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Genomics of post-prandial lipidomic phenotypes in the Genetics of Lipid lowering Drugs and Diet Network (GOLDN) study.

Irvin MR, Zhi D, Aslibekyan S, Claas SA, Absher DM, Ordovas JM, Tiwari HK, Watkins S, Arnett DK - PLoS ONE (2014)

Bottom Line: After the PPL challenge, fatty acids increased as well as sterols associated with cholesterol absorption, while sterols associated with cholesterol synthesis decreased.Two SNPs (rs12247017 and rs12240292) in the sorbin and SH3 domain containing 1 (SORBS1) gene were associated with b-Sitosterol after correction for multiple testing (P≤4.5*10(-10)).Integration of lipidomic and genomic data has the potential to identify new biomarkers of CVD risk.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.

ABSTRACT

Background: Increased postprandial lipid (PPL) response to dietary fat intake is a heritable risk factor for cardiovascular disease (CVD). Variability in postprandial lipids results from the complex interplay of dietary and genetic factors. We hypothesized that detailed lipid profiles (eg, sterols and fatty acids) may help elucidate specific genetic and dietary pathways contributing to the PPL response.

Methods and results: We used gas chromatography mass spectrometry to quantify the change in plasma concentration of 35 fatty acids and 11 sterols between fasting and 3.5 hours after the consumption of a high-fat meal (PPL challenge) among 40 participants from the GOLDN study. Correlations between sterols, fatty acids and clinical measures were calculated. Mixed linear regression was used to evaluate associations between lipidomic profiles and genomic markers including single nucleotide polymorphisms (SNPs) and methylation markers derived from the Affymetrix 6.0 array and the Illumina Methyl450 array, respectively. After the PPL challenge, fatty acids increased as well as sterols associated with cholesterol absorption, while sterols associated with cholesterol synthesis decreased. PPL saturated fatty acids strongly correlated with triglycerides, very low-density lipoprotein, and chylomicrons. Two SNPs (rs12247017 and rs12240292) in the sorbin and SH3 domain containing 1 (SORBS1) gene were associated with b-Sitosterol after correction for multiple testing (P≤4.5*10(-10)). SORBS1 has been linked to obesity and insulin signaling. No other markers reached the genome-wide significance threshold, yet several other biologically relevant loci are highlighted (eg, PRIC285, a co-activator of PPARa).

Conclusions: Integration of lipidomic and genomic data has the potential to identify new biomarkers of CVD risk.

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Related in: MedlinePlus

Manhattan plots for markers with P<0.0001 from epigenome-wide association study and genome-wide association study.Phenotypes include 11 sterols and 35 fatty acids measured at fasting.
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pone-0099509-g002: Manhattan plots for markers with P<0.0001 from epigenome-wide association study and genome-wide association study.Phenotypes include 11 sterols and 35 fatty acids measured at fasting.

Mentions: Manhattan plots of association signals with P<1.0*10−4 from EWAS and GWAS of fasting sterols and fatty acids are presented in Figure 2. GWAS results from each of 11 sterols are combined in the upper right hand quadrant of Figure 2 and, likewise, GWAS results for each of the 35 fatty acids are combined in the bottom right hand quadrant. For a complete list of all CpGs and SNPs (including annotations) shown in Figure 2, see Spreadsheet S1. Table 2 highlights GWAS and EWAS results from analysis of fasting sterol and fatty acids with P<1.0*10−7. For sterols the strongest genetic signal (P<4.5*10−9 for each of 5 SNPs) came from a region on chromosome 10 within the sorbin and SH3 domain containing 1 gene (SORBS1). The top two SNPs (rs12247017 and rs12240292) met significance criteria (P<2.6*10−9) after correction for multiple testing. The second strongest signal (P<1.8*10−8 for each of 3 SNPs) was for 7a-hydroxycholesterol in the semaphorin 6D (SEMA6D) gene on chromosome 15. Each of the 3 SNPs lies in the first intron of an alternate transcript of SEMA6D. Fourteen intronic SNPs in SEMA5D on chromosome 5 were strongly associated with fasting b-sitosterol (P≤9.8*10−8). A SNP (rs3918278) on chromosome 20 upstream of the matrix metallopeptidase 9 (MMP9) gene was also associated with b-sitosterol (P = 5.6*10−8). In EWAS, no CpG was statistically significantly associated with any sterol after correction for multiple testing. Table 2 highlights a CpG (cg02621636) on chromosome 11 associated with coprostanol in the 3′ UTR of the membrane-spanning 4-domains, subfamily A, member 7 gene (MS4A7). Finally, no SNP or CpG was statistically significantly associated with any of the 35 fasting fatty acid concentrations considered (Figure 2). Marginally significant markers are highlighted in Table 2 and include 2 SNPs on chromosome 5 upstream of the protein phosphatase 2, regulatory subunit B, beta gene (PPP2R2B) associated with di-homo-γ-linoleic acid (DGLA).


Genomics of post-prandial lipidomic phenotypes in the Genetics of Lipid lowering Drugs and Diet Network (GOLDN) study.

Irvin MR, Zhi D, Aslibekyan S, Claas SA, Absher DM, Ordovas JM, Tiwari HK, Watkins S, Arnett DK - PLoS ONE (2014)

Manhattan plots for markers with P<0.0001 from epigenome-wide association study and genome-wide association study.Phenotypes include 11 sterols and 35 fatty acids measured at fasting.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4048279&req=5

pone-0099509-g002: Manhattan plots for markers with P<0.0001 from epigenome-wide association study and genome-wide association study.Phenotypes include 11 sterols and 35 fatty acids measured at fasting.
Mentions: Manhattan plots of association signals with P<1.0*10−4 from EWAS and GWAS of fasting sterols and fatty acids are presented in Figure 2. GWAS results from each of 11 sterols are combined in the upper right hand quadrant of Figure 2 and, likewise, GWAS results for each of the 35 fatty acids are combined in the bottom right hand quadrant. For a complete list of all CpGs and SNPs (including annotations) shown in Figure 2, see Spreadsheet S1. Table 2 highlights GWAS and EWAS results from analysis of fasting sterol and fatty acids with P<1.0*10−7. For sterols the strongest genetic signal (P<4.5*10−9 for each of 5 SNPs) came from a region on chromosome 10 within the sorbin and SH3 domain containing 1 gene (SORBS1). The top two SNPs (rs12247017 and rs12240292) met significance criteria (P<2.6*10−9) after correction for multiple testing. The second strongest signal (P<1.8*10−8 for each of 3 SNPs) was for 7a-hydroxycholesterol in the semaphorin 6D (SEMA6D) gene on chromosome 15. Each of the 3 SNPs lies in the first intron of an alternate transcript of SEMA6D. Fourteen intronic SNPs in SEMA5D on chromosome 5 were strongly associated with fasting b-sitosterol (P≤9.8*10−8). A SNP (rs3918278) on chromosome 20 upstream of the matrix metallopeptidase 9 (MMP9) gene was also associated with b-sitosterol (P = 5.6*10−8). In EWAS, no CpG was statistically significantly associated with any sterol after correction for multiple testing. Table 2 highlights a CpG (cg02621636) on chromosome 11 associated with coprostanol in the 3′ UTR of the membrane-spanning 4-domains, subfamily A, member 7 gene (MS4A7). Finally, no SNP or CpG was statistically significantly associated with any of the 35 fasting fatty acid concentrations considered (Figure 2). Marginally significant markers are highlighted in Table 2 and include 2 SNPs on chromosome 5 upstream of the protein phosphatase 2, regulatory subunit B, beta gene (PPP2R2B) associated with di-homo-γ-linoleic acid (DGLA).

Bottom Line: After the PPL challenge, fatty acids increased as well as sterols associated with cholesterol absorption, while sterols associated with cholesterol synthesis decreased.Two SNPs (rs12247017 and rs12240292) in the sorbin and SH3 domain containing 1 (SORBS1) gene were associated with b-Sitosterol after correction for multiple testing (P≤4.5*10(-10)).Integration of lipidomic and genomic data has the potential to identify new biomarkers of CVD risk.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.

ABSTRACT

Background: Increased postprandial lipid (PPL) response to dietary fat intake is a heritable risk factor for cardiovascular disease (CVD). Variability in postprandial lipids results from the complex interplay of dietary and genetic factors. We hypothesized that detailed lipid profiles (eg, sterols and fatty acids) may help elucidate specific genetic and dietary pathways contributing to the PPL response.

Methods and results: We used gas chromatography mass spectrometry to quantify the change in plasma concentration of 35 fatty acids and 11 sterols between fasting and 3.5 hours after the consumption of a high-fat meal (PPL challenge) among 40 participants from the GOLDN study. Correlations between sterols, fatty acids and clinical measures were calculated. Mixed linear regression was used to evaluate associations between lipidomic profiles and genomic markers including single nucleotide polymorphisms (SNPs) and methylation markers derived from the Affymetrix 6.0 array and the Illumina Methyl450 array, respectively. After the PPL challenge, fatty acids increased as well as sterols associated with cholesterol absorption, while sterols associated with cholesterol synthesis decreased. PPL saturated fatty acids strongly correlated with triglycerides, very low-density lipoprotein, and chylomicrons. Two SNPs (rs12247017 and rs12240292) in the sorbin and SH3 domain containing 1 (SORBS1) gene were associated with b-Sitosterol after correction for multiple testing (P≤4.5*10(-10)). SORBS1 has been linked to obesity and insulin signaling. No other markers reached the genome-wide significance threshold, yet several other biologically relevant loci are highlighted (eg, PRIC285, a co-activator of PPARa).

Conclusions: Integration of lipidomic and genomic data has the potential to identify new biomarkers of CVD risk.

Show MeSH
Related in: MedlinePlus