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Comparison of methods for transcriptome imputation through application to two common complex diseases.

ABSTRACT: Transcriptome imputation has become a popular method for integrating genotype data with publicly available expression data to investigate the potentially causal role of genes in complex traits. Here, we compare three approaches (PrediXcan, MetaXcan and FUSION) via application to genome-wide association study (GWAS) data for Crohn's disease and type 1 diabetes from the Wellcome Trust Case Control Consortium. We investigate: (i) how the results of each approach compare with each other and with those of standard GWAS analysis; and (ii) how variants in the models used by the prediction tools compare with variants previously reported as eQTLs. We find that all approaches produce highly correlated results when applied to the same GWAS data, although for a subset of genes, mostly in the major histocompatibility complex, the approaches strongly disagree. We also observe that most associations detected by these methods occur near known GWAS risk loci. PrediXcan and MetaXcan's models for predicting expression more consistently recapitulate known effects of genotype on expression, suggesting they are more robust than FUSION. Application of these transcriptome imputation approaches to summary statistics from meta-analyses in Crohn's disease and type 1 diabetes detects 53 significant expression-Crohn's disease associations and 154 significant expression-type 1 diabetes associations, providing insight into biology underlying these diseases. We conclude that while current implementations of transcriptome imputation typically detect fewer associations than GWAS, they nonetheless provide an interesting way of interpreting association signals to identify potentially causal genes, and that PrediXcan and MetaXcan generally produce more reliable results than FUSION.

SUBMITTER: Fryett JJ

PROVIDER: S-EPMC6189136 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Comparison of methods for transcriptome imputation through application to two common complex diseases.

Fryett James J JJ Inshaw Jamie J Morris Andrew P AP Cordell Heather J HJ

European journal of human genetics : EJHG 20180705 11

Transcriptome imputation has become a popular method for integrating genotype data with publicly available expression data to investigate the potentially causal role of genes in complex traits. Here, we compare three approaches (PrediXcan, MetaXcan and FUSION) via application to genome-wide association study (GWAS) data for Crohn's disease and type 1 diabetes from the Wellcome Trust Case Control Consortium. We investigate: (i) how the results of each approach compare with each other and with tho ...[more]

PMID: 29976976

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Project description:Objective: RNA extraction and library preparation from formalin-fixed, paraffin-embedded (FFPE) samples are crucial pre-analytical steps towards achieving optimal downstream RNA sequencing (RNASeq) results. In this study, we assessed 2 Illumina library preparation methods for RNA-Seq analysis using archived FFPE samples from human cancer indications at 2 independent vendors. Methods: Twenty-five FFPE samples from 5 indications (non-small cell lung cancer, colorectal cancer, renal carcinoma, breast cancer, and hepatocellular carcinoma) were included, covering a wide range of sample storage durations (3-25 years-old), sample qualities, and specimen types (resection vs core needle biopsy). Each sample was processed independently by both vendors. Total RNA was isolated using the Qiagen miRNeasy FFPE kit followed by library construction using either TruSeq Stranded Total RNA library preparation kit with Ribo-Zero Gold, or TruSeq RNA Access library preparation kit. Libraries were normalized to 20 pM and sequenced on an Illumina HiSeq 2500 using V3 chemistry in paired-end mode with a read length of 2 × 50 bp. The data were processed through a standard RNASeq pipeline to produce counts and transcripts per millions for each gene in each sample to compare 2 library kits at 2 different vendors. Results: Our data showed that TruSeq RNA Access libraries yield over 80% exonic reads across different quality samples, indicating higher selectivity of the exome pull down by the capture approach compared to the random priming of the TruSeq Stranded Total kit. The overall QC data for FFPE RNA extraction, library preparation, and sequencing generated by the 2 vendors are comparable, and downstream gene expression quantification results show high concordance as well. With the TruSeq Stranded Total kit, the mean Spearman correlation between vendors was 0.87 and the mean Pearson correlation was 0.76. With the TruSeq RNA Access kit, the mean Spearman correlation between vendors was 0.89 and the mean Pearson correlation was 0.73. Interestingly, examination of the cross-vendor correlations compared to various common QC statistics suggested that library concentration is better correlated with consistency between vendors than is the RNA quantity. Conclusions: Our analyses provide evidence to guide selection of sequencing methods for FFPE samples in which the sample quality may be severely compromised.

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Comparison of methods for transcriptome imputation through application to two common complex diseases.

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Comparison of methods for transcriptome imputation through application to two common complex diseases.

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