A dynamic model for genome-wide association studies

Das, Kiranmoy; Li, Jiahan; Wang, Zhong; Tong, Chunfa; Fu, Guifang; Li, Yao; Xu, Meng; Ahn, Kwangmi; Mauger, David T.; Li, Runze; Wu, Rongling

doi:10.1007/s00439-011-0960-6

Cited by 89 publications

(91 citation statements)

References 47 publications

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Section: Discussionmentioning

confidence: 99%

“…Our results provide support for the need to further investigate time-dependent effects of significantly associated SNPs identified in GWAS. In fact, multiple significant SNPs have been found to affect BMI with different temporal patterns during middle age (Das et al 2011).Future work of the proposed models can be focused on the extension to other types of traits such as binary and categorical traits. A full Bayesian method may be utilized to obtain a more accurate estimate and C.I.'s.…”

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confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…On the other hand, in human genetics, the SNP effect with genotype differentiation trajectories over time has been studied to detect the genetic influence on dynamic traits. Das et al (2011a) plotted and interpreted age-specific trajectories of the body mass index (BMI) in different sexes for three genotypes at each significant SNP that was detected from the various chromosomes. Analogously, Das et al (2011b) fitted mean curves for different genotypes and computed the additive and dominant SNP effects over time for blood pressure in the different sexes.…”

Section: Genome Association Analyses Through Nonlinear Mixed Models Amentioning

confidence: 99%

“…There are studies considering this gene identification for growth curve parameters in humans (Das et al, 2011a) and cattle (Crispim et al, 2015), but for pig growth curves, this approach has not been employed in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Silva

Zambrano

Varona

et al. 2017

Sci. agric. (Piracicaba, Braz.)

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Genome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied.

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“…In contrast, another SNP (rs40953127) was determined not to be related in the static study but was significant in the dynamic study. This suggests that phenotypic records which are measured at a single time point may not capture complete information on phenotypic expression [22].…”

Section: Selection Of Candidate Genes and Snpsmentioning

confidence: 99%

Associations between gene polymorphisms in two crucial metabolic pathways and growth traits in pigs

Yang

Wang

et al. 2012

Chin. Sci. Bull.

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Propanoate metabolism and fatty acid metabolism are crucial metabolic pathways in vivo that have been studied extensively and have shown a significant influence on the growth and diseases of animals. In order to identify the essential genes significantly associated with porcine growth traits at the pathway level, we selected 14 candidate genes with 58 SNPs, including 24 tag SNPs and 34 predicted SNPs, in the two essential pathways to test using the SNaPshot mini-sequencing method. Association analysis was performed using both static and dynamic phenotypic records. The results showed that four SNPs, respectively in SUCLA2, SUCLG2, ACADS, and ALDH1B1, were significantly associated with growth in pigs. In addition, an exciting finding was that the change of transcription factor binding site that resulted from alteration of the bases in SUCLG2 brought out a transcription factor binding site for POU1F1a, which is the product of a notable marker gene for growth of animals. Growth traits, including total feed intake, daily feed intake, feed conversion ratio and daily weight gain, have a direct impact on production efficiency in the swine industry. Studies on the relationship between noted growth-related genes and growth traits have attracted a great deal of attention for decades. Unfortunately, despite the fact that numerous studies have been performed, few of them have demonstrated the nature and mechanism of genetic variation that underlies the quantitative traits. The methods for studying quantitative traits should be reconsidered, because the use of only SNPs as the basic units of association analysis has shown several limitations. The functions of SNPs and genes are usually carried out through intricate pathways of reactions and interactions [1]. Therefore, studies based on the candidate pathways approach may not only provide us with the possibility to gain more extensive insight into the functional basis of association but also facilitate and unravel the detailed genetic control mechanisms of complex phenotypes. Previous study in our laboratory has shown that the propanoate and fatty acid metabolic pathways exhibit significant down-regulation under PPARα knockout conditions in the livers of mice, which was detected by the analysis of mouse PPARα dependent or independent datasets by GSEA (Gene Set Enrichment Analysis) [2]. The propanoate and fatty acid metabolic processes are crucial metabolic pathways in vivo, and may be connected with the TCA (tricarboxylic acid cycle) cycle. Metabolism of fatty acids is essential for the organism because they are a major source of energy and the structural components of membranes. In addition, a variety of fatty acids perform key biological functions such as the regulation of lipid metabolism, cell division and inflammation [3][4][5]. The metabolism of propanoate (propionic acid) begins with its conversion to propionyl coenzyme A (propionyl-CoA), which is usually the first step in the metabolism of carboxylic acids. The regulatory mechanisms of the propanoate and fatty acid met...

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A dynamic model for genome-wide association studies

Cited by 89 publications

References 47 publications

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

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

Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves

Associations between gene polymorphisms in two crucial metabolic pathways and growth traits in pigs

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