Dataset Information

Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks.

ABSTRACT: A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes show substantial overlap, highlighting common mechanisms including synaptic signaling, epigenetic modification, and hormonal regulation. Genetic perturbations within these networks cause abnormal BMI phenotypes in mice, too, supporting their broad conservation across mammals. Other mechanisms appear species specific, including carbohydrate biosynthesis (humans) and glycerolipid metabolism (rodents). Finally, network co-localization also identifies cross-species convergence for height/body length. This study advances a general paradigm for determining whether and how phenotypes measured in model species recapitulate human biology.

SUBMITTER: Wright SN

PROVIDER: S-EPMC10546330 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks.

Wright Sarah N SN Leger Brittany S BS Rosenthal Sara Brin SB Liu Sophie N SN Jia Tongqiu T Chitre Apurva S AS Polesskaya Oksana O Holl Katie K Gao Jianjun J Cheng Riyan R Garcia Martinez Angel A George Anthony A Gileta Alexander F AF Han Wenyan W Netzley Alesa H AH King Christopher P CP Lamparelli Alexander A Martin Connor C St Pierre Celine L CL Wang Tengfei T Bimschleger Hannah H Richards Jerry J Ishiwari Keita K Chen Hao H Flagel Shelly B SB Meyer Paul P Robinson Terry E TE Solberg Woods Leah C LC Kreisberg Jason F JF Ideker Trey T Palmer Abraham A AA

Cell reports 20230731 8

A vexing observation in genome-wide association studies (GWASs) is that parallel analyses in different species may not identify orthologous genes. Here, we demonstrate that cross-species translation of GWASs can be greatly improved by an analysis of co-localization within molecular networks. Using body mass index (BMI) as an example, we show that the genes associated with BMI in humans lack significant agreement with those identified in rats. However, the networks interconnecting these genes sho ...[more]

PMID: 37527041

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Project description:BackgroundIn DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.ResultsWe present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.ConclusionsThe usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

Dataset Information

Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks.

Publications

Genome-wide association studies of human and rat BMI converge on synapse, epigenome, and hormone signaling networks.

Similar Datasets

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