Genetic association with lipids in Filipinos: waist circumference modifies an APOA5 effect on triglyceride levels

Wu, Ying; Marvelle, Amanda F.; Li, Jin; Croteau‐Chonka, Damien C.; Feranil, Alan B.; Kuzawa, Christopher W.; Li, Yun; Adair, Linda S.; Mohlke, Karen L.

doi:10.1194/jlr.p042077

Cited by 31 publications

(38 citation statements)

References 40 publications

(46 reference statements)

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“…Little is known about CILP2 and how it might relate to lipid levels. Finally, our result suggesting that the association of rs964184 near APOA1-5 with TG is accentuated in the context of high WHR is consistent with previous studies 4,5 .…”

Section: Discussionsupporting

confidence: 93%

Interaction of Insulin Resistance and Related Genetic Variants With Triglyceride-Associated Genetic Variants

Klimentidis

Arora

2016

Circ Cardiovasc Genet

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Background Several studies suggest that some triglyceride (TG)-associated single nucleotide polymorphisms (SNPs) have pleiotropic and opposite effects on glycemic traits. This potentially implicates them in pathways such as de novo lipogenesis, which is presumably up-regulated in the context of insulin resistance. We therefore tested whether the association of TG-associated SNPs with TG levels differs according to one’s level of insulin resistance. Methods and Results In three cohort studies (combined n=12,487), we tested the interaction of established TG-associated SNPs (individually and collectively) with several traits related to insulin resistance, on TG levels. We also tested the interaction of TG SNPs with fasting insulin (FI)-associated SNPs, individually and collectively, on TG levels. We find significant interactions of a weighted genetic risk score (GRS) for TG with insulin resistance on TG (pinteraction=2.73 × 10−11 and pinteraction=2.48 × 10−11 for FI and HOMA-IR, respectively). The association of the TG GRS with TG is over 60% stronger among those in the highest tertile of HOMA-IR, compared to those in the lowest tertile. Individual SNPs contributing to this trend include those in/near GCKR, CILP2, and IRS1, while PIGV-NROB2 and LRPAP1 display an opposite trend of interaction. In the pooled dataset, we also identify a SNP-by-SNP interaction involving a TG-associated SNP, rs4722551 near MIR148A, with a FI-associated SNP, rs4865796 in ARL15 (pinteraction=4.1 × 10−5). Conclusions Our findings may thus provide genetic evidence for the upregulation of TG levels in insulin resistant individuals, in addition to identifying specific genetic loci and a SNP-by-SNP interaction implicated in this process.

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Section: Discussionsupporting

confidence: 93%

Interaction of Insulin Resistance and Related Genetic Variants With Triglyceride-Associated Genetic Variants

Klimentidis

Arora

2016

Circ Cardiovasc Genet

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“…Finally, we identified an association between increased RDW and the SNP encoding the canonical APOE -ɛ2 variant rs7412, which is known to be associated with cholesterol 22–25 and inversely associated with dementia. 26,27 Additional adjustment for LDL-cholesterol, HDL-cholesterol, and triglyceride levels did not attenuate the APOE -ɛ2/RDW association in the ARIC study.…”

Section: Resultsmentioning

confidence: 96%

Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits

2016

Nat Genet

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Hematologic measures such as hematocrit and white blood cell (WBC) count are heritable and clinically relevant. Erythrocyte and WBC phenotypes were analyzed with Illumina HumanExome BeadChip genotypes in 52,531 individuals (37,775 of European ancestry; 11,589 African Americans; 3,167 Hispanic Americans) from 16 population-based cohorts. We then performed replication analyses of novel discoveries in 18,018 European American women and 5,261 Han Chinese. We identified and replicated four novel erythrocyte trait-locus associations (CEP89, SHROOM3, FADS2, and APOE) and six novel WBC loci for neutrophil count (S1PR4), monocyte count (BTBD8, NLRP12, and IL17RA), eosinophil count (IRF1), and total WBC (MYB). The novel association of a rare missense variant in S1PR4 supports the role of sphingosine-1-phosphate signaling in leukocyte trafficking and circulating neutrophil counts. Loss-of-function experiments of S1pr4 in mouse and zebrafish demonstrated phenotypes consistent with the association observed in humans and altered kinetics of neutrophil recruitment and resolution in response to tissue injury.

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“…Environmental factors that distinguish higher vs. lower triglycerides (e.g., obesity, physical inactivity, smoking, alcohol, high-carbohydrate diets, T2DM) are predicted to produce different genetic estimates under quantile-dependent expressivity. Traditionally, these differences have been attributed to gene-environment interactions, where: 1) the effect of the genotype on the phenotype differs by environment [26][27][28][29][30][31][32][33][34][35][36][37][41][42][43][45][46][47][48][49][50][51][52][53][54][55][58][59][60][61][62][63][65][66][67][74][75][76][77][78][79][80] , or equivalently: 2) the effect of the environment on the phenotype differs by genotype 36,[38][39][40]44,…”

Section: Discussionmentioning

confidence: 99%

Gene-environment interactions due to quantile-specific heritability of triglyceride and VLDL concentrations

Williams

2020

Sci Rep

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Gene-environment interactions due to quantile-specific heritability of triglyceride and VLDL concentrations paul t. Williams "Quantile-dependent expressivity" is a dependence of genetic effects on whether the phenotype (e.g., triglycerides) is high or low relative to its distribution in the population. Quantile-specific offspringparent regression slopes (β op) were estimated by quantile regression for 6227 offspring-parent pairs. Quantile-specific heritability (h 2), estimated by 2β op /(1 + r spouse), decreased 0.0047 ± 0.0007 (P = 2.9 × 10 −14) for each one-percent decrement in fasting triglyceride concentrations, i.e., h 2 ± Se were: 0.428 ± 0.059, 0.230 ± 0.030, 0.111 ± 0.015, 0.050 ± 0.016, and 0.033 ± 0.010 at the 90th, 75th, 50th, 25th, and 10th percentiles of the triglyceride distribution, respectively. Consistent with quantile-dependent expressivity, 11 drug studies report smaller genotype differences at lower (posttreatment) than higher (pre-treatment) triglyceride concentrations. This meant genotype-specific triglyceride changes could not move in parallel when triglycerides were decreased pharmacologically, so that subtracting pre-treatment from post-treatment triglyceride levels necessarily created a greater triglyceride decrease for the genotype with a higher pre-treatment value (purported precision-medicine genetic markers). In addition, sixty-five purported gene-environment interactions were found to be potentially attributable to triglyceride's quantile-dependent expressivity, including gene-adiposity

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Genetic association with lipids in Filipinos: waist circumference modifies an APOA5 effect on triglyceride levels

Cited by 31 publications

References 40 publications

Interaction of Insulin Resistance and Related Genetic Variants With Triglyceride-Associated Genetic Variants

Interaction of Insulin Resistance and Related Genetic Variants With Triglyceride-Associated Genetic Variants

Meta-analysis of rare and common exome chip variants identifies S1PR4 and other loci influencing blood cell traits

Gene-environment interactions due to quantile-specific heritability of triglyceride and VLDL concentrations

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