriasis, Behcet's disease, and other related conditions. This will provide critical insights into disease mechanisms to guide targeted prevention and treatment strategies for this nexus of related diseases.

4. lucidate Potential Interactions Between Genetic and Environmental Influences on This Disease Cluster. Uncover and elucidate potential interactions between genetic factors and environmental influences in the development and progression of IBD and related conditions. This integrated approach aims to provide a nuanced understanding of how genetic predispositions and environmental exposures collaboratively contribute to the manifestation of this disease cluster, offering a foundation for personalised and precision medicine strategies.

• Lead PI: Chew Fook Tim
  National University of Singapore

1. Validate disease-associated genetic polymorphisms and enviromental factors that were previously identified and functionally characterised in the SMCGES cohort, using the PRECISE-SG100K dataset.

2. Investigate the associations of previously identified asthma/AR/AD/acne candidate genes with the other clinical parameters relevant to the disease of interest. For instance, whether the allelic/genotypic differences of genetic variants would affect the treatment response, lung functon (spirometry), skin condition (sites of flexural dermatitis and psoriasis, etc.) in complex disease.

3. Reproduce and validate the observed associations between specific dietary habits and allergic diseases, using a more extensive and culturally relevant FFQ.

4. Explore causal relationship between dietary habits and allergic diseases by understanding how changes in dietary patterns influence the development and progression of allergic diseases.

• Lead PI: Ho Han Kiat
  National University of Singapore

1. Determine the prevalence of CYP7A1 single nucleotide polymorphisms (SNP) locally, on extrapolation, to the region that presents similar ethnicities.

2. Ascertain the relationship between CYP7A1 SNPs and hypercholesterolemia in our local population.

3. Identify the target population most likely to benefit from targeting CYP7A1 as an orthogonal approach to cholesterol control.

4. Study the impact of non-genetic extrinsic factors, such as comorbidities and comedications, on the genotypes to discern the possibility of phenoconversion.

• Lead PI: Mu Yuguang
  Nanyang Technological University

• Co-Lead PI: Bernett Lee
  Lee Kong Chian School of Medicine

• Co-Lead PI: Geoffrey Tan Chern-Yee
  Institute of Mental Health

1.1 Build machine learning and deep learning models specific to Singaporean demographics which aid in prediction of depression.

1.2 To identify gene by marker and gene by environment interactions predicting depressive and anxiety symptomswith dietary, nutrient, metabolic, lifestyle, sociodemographic and cognitive factors

1.3 Apply feature selection approaches to discover important environmental and genetic features used by the machine learning and deep learning models to predict depression to ensure the model is explainable and reasonable to physicians.

1.4 Explore the potential for the depression machine learning models to be deployed in clinical settings.

2.1 Construct a language model which could extract complex interrelations between patient features under Singapore context.

2.2 Assess the capability of the language model in imputing missing features of a patient when other features were provided to extrapolate patient features in clinical settings.

2.3 Assess suitability of leveraging the trained language model for transfer learning.

• Lead PI: Tan Eng King
  National Neuroscience Institute

• Co-Lead PI: Thomas Welton
  Duke-NUS Medical School

• Co-Lead PI: Chan Ling Ling
  Duke-NUS Medical School

1a. Determine the prevalence of Asian specific LRRK2 coding variants and other PD risk genes (e.g., APOE, SNCA, GBA1, HLA alleles, etc) in Malays, Indians and Chinese in the SG100k cohort.

1b. Investigate the association of PD risk genes with comorbidities (Diabetes, Hypertension, Heart disease, Autoimmune diseases, Infectious diseases, vaccination history etc) in carriers and compare the associations with non-carriers.

2. Investigate the association with motor and non-motor features between PD risk gene carriers and non-carriers based on the quantitative outcome measures (e.g. eye, cognition, bone mass, etc).

3. Investigate the differences in MRI regional volumes, lesion burden and tissue microstructure between carriers and non- carriers.

• Lead PI: Neerja Karnani
  A*STAR Bioinformatics Institute

• Co-Lead PI: Joanne Ngeow
  Lee Kong Chian School of Medicine

• Co-Lead PI: Brian Kennedy
  National University of Singapore

• Co-Lead PI: Rajkumar s/o Dorajoo
  A*STAR Genome Institute of Singapore

1. Investigate the stressors associated with aging and identify the factors contributing to resilience.

2. Investigate gender-specific variations in aging stressors and assess the influence of reproductive aging.

3. Examine the effects of Asian ethnicity on the aging process and healthspan.

4. Evaluate the pharmacogenomic effects of medications on lifespan and overall health during aging

• Lead PI: Pierre-Alexis Goy
  A*STAR Genome Institute of Singapore

• Co-Lead PI: Li Jingmei
  A*STAR Genome Institute of Singapore

1. Integration of Research Phenotypes with PRS Catalogue Ontologies and Identification of Applicable PRS Models:

   • Align research phenotypes with the trait ontologies in the EBI-maintained PRS catalogue.

   • Systematically select pertinent PRS models based on the relevance to mapped research phenotypes.

2. Assessment of Performance Across Diverse Ancestries within SG100K:

   • Evaluate the effectiveness of selected PRS models across the major ancestries represented in the SG100K dataset (i.e. distribution, discrimination, calibration).

   • Analyse and compare performance metrics to identify any ancestry-specific nuances in predictive accuracy.

   • Recommendations and portability assessment of EBI-catalogued published PRS in a Singapore context.

• Lead PI: Tan Nguan Soon
  Lee Kong Chian School of Medicine

• Co-Lead PI: Yew Kuo Chao
  Tan Tock Seng Hospital

• Co-Lead PI: Cheng Hong Sheng
  Lee Kong Chian School of Medicine

1. Investigate the genomic risk of MASLD and associated metabolic traits in Asian populations.

2. Interrogate the contribution of dietary components, alcohol intake and physical activity to MASLD and MetALD disease spectrum.

3. Develop machine learning frameworks for risk stratification and identification of predictive markers for MASLD disease spectrum.

• Lead PI: Teh Bin Tean
  National Cancer Centre Singapore

• Co-Lead PI: Frederick Koh Hong Xiang
  SingHealth

1. Validate the multi-omics signature of people with various stages of sarcopenia.

2. Identify the influence of sarcopenia on comorbidities and its impact on clinically relevant outcomes.

3. Evaluate correlation of biomarkers of sarcopenia with social economic status

• Lead PI: Yusuf Ali
  Lee Kong Chian School of Medicine

• Co-Lead PI: Sunny Wong
  Lee Kong Chian School of Medicine

• Co-Lead PI: Fan Xiuyi
  Lee Kong Chian School of Medicine

1. Correlates of obesity before age 45 and incidence of cancer (EHR). Compare risk relationships by ethnicity and sex.

2. Develop a dietary pattern score characterising inflammatory potential of diet. Relate this score to circulating markers of inflammation and metabolic health among PRECISE-SG100K participants under age 45.

3. Determine whether medications and bariatric surgery mitigate cancer risk in young onset obese PRECISE-SG100K participants. Compare effectiveness across ethnic groups and cancer sites.

• Lead PI: Soong Tuck Wah
  National University of Singapore

Profile the frequency of splicing associated genetic variations of VGCCs and their auxiliary subunits, and their potential association with schizophrenia in the SG100K cohort.

• Lead PI: Tan Kar-Tong
  National University of Singapore

1. Assess the impact of genetic variations the rate of somatic mutagenesis, and the risk of CVDs.

2. Assess the impact of environmental exposures on the rate of somatic mutagenesis, and the risk of CVDs.

3. Assess the impact of DNA damaging drugs on the rate of somatic mutagenesis and CVDs.

• Lead PI: Ng Kar Hui
  National University of Singapore

• Co-Lead PI: David Bryce Matchar
  Duke-NUS Medical School

• Co-Lead PI: Jason Choo Chon Jun
  Duke-NUS Medical School

1.Determine the prevalence of autosomal dominant (AD), X-linked (XL), autosomal recessive (AR) and digenic Alport syndrome in Singapore; and differences in these prevalences among the Chines, Malay and Indian populations in Singapore.

2a. Determine the penetrance of Kidney, eye and hearing phenotypes in AD Alport, XL male Alport and XL female Alport syndrome, stratified according to age groups and gender in Singapore.

2b. Estimate the number of diagnosed Alport and versus mis-diagnosed or undiagnosed Alport cases in Singapore and the differences in healthcare costs, service utilisation and patterns of care among these groups.

2c. Correlate the severity of the kidney phenotypes with the genotypes in COL4A3 and COL4A4, specifically comparing collagenous domain glycine missense variants with other types of genetic variants.

3a. Determine the clinical features that predict an Alport genetic diagnosis.

3b. Determine the added risk of AD Alport on bad kidney outcomes (ESKD, rapid GFR decline or heavy proteinuria).

3c. Determine the clinical features that predict a poor kidney outcome in AD Alport subjects.

• Lead PI: Joshua Tay
  National University of Singapore

• Co-Lead PI: Tan Ene Choo
  KK Women's & Children's Hospital

• Co-Lead PI: Goh Xueying
  National University of Singapore

1. Describe the genetic landscape of congenital and early-onset hearing loss in multi-ethnic Singapore. Identify the prevalence of known genetic variants and novel variants associated with hearing loss.

2. Analyse interactions between hearing loss-associated genetic variants and clinical events that may potentiate hearing loss (e.g. use of ototoxic drugs).

3. Develop and validate a polygenic risk score for congenital and early onset hearing loss based on an individual's genotype.

• Lead PI: Andrea B. Maier
  National University of Singapore

• Co-Lead PI: Weilan Wang
  National University of Singapore

1. Investigate the optimal versus current reference ranges of organ systems against the risk of age-related diseases using Singaporean data.

2. Develop and validate a biological clock on organ systems (cardiovascular, pulmonary, metabolic, immune, hepatic, and musculoskeletal systems) based on Singaporean data.

3. Explore the lifestyle and genetic risk factors associated with advanced biological organ age.

• Lead PI: Patrick Tan
  Duke-NUS Medical School

• Co-Lead PI: Lim Weng Khong
  Duke-NUS Medical School

• Co-Lead PI: Rajkumar s/o Dorajoo
  A*STAR Genome Institute of Singapore

1. Systematically identify genes associated with gastrointestinal cancer risks and survival and estimate penetrance (the cancer risk associated with gene variants) of both novel and known pathogenic genes in gastrointestinal cancer.

2. Investigate the interactions between the human genome, lifestyle factors and presence of precursor lesions. This aim to determine the extent that a healthier lifestyle can mitigate gastrointestinal cancer risk in subjects with premalignant lesions or carrying a cancer predisposition gene.

3. Quantify the proportional contribution of human genome and lifestyle factors to risks and survival outcomes of gastrointestinal cancers. We also aim to develop predictive models for gastrointestinal risks by integrating these factors.

• Lead PI: Kelvin Bryan Tan
  Ministry of Health

1. Mine and catalogue published disease severity and susceptibility genetic variants and testing their prevalence in Singaporeans. This will include not just previously published variants but identify potential new variants which are associated with COVID severity, susceptibility and long COVID outcomes. Prevalence and allele frequencies of these variants will be further studied.

2. Genome wide association study (GWAS) to identify novel host genetic factors in our population.

3. Assessing and developing genetic risk scores of disease severity, susceptibility and long COVID outcomes in our population.