Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia

Autoři: Shizhen Tang aff001;  Aron S. Buchman aff003;  Philip L. De Jager aff004;  David A. Bennett aff003;  Michael P. Epstein aff001;  Jingjing Yang aff001
Působiště autorů: Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia, United States of America aff001;  Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, Georgia, United States of America aff002;  Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, United States of America aff003;  Center for Translational and Computational Neuroimmunology, Department of Neurology and Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, New York, United States of America aff004
Vyšlo v časopise: Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer’s dementia. PLoS Genet 17(4): e1009482. doi:10.1371/journal.pgen.1009482
Kategorie: Research Article
doi: 10.1371/journal.pgen.1009482


Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL’s estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer’s dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

Klíčová slova:

Alzheimer's disease – Gene expression – Genome-wide association studies – Medical risk factors – Phenotypes – Simulation and modeling – Single nucleotide polymorphisms – Transcriptome analysis


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