TY - JOUR
T1 - The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1
AU - Ma, Lijiang
AU - Bryce, Nicole S.
AU - Turner, Adam W.
AU - Di Narzo, Antonio F.
AU - Rahman, Karishma
AU - Xu, Yang
AU - Ermel, Raili
AU - Sukhavasi, Katyayani
AU - d’Escamard, Valentina
AU - Chandel, Nirupama
AU - V’Gangula, Bhargavi
AU - Wolhuter, Kathryn
AU - Kadian-Dodov, Daniella
AU - Franzen, Oscar
AU - Ruusalepp, Arno
AU - Hao, Ke
AU - Miller, Clint L.
AU - Björkegren, Johan L.M.
AU - Kovacic, Jason C.
N1 - Funding Information:
KH acknowledges support from NIH (1R01ES029212). CLM acknowledges support from NIH (R01HL148239, R00HL125912) and Fondation Leducq. JLMB acknowledges support from NIH R01HL125863, Swedish Research Council (2018-02529) and Heart Lung Foundation (20170265), Foundation Leducq (PlaqueOmics, 18CVD02; and CADgenomics, 12CVD02) and Astra-Zeneca. DKD acknowledges support from NIH (R01HL148167). JCK acknowledges support from NIH (R01HL130423, R01HL135093, R01HL148167), New South Wales health grant RG194194, the Bourne Foundation and Agilent. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2022 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2022/6/17
Y1 - 2022/6/17
N2 - Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The ‘gold standard’ method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.
AB - Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The ‘gold standard’ method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.
UR - http://www.scopus.com/inward/record.url?scp=85133048073&partnerID=8YFLogxK
U2 - 10.1371/journal.pgen.1010261
DO - 10.1371/journal.pgen.1010261
M3 - Article
C2 - 35714152
AN - SCOPUS:85133048073
VL - 18
JO - PLoS Genetics
JF - PLoS Genetics
SN - 1553-7390
IS - 6
M1 - e1010261
ER -