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Genetic variants associated with physiological and biochemical indicators: A multi‐centre whole‐exome sequencing study of Chinese healthy participants

Year: 2025

Type: article

Abstract: Genetic variants associated with physiological and biochemical indicators: A multi-centre whole-exome sequencing study of Chinese healthy participantsDear Editor, Disease onset and progression manifest through quantifiable alterations in organ function biomarkers, including hepatic, renal and lipid profiles.Even within normal reference ranges, these indicators could predict disease risk. 1 Physiological and biochemical profiles in Asians display distinct characteristics compared to those in Caucasians. 2 Identifying factors that influence these markers in Asian populations may therefore enhance early disease prediction and intervention, improving clinical outcomes.Given the heritability of physiological and biochemical indicators, 3 linking genetic variations with these indicators differences through genome-wide association studies (GWAS) has become a highly promising approach, particularly in lipid metabolism.4 However, databases such as UK Biobank and FinnGen present limitation due to ethnic diversity and lack of stringent screening.This study employed whole-exome sequencing to analyse genes associated with key physiological indicators in clinically screened Chinese healthy participants, providing insights into their genetic correlations.A total of 778 participants in IMPACT study were included in this cohort.[5][6][7][8] Briefly, healthy volunteers were enrolled in this study based on the absence of clinically significant abnormalities in blood pressure, heart rate, routine blood, blood biochemistry, chest X-ray, urinalysis and electrocardiogram, as well as confirmation of no infectious diseases or non-pregnancy status. Figure 1 shows the flow diagram.The physiological and biochemical indicators of participants are presented in Table 1.In discovery cohort, correlation analysis and linear regression were conducted to choose the covariates (Figure S1 and Table S1).Consequently, age, male proportion and body mass index were included as covariates in subsequent genetic association analyses.Identity-by-descent analysis

Source: Clinical and Translational Medicine

DOI: 10.1002/ctm2.70300