RT Journal Article SR Electronic T1 Polygenic Prediction of Molecular Traits using Large-Scale Meta-analysis Summary Statistics JF bioRxiv FD Cold Spring Harbor Laboratory SP 2022.11.23.517213 DO 10.1101/2022.11.23.517213 A1 Oliver Pain A1 Zachary Gerring A1 Eske Derks A1 Naomi R. Wray A1 Alexander Gusev A1 Ammar Al-Chalabi YR 2022 UL http://biorxiv.org/content/early/2022/11/25/2022.11.23.517213.abstract AB Introduction Transcriptome-wide association study (TWAS) integrates expression quantitative trait loci (eQTL) data with genome-wide association study (GWAS) results to infer differential expression. TWAS uses multi-variant models trained using individual-level genotype-expression datasets, but methodological development is required for TWAS to utilise larger eQTL summary statistics.Methods TWAS models predicting gene expression were derived using blood-based eQTL summary statistics from eQTLGen, the Young Finns Study (YFS), and MetaBrain. Summary statistic polygenic scoring methods were used to derive TWAS models, evaluating their predictive utility in GTEx v8. We investigated gene inclusion criteria and omnibus tests for aggregating TWAS associations for a given gene. We performed a schizophrenia TWAS using summary statistic-based TWAS models, comparing results to existing resources and methods.Results TWAS models derived using eQTL summary statistics performed comparably to models derived using individual-level data. Multi-variant TWAS models significantly improved prediction over single variant models for 8.6% of genes. TWAS models derived using eQTLGen summary statistics significantly improved prediction over models derived using a smaller individual-level dataset. The eQTLGen-based schizophrenia TWAS, using the ACAT omnibus test to aggregate associations for each gene, identified novel significant and colocalised associations compared to summary-based mendelian randomisation (SMR) and SMR-multi.Conclusions Using multi-variant TWAS models and larger eQTL summary statistic datasets can improve power to detect differential expression associations. We provide TWAS models based on eQTLGen and MetaBrain summary statistics, and software to easily derive and apply summary statistic-based TWAS models based on eQTL and other molecular QTL datasets released in the future.Competing Interest StatementThe authors have declared no competing interest.