Characteristics of study sample and bivariate associations with Urinary UA excretion and gene expression levels from uric acid absorption/secretion associated genes, n = 541.

<p>*statistically significant association at α = 0.05.</p><p>UA = uric acid; BMI = body mass index; Bivariate association results were obtained from linear mixed models accounting for sibship (n = 541). ABCG2 ENSG00000118777, SLC17A1 ENSG00000124568, SLC17A3 ENSG00000124564, SLC22A12 ENSG00000197891, SLC2A9 ENSG00000109667, SLC2A9-001 ENST00000506583, SLC2A9-201 ENST00000309065, SLC2A9-002 ENST00000264784.</p><p>β coefficients for association with gene expression were standardized.</p><p>Characteristics of study sample and bivariate associations with Urinary UA excretion and gene expression levels from uric acid absorption/secretion associated genes, n = 541.</p

Gene expression by dietary protein intake interaction associations for urinary uric acid.

<p>*statistically significant association at α = 0.05.</p><p>ABCG2 ENSG00000118777, SLC17A1 ENSG00000124568, SLC17A3 ENSG00000124564, SLC22A12 ENSG00000197891, SLC2A9 ENSG00000109667, SLC2A9-001 ENST00000506583, SLC2A9-201 ENST00000309065, SLC2A9-002 ENST00000264784.</p><p>Gene expression by dietary protein intake interaction associations for urinary uric acid.</p

Urinary UA association results for 880 SNPs in the <i>SLC2A9</i> gene region.

<p>Left Y-axis:–log₁₀(p-value) from association between SNPs and urinary UA, adjusted for age, sex, BMI, and urinary sodium, and accounting for sibship; Right Y-axis: SNP recombination rate based on HapMap hg18 CEU; X-axis: chromosomal location and gene regions; r² color code: degree of linkage disequilibrium with index (most strongly associated) SNP, rs12509955 (purple diamond).</p

<i>SLC2A9</i> gene expression association results for 880 SNPs in the <i>SLC2A9</i> gene region.

<p>Left Y-axis:–log₁₀(p-value) from association between SNPs and <i>SLC2A9</i> gene expression, accounting for sibship; Right Y-axis: SNP recombination rate based on HapMap hg18 CEU; X-axis: chromosomal location and gene regions; r² color code: degree of linkage disequilibrium with index (most strongly associated) SNP, rs2240724 (purple diamond).</p

<i>SLC2A9</i> Genotype Is Associated with <i>SLC2A9</i> Gene Expression and Urinary Uric Acid Concentration

<div><p>Objectives</p><p><i>SLC2A9</i> gene variants have been associated with urinary uric acid (UA) concentration, but little is known about the functional mechanism linking these gene variants with UA. <i>SLC2A9</i> encodes a UA transporter present in the proximal tubule of the kidney, and gene expression levels of <i>SLC2A9</i> and other genes in the uricosuric pathway (<i>ABCG2</i>, <i>SLC17A1</i>, <i>SLC17A3</i>, <i>and SLC22A12) </i> could potentially mediate the relationship between <i>SLC2A9</i> gene variants and urinary UA excretion.</p><p>Methods</p><p>The association between urinary UA concentrations and single nucleotide polymorphisms (SNPs) within the <i>SLC2A9</i> gene region, expression levels of genes in the uricosuric pathway, and dietary protein intake were analyzed for a sample of non-Hispanic white participants from the Genetic Epidemiology Network of Arteriopathy (GENOA) cohort. The <i>SLC2A9 </i> SNP most significantly associated with urinary UA concentration was then tested for associations with gene expression levels from uric acid absorption/secretion associated genes. Models including interactions between dietary protein (total, animal, and vegetable) and genetic factors were also assessed.</p><p>Results</p><p>The most significant <i>SLC2A9 </i> SNP associated with urinary UA (rs12509955, corrected p = 0.001) was also associated with <i>SLC2A9</i> gene expression levels (corrected p = 0.0084); however, <i>SLC2A9</i> gene expression levels were not significantly associated with urinary UA concentrations (p = 0.509). The interactions between rs12509955 and total dietary protein, and <i>SLC2A9 </i> gene-level gene expression and dietary vegetable protein on the outcome of urinary UA were marginally significant (p = 0.11 and p = 0.07, respectively). Gene expression level of one <i>SLC2A9</i> transcript had a significant interaction with dietary animal protein (<i>SLC2A9-001</i> ENST00000506583, p = 0.01) and a marginally significant interaction with total dietary protein (p = 0.07) on urinary UA.</p><p>Conclusion</p><p>Our results illustrate that SNPs in the <i>SLC2A9</i> gene influence <i>SLC2A9</i> gene expression as well as urinary UA excretion. Evidence is also suggestive that gene-by-diet interactions may disproportionately increase urinary UA in genetically susceptible individuals that consume higher amounts of protein.</p></div

Association between Stress Response Genes and Features of Diurnal Cortisol Curves in the Multi-Ethnic Study of Atherosclerosis: A New Multi-Phenotype Approach for Gene-Based Association Tests

<div><p>The hormone cortisol is likely to be a key mediator of the stress response that influences multiple physiologic systems that are involved in common chronic disease, including the cardiovascular system, the immune system, and metabolism. In this paper, a candidate gene approach was used to investigate genetic contributions to variability in multiple correlated features of the daily cortisol profile in a sample of European Americans, African Americans, and Hispanic Americans from the Multi-Ethnic Study of Atherosclerosis (MESA). We proposed and applied a new gene-level multiple-phenotype analysis and carried out a meta-analysis to combine the ethnicity specific results. This new analysis, instead of a more routine single marker-single phenotype approach identified a significant association between one gene (ADRB2) and cortisol features (meta-analysis p-value=0.0025), which was not identified by three other commonly used existing analytic strategies: 1. Single marker association tests involving each single cortisol feature separately; 2. Single marker association tests jointly testing for multiple cortisol features; 3. Gene-level association tests separately carried out for each single cortisol feature. The analytic strategies presented consider different hypotheses regarding genotype-phenotype association and imply different costs of multiple testing. The proposed gene-level analysis integrating multiple cortisol features across multiple ethnic groups provides new insights into the gene-cortisol association.</p></div

Representation of the diurnal cortisol curve describing our summary features of interest.

<p>In our study we specifically used Wakeup, Bedtime, Cortisol awakening response (CAR), Area under the curve (AUC) from 0–16 hours, Early Decline Slope, Late Decline Slope, and Overall Decline Slope.</p

LocusZoom plot of the association between all SNPs in the <i>ADRB2</i> gene region and cortisol features.

<p>Each SNP in the <i>ADRB2</i> gene region was analyzed by MultiPhen (O’Reilly et al., 2012) stratified by ethnicity. Then the meta-analysis p-values were calculated using Fisher’s probability test and plotted using LocusZoom (Pruim et al., 2010). The linkage disequilibrium is based on the European Americans. This is labeled as Method 2 in the paper.</p

Gene based analysis testing the association between the cortisol features and common variants (minor allele frequency > 0.05) in the stress response genes using SKAT (Method 3).

<p>MP-Fisher in the last column is the proposed multi-phenotype analysis (Method 4) that combines the gene-based p-values across the seven cortisol features.</p><p>CAR: cortisol awakening response. AUC: area under the diurnal cortisol curve. EDSlope: early decline slope. ODSlope: overall decline slope. LDSlope: Late decline slope. MP-Fisher: The Fisher’s probability test combining the seven cortisol features where permutation is used to account for the correlation among cortisol features. EUR: European Americans. AFA: African Americans. HIS: Hispanic Americans. Meta-WZ [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126637#pone.0126637.ref036" target="_blank">36</a>]: meta-analysis using weighted Z-score test. Meta-F: meta-analysis using Fisher’s probability test. Meta-WF [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126637#pone.0126637.ref037" target="_blank">37</a>]: meta-analysis using weighted Fisher’s probability test. Each cell presents the p-value. Age, gender and top five principal components were adjusted as covariates. Each cell presents the p-value. P-values less than 0.05 are bolded. Gene-level Bonferroni threshold is 0.0012 for single cortisol feature analysis, and 0.0083 for multiple cortisol features analysis.</p><p>Gene based analysis testing the association between the cortisol features and common variants (minor allele frequency > 0.05) in the stress response genes using SKAT (Method 3).</p

Characteristics of the stress response genes.

<p>Start/End position is according to the smallest start / largest end of the gene by UCSC genome browser based on the March 2006 human reference sequence (NCBI Build 36.1) produced by the International Human Genome Sequencing Consortium. Genotype data used for the main analysis includes both measured and imputed common SNPs (minor allele frequency > 0.05) in each gene +- 5kb. The imputation is based on ethnic specific reference panels. EUR: European Americans. AFA: African Americans. HIS: Hispanic Americans.</p><p>Characteristics of the stress response genes.</p