Genetic random effects model for family data with long-term survivors: analysis of diabetic nephropathy in type 1 diabetes

Estimating Modifying Effect of Age on Genetic and Environmental Variance Components in Twin Models

Twin studies have been adopted for decades to disentangle the relative genetic and environmental contributions for a wide range of traits. However, heritability estimation based on the classical twin models does not take into account dynamic behavior of the variance components over age. Varying variance of the genetic component over age can imply the existence of gene-environment (G×E) interactions that general genome-wide association studies (GWAS) fail to capture, which may lead to the inconsistency of heritability estimates between twin design and GWAS. Existing parametricG×Einteraction models for twin studies are limited by assuming a linear or quadratic form of the variance curves with respect to a moderator that can, however, be overly restricted in reality. Here we propose spline-based approaches to explore the variance curves of the genetic and environmental components. We choose the additive genetic, common, and unique environmental variance components (ACE) model as the starting point. We treat the component variances as variance functions with respect to age modeled by B-splines or P-splines. We develop an empirical Bayes method to estimate the variance curves together with their confidence bands and provide an R package for public use. Our simulations demonstrate that the proposed methods accurately capture dynamic behavior of the component variances in terms of mean square errors with a data set of >10,000 twin pairs. Using the proposed methods as an alternative and major extension to the classical twin models, our analyses with a large-scale Finnish twin data set (19,510 MZ twins and 27,312 DZ same-sex twins) discover that the variances of the A, C, and E components for body mass index (BMI) change substantially across life span in different patterns and the heritability of BMI drops to ∼50% after middle age. The results further indicate that the decline of heritability is due to increasing unique environmental variance, which provides more insights into age-specific heritability of BMI and evidence ofG×Einteractions. These findings highlight the fundamental importance and implication of the proposed models in facilitating twin studies to investigate the heritability specific to age and other modifying factors.

Association of intercellular adhesion molecule 1 (ICAM1) with diabetes and diabetic nephropathy

Diabetes and diabetic nephropathy are complex diseases affected by genetic and environmental factors. Identification of the susceptibility genes and investigation of their roles may provide useful information for better understanding of the pathogenesis and for developing novel therapeutic approaches. Intercellular adhesion molecule 1 (ICAM1) is a cell surface glycoprotein expressed on endothelial cells and leukocytes in the immune system. The ICAM1 gene is located on chromosome 19p13 within the linkage region of diabetes. In the recent years, accumulating reports have implicated that genetic polymorphisms in the ICAM1 gene are associated with diabetes and diabetic nephropathy. Serum ICAM1 levels in diabetes patients and the icam1 gene expression in kidney tissues of diabetic animals are increased compared to the controls. Therefore, ICAM1 may play a role in the development of diabetes and diabetic nephropathy. In this review, we present genomic structure, variation, and regulation of the ICAM1 gene, summarized genetic and biological studies of this gene in diabetes and diabetic nephropathy and discussed about the potential application using ICAM1 as a biomarker and target for prediction and treatment of diabetes and diabetic nephropathy.

Fitting ACE structural equation models to case-control family data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for members of families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). We describe an ACE model for binary family data; this structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. We then introduce our contribution, a likelihood-based approach to fitting the model to singly ascertained case-control family data. The approach, which involves conditioning on the proband's disease status and also setting prevalence equal to a prespecified value that can be estimated from the data, makes it possible to obtain valid estimates of the A, C, and E variance components from case-control (rather than only from population-based) family data. In fact, simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly made assumptions hold. Further, when our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.

Gene polymorphisms of TNF-alpha-308 (G/A), IL-10(-1082) (G/A), IL-6(-174) (G/C) and IL-1Ra (VNTR) in Egyptian cases with type 1 diabetes mellitus

BACKGROUND

Type 1 diabetes (T1D) is a genetically conditioned autoimmune disease in which cytokines play an important role. Objectives. To check for the association of polymorphisms of cytokine genes with type 1 diabetes. Subjects. This work included 50 cases with T1D and 98 healthy individuals from the Nile Delta region of Egypt. Cases included 20 males and 30 females with a median age of 25 and range of 15-50 years.

METHODS

DNA was amplified using PCR with sequence-specific primers for detection of polymorphisms related to tumor necrosis factor (TNF)-alpha(- 308) (G/A), interleukin (IL)-10(- 1082) (G/A), IL-6(- 174) (G/C), and IL-1Ra (VNTR).

RESULTS

Cases with T1D showed significant higher frequency of genotypes of TNF-alpha(- 308) AA (p < 0.001, odds ratio (OR) = 7.91), IL-6-17CC (p < 0.05, OR = 3.36) and IL-1Ra A1A1 (p < 0.05, OR = 3.68) with significant lower frequencies of TNF-alpha(- 308) GA, and IL-1Ra A1A2 genotypes (p < 0.001 and < 0.05, respectively). They also showed significant higher frequency of TNF-alpha(- 308) allele A (p < 0.05, OR = 2.0), IL-1Ra allele A1 (p < 0.05, OR = 2.98) with a significant lower frequency of TNF-alpha(- 308) G allele and IL-1Ra A2 allele (p < 0.05). No significant difference was detected among cases in relation to IL-10(- 1082) (G/A) genotypes or alleles nor in relation to age, sex, consanguinity or family history of the disease.

CONCLUSIONS

Polymorphisms related to TNF-alpha and IL-1Ra genes may be considered genetic markers for T1D among Egyptians with a potential impact on family counseling and management.