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

Pitkäniemi, Janne; Moltchanova, Elena; Haapala, Laura; Harjutsalo, Valma; Tuomilehto, Jaakko; Hakulinen, Timo

doi:10.1002/gepi.20234

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…The treatment cost for diabetes patients has been increasing staggering in the recent decades and becomes a further burden of the healthcare system. Diabetes and DN are multi-factorial diseases, which are influenced by both genetic and environmental factors (Satko et al, 2005; Pitkäniemi et al, 2007; Ashcroft and Rorsman, 2012; Gonzalez-Bulnes and Ovilo, 2012; Morahan, 2012). Therefore, identification of the susceptibility genes in development of diabetes and diabetic complications and investigation of their roles are of importance to provide useful information for improvement of the prevention and medication programs.…”

Section: Introductionmentioning

confidence: 99%

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

Gu¹,

Ma²,

Gu³

et al. 2013

Front. Endocrin.

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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.

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Section: Introductionmentioning

confidence: 99%

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

Gu¹,

Ma²,

Gu³

et al. 2013

Front. Endocrin.

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“…Extensions for longitudinal data or time-to-event phenotypes based on the classical twin models have been proposed (Boomsma et al 1989;Pitkäniemi et al 2007). Nevertheless, one of the major issues posing potential challenge is that many twin data sets contain individuals across a wide range of age and the variances of genetic and environmental components for many complex traits such as body mass index (BMI) and height change over age (Lajunen et al 2009;Elks et al 2012) or birth cohorts (Silventoinen et al 2000).…”

mentioning

confidence: 99%

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

Sillanpää

Silventoinen

et al. 2016

Genetics

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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 geneenvironment (G 3 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 parametric G 3 E interaction 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 of G 3 E interactions. 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.KEYWORDS twin models; penalized B-splines; age-specific heritability; empirical Bayes predictor; body mass index T WIN studies have been broadly used as a tool to investigate heritability for decades. A recent comprehensive meta-analysis reported 17,804 heritability estimates for various traits based on twin studies (Polderman et al. 2015).Estimation methods and software such as structural equation modeling (SEM) based on the classical twin models consisting of additive genetic, common, and unshared environmental effects have been well established and extensively adopted (Rijsdijk and Sham 2002). Extensions for longitudinal data or time-to-event phenotypes based on the classical twin models have been proposed (Boomsma et al. 1989;Pitkäniemi et al. 2007). Nevertheless, one of...

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“…The application of the Cox model for analyzing rare and common diseases can also be extended to accommodate situations in which there are forces accelerating or decelerating the time to event of interest. The Birnbaum-Saunders [Birnbaum and Saunders, 1969;Kundu et al, 2008] parametric LTS models [Locatelli et al, 2007, Pitkäniemi et al, 2007 and also a nonparametric LTS model, for instance, might be appropriate alternatives for modeling each one of these situations. Since genetic and environmental factors might be involved in that acceleration or deceleration process, extending these models in genetic analysis would be useful.…”

Section: Discussionmentioning

confidence: 99%

“…This model was applied by authors for assessing genetic effects on age-at-onset of breast cancer in a large family study. More recently, a Bayesian shared and additive genetic random effects model for family data with longterm survivors (LTS) was proposed by Pitkäniemi et al [2007]. In this model, a fraction of the population under study is considered to be non-susceptible or immune to the disease.…”

mentioning

confidence: 99%

Genetic Analysis of Age-at-Onset for Cardiovascular Risk Factors in a Brazilian Family Study

Giolo¹,

Pereira²,

Andrade

et al. 2009

Hum Hered

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Background/Aims: Statistical analysis of age-at-onset involving family data is particularly complicated because there is a correlation pattern that needs to be modeled and also because there are measurements that are censored. In this paper, our main purpose was to evaluate the effect of genetic and shared family environmental factors on age-at-onset of three cardiovascular risk factors: hypertension, diabetes and high cholesterol. Methods: The mixed-effects Cox model proposed by Pankratz et al. [2005] was used to analyze the data from 81 families, involving 1,675 individuals from the village of Baependi, in the state of Minas Gerais, Brazil. Results: The analyses performed showed that the polygenic effect plays a greater role than the shared family environmental effect in explaining the variability of the age-at-onset of hypertension, diabetes and high cholesterol. The model which simultaneously evaluated both effects indicated that there are individuals which may have risk of hypertension due to polygenic effects 130% higher than the overall average risk for the entire sample. For diabetes and high cholesterol the risks of some individuals were 115 and 45%, respectively, higher than the overall average risk for the entire population. Conclusions: Results showed evidence of significant polygenic effects indicating that age-at-onset is a useful trait for gene mapping of the common complex diseases analyzed. In addition, we found that the polygenic random component might absorb the effects of some covariates usually considered in the risk evaluation, such as gender, age and BMI.

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

Cited by 6 publications

References 33 publications

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

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

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

Genetic Analysis of Age-at-Onset for Cardiovascular Risk Factors in a Brazilian Family Study

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