Quantile-Dependent Expressivity of Serum Uric Acid Concentrations

Abstract

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 (β _OP, h ² = 2β _OP/(1 + r _spouse)) and full-sib regression slopes (β _FS, h ² = {(1 + 8r _spouse β _FS)^0.5 - 1}/(2r _spouse)) was robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples.

Results: Quantile-specific h ² (±SE) increased with increasing percentiles of the offspring's sex- and age-adjusted uric acid distribution when estimated from β _OP (P _trend = 0.001): 0.34 ± 0.03 at the 10^th, 0.36 ± 0.03 at the 25^th, 0.41 ± 0.03 at the 50^th, 0.46 ± 0.04 at the 75^th, and 0.49 ± 0.05 at the 90^th percentile and when estimated from β _FS (P _trend = 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.

Figure 1

(a) Offspring-parent regression slopes (β_OP) for selected quantiles of the offspring's uric concentrations from 12,068 offspring-parent pairs, with corresponding estimates of heritability (h² = 2β_OP/(1 + r_spouse) [32], where the correlation between spouses was r_spouse = 0.1062. The slopes became progressively greater (i.e., steeper) with increasing quantiles of the uric acid distribution. (b) The selected quantile-specific regression slopes were included with those of other quantiles to create the quantile-specific heritability function in the lower panel. Significance of the linear, quadratic, and cubic trends and the 95% confidence intervals (shaded region) determined by 1000 bootstrap samples.

Figure 2

Quantile-specific full-sib regression slopes (β_FS) from 5703 full-sibs in 2036 sibships, with corresponding estimates of heritability as calculated by h² = {(8r_spouseβ_FS + 1)^0.5 − 1}/(2r_spouse) [32].

Figure 3

Precision medicine perspective of genotype-specific uric acid differences (histogram inserts) vs. quantile-dependent expressivity perspective (line graphs showing larger genetic effect size when average uric acid concentrations were high) for (a) Das Gupta et al.'s [37] 2018 report on the uric acid difference between gout patients and healthy controls by SLC2A9 rs11722228 genotypes; (b) Yang et al.'s [42] 2014 report on the uric acid difference between males and females by ABCG2 rs2231142 genotypes; (c) Lin et al.'s [43] report on the uric acid difference between males and females by ABCG2 rs2231142 genotypes; (d) Lin et al.'s [43] 2020 report on the uric acid difference between males and females by rs13120819 genotypes located 5′ of ABCG2; (e) Sarzynski et al.'s [47] 2012 report on the uric acid difference before and after 29 kg weight loss following bariatric surgery by SLC2A9 rs13113918 genotypes; (f) Cheng et al.'s [48] 2017 report on the uric acid difference between obese and nonobese women by ABCG2 rs2231142 genotypes.

Figure 4

Precision medicine perspective of genotype-specific uric acid differences (histogram inserts) vs. quantile-dependent expressivity perspective (line graphs showing larger genetic effect size when average uric acid concentrations were high) for (a) Yang et al. [51] 2020 report on the uric acid difference due to drinking more vs. less than 10 g/d of alcohol by major, heterozygotic (het), and minor alleles of the SLC2A9 rs3733591, PKD2 rs2725220, and ABCG2 rs2231142 haplotype; (b) Hamajima et al. [52] 2012 report on the uric acid difference between drinking >5 times per week vs. abstaining by the LRP2 rs2544390 genotypes; (c) Yang et al. [51] 2020 report on the uric acid difference due to consuming more vs. less than 6.3 g/d of chicken by major, heterozygotic (het), and minor alleles of the SLC2A9 rs3733591, PKD2 rs2725220, and ABCG2 rs2231142 haplotype; (d) Yang et al. [51] 2020 report on the uric acid difference due to consuming more vs. less than 3.0 g/d of processed meat by major, heterozygotic (het), and minor alleles of the SLC2A9 rs3733591, PKD2 rs2725220, and ABCG2 rs2231142 haplotype.