Widespread epistasis regulates glucose homeostasis and gene expression

<div><p>The relative contributions of additive versus non-additive interactions in the regulation of complex traits remains controversial. This may be in part because large-scale epistasis has traditionally been difficult to detect in complex, multi-cellular organisms. We hypothesized that it would be easier to detect interactions using mouse chromosome substitution strains that simultaneously incorporate allelic variation in many genes on a controlled genetic background. Analyzing metabolic traits and gene expression levels in the offspring of a series of crosses between mouse chromosome substitution strains demonstrated that inter-chromosomal epistasis was a dominant feature of these complex traits. Epistasis typically accounted for a larger proportion of the heritable effects than those due solely to additive effects. These epistatic interactions typically resulted in trait values returning to the levels of the parental CSS host strain. Due to the large epistatic effects, analyses that did not account for interactions consistently underestimated the true effect sizes due to allelic variation or failed to detect the loci controlling trait variation. These studies demonstrate that epistatic interactions are a common feature of complex traits and thus identifying these interactions is key to understanding their genetic regulation.</p></div

SHBG Gene Polymorphism (rs1799941) Associates with Metabolic Syndrome in Children and Adolescents

<div><p>Background</p><p>Metabolic syndrome (MetS) is a complex disorder characterized by coexistence of several cardiometabolic (CM) factors, i.e. hyperlipidemia, obesity, high blood pressure and insulin resistance. The presence of MetS is strongly associated with increased risk of cardiovascular disease (CVD). The syndrome was originally defined as an adult disorder, but MetS has become increasingly recognized in children and adolescents.</p><p>Methods</p><p>Genetic variants influence biological components common to the CM factors that comprise MetS. We investigated single locus associations between six single nucleotide polymorphisms (SNPs), previously shown to modulate lipid or sex hormone binding globulin (SHBG) levels, with MetS in a Turkish pediatric cohort (37 cases, 323 controls).</p><p>Results</p><p>Logistic regression analysis revealed a significant association between rs1799941, located in SHBG, and MetS (OR = 3.09, p-value = 0.006). The association with MetS remained after sequential adjustment for each CM factor included in the syndrome definition, indicating that the identified association is not being driven by any single trait. A relationship between rs1799941 and SHBG levels, was also discovered, but it was dependent on MetS status. In control subjects, the A allele of rs1799941 associated with a significant increase in SHBG levels (p = 0.012), while in cases there was no association between rs1799941 and SHBG levels (p = 0.963).</p><p>Conclusions</p><p>The significant association between rs1799941 and MetS in children is not contingent on any single CM trait. Additionally, the presence of MetS may abrogate effect of rs1799941 polymorphism on SHBG levels in children.</p></div

Logistic regression results of Metabolic Syndrome Adjusted for known Metabolic Syndrome Risk Factors.

<p><u><i>Regression models above were adjusted for age</i>, <i>gender</i>, <i>and SHBG levels</i>. <i>Significant results are highlighted in</i><b><i>bold</i></b>.</u></p><p><u><i>Note</i></u>: SNPs were coded coded dominantly for the effect of the minor allele (homozygous major = 0, heterozygote = 1, homozygous minor = 1). Odds ratios presented above represent the change in odds per the addition of at least one copy of the minor allele for each analyzed SNP.</p><p>¹: OR = Odds Ratio</p><p>*Effect remained significant after Bonferronni correction for multiple testing</p><p>Logistic regression results of Metabolic Syndrome Adjusted for known Metabolic Syndrome Risk Factors.</p

Examples of synergistic and antagonistic ieQTLs.

<p>Each dot represents the gene expression data from one mouse. The horizontal bar indicates the mean value for each strain (A) An antagonistic ieQTL regulates the expression of <i>Agxt</i> in the liver. (B) A synergistic ieQTL regulates the expression of <i>Cyp3a16</i> in the liver. The red horizontal line indicates the predicted trait level based on a model of additivity.</p

Regression Analysis of Sex Hormone Binding Globulin Levels by rs1799941 genotype in Metabolic Syndrome Cases and Controls.

<p><u><i>All regression models were adjusted for age and gender</i>. <i>Significant results are highlighted in</i><b><i>bold</i></b>.</u></p><p>*Describes the coding scheme used for rs1799941 1</p><p>¹ rs1799941 genotype was coded dominantly with the minor allele as the reference: homozygous major = 0, heterozygote = 1, homozygous minor = 1</p><p>² rs1799941 genotype was coded additively with the minor allele as reference: homozygous major = 0, heterozygote = 1, homozygous minor = 2</p><p>^# There are no Metabolic Syndrome cases with the AA genotype; therefore, additive and dominant genotype coding in these individuals is identical</p><p>³ Regresssion coefficient that describes the change in median SHBG levels per the addition of one (rs1799941_ADD) or at least one (rs1799941_DOM) minor allele at SNP rs1799941</p><p>⁴ Standard Error of regression coefficient</p><p>⁵ 95% Confidence Interval for indicated regression coeficient</p><p>Regression Analysis of Sex Hormone Binding Globulin Levels by rs1799941 genotype in Metabolic Syndrome Cases and Controls.</p

Contribution of epistasis to the genetic regulation of hepatic gene expression.

<p>Diagrams representing the estimated proportion of genetic variation due to interactions for (A) all genes expressed in the mouse liver whose expression was under genetic control in the CSS strains studied, (B) the same data segregated based on the statistical evidence supporting an effect of interaction on gene expression, and (C) a comparison of the genes with the most significant evidence for regulation by genetic interactions (FDR < 0.05) and a simulation study with artificial data that model the absence of any genetic interactions.</p

Identification of meQTLs that regulate hepatic gene expression.

<p>A circos plot of meQTL locations in the genome where each layer of the circle represents the comparison between a CSS strain and control B6 mice. From the inner circle, the CSS strains are (B6 x B6.A5)F1, (B6.17 x B6)F1, (B6.A3 x B6)F1, (B6.A6 x B6)F1, (B6 x B6.A10)F1, (B6 x B6.A4)F1, (B6.A14 x B6)F1 and (B6 x B6.A8)F1. Cis-meQTLs and trans-meQTLs are marked with red and blue, respectively. The width of each chromosome is proportional to its physical size. The height of each meQTL bar is proportional to the number of meQTLs in that genomic interval.</p

Median Circulating Sex Hormone Binding Globulin Levels vs. rs1799941 genotype (additive coding).

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0116915#pone.0116915.g001" target="_blank">Fig. 1</a> presents the relationship between median SHBG levels and rs1799941 coded additively for increasing numbers of the minor allele (A allele). Dashed line represents overall median SHBG level (65.0 nmol/l) in full cohort. Red circles represent denoted genotypic medians, and blue dotted lines with triangular end bars represent the interquartile range for SHBG in the rs1799941 genotype subgroup indicated. Connecting red lines illustrate trend in median SHBG by rs179941 genotype.</p

Pleiotropic Effects of Immune Responses Explain Variation in the Prevalence of Fibroproliferative Diseases

<div><p>Many diseases are differentially distributed among human populations. Differential selection on genetic variants in ancestral environments that coincidentally predispose to disease can be an underlying cause of these unequal prevalence patterns. Selected genes may be pleiotropic, affecting multiple phenotypes and resulting in more than one disease or trait. Patterns of pleiotropy may be helpful in understanding the underlying causes of an array of conditions in a population. For example, several fibroproliferative diseases are more prevalent and severe in populations of sub-Saharan ancestry. We propose that this disparity is due to selection for an enhanced Th2 response that confers resistance to helminthic infections, and concurrently increases susceptibility to fibrosis due to the profibrotic action of Th2 cytokines. Many studies on selection of Th2-related genes for host resistance to helminths have been reported, but the pleiotropic impact of this selection on the distribution of fibrotic disorders has not been explicitly investigated. We discuss the disproportionate occurrence of fibroproliferative diseases in individuals of African ancestry and provide evidence that adaptation of the immune system has shaped the genetic structure of these human populations in ways that alter the distribution of multiple fibroproliferative diseases.</p></div

Sex Hormone Binding Globulin Levels (SHBG) by rs1799941 genotype in Metabolic Syndrome Cases and Controls.

<p><u>Significant results are highlighted in <b><i>bold</i></b>.</u></p><p>NA: There were no individuals in this genotype group</p><p>¹P-value presented is for the Kruskal Wallis test for differences between groups</p><p>²P-value presented is from the Non-Parametric test for Increasing Trend in STATA 11</p><p>Sex Hormone Binding Globulin Levels (SHBG) by rs1799941 genotype in Metabolic Syndrome Cases and Controls.</p