PRECISE-SG100K Projects

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PRECISESG100KProjects.csv (36 rows, 29.4 KB)

No Text	Project Text	Project Team Text	Project Aims Text
(Null)0.0%	(Null)0.0%	(Null)0.0%	(Null)0.0%
1	Advancing Precision Medicine for Cardiovascular Disease and Diabetes in Asian Populations	Lead PI: Prof John Chambers, Lee Kong Chian School of Medicine Co-Lead PI: A/Prof Sim Xueling, National University of Singapore Co-Lead PI: Prof Cheng Ching-Yu, Duke-NUS Medical School Co-Lead PI: Prof Yeo Khung Keong, National Heart Centre Singapore	1. Determine the behavioural (including nutrition and physical activity), environmental, genetic, and other molecular factors that underpin CVD and diabetes in the multi-ethnic Asian population in Singapore. 2. Develop and validate algorithms for accurate identification of Asian individuals who are at increased risk of CVD and diabetes.
2	The SG100K Cognitive Health Programme	Lead PI: Adj Asst Prof Max Lam, Lee Kong Chian School of Medicine Co-Lead PI: A/Prof Jimmy Lee, Institute of Mental Health Co-Lead PI: Prof Liu Jianjun, A*STAR Genome Institute of Singapore	1. Establish the biological underpinnings for cognitive function in diverse Asian and global populations. 2. Establish the biological convergence between cognitive function and disease traits. 3. Establish epidemiological and genomic risk predictors of cognitive health.
3	The SG100K_Med Alliance - Clinical Genetics Researchers United for the Analysis of Mendelian Disease Variation in SG100K	Lead PI: Asst Prof Lim Weng Khong, Duke-NUS Medical School Co-Lead PI: A/Prof Joanne Ngeow, Lee Kong Chian School of Medicine Co-Lead PI: A/Prof Saumya Jamuar, Duke-NUS Medical School	1. Seek a deeper understanding of genetic disease burden in major Asian populations through a comprehensive analysis of structural variation and short tandem repeat expansions. 2. Demonstrate how SG100K data can resolve variants of uncertain significance. 3. Explore impact of polygenic backgrounds on penetrance in autosomal dominant conditions for under-represented Asian populations.
4	Identification of Asian-specific Genetic Association with Fat and Lean Muscle Mass Distribution	Lead PI: Asst Prof Liu Boxiang, National University of Singapore Co-Lead PI: A/Prof Sim Xueling, National University of Singapore Co-Lead PI: Prof Tai E Shyong, National University of Singapore	1. Perform multi-ethnic meta-analysis of fat and lean muscle mass using SG100K and UKBB datasets. 2. Mendelian randomisation analysis to identify the contribution of fat and lean muscle mass to cardiometabolic diseases. 3. Colocalisation analysis to identify risk genes affecting fat and lean muscle mass. 4. Conduct functional validation studies of identified genetic loci.
5	HLA alleles and its Association with Auto-immune Diseases and Pharmacogenomics in Multi-Ancestral Asian Populations	Lead PI: A/Prof Sim Xueling, National University of Singapore Co-Lead PI: Adj A/Prof Leong Khai Pang, Tan Tock Seng Hospital Co-Lead PI: Dr Wharton Chan, Duke-NUS Medical School	1. Generate a high-resolution human leukocyte antigen (HLA) reference panel in Asian populations. 2. Generate frequencies of HLA alleles and haplotypes in Asian populations for local reference and for global population comparisons. 3. Conduct association analyses of HLA alleles in outcomes including auto-immune diseases and pharmacogenomic responses.
6	Unraveling the Determinants of Kidney Health in a Multi-Ethnic Asian Population	Lead PI: A/Prof Yeo See Cheng, Tan Tock Seng Hospital Co-Lead PI: Prof John Chambers, Lee Kong Chian School of Medicine	1. Determine prevalence of chronic kidney disease (CKD) among adults. 2. Examine association of CKD with genetic, clinical, and socio-behavioural predictors. 3. Examine relative contribution of key predictors driving differences in CKD risks across different sub-population. 4. Develop and validate an integrated risk score for the development of CKD in a representative multi-ethnic Asian population-based cohort in Singapore.
7	The High Variability of Tandem Repeats Offers Insights into Population Diversity and may Explain the Missing Heritability of Complex Neurological and Neurocognitive Disorders in Asian Populations	Lead PI: Prof Liu Jianjun ASTAR Genome Institute of Singapore Co-Lead PI: Dr Nicolas Bertin, ASTAR Genome Institute of Singapore Co-Lead PI: Asst Prof Lim Weng Khong, Duke-NUS Medical School	1. Generate SG100K genome wide tandem repeats (TR) variation catalogue and characterise their respective prevalence in Asian populations. 2. Characterise contributions of TR variations to the aetiology of complex neurological and neurocognitive disorders.
8	An Integrated Pharmacoeconomic-Pharmacokinetic Framework for Prioritising and Testing Clinically Important Drug-Gene Interactions	Lead PI: Dr Janice Goh, ASTAR Bioinformatics Institute Co-Lead PI: A/Prof Wee Hwee Lin, National University of Singapore Co-Lead PI: Dr Nicolas Bertin, ASTAR Genome Institute of Singapore	1. Evaluate the occurrence of known drug-gene interactions based on HER data and its impact on efficacy and toxicity. 2. Explore genotype-drug response associations using SG100K and linked HER datasets augmented by a dedicated pipeline for haplotyping highly polymorphic drug metabolising enzyme CYP2D6. 3. Develop a pharmacokinetics-informed framework for evaluating and ranking both known and novel drug-gene sets for clinical action to make dose recommendations.
9	Genetic Variants Contributing to Clonal Haematopoiesis across Diverse Asian Genomes	Lead PI: Prof Ong Sin Tiong, Duke-NUS Medical School Co-Lead PI: Prof Ashok Vekitaraman, National University of Singapore Co-Lead PI: Prof Chng Wee Joo, National University of Singapore Co-Lead PI: Prof John Chambers, Lee Kong Chian School of Medicine Co-lead PI: Dr Nicolas Bertin, A*STAR Genome Institute of Singapore	1. Determine age-related incidence of clonal haematopoiesis (CH) among our three major ancestry groups. 2. Correlate CH status with clinical metadata, measures of ageing and disease-incidence, and disease-related variables including biomarkers. 3. Discover novel genetic associations with CH. 4. Integrate functional genomics for novel Asian CH driver mutation discovery and validation. 5. Correlate CH status with cell clusters and gene expression signatures in the AIDA scRNA-seq dataset.
10	Computation of Genome-Wide LD Scores and Matrices from the SG100K resource	Lead PI: Li Jingmei, ASTAR Genome Institute of Singapore Co-Lead PI: Rajkumar s/o Dorajoo, ASTAR Genome Institute of Singapore Co-Lead PI: Khor Chiea Chuen, A*STAR Genome Institute of Singapore	1. Compute in-sample dosage-based LD matrices and scores for each of the three major ancestry groups in SG100K, taking reference from similar work performed by the Pan-UK Biobank. 2. Use LD score regression analysis to estimate heritabilities. 3. Use fine-mapping analysis to identify causal variants of well-powered complex traits.

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Title	Column name	Data type	Unit of measure	Description
No	No	Text	-	-
Project	Project	Text	-	-
Project Team	Project_Team	Text	-	-
Project Aims	Project_Aims	Text	-	-

Sample OpenAPI query

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import requests
          
dataset_id = "d_4dbb5c7995589918858f3c9c98b34706"
url = "https://data.gov.sg/api/action/datastore_search?resource_id="  + dataset_id 
        
response = requests.get(url)
print(response.json())

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PRECISESG100KProjects.csv (36 rows, 29.4 KB)

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