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Approved Research

Association analysis between inflammatory diseases and clonal hematopoiesis of indeterminate potential (CHIP)

Principal Investigator: Dr Denny Sun
Approved Research ID: 86996
Approval date: April 28th 2022

Lay summary

Clonal hematopoiesis of indeterminate potential (CHIP), is an age-related phenomenon characterized by a gradual replacement of polyclonal leucocytes by one or more clones marked by somatic mutations. CH is associated with an elevated relative risk of developing hematological malignancies compared to age and sex-matched controls without CH, and an elevated risk of developing non-malignant, immune and inflammatory disorders such as atherosclerotic cardiovascular disease (CVD), Alzheimer's disease, and COPD.

Our company recently generated high quality and deep coverage NGS data with 1,000x from various diseases and healthy control cohorts. Our aim is to assess the relationship between CH and non-malignant diseases in our dataset and UK Biobank (UKBB), an ongoing, prospective UK cohort study of approximately 500,000 community-dwelling participants aged 40-69 years when recruited between 2006 and 2010.

CHIP affects 10% of the population older than 70 years and has been linked with an increase in cardiovascular and hematological malignancies based on 2% variant allele frequency. However, CHIP somatic mutation calling is very various and limited depending on their data quality and depth of coverage. Recently. a few studies have also reported 1% VAF as a result of statistically clinical significance. Using the UK, we will characterize CHIP and then validate statistical clinical significance for our disease cohorts. Also we will assess for a reasonable VAF cut-off to show statistical significance. Over the next three years, we will use data from UKBB to identify and validate novel disease associations of CHIP as below:

1) Characterize the presence of CHIP in the UKBB. CHIP somatic mutation calling by various cutoff conditions

2) Assess for variant allele frequency distribution between out dataset and UKBB, and limitation for characterize the presence of CHIP

3) Utilize the breadth of available phenotypic data to conduct a comprehensive understanding of the driver mutations (or genes) of CHIP