Location
Moultrie, GA
Start Date
7-5-2025 1:00 PM
End Date
7-5-2025 4:00 PM
Description
Introduction
Heart failure (HF) is a complex clinical syndrome characterized by the heart’s inability to pump blood efficiently. This can be significantly influenced by genetic factors. Genetic predisposition varies across ancestries, which can impact disease susceptibility. This study identifies ancestry-specific and shared genetic variants associated with HF.
Methods
We analyzed the “All by All” genome-wide association study (GWAS) result tables within the All-of-Us NIH Database using phenotype code CV_424 for HF. Heart failure (HF) patients were stratified into American (AMR), African (AFR), and European (EUR) ancestry groups. The analysis of GWAS identified significant ancestry-specific variants, followed by a meta-analysis to determine shared and unique associations. Rare variant gene associations were also assessed through meta-analysis.
Results
Significant variants were identified across ancestries:
-
AMR:
-
ZNF608 or LINC02240 (Chr5:125045705, P = 1.948×10⁻9)
-
ENSG00000293803 or EFNB2 (Chr13:104556816, P = 1.6771×10⁻8)
-
AFR:
-
THUMPD2 (Chr2:39751537, P = 3.9006×10⁻9)
-
ENSG00000297786 or GATA3 (Chr10:8541268, P = 4.6294×10⁻9)
-
EUR:
-
ST6GALNAC5 (Chr1:76852538, P = 2.2522×10⁻9)
-
ENSG00000294728 or UBL3 (Chr13:29995444, P = 3.3422×10⁻9)
-
SOX2 (Chr3:181688544, P = 1.3921×10⁻8)
-
FTO (Chr16:53775211, P = 2.9942×10⁻8)
-
Significant rare genetic variants with loss of function of TTN (P = 7.66×10⁻15) and SLC16A12 (9.07×10⁻7); and synonymous of ZBTB22 (P = 1.31×10⁻6)
-
Heart Failure Meta-analysis: Common variants at Chr1:76852538- ST6GALNAC5, Chr10:8553206- GATA3, and Chr 16:53775211- FTO, suggest shared genetic influences.
Discussion
This study highlights ancestry-specific and shared genetic associations with HF, reinforcing the importance of diverse genomic research. Limitations to this study include the limited sample size, which may affect statistical power. Future directions for this research are functional studies on identified variants and broader population analysis for genetic insights.
Embargo Period
6-4-2025
Included in
Genetic associations with heart failure across ancestries: an analysis of the All-of-Us NIH database.
Moultrie, GA
Introduction
Heart failure (HF) is a complex clinical syndrome characterized by the heart’s inability to pump blood efficiently. This can be significantly influenced by genetic factors. Genetic predisposition varies across ancestries, which can impact disease susceptibility. This study identifies ancestry-specific and shared genetic variants associated with HF.
Methods
We analyzed the “All by All” genome-wide association study (GWAS) result tables within the All-of-Us NIH Database using phenotype code CV_424 for HF. Heart failure (HF) patients were stratified into American (AMR), African (AFR), and European (EUR) ancestry groups. The analysis of GWAS identified significant ancestry-specific variants, followed by a meta-analysis to determine shared and unique associations. Rare variant gene associations were also assessed through meta-analysis.
Results
Significant variants were identified across ancestries:
-
AMR:
-
ZNF608 or LINC02240 (Chr5:125045705, P = 1.948×10⁻9)
-
ENSG00000293803 or EFNB2 (Chr13:104556816, P = 1.6771×10⁻8)
-
AFR:
-
THUMPD2 (Chr2:39751537, P = 3.9006×10⁻9)
-
ENSG00000297786 or GATA3 (Chr10:8541268, P = 4.6294×10⁻9)
-
EUR:
-
ST6GALNAC5 (Chr1:76852538, P = 2.2522×10⁻9)
-
ENSG00000294728 or UBL3 (Chr13:29995444, P = 3.3422×10⁻9)
-
SOX2 (Chr3:181688544, P = 1.3921×10⁻8)
-
FTO (Chr16:53775211, P = 2.9942×10⁻8)
-
Significant rare genetic variants with loss of function of TTN (P = 7.66×10⁻15) and SLC16A12 (9.07×10⁻7); and synonymous of ZBTB22 (P = 1.31×10⁻6)
-
Heart Failure Meta-analysis: Common variants at Chr1:76852538- ST6GALNAC5, Chr10:8553206- GATA3, and Chr 16:53775211- FTO, suggest shared genetic influences.
Discussion
This study highlights ancestry-specific and shared genetic associations with HF, reinforcing the importance of diverse genomic research. Limitations to this study include the limited sample size, which may affect statistical power. Future directions for this research are functional studies on identified variants and broader population analysis for genetic insights.