Commingling and segregation analysis of serum uric acid in five North American populations: the Lipid Research Clinics family study

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 (β_OP, h² = 2β_OP/(1 + r_spouse)) and full-sib regression slopes (β_FS, h² = {(1 + 8r_spouseβ_FS)^0.5 − 1}/(2r_spouse)) 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 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.

Genome-wide search for genes affecting serum uric acid levels: the Framingham Heart Study

Serum uric acid levels are associated with hypertension, cardiovascular disease, and renal disease. Uric acid has been shown to be heritable; however, genome-wide linkage analyses have not been reported. Genome-wide multipoint variance components linkage analyses with 401 markers spaced at approximately 10 centimorgan (cM) were conducted on 1258 subjects of the Framingham Heart Study, using the average of two serum uric acid measurements obtained in examinations 1 and 2 around 1971 and 1979. Covariates in fully adjusted model included sex, age, body mass index (BMI), serum creatinine, alcohol consumption, diabetes, diuretic treatment, and triglycerides. To investigate possible pleiotropic effects between uric acid and covariates that may have a genetic component, bivariate linkage analyses of uric acid with BMI, triglycerides, and glucose were conducted at the uric acid linkage regions. The heritability of uric acid was 0.63. The highest multipoint log-of-the-odds (LOD) score was 3.3 at 50 cM on chromosome 15 for age-sex-adjusted uric acid, but decreased to 1.5 after multivariable adjustment. Additional evidence of linkage was seen on chromosomes 2 (LOD score 1.1 at 4 cM) and 8 (LOD score 1.7 at 6 cM) for multivariable-adjusted uric acid. Pleiotropic effects were only found between uric acid and glucose and BMI at chromosomes 8 and 15 linkage locations, respectively. We have identified several novel loci linked to uric acid. We found possible pleiotropic effects between uric acid and BMI and glucose. Further research is necessary to identify the genes involved in uric acid metabolism and their roles in hypertension, cardiovascular disease, and renal disease.

Mixture distributions in human genetics research

The use of mixture distributions in genetics research dates back to at least the late 1800s when Karl Pearson applied them in an analysis of crab morphometry. Pearson's use of normal mixture distributions to model the mixing of different species of crab (or 'families' of crab as he referred to them) within a defined geographic area motivated further use of mixture distributions in genetics research settings, and ultimately led to their development and recognition as intuitive modelling devices for the effects of underlying genes on quantitative phenotypic (i.e. trait) expression. In addition, mixture distributions are now used routinely to model or accommodate the genetic heterogeneity thought to underlie many human diseases. Specific applications of mixture distribution models in contemporary human genetics research are, in fact, too numerous to count. Despite this long, consistent and arguably illustrious history of use, little mention of mixture distributions in genetics research is made in many recent reviews on mixture models. This review attempts to rectify this by providing insight into the role that mixture distributions play in contemporary human genetics research. Tables providing examples from the literature that describe applications of mixture models in human genetics research are offered as a way of acquainting the interested reader with relevant studies. In addition, some of the more problematic aspects of the use of mixture models in genetics research are outlined and addressed.