Epigenome-Wide Association of Liver Methylation Patterns and Complex Metabolic Traits in Mice

Orozco, Luz D.; Morselli, Marco; Rubbi, Liudmilla; Guo, Weilong; Go, James; Shi, Huwenbo; López, David; Furlotte, Nicholas A.; Bennett, Brian J.; Farber, Charles R.; Ghazalpour, Anatole; Zhang, Michael Qiwei; Bahous, Renata H.; Rozen, Rima; Lusis, Aldons J.; Pellegrini, Matteo

doi:10.1016/j.cmet.2015.04.025

Cited by 88 publications

(102 citation statements)

References 34 publications

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“…The causal inference test (CIT) was conducted using the statistical package CIT 25 in R based on the following conditions: (1) the trait (T) is associated with the locus (L); (2) L is associated with the eGene mediator (G) after adjusting for T; (3) G is associated with T after adjusting for L; and (4) L is independent of T after adjusting for G. The p value of CIT is defined as the maximum of the four-component test p values by the intersection-union test framework ( Figure S1). 26 To determine whether cis-eGenes or trans-eGenes are causal mediators for a trait, CIT was performed for cis-eGenes and trans-eGenes separately. For a cis-eGene, we used its cis-eQTL with the smallest p value as an instrumental variable.…”

Section: Mediation and Causal Testingmentioning

confidence: 99%

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Chen¹,

Joehanes²,

Johnson³

et al. 2017

The American Journal of Human Genetics

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Identifying causal genetic variants and understanding their mechanisms of effect on traits remains a challenge in genome-wide association studies (GWASs). In particular, how genetic variants (i.e., trans-eQTLs) affect expression of remote genes (i.e., trans-eGenes) remains unknown. We hypothesized that some trans-eQTLs regulate expression of distant genes by altering the expression of nearby genes (cis-eGenes). Using published GWAS datasets with 39,165 single-nucleotide polymorphisms (SNPs) associated with 1,960 traits, we explored whole blood gene expression associations of trait-associated SNPs in 5,257 individuals from the Framingham Heart Study. We identified 2,350 trans-eQTLs (at p < 10 À7 ); more than 80% of them were found to have cis-associated eGenes. Mediation testing suggested that for 35% of trans-eQTL-transeGene pairs in different chromosomes and 90% pairs in the same chromosome, the disease-associated SNP may alter expression of the transeGene via cis-eGene expression. In addition, we identified 13 trans-eQTL hotspots, affecting from ten to hundreds of genes, suggesting the existence of master genetic regulators. Using causal inference testing, we searched causal variants across eight cardiometabolic traits (BMI, systolic and diastolic blood pressure, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, and fasting blood glucose) and identified several cis-eGenes (ALDH2 for systolic and diastolic blood pressure, MCM6 and DARS for total cholesterol, and TRIB1 for triglycerides) that were causal mediators for the corresponding traits, as well as examples of trans-mediators (TAGAP for LDL cholesterol). The finding of extensive evidence of genome-wide mediation effects suggests a critical role of cryptic gene regulation underlying many disease traits.

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Section: Mediation and Causal Testingmentioning

confidence: 99%

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Chen¹,

Joehanes²,

Johnson³

et al. 2017

The American Journal of Human Genetics

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“…At present no example of this in livestock science exists. Orozco et al (2015) provide a good example comparing 90 laboratory strains of mice, investigating genome-wide genotype (genetics), genome-wide methylation patterns (epigenetics), transcriptomics, proteomics, and metabolomic patterns. The analysis integrates all data together, clearly showing that genetic analysis using GWAS (genome-wide association study) provides only part of the genomic effect.…”

Section: Integration Of Knowledge and Potential Uses Of The Biomarkersmentioning

confidence: 99%

Invited review: Measurable biomarkers linked to meat quality from different pig production systems

2017

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Abstract. Biological processes underlie all livestock traits, including post-mortem meat quality traits. Biomarkers are molecular components of the biological processes showing differential expression associated with the phenotype of the trait. The phenotypes of the meat quality traits are determined by the animal's genotype interacting with the environment affecting the expression of the genome. The "omics" technologies enable measuring the expression of the genome at all levels: transcriptome, proteome, and metabolome. Associations between the phenotype of the traits and expressions measured with the omics techniques are a first step in developing biomarkers. Biomarkers enable the monitoring, diagnosis, and prediction of changes in meat quality related to external (environmental, e.g. feed and animal management conditions) stimuli and interactions with the genotype. In this paper we review the development of biomarkers for meat quality of pigs in diverse pig breeds, environments, and pork production chains.

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“…We begin with simulations in which the true value of β is known, and the over-dispersion parameter and genetic covariance between samples are motivated by the real data sets. We also motivate our choice of simulated sample sizes based on real bisulfite sequencing data sets, which currently range from~20-150 samples [19,26,46,53,[79][80][81][82]. However, because sample sizes are only likely to grow in the future, for the data set types of most direct interest (i.e., those that contain population structure and heritable DNA methylation levels) we further consider sample sizes that are much larger than currently represented in the literature (n = 500 and n = 1000).…”

Section: The Binomial Mixed Model and The Macau Algorithmmentioning

confidence: 99%

“…However, despite growing interest in environmental epigenetics and epigenome-wide association studies (EWAS), none of the currently available count-based methods appropriately control for genetic effects on DNA methylation levels in bisulfite sequencing data (Table 1). Consequently, even though count-based methods have been shown to be more powerful, recent bisulfite sequencing studies have turned to linear mixed models to deal with the confounding effects of population structure [19,46]. To address this gap, we present a binomial mixed model (BMM) for identifying differentially methylated sites that directly models raw read counts while accounting for both covariance between samples and extra over-dispersion caused by independent noise.…”

Section: Introductionmentioning

confidence: 99%

A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data

Lea

Alberts

Tung

et al. 2015

Preprint

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Identifying sources of variation in DNA methylation levels is important for understanding gene regulation. Recently, bisulfite sequencing has become a popular tool for investigating DNA methylation levels. However, modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples, and because of the computational challenges of controlling for genetic covariance in count data. To address these challenges, we present a binomial mixed model and an efficient, sampling-based algorithm (MACAU: Mixed model association for count data via data augmentation) for approximate parameter estimation and p-value computation. This framework allows us to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. Using simulations and two real data sets (whole genome bisulfite sequencing (WGBS) data from Arabidopsis thaliana and reduced representation bisulfite sequencing (RRBS) data from baboons), we show that our method provides well-calibrated test statistics in the presence of population structure. Further, it improves power to detect differentially methylated sites: in the RRBS data set, MACAU detected 1.6-fold more age-associated CpG sites than a beta-binomial model (the next best approach). Changes in these sites are consistent with known age-related shifts in DNA methylation levels, and are enriched near genes that are differentially expressed with age in the same population. Taken together, our results indicate that MACAU is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations. MACAU is freely available at www.xzlab. org/software.html. Author SummaryDNA methylation is an important epigenetic modification involved in regulating gene expression. It can be measured at base-pair resolution, on a genome-wide scale, by coupling sodium bisulfite conversion with high-throughput sequencing (a technique known as 'bisulfite sequencing'). However, the data generated by such methods present several challenges for statistical analysis. In particular, while the raw data generated from bisulfite sequencing experiments are read counts, they are often converted to proportions for ease of modeling, resulting in loss of information. Furthermore, although DNA methylation levels are known to be heritable-and are thus affected by kinship and population structure-existing approaches for modeling bisulfite sequencing data fail to account for this covariance. Such failure can lead to spurious associations and reduced power. Here, we present a new approach that models bisulfite sequencing data using raw read counts, while also taking into account population structure and other sources of data over-dispersion. Using simulations and two real data sets (publicly available data from Arabidopsis thaliana and newly generated data from Papio cynocephalus), we demonstrate that our model provides well-calibrated p-values and i...

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Epigenome-Wide Association of Liver Methylation Patterns and Complex Metabolic Traits in Mice

Cited by 88 publications

References 34 publications

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Dynamic Role of trans Regulation of Gene Expression in Relation to Complex Traits

Invited review: Measurable biomarkers linked to meat quality from different pig production systems

A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data

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