Quantile-dependent Expressivity Research Articles

Quantile-specific heritability of plasma fibrinogen concentrations.

BackgroundFibrinogen is a moderately heritable blood protein showing different genetic effects by sex, race, smoking status, pollution exposure, and disease status. These interactions may be explained in part by “quantile-dependent expressivity”, where the effect size of a genetic variant depends upon whether the phenotype (e.g. plasma fibrinogen concentration) is high or low relative to its distribution.PurposeDetermine whether fibrinogen heritability (h2) is quantile-specific, and whether quantile-specific h2 could account for fibrinogen gene-environment interactions.MethodsPlasma fibrinogen concentrations from 5689 offspring-parent pairs and 1932 sibships from the Framingham Heart Study were analyzed. Quantile-specific heritability from offspring-parent (βOP, h2 = 2βOP/(1+rspouse)) and full-sib regression slopes (βFS, h2 = {(1+8rspouseβFS)0.05–1}/(2rspouse)) were robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples.ResultsQuantile-specific h2 (±SE) increased with increasing percentiles of the offspring’s age- and sex-adjusted fibrinogen distribution when estimated from βOP (Ptrend = 5.5x10-6): 0.30±0.05 at the 10th, 0.37±0.04 at the 25th, 0.48±0.05 at the 50th, 0.61±0.06 at the 75th, and 0.65±0.08 at the 90th percentile, and when estimated from βFS (Ptrend = 0.008): 0.28±0.04 at the 10th, 0.31±0.04 at the 25th, 0.36±0.03 at the 50th, 0.41±0.05 at the 75th, and 0.50±0.06 at the 90th percentile. The larger genetic effect at higher average fibrinogen concentrations may contribute to fibrinogen’s greater heritability in women than men and in Blacks than Whites, and greater increase from smoking and air pollution for the FGB -455G>A A-allele. It may also explain greater fibrinogen differences between: 1) FGB -455G>A genotypes during acute phase reactions than usual conditions, 2) GTSM1 and IL-6 -572C>G genotypes in smokers than nonsmokers, 3) FGB -148C>T genotypes in untreated than treated diabetics, and LPL PvuII genotypes in macroalbuminuric than normoalbuminuric patients.ConclusionFibrinogen heritability is quantile specific, which may explain or contribute to its gene-environment interactions. The analyses do not disprove the traditional gene-environment interpretations of these examples, rather quantile-dependent expressivity provides an alternative explanation that warrants consideration.

Quantile-Dependent Expressivity of Serum Interleukin-6 Concentrations as a Possible Explanation of Gene-Disease Interactions, Gene-Environment Interactions, and Pharmacogenetic Effects.

Interleukin 6 (IL-6) is a moderately heritable pleiotropic cytokine whose elevated concentrations in coronary artery disease, peripheral arterial disease, pulmonary arterial hypertension, Eales' disease, Sjògren's syndrome, osteoarthritis, adenocarcinoma, neuroblastoma, polymyalgia rheumatica, pulmonary tuberculosis, and enterovirus 71 infection, and following coronary artery bypass graft show larger genetic effects than in unaffected low IL-6 controls. We hypothesize that genetic effects may depend upon whether average IL-6 concentrations are high or low, i.e., quantile-dependent expressivity. Quantile-specific offspring-parent (βOP) and full-sib regression slopes (βFS) were estimated by applying quantile regression to the age- and sex-adjusted serum IL-6 concentrations in families surveyed in theFramingham Heart Study. Quantile-specific heritabilities were calculated as h2 = 2βOP / (1 + rspouse) and h2 = {(1 + 8rspouseβFS)0.5 -1} / (2rspouse)). Heritability (h2 ± SE) of IL-6 concentrations increased from 0.01 ± 0.01 at the 10th percentile (NS), 0.02 ± 0.01 at the 25th (P = 0.009), 0.03 ± 0.01 at the 50th (P = 0.007), 0.04 ± 0.02 at the 75th (P = 0.004), and 0.13 ± 0.05 at the 90th percentile (P = 0.03), or 0.0005 ± 0.0002 for each 1% increase in the offspring's phenotype distribution (Plinear trend = 0.02) when estimated from βOP and from 0.02 ± 0.02 at the 10th (NS), 0.02 ± 0.02 at the 25th (NS), 0.06 ± 0.02 at the 50th (P = 0.01), 0.12 ± 0.04 at the 75th (P = 0.001), and 0.30 ± 0.03 at the 90th percentile (P < 10-16), or 0.0015 ± 0.0007 for each 1% increase in the sibling phenotype distribution (Plinear trend = 0.02) when estimated from βFS. Thus the heritability of serum IL-6 concentrations isquantile dependent, which may contribute in part to the larger genetic effect size reported for diseases and environmental conditions that elevate IL-6 concentrations vis-à-vis unaffected controls.

Quantile-Specific Heritability of Mean Platelet Volume, Leukocyte Count, and Other Blood Cell Phenotypes

Introduction: “Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., mean platelet volume, MPV) is high or low relative to its distribution. Methods: Offspring-parent regression slopes (β_OP) were estimated by quantile regression, from which quantile-specific heritabilities (h²) were calculated (h² = 2β_OP/[1 + r_spouse]) for blood cell phenotypes in 3,929 parent-offspring pairs from the Framingham Heart Study. Results: Quantile-specific h² (±SE) increased with increasing percentiles of the offspring’s age- and sex-adjusted MPV distribution (p_linear = 0.0001): 0.48 ± 0.09 at the 10th, 0.53 ± 0.04 at the 25th, 0.70 ± 0.06 at the 50th, 0.74 ± 0.06 at the 75th, and 0.90 ± 0.12 at the 90th percentile. Quantile-specific h² also increased with increasing percentiles of the offspring’s white blood cell (WBC, p_linear = 0.002), monocyte (p_linear = 0.01), and eosinophil distributions (p_linear = 0.0005). In contrast, heritibilities of red blood cell (RBC) count, hematocrit (HCT), and hemoglobin (HGB) showed little evidence of quantile dependence. Quantile-dependent expressivity is consistent with gene-environment interactions reported by others, including (1) greater increases in WBC and PLT concentrations in subjects who are glutathione-S-transferase Mu1 (GSTM1) null homozygotes than GSTM1 sufficient when exposed to endotoxin; (2) significantly higher WBC count in AA homozygotes than carriers of the G-allele of the glutathione S-transferase P1 (GSTP1) rs1695 polymorphism at low but not high benzene exposure in shoe factory workers; (3) higher WBC counts in TT homozygotes than C-allele carriers of the interleukin-1β (IL1B) c.315C>T polymorphism after undergoing surgery for infective endocarditis but not before surgery. Discussion/Conclusion: Quantile-dependent expressivity may explain several purported gene-environment interactions involving blood cell phenotypes.

Quantile-specific heritability of monocyte chemoattractant protein-1, and relevance to rs1024611-disease interactions.

Monocyte chemoattractant protein-1 (MCP-1) concentrations are 34% to 47% heritable. Larger -2518 G/A (rs1024611) genotypes differences are reported for: 1) MCP-1 production in stimulated vs. basal cells; and 2) MCP-1 concentrations in diseased (sepsis, brain abscess, hepatitis B virus, Alzheimer's disease, Behcet's disease, and systemic lupus erythematosus) vs. healthy patients. Those results suggest that the -2518 G/A effect size may depend on whether the phenotype is high or low relative to its distribution (quantile-dependent expressivity). To test whether quantile-dependent expressivity applies more broadly to genetic influences on MCP-1 concentrations, quantile-specific offspring-parent (βOP) and full-sib regression slopes (βFS) were estimated by applying quantile regression to the age- and sex-adjusted serum MCP-1 concentrations of Framingham Heart Study families. Quantile-specific heritabilities were calculated as h2=2βOP/(1+rspouse) and h2={(1+8rspouseβFS)0.5-1}/(2rspouse)). Heritability (h2±SE) of MCP-1 concentrations increased from 0.15±0.05 at the 10th percentile of the MCP-1 distribution, 0.23±0.04 at the 25th, 0.32±0.05 at the 50th, 0.43±0.07 at the 75th, and 0.44±0.07 at the 90th percentile, or an 0.0041±0.0009 increase for each one-percent increment in the MCP-1 distribution (Plinear trend=2.4×10-5) when estimated from βOP, and (Plinear trend=7.7×10-9) when estimated from βFS. Compared to the 10th percentile, βOP-estimated h2 was 3-fold greater at the 90th percentile (Pdifference=0.0003), and 6.9-fold greater when estimated from βFS (Pdifference=3.3×10-6). Re-analysis of in vivo comparison of MCP-1 concentrations in controls vs. patients with MCP-1-elevating conditions, and in vitro studies of MCP-1 production in basal vs. stimulated cells, show rs1024611 genotypes differences that were consistent with quantile-dependent expressivity. The heritability of circulating MCP-1 concentrations is quantile-dependent.

Quantile-Specific Heritability of Inflammatory and Oxidative Stress Biomarkers Linked to Cardiovascular Disease.

PurposeHeritability (h2, the proportion of the phenotypic variance attributable to additive genetic effects) is traditionally assumed to be constant throughout the distribution of the phenotype. However, the heritabilities of circulating C-reactive protein, interleukin-6, plasminogen activator inhibitor type-1 (PAI-1), and monocyte chemoattractant protein-1 (MCP-1) concentrations depend upon whether the phenotype is high or low relative to their distributions (quantile-dependent expressivity), which may account for apparent gene–environment interactions. Whether the heritabilities of other inflammatory biomarkers linked to cardiovascular disease are quantile-dependent remain to be determined.Patients and MethodsQuantile-specific offspring-parent (βOP) and full-sib regression slopes (βFS) were estimated by applying quantile regression to the age- and sex-adjusted phenotypes of families surveyed as part of the Framingham Heart Study. Quantile-specific heritabilities were calculated as: h2=2βOP/(1+rspouse) and h2={(1+8rspouseβFS)0.5–1}/(2rspouse).ResultsHeritability (h2 ± SE) of lipoprotein-associated phospholipase A2 (Lp-PLA2) mass concentrations increased from 0.11 ± 0.03 at the 10th percentile, 0.08 ± 0.03 at the 25th, 0.12 ± 0.03 at the 50th, 0.20 ± 0.04 at the 75th, and 0.26 ± 0.06 at the 90th percentile, or 0.0023 ± 0.0006 per each one-percent increase in the phenotype distribution (Plinear trend= 0.0004). Similarly, h2 increased 0.0029 ± 0.0011 (Plinear trend= 0.01) for sP-selectin, 0.0032 ± 0.0009 (Plinear trend= 0.0001) for soluble intercellular adhesion molecule 1 (sICAM-1), and 0.0026 ± 0.0006 for tumor necrosis factor receptor 2 (TNFR2) (Plinear trend= 5.0 × 10−6) per each one-percent increase in their distributions when estimated from βOP. Osteoprotegerin and soluble ST2 heritability also increased significantly with increasing percentiles of their distributions when estimated from βFS. Lp-PLA2 activity, CD40 ligand, TNFα, interleukin-18, and myeloperoxidase heritability showed no significant quantile-dependence.ConclusionThe heritabilities of circulating Lp-PLA2-mass, sP-selectin, sICAM-1, TNFR2, osteoprotegerin and soluble ST2 concentrations are quantile-dependent, which may contribute to purported genetic modulations of: 1) sP-selectin’s relationships to venous thrombosis, pulmonary hypertension, type 2 diabetes and atorvastatin treatment; 2) sICAM-I’s relationships to brain abscess and atorvastatin treatment; and 3) Lp-PLA2’s relationships to myocardial infarction and preeclampsia.

Quantile-Dependent Heritability of Glucose, Insulin, Proinsulin, Insulin Resistance, and Glycated Hemoglobin

Background: “Quantile-dependent expressivity” is a dependence of genetic effects on whether the phenotype (e.g., insulin resistance) is high or low relative to its distribution. Methods: Quantile-specific offspring-parent regression slopes (β_OP) were estimated by quantile regression for fasting glucose concentrations in 6,453 offspring-parent pairs from the Framingham Heart Study. Results: Quantile-specific heritability (h²), estimated by 2β_OP/(1 + r_spouse), increased 0.0045 ± 0.0007 (p = 8.8 × 10⁻¹⁴) for each 1% increment in the fasting glucose distribution, that is, h² ± SE were 0.057 ± 0.021, 0.095 ± 0.024, 0.146 ± 0.019, 0.293 ± 0.038, and 0.456 ± 0.061 at the 10th, 25th, 50th, 75th, and 90th percentiles of the fasting glucose distribution, respectively. Significant increases in quantile-specific heritability were also suggested for fasting insulin (p = 1.2 × 10⁻⁶), homeostatic model assessment of insulin resistance (HOMA-IR, p = 5.3 × 10⁻⁵), insulin/glucose ratio (p = 3.9 × 10⁻⁵), proinsulin (p = 1.4 × 10⁻⁶), proinsulin/insulin ratio (p = 2.7 × 10⁻⁵), and glucose concentrations during a glucose tolerance test (p = 0.001), and their logarithmically transformed values. Discussion/Conclusion: These findings suggest alternative interpretations to precision medicine and gene-environment interactions, including alternative interpretation of reported synergisms between ACE, ADRB3, PPAR-γ2, and TNF-α polymorphisms and being born small for gestational age on adult insulin resistance (fetal origin theory), and gene-adiposity (APOE, ENPP1, GCKR, IGF2BP2, IL-6, IRS-1, KIAA0280, LEPR, MFHAS1, RETN, TCF7L2), gene-exercise (INS), gene-diet (ACSL1, ELOVL6, IRS-1, PLIN, S100A9), and gene-socioeconomic interactions.

Quantile-Dependent Expressivity of Serum Uric Acid Concentrations.

Objective “Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., serum uric acid) is high or low relative to its distribution. Analyses were performed to test whether serum uric acid heritability is quantile-specific and whether this could explain some reported gene-environment interactions. Methods Serum uric acid concentrations were analyzed from 2151 sibships and 12,068 offspring-parent pairs from the Framingham Heart Study. Quantile-specific heritability from offspring-parent regression slopes (βOP, h2 = 2βOP/(1 + rspouse)) and full-sib regression slopes (βFS, h2 = {(1 + 8rspouseβFS)0.5 − 1}/(2rspouse)) was robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. Results Quantile-specific h2 (±SE) increased with increasing percentiles of the offspring's sex- and age-adjusted uric acid distribution when estimated from βOP (Ptrend = 0.001): 0.34 ± 0.03 at the 10th, 0.36 ± 0.03 at the 25th, 0.41 ± 0.03 at the 50th, 0.46 ± 0.04 at the 75th, and 0.49 ± 0.05 at the 90th percentile and when estimated from βFS (Ptrend = 0.006). This is consistent with the larger genetic effect size of (1) the SLC2A9 rs11722228 polymorphism in gout patients vs. controls, (2) the ABCG2 rs2231142 polymorphism in men vs. women, (3) the SLC2A9 rs13113918 polymorphism in obese patients prior to bariatric surgery vs. two-year postsurgery following 29 kg weight loss, (4) the ABCG2 rs6855911 polymorphism in obese vs. nonobese women, and (5) the LRP2 rs2544390 polymorphism in heavier drinkers vs. abstainers. Quantile-dependent expressivity may also explain the larger genetic effect size of an SLC2A9/PKD2/ABCG2 haplotype for high vs. low intakes of alcohol, chicken, or processed meats. Conclusions Heritability of serum uric acid concentrations is quantile-specific.

Quantile-specific heritability of serum growth factor concentrations

Background “Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g. growth factor concentration) is high or low relative to its distribution. Methods Quantile-regression analysis was applied to family sets from the Framingham Heart Study to determine whether the heritability (h2 ) of vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), angiopoietin-2, and angiopoietin-2 (sTie-2) and VEGFR1 (sFlt-1) receptor concentrations were quantile-specific. Results Quantile-specific h2 (±SE) increased with increasing percentiles of the age- and sex-adjusted VEGF (P trend<10−16), HGF (P trend=0.0004), angiopoietin-2 (P trend=0.0002), sTie-2 (P trend=1.2 × 10−5), and sFlt-1 distributions (P trend=0.04). Conclusion Heritabilities of VEGF, HGF, angiopoitein-2, sTie-2 and sFlt-1 concentrations are quantile dependent. This may explain reported interactions of genetic loci (rs10738760, rs9472159, rs833061, rs3025039, rs2280789, rs1570360, rs2010963) with metabolic syndrome, diet, recurrent miscarriage, hepatocellular carcinoma, erysipelas, diabetic retinopathy, and bevacizumab treatment in their effect on VEGF concentrations.

Quantile-dependent expressivity of serum C-reactive protein concentrations in family sets.

Background“Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., C-reactive protein, CRP) is high or low relative to its distribution. We have previously shown that the heritabilities (h2) of coffee and alcohol consumption, postprandial lipemia, lipoproteins, leptin, adiponectin, adiposity, and pulmonary function are quantile-specific. Whether CRP heritability is quantile-specific is currently unknown.MethodsSerum CRP concentrations from 2,036 sibships and 6,144 offspring-parent pairs were analyzed from the Framingham Heart Study. Quantile-specific heritability from full-sib (βFS, h2 ={(1 + 8rspouseβFS)0.5 − 1}/(2rspouse)) and offspring-parent regression slopes (βOP, h2 = 2βOP/(1 + rspouse)) were estimated robustly by quantile regression with nonparametric significance determined from 1,000 bootstrap samples.ResultsQuantile-specific h2 (±SE) increased with increasing percentiles of the offspring’s age- and sex-adjusted CRP distribution when estimated from βOP (Ptrend = 0.0004): 0.02 ± 0.01 at the 10th, 0.04 ± 0.01 at the 25th, 0.10 ± 0.02 at the 50th, 0.20 ± 0.05 at the 75th, and 0.33 ± 0.10 at the 90th percentile, and when estimated from βFS (Ptrend = 0.0008): 0.03±0.01 at the 10th, 0.06 ± 0.02 at the 25th, 0.14 ± 0.03 at the 50th, 0.24 ± 0.05 at the 75th, and 0.53 ± 0.21 at the 90th percentile.ConclusionHeritability of serum CRP concentration is quantile-specific, which may explain or contribute to the inflated CRP differences between CRP (rs1130864, rs1205, rs1800947, rs2794521, rs3091244), FGB (rs1800787), IL-6 (rs1800795, rs1800796), IL6R (rs8192284), TNF-α (rs1800629) and APOE genotypes following CABG surgery, stroke, TIA, curative esophagectomy, intensive periodontal therapy, or acute exercise; during acute coronary syndrome or Staphylococcus aureus bacteremia; or in patients with chronic rheumatoid arthritis, diabetes, peripheral arterial disease, ankylosing spondylitis, obesity or inflammatory bowel disease or who smoke.

Quantile-specific heritability of total cholesterol and its pharmacogenetic and nutrigenetic implications

Background“Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g. cholesterol) is high or low relative to its distribution. We have previously shown that the effect of a 52-SNP genetic-risk score was 3-fold larger at the 90th percentile of the total cholesterol distribution than at its 10th percentile. The objective of this study is to assess quantile-dependent expressivity for total cholesterol in 7006 offspring with parents and 2112 sibships from Framingham Heart Study. MethodsQuantile-specific heritability (h2) was estimated as twice the offspring-parent regression slope as robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. ResultsQuantile-specific h2 increased linearly with increasing percentiles of the offspring's cholesterol distribution (P = 3.0 × 10−9), i.e. h2 = 0.38 at the 10th percentile, h2 = 0.45 at the 25th percentile, h2 = 0.52 at the 50th, h2 = 0.61 at the 75th percentile, and h2 = 0.65 at the 90th percentile of the total cholesterol distribution. Average h2 decreased from 0.55 to 0.34 in 3564 offspring who started cholesterol-lowering medications, but this was attributable to quantile-dependent expressivity and the offspring's 0.94 mmol/L average drop in total cholesterol. Quantile-dependent expressivity likely explains the reported effect of the CELSR2/PSRC1/SORT1 rs646776 and APOE rs7412 gene loci on statin efficacy. Specifically, a smaller genetic effect size at the lower (post-treatment) than higher (pre-treatment) cholesterol concentrations mandates that the trajectories of the genotypes cannot move in parallel when cholesterol is decreased pharmacologically. ConclusionCholesterol concentrations exhibit quantile-dependent expressivity, which may provide an alternative interpretation to pharmacogenetic and nutrigenetic interactions.

Quantile-specific heritability of sibling leptin concentrations and its implications for gene-environment interactions

“Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g., leptin) is high or low relative to its distribution. Leptin concentrations are strongly related to adiposity, whose heritability is quantile dependent. Whether inheritance of leptin concentrations is quantile dependent, and whether this explains the greater heritability in women than men in accordance with their greater adiposity, and explains other gene-environment interactions, remains to be determined. Therefore, leptin and leptin receptor concentrations from 3068 siblings in 1133 sibships from the Framingham Heart Study Third Generation Cohort were analyzed. Free leptin index (FLI) was calculated as the ratio of leptin to soluble leptin receptor concentrations. Full-sib (βFS) regression slopes were robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. The analyses showed βFS increased significantly with increasing percentiles of the offspring’s age- and sex-adjusted leptin distribution (Plinear = 0.0001), which was accelerated at the higher concentrations (Pquadratic = 0.0003). βFS at the 90th percentile (0.418 ± 0.066) was 4.7-fold greater than at the 10th percentile (0.089 ± 0.032, Pdifference = 3.6 × 10−6). Consistent with quantile-dependent expressivity, the βFS was greater in female sibs, which was attributable to their higher leptin concentrations. Reported gene-environment interactions involving adiposity and LEP, LEPR, MnSOD, PPARγ, PPARγ2, and IRS-1 polymorphisms were consistent with quantile-dependent expressivity of leptin concentrations. βFS for leptin receptor concentrations and free leptin index also increased significantly with increasing percentiles of their distributions (Plinear = 0.04 and Plinear = 8.5 × 10−6, respectively). In conclusion, inherited genetic and shared environmental effects on leptin concentrations were quantile dependent, which likely explains male–female differences in heritability and some gene-environment interactions.

Quantile-dependent expressivity of plasma adiponectin concentrations may explain its sex-specific heritability, gene-environment interactions, and genotype-specific response to postprandial lipemia.

Background“Quantile-dependent expressivity” occurs when the effect size of a genetic variant depends upon whether the phenotype (e.g. adiponectin) is high or low relative to its distribution. We have previously shown that the heritability (h2) of adiposity, lipoproteins, postprandial lipemia, pulmonary function, and coffee and alcohol consumption are quantile-specific. Whether adiponectin heritability is quantile specific remains to be determined.MethodsPlasma adiponectin concentrations from 4,182 offspring-parent pairs and 1,662 sibships from the Framingham Heart Study were analyzed. Quantile-specific heritability from offspring-parent (βOP,h2 = 2βOP/(1 + rspouse)) and full-sib regression slopes (βFS, h2 = {(1 + 8rspouseβFS)0.05-1}/(2rspouse)) were robustly estimated by quantile regression with nonparametric significance assigned from 1,000 bootstrap samples.ResultsQuantile-specific h2 (± SE) increased with increasing percentiles of the offspring’s age- and sex-adjusted adiponectin distribution when estimated from βOP (Ptrend = 2.2 × 10−6): 0.30 ± 0.03 at the 10th, 0.33 ± 0.04 at the 25th, 0.43 ± 0.04 at the 50th, 0.55 ± 0.05 at the 75th, and 0.57 ± 0.08 at the 90th percentile, and when estimated from βFS (Ptrend = 7.6 × 10−7): 0.42 ± 0.03 at the 10th, 0.44 ± 0.04 at the 25th, 0.56 ± 0.05 at the 50th, 0.73 ± 0.08 at the 75th, and 0.79 ± 0.11 at the 90th percentile. Consistent with quantile-dependent expressivity, adiponectin’s: (1) heritability was greater in women in accordance with their higher adiponection concentrations; (2) relationships to ADIPOQ polymorphisms were modified by adiposity in accordance with its adiponectin-lowering effect; (3) response to rosiglitazone was predicted by the 45T> G ADIPOQ polymorphism; (4) difference by ADIPOQ haplotypes increased linearly with increasing postprandial adiponectin concentrations.ConclusionAdiponectin heritability is quantile dependent, which may explain sex-specific heritability, gene-environment and gene-drug interactions, and postprandial response by haplotypes.

Quantile-dependent heritability of computed tomography, dual-energy x-ray absorptiometry, anthropometric, and bioelectrical measures of adiposity.

Background/Objectives:Quantile-dependent expressivity occurs when a gene’s phenotypic expression depends upon whether the trait (e.g., BMI) is high or low relative to its distribution. We have previously shown that the obesity effects of a genetic risk score (GRSBMI) increased significantly with increasing quantiles of BMI. However, BMI is an inexact adiposity measure and GRSBMI explains <3% of the BMI variance. The purpose of this paper is to test BMI for quantile-dependent expressivity using a more inclusive genetic measure (h2, heritability in the narrow sense), extend the result to other adiposity measures, and demonstrate its consistency with purported gene-environment interactions.Subjects/Methods:Quantile-specific offspring-parent regression slopes (βOP) were obtained from quantile regression for height (ht) and computed tomography (CT), dual-energy x-ray absorptiometry (DXA), anthropometric, and bioelectrical impedance (BIA) adiposity measures. Heritability was estimated by 2βOP/(1+rspouse) in 6,227 offspring-parent pairs from the Framingham Heart Study, where rspouse is the spouse correlation.Results:Compared to h2 at the 10th percentile, genetic heritability was significantly greater at the 90th population percentile for BMI (3.14-fold greater, P<10−15), waist girth/ht (3.27-fold, P<10−15), hip girth/ht (3.12-fold, P=6.3×10−14), waist-to-hip ratio (1.75-fold, P=0.01), sagittal diameter/ht (3.89-fold, P=3.7×10−7), DXA total fat/ht2 (3.62-fold, P=0.0002), DXA leg fat/ht2 (3.29-fold, P=2.0×10−11), DXA arm fat/ht2 (4.02-fold, P=0.001), CT-visceral fat/ht2 (3.03-fold, P=0.002), and CT-subcutaneous fat/ht2 (3.54-fold, P=0.0004). External validity was suggested by the phenomenon’s consistency with numerous published reports. Quantile-dependent expressivity potentially explains precision medicine markers for weight gain from overfeeding or antipsychotic medications, and the modifying effects of physical activity, sleep, diet, polycystic ovary syndrome, socioeconomic status, and depression on gene-BMI relationships.Conclusion:Genetic heritabilities of anthropometric, CT, and DXA adiposity measures increase with increasing adiposity. Some gene-environment interactions may arise from analyzing subjects by characteristics that distinguish high vs. low adiposity rather than the effects of environmental stimuli on transcriptional and epigenetic processes.

Quantile-Specific Heritability of Intakes of Alcohol but not Other Macronutrients.

Genetic heritability (h2) of alcohol use is reported to be greater in rural dwellers, distressed marriages, low socioeconomic status, in girls who are unmarried or lacking closeness with their parents or religious upbringing, in less-educated men, and in adolescents with peers using alcohol. However, these are all risk factors for heavy drinking, and the greater heritability could be due to quantile-dependent expressivity, i.e., h2dependent upon whether the phenotype (alcohol intake) is high or low relative to its distribution. Quantile regression showed that h2 estimated from the offspring-parent regression slope increased significantly from lowest to highest gram/day of alcohol consumption (0.006 ± 0.001 per percent, P = 1.1 × 10-7). Heritability at the 90th percentile of the sample distribution (0.557 ± 0.116) was 4.5-fold greater than at the 10th percentile (0.122 ± 0.037). Heritabilities for intakes of other macronutrients were not quantile-dependent. Thus quantile-dependent expressivity may explain the higher estimated heritability associated with risk factors for high alcohol consumption.

Quantile-specific heritability of high-density lipoproteins with implications for precision medicine

We have previously shown that the effect of a high-density lipoprotein (HDL) genetic risk score depends on whether the phenotype (HDL cholesterol) is high or low relative to its distribution (quantile-dependent expressivity). Evidence for quantile-dependent expressivity was sought using a more inclusive genetic measure (quantile-specific heritability, h2) in a larger population (Framingham cohort). Quantile regression was used to test whether the offspring-parent (βOP) and full-sib (βFS) regression slopes increased with the percentiles of the offspring's HDL distribution in 10,650 parent-offspring pairs and 2130 sibships. Quantile-specific heritability was estimated by 2βOP/(1+ rspouse) and [(8βFSrspouse+ 1)0.5-1]/(2rspouse), where rspouse is the spouse correlation. HDL cholesterol heritability estimated from βOP increased significantly (P=4.2×10-5) from the 10th (h2 ± SE: 0.44±0.03), 25th (0.45±0.03), 50th (0.47±0.03), and 75th (0.56±0.04) to the 90th percentiles (0.65±0.06) of the offspring's age- and sex-adjusted HDL cholesterol distribution. Heritability estimated from βFS also increased significantly with the percentiles of the offspring's HDL cholesterol (P=.002), apo A1 (P=.006), HDL2 cholesterol (P=.003), and HDL3 cholesterol distribution (P=.02). Consistent with quantile-dependent expressivity, published pharmacologic and nutritional interventions that raised (eg, statin, fibrates, estrogen replacement therapy, efavirenz, and dietary fat) or lowered HDL cholesterol concentrations (tamoxifen, dietary carbohydrate) correspondingly increased and decreased genetic effects. HDL cholesterol heritability increased with increasing percentile of the offspring's HDL distribution. Whereas precision medicine is based on the premise that genetic markers identify patients most likely to benefit from drugs and diet, quantile-dependent expressivity postulates that the strong signals from these genetic markers simply trace the heritability increase with increasing plasma HDL concentrations. Thus, quantile-dependent expressivity provides an alternative interpretation to these genotype-specific effects.

Spirometric traits show quantile-dependent heritability, which may contribute to their gene-environment interactions with smoking and pollution.

Background“Quantile-dependent expressivity” refers to a genetic effect that is dependent upon whether the phenotype (e.g., spirometric data) is high or low relative to its population distribution. Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), and the FEV1/FVC ratio are moderately heritable spirometric traits. The aim of the analyses is to test whether their heritability (h2) is constant over all quantiles of their distribution.MethodsQuantile regression was applied to the mean age, sex, height and smoking-adjusted spirometric data over multiple visits in 9,993 offspring-parent pairs and 1,930 sibships from the Framingham Heart Study to obtain robust estimates of offspring-parent (βOP), offspring-midparent (βOM), and full-sib regression slopes (βFS). Nonparametric significance levels were obtained from 1,000 bootstrap samples. βOPs were used as simple indicators of quantile-specific heritability (i.e., h2 = 2βOP/(1+rspouse), where rspouse was the correlation between spouses).ResultsβOP ± standard error (SE) decreased by 0.0009 ± 0.0003 (P = 0.003) with every one-percent increment in the population distribution of FEV1/FVC, i.e., βOP ± SE were: 0.182 ± 0.031, 0.152 ± 0.015; 0.136 ± 0.011; 0.121 ± 0.013; and 0.099 ± 0.013 at the 10th, 25th, 50th, 75th, and 90th percentiles of the FEV1/FVC distribution, respectively. These correspond to h2 ± SEs of 0.350 ± 0.060 at the 10th, 0.292 ± 0.029 at the 25th, 0.262 ± 0.020 at the 50th, 0.234 ± 0.025 at the 75th, and 0.191 ± 0.025 at the 90th percentiles of the FEV1/FVC ratio. Maximum mid-expiratory flow (MMEF) h2 ± SEs increased 0.0025 ± 0.0007 (P = 0.0004) with every one-percent increment in its distribution, i.e.: 0.467 ± 0.046, 0.467 ± 0.033, 0.554 ± 0.038, 0.615 ± 0.042, and 0.675 ± 0.060 at the 10th, 25th, 50th, 75th, and 90th percentiles of its distribution. This was due to forced expiratory flow at 75% of FVC (FEF75%), whose quantile-specific h2 increased an average of 0.0042 ± 0.0008 for every one-percent increment in its distribution. It is speculated that previously reported gene-environment interactions may be partially attributable to quantile-specific h2, i.e., greater heritability in individuals with lower FEV1/FVC due to smoking or airborne particles exposure vs. nonsmoking, unexposed individuals.ConclusionHeritabilities of FEV1/FVC, MMEF, and FEF75% from quantile-regression of offspring-parent and sibling spirometric data suggest their quantile-dependent expressivity.

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

“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 offspring-parent regression slopes (βOP) were estimated by quantile regression for 6227 offspring-parent pairs. Quantile-specific heritability (h2), estimated by 2βOP/(1 + rspouse), decreased 0.0047 ± 0.0007 (P = 2.9 × 10−14) for each one-percent decrement in fasting triglyceride concentrations, i.e., h2 ± 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 (post-treatment) 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 (APOA5, APOB, APOE, GCKR, IRS-1, LPL, MTHFR, PCSK9, PNPLA3, PPARγ2), gene-exercise (APOA1, APOA2, LPL), gene-diet (APOA5, APOE, INSIG2, LPL, MYB, NXPH1, PER2, TNFA), gene-alcohol (ALDH2, APOA5, APOC3, CETP, LPL), gene-smoking (APOC3, CYBA, LPL, USF1), gene-pregnancy (LPL), and gene-insulin resistance interactions (APOE, LPL).

Quantile-dependent expressivity of postprandial lipemia.

Purpose“Quantile-dependent expressivity” describes an effect of the genotype that depends upon the level of the phenotype (e.g., whether a subject’s triglycerides are high or low relative to its population distribution). Prior analyses suggest that the effect of a genetic risk score (GRS) on fasting plasma triglyceride levels increases with the percentile of the triglyceride distribution. Postprandial lipemia is well suited for testing quantile-dependent expressivity because it exposes each individual’s genotype to substantial increases in their plasma triglyceride concentrations. Ninety-seven published papers were identified that plotted mean triglyceride response vs. time and genotype, which were converted into quantitative data. Separately, for each published graph, standard least-squares regression analysis was used to compare the genotype differences at time t (dependent variable) to average triglyceride concentrations at time t (independent variable) to assess whether the genetic effect size increased in association with higher triglyceride concentrations and whether the phenomenon could explain purported genetic interactions with sex, diet, disease, BMI, and drugs.ResultsConsistent with the phenomenon, genetic effect sizes increased (P≤0.05) with increasing triglyceride concentrations for polymorphisms associated with ABCA1, ANGPTL4, APOA1, APOA2, APOA4, APOA5, APOB, APOC3, APOE, CETP, FABP2, FATP6, GALNT2, GCKR, HL, IL1b, LEPR, LOX-1, LPL, MC4R, MTTP, NPY, SORT1, SULF2, TNFA, TCF7L2, and TM6SF2. The effect size for these polymorphisms showed a progressively increasing dose-response, with intermediate effect sizes at intermediate triglyceride concentrations. Quantile-dependent expressivity provided an alternative interpretation to their interactions with sex, drugs, disease, diet, and age, which have been traditionally ascribed to gene-environment interactions and genetic predictors of drug efficacy (i.e., personalized medicine).ConclusionQuantile-dependent expressivity applies to the majority of genetic variants affecting postprandial triglycerides, which may arise because the impaired functionalities of these variants increase at higher triglyceride concentrations. Purported gene-drug interactions may be the manifestations of quantile-dependent expressivity, rather than genetic predictors of drug efficacy.