A quantitative genetic and epigenetic model of complex traits

Wang, Zhong; Wang, Zuoheng; Wang, Jianxin; Sui, Yihan; Zhang, Jian; Liao, Duanping; Wu, Rongling

doi:10.1186/1471-2105-13-274

Cited by 12 publications

(11 citation statements)

References 37 publications

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“…Although this has helped greatly to improve our understanding of the origin of epigenetic alterations, a systematic detection of this phenomenon through GWAS in a commonly used case-control design is still not largely feasible [19]. Here, we have integrated quantitative genetic and epigenetic principles established by Wang et al [18] into a case-control setting, allowing the significance of genetic and epigenetic effects on a disease to be tested and analyzed. In the previous studies, quantitative genetic principles were incorporated with a case-control design to analyze the associations between epistasis and disease [27,28].…”

Section: Discussionmentioning

confidence: 99%

“…By making crosses derived from epigenomically perturbed isogenic lines, Johannes and Colome-Tatche [17] developed an approach to estimating epigenetic variation, allowing the effects of epiallelic instability, recombination, parent-oforigin effects and transgressive segregation to be characterized. Wang et al [18] integrated conventional quantitative genetic theory with DNA methylation mechanisms to provide an approach for quantifying epigenetic effects and variation in complex traits.…”

Section: Introductionmentioning

confidence: 99%

“…Variation in DNA methylation at a single or multiple sites is called a methylation variable position (MVP) and a differentially methylated region (DMP), respectively. By considering MVP or DMP as the epigenetic equivalent of a SNP or multiple SNPs, Wang et al [18] are able to separate and estimate the genetic and epigenetic contributions to a complex trait using a quantitative genetic and epigenetic theory. Here, we test each of these two types of contributions in a case-control design.…”

Section: Introductionmentioning

confidence: 99%

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A case-control design for testing and estimating epigenetic effects on complex diseases

Sui

Wang

et al. 2013

Briefings in Bioinformatics

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Epigenetic modifications may play an important role in the formation and progression of complex diseases through the regulation of gene expression. The systematic identification of epigenetic variants that contribute to human diseases can be made possible using genome-wide association studies (GWAS), although epigenetic effects are currently not included in commonly used case-control designs for GWAS. Here, we show that epigenetic modifications can be integrated into a case-control setting by dissolving the overall genetic effect into its different components, additive, dominant and epigenetic. We describe a general procedure for testing and estimating the significance of each component based on a conventional chi-squared test approach. Simulation studies were performed to investigate the power and false-positive rate of this procedure, providing recommendations for its practical use. The integration of epigenetic variants into GWAS can potentially improve our understanding of how genetic, environmental and stochastic factors interact with epialleles to construct the genetic architecture of complex diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A case-control design for testing and estimating epigenetic effects on complex diseases

Sui

Wang

et al. 2013

Briefings in Bioinformatics

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“…The potential role of epigenetics on complex traits has led to studies of the impact of epigenetic variation on phenotypic and genetic variations. For example, in a recent in silico study, it was shown that epigenetic modification of one allele at a biallelic locus can result in an 11 % of total genetic variance attributed to epigenetic variation at moderate allele frequency even if u , the epigenetic modification rate, is as low as 0.01 [ 21 ]. The proportion of genetic variance explained by epigenetic variation could be as large as 18 % if u increases to 0.5.…”

Section: Contribution Of Imprinting To Genetic Variationmentioning

confidence: 99%

“…The ratio defines the proportion of total genetic variance explained by imprinting. This ratio is, to some extent, equivalent to the definition of in [ 21 ], with the only difference being that these authors were interested in a broader concept of epigenetic mechanism while here we are interested in imprinting only. We graphically illustrate how imprinting can impact the evaluation of marked variance, and its consequences if ignored.…”

Section: Contribution Of Imprinting To Genetic Variationmentioning

confidence: 99%

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice

Rosa

Gianola

2015

BMC Genomics

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BackgroundGenomic imprinting is an epigenetic mechanism that can lead to differential gene expression depending on the parent-of-origin of a received allele. While most studies on imprinting address its underlying molecular mechanisms or attempt at discovering genomic regions that might be subject to imprinting, few have focused on the amount of phenotypic variation contributed by such epigenetic process. In this report, we give a brief review of a one-locus imprinting model in a quantitative genetics framework, and provide a decomposition of the genetic variance according to this model. Analytical deductions from the proposed imprinting model indicated a non-negligible contribution of imprinting to genetic variation of complex traits. Also, we performed a whole-genome scan analysis on mouse body mass index (BMI) aiming at revealing potential consequences when existing imprinting effects are ignored in genetic analysis.Results10,021 SNP markers were used to perform a whole-genome single marker regression on mouse BMI using an additive and an imprinting model. Markers significant for imprinting indicated that BMI is subject to imprinting. Marked variance changed from 1.218 ×10−4 to 1.842 ×10−4 when imprinting was considered in the analysis, implying that one third of marked variance would be lost if existing imprinting effects were not accounted for. When both marker and pedigree information were used, estimated heritability increased from 0.176 to 0.195 when imprinting was considered.ConclusionsWhen a complex trait is subject to imprinting, using an additive model that ignores this phenomenon may result in an underestimate of additive variability, potentially leading to wrong inferences about the underlying genetic architecture of that trait. This could be a possible factor explaining part of the missing heritability commonly observed in genome-wide association studies (GWAS).

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Evolutionary Perspectives on Transgenerational Epigenetics

Uller

2014

Transgenerational Epigenetics

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A quantitative genetic and epigenetic model of complex traits

Cited by 12 publications

References 37 publications

A case-control design for testing and estimating epigenetic effects on complex diseases

A case-control design for testing and estimating epigenetic effects on complex diseases

A GWAS assessment of the contribution of genomic imprinting to the variation of body mass index in mice

Evolutionary Perspectives on Transgenerational Epigenetics

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