NDUFA13/YJEFN3 is differentially expressed in the brains of patients with schizophrenia.

Mamoor, Shahan

doi:10.31219/osf.io/r6uyd

Cited by 1 publication

(1 citation statement)

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“…A few of these genes have been implicated in neuropsychiatric traits or disorders. For example, genetic and methylomic variation in YJEFN3 has shown associations with schizophrenia [ 74 , 75 ] and common and rare genetic variants in GCH1 are associated with Parkinson’s disease [ 76 ]. Furthermore, KCTD10 encodes a protein that belongs to a family implicated in fetal development of many psychiatric traits [ 77 , 78 ].…”

Section: Discussionmentioning

confidence: 99%

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

Bhattacharya

Love

2021

PLoS Genet

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Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or other molecular effects underlying the SNP-gene association. Here, we outline multi-omics strategies for transcriptome imputation from germline genetics to allow more powerful testing of gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final predictive model of gene expression, along with local SNPs. In the second extension, we assess distal-eQTLs (SNPs associated with genes not in a local window around it) for their mediation effect through mediating biomarkers local to these distal-eSNPs. Distal-eSNPs with large indirect mediation effects are then included in the transcriptomic prediction model with the local SNPs around the gene of interest. Using simulations and real data from ROS/MAP brain tissue and TCGA breast tumors, we show considerable gains of percent variance explained (1–2% additive increase) of gene expression and TWAS power to detect gene-trait associations. This integrative approach to transcriptome-wide imputation and association studies aids in identifying the complex interactions underlying genetic regulation within a tissue and important risk genes for various traits and disorders.

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