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Association and interaction of PPAR-complex gene variants with latent traits of left ventricular diastolic function.

Juang JM, de Las Fuentes L, Waggoner AD, Gu CC, Dávila-Román VG - BMC Med. Genet. (2010)

Bottom Line: By linear regression analysis, 7 SNPs (4 in PPARA, 2 in PPARGC1A, 1 in PPARG) were significantly associated with the latent LVDF trait, whereas a range of 0-4 SNPs were associated with each of the 14 measured echocardiography-derived endophenotypes.Frequency distribution of P values showed a greater proportion of significant associations with the latent LVDF trait than for the measured endophenotypes, suggesting that analyses of the latent trait improved detection of the genetic underpinnings of LVDF.In the gene-gene analysis, significant interactions were found between rs4253655 in PPARA and rs1873532 (p = 0.02) and rs7672915 (p = 0.02), both in PPARGC1A, and between rs1151996 in PPARG and rs4697046 in PPARGC1A (p = 0.01).

View Article: PubMed Central - HTML - PubMed

Affiliation: Cardiovascular Division, Department of Medicine, Cardiovascular Imaging and Clinical Research Core Laboratory, Washington University School of Medicine, St Louis, Missouri, USA.

ABSTRACT

Background: Abnormalities in myocardial metabolism and/or regulatory genes have been implicated in left ventricular systolic dysfunction. However, the extent to which these modulate left ventricular diastolic function (LVDF) is uncertain.

Methods: Independent component analysis was applied to extract latent LVDF traits from 14 measured echocardiography-derived endophenotypes of LVDF in 403 Caucasians. Genetic association was assessed between measured and latent LVDF traits and 64 single nucleotide polymorphisms (SNPs) in three peroxisome proliferator-activated receptor (PPAR)-complex genes involved in the transcriptional regulation of fatty acid metabolism.

Results: By linear regression analysis, 7 SNPs (4 in PPARA, 2 in PPARGC1A, 1 in PPARG) were significantly associated with the latent LVDF trait, whereas a range of 0-4 SNPs were associated with each of the 14 measured echocardiography-derived endophenotypes. Frequency distribution of P values showed a greater proportion of significant associations with the latent LVDF trait than for the measured endophenotypes, suggesting that analyses of the latent trait improved detection of the genetic underpinnings of LVDF. Ridge regression was applied to investigate within-gene and gene-gene interactions. In the within-gene analysis, there were five significant pair-wise interactions in PPARGC1A and none in PPARA or PPARG. In the gene-gene analysis, significant interactions were found between rs4253655 in PPARA and rs1873532 (p = 0.02) and rs7672915 (p = 0.02), both in PPARGC1A, and between rs1151996 in PPARG and rs4697046 in PPARGC1A (p = 0.01).

Conclusions: Myocardial metabolism PPAR-complex genes, including within and between genes interactions, may play an important role modulating left ventricular diastolic function.

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Related in: MedlinePlus

Significant SNPs and their interactions (within-gene and gene-gene), with corresponding p-values identified by the two-step ridge regression analysis are shown. SNPs are grouped by the candidate genes (i.e., PPARGC1A, PPARA and PPARG); significant interactions are shown by bidirectional arrows between two interacting SNPs: within-gene (arrows within boxes) and gene-gene interactions (arrows between boxes).
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Figure 2: Significant SNPs and their interactions (within-gene and gene-gene), with corresponding p-values identified by the two-step ridge regression analysis are shown. SNPs are grouped by the candidate genes (i.e., PPARGC1A, PPARA and PPARG); significant interactions are shown by bidirectional arrows between two interacting SNPs: within-gene (arrows within boxes) and gene-gene interactions (arrows between boxes).

Mentions: In the within-gene analysis, significant SNPs (8 in PPARA, 12 in PPARGC1A, and 4 in PPARG) for each of the three genes were retained for the final best-fit model (log likelihood: -276.1, -281.2 and -281.6, respectively; Table 6 and Figure 2). PPARGC1A SNPs rs12500214 significantly interacted with both rs2970847 and rs7672915 (p = 0.02 and 0.009, respectively); rs768695 significantly interacted with both rs2970847 and rs2970853 (both p = 0.03); and rs4235308 significantly interacted with rs7672915 (p = 0.007). No significant within-gene interactions were found among PPARA and PPARG SNPs.


Association and interaction of PPAR-complex gene variants with latent traits of left ventricular diastolic function.

Juang JM, de Las Fuentes L, Waggoner AD, Gu CC, Dávila-Román VG - BMC Med. Genet. (2010)

Significant SNPs and their interactions (within-gene and gene-gene), with corresponding p-values identified by the two-step ridge regression analysis are shown. SNPs are grouped by the candidate genes (i.e., PPARGC1A, PPARA and PPARG); significant interactions are shown by bidirectional arrows between two interacting SNPs: within-gene (arrows within boxes) and gene-gene interactions (arrows between boxes).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2874543&req=5

Figure 2: Significant SNPs and their interactions (within-gene and gene-gene), with corresponding p-values identified by the two-step ridge regression analysis are shown. SNPs are grouped by the candidate genes (i.e., PPARGC1A, PPARA and PPARG); significant interactions are shown by bidirectional arrows between two interacting SNPs: within-gene (arrows within boxes) and gene-gene interactions (arrows between boxes).
Mentions: In the within-gene analysis, significant SNPs (8 in PPARA, 12 in PPARGC1A, and 4 in PPARG) for each of the three genes were retained for the final best-fit model (log likelihood: -276.1, -281.2 and -281.6, respectively; Table 6 and Figure 2). PPARGC1A SNPs rs12500214 significantly interacted with both rs2970847 and rs7672915 (p = 0.02 and 0.009, respectively); rs768695 significantly interacted with both rs2970847 and rs2970853 (both p = 0.03); and rs4235308 significantly interacted with rs7672915 (p = 0.007). No significant within-gene interactions were found among PPARA and PPARG SNPs.

Bottom Line: By linear regression analysis, 7 SNPs (4 in PPARA, 2 in PPARGC1A, 1 in PPARG) were significantly associated with the latent LVDF trait, whereas a range of 0-4 SNPs were associated with each of the 14 measured echocardiography-derived endophenotypes.Frequency distribution of P values showed a greater proportion of significant associations with the latent LVDF trait than for the measured endophenotypes, suggesting that analyses of the latent trait improved detection of the genetic underpinnings of LVDF.In the gene-gene analysis, significant interactions were found between rs4253655 in PPARA and rs1873532 (p = 0.02) and rs7672915 (p = 0.02), both in PPARGC1A, and between rs1151996 in PPARG and rs4697046 in PPARGC1A (p = 0.01).

View Article: PubMed Central - HTML - PubMed

Affiliation: Cardiovascular Division, Department of Medicine, Cardiovascular Imaging and Clinical Research Core Laboratory, Washington University School of Medicine, St Louis, Missouri, USA.

ABSTRACT

Background: Abnormalities in myocardial metabolism and/or regulatory genes have been implicated in left ventricular systolic dysfunction. However, the extent to which these modulate left ventricular diastolic function (LVDF) is uncertain.

Methods: Independent component analysis was applied to extract latent LVDF traits from 14 measured echocardiography-derived endophenotypes of LVDF in 403 Caucasians. Genetic association was assessed between measured and latent LVDF traits and 64 single nucleotide polymorphisms (SNPs) in three peroxisome proliferator-activated receptor (PPAR)-complex genes involved in the transcriptional regulation of fatty acid metabolism.

Results: By linear regression analysis, 7 SNPs (4 in PPARA, 2 in PPARGC1A, 1 in PPARG) were significantly associated with the latent LVDF trait, whereas a range of 0-4 SNPs were associated with each of the 14 measured echocardiography-derived endophenotypes. Frequency distribution of P values showed a greater proportion of significant associations with the latent LVDF trait than for the measured endophenotypes, suggesting that analyses of the latent trait improved detection of the genetic underpinnings of LVDF. Ridge regression was applied to investigate within-gene and gene-gene interactions. In the within-gene analysis, there were five significant pair-wise interactions in PPARGC1A and none in PPARA or PPARG. In the gene-gene analysis, significant interactions were found between rs4253655 in PPARA and rs1873532 (p = 0.02) and rs7672915 (p = 0.02), both in PPARGC1A, and between rs1151996 in PPARG and rs4697046 in PPARGC1A (p = 0.01).

Conclusions: Myocardial metabolism PPAR-complex genes, including within and between genes interactions, may play an important role modulating left ventricular diastolic function.

Show MeSH
Related in: MedlinePlus