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Publicly Available Data Provide Evidence against NR1H3 R415Q Causing Multiple Sclerosis.

ABSTRACT: It has recently been reported that an NR1H3 missense variant, R415Q, causes a novel familial form of multiple sclerosis (Wang et al., 2016a). This claim is at odds with publicly available data from the Exome Aggregation Consortium (ExAC; http://exac.broadinstitute.org). The allele frequency of R415Q is not significantly higher in cases (0.024%-0.049%) than in ExAC population controls (0.031%), whereas if R415Q conferred even 50% lifetime risk of developing MS, it would be hundreds of times more common in cases than in controls. The upper bound of the 95% confidence interval of penetrance for R415Q can be estimated at 2.2% for women and 1.2% for men, indicating that even if this variant is disease associated, individuals harboring the variant would have a lifetime risk of developing MS no higher than a few percent. ExAC data should be considered when evaluating claims of variant pathogenicity. This Matters Arising paper is in response to Wang et al. (2016a), published in Neuron. See also the related Matters Arising paper by The International Multiple Sclerosis Genetics Consortium (2016) and the response by Wang et al. (2016b), published in this issue.

SUBMITTER: Minikel EV

PROVIDER: S-EPMC5123684 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publicly Available Data Provide Evidence against NR1H3 R415Q Causing Multiple Sclerosis.

Minikel Eric Vallabh EV MacArthur Daniel G DG

Neuron 20161001 2

It has recently been reported that an NR1H3 missense variant, R415Q, causes a novel familial form of multiple sclerosis (Wang et al., 2016a). This claim is at odds with publicly available data from the Exome Aggregation Consortium (ExAC; http://exac.broadinstitute.org). The allele frequency of R415Q is not significantly higher in cases (0.024%-0.049%) than in ExAC population controls (0.031%), whereas if R415Q conferred even 50% lifetime risk of developing MS, it would be hundreds of times more ...[more]

PMID: 27764668

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Project description:Transcriptome wide association studies (TWAS) can be used as a powerful method to identify and interpret the underlying biological mechanisms behind GWAS by mapping gene expression levels with phenotypes. In TWAS, gene expression is often imputed from individual-level genotypes of regulatory variants identified from external resources, such as Genotype-Tissue Expression (GTEx) Project. In this setting, a straightforward approach to impute expression levels of a specific tissue is to use the model trained from the same tissue type. When multiple tissues are available for the same subjects, it has been demonstrated that training imputation models from multiple tissue types improves the accuracy because of shared eQTLs between the tissues and increase in effective sample size. However, existing joint-tissue methods require access of genotype and expression data across all tissues. Moreover, they cannot leverage the abundance of various expression datasets across various tissues for non-overlapping individuals. Here, we explore the optimal way to combine imputed levels across training models from multiple tissues and datasets in a flexible manner using summary-level data. Our proposed method (SWAM) combines arbitrary number of transcriptome imputation models to linearly optimize the imputation accuracy given a target tissue. By integrating models across tissues and/or individuals, SWAM can improve the accuracy of transcriptome imputation or to improve power to TWAS while only requiring individual-level data from a single reference cohort. To evaluate the accuracy of SWAM, we combined 49 tissue-specific gene expression imputation models from the GTEx Project as well as from a large eQTL study of Depression Susceptibility Genes and Networks (DGN) Project and tested imputation accuracy in GEUVADIS lymphoblastoid cell lines samples. We also extend our meta-imputation method to meta-TWAS to leverage multiple tissues in TWAS analysis with summary-level statistics. Our results capitalize on the importance of integrating multiple tissues to unravel regulatory impacts of genetic variants on complex traits.

Dataset Information

Publicly Available Data Provide Evidence against NR1H3 R415Q Causing Multiple Sclerosis.

Publications

Publicly Available Data Provide Evidence against NR1H3 R415Q Causing Multiple Sclerosis.

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