From Transcript to Assembly

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.

/pdf/mostwas-multi-omic-strategies-for-transcriptome-wide-1qvvu7o37v.pdf

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

As the master regulator in utero, the placenta is core to the Developmental Origins of Health and Disease (DOHaD) hypothesis but is historically understudied. To identify placental gene-trait associations (GTAs) across the life course, we perform distal mediator-enriched transcriptome-wide association studies (TWAS) for 40 traits, integrating placental multi-omics from the Extremely Low Gestational Age Newborn Study. At [Formula: see text], we detect 248 GTAs, mostly for neonatal and metabolic traits, across 176 genes, enriched for cell growth and immunological pathways. In aggregate, genetic effects mediated by placental expression significantly explain 4 early-life traits but no later-in-life traits. 89 GTAs show significant mediation through distal genetic variants, identifying hypotheses for distal regulation of GTAs. Investigation of one hypothesis in human placenta-derived choriocarcinoma cells reveal that knockdown of mediator gene EPS15 upregulates predicted targets SPATA13 and FAM214A, both associated with waist-hip ratio in TWAS, and multiple genes involved in metabolic pathways. These results suggest profound health impacts of placental genomic regulation in developmental programming across the life course. 

/pdf/placental-genomics-mediates-genetic-associations-with-e87iw9nx.pdf

Placental genomics mediates genetic associations with complex health traits and disease

/pdf/mostwas-multi-omic-strategies-for-transcriptome-wide-188ihtgrt1.pdf

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

/pdf/placental-genomics-mediates-genetic-associations-with-2iq56je3.pdf

The transcriptome-wide association study (TWAS) has emerged as one of several promising techniques for integrating multi-scale 'omics' data into traditional genome-wide association studies (GWAS). Unlike GWAS, which associates phenotypic variance directly with genetic variants, TWAS uses a reference dataset to train a predictive model for gene expressions, which allows it to associate phenotype with variants through the mediating effect of expressions. Although effective, this core innovation of TWAS is poorly understood, since the predictive accuracy of the genotype-expression model is generally low and further bounded by expression heritability. This raises the question: to what degree does the accuracy of the expression model affect the power of TWAS? Furthermore, would replacing predictions with actual, experimentally determined expressions improve power? To answer these questions, we compared the power of GWAS, TWAS, and a hypothetical protocol utilizing real expression data. We derived non-centrality parameters (NCPs) for linear mixed models (LMMs) to enable closed-form calculations of statistical power that do not rely on specific protocol implementations. We examined two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression), and also tested the effects of various properties including expression heritability. Our analysis reveals two main outcomes: (1) Under pleiotropy, the use of predicted expressions in TWAS is superior to actual expressions. This explains why TWAS can function with weak expression models, and shows that TWAS remains relevant even when real expressions are available. (2) GWAS outperforms TWAS when expression heritability is below a threshold of 0.04 under causality, or 0.06 under pleiotropy. Analysis of existing publications suggests that TWAS has been misapplied in place of GWAS, in situations where expression heritability is low.

/pdf/power-analysis-of-transcriptome-wide-association-study-26bada7v4b.pdf

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

Standard Genome-wide association study (GWAS) discovers genetic variants explaining phenotypic variance by directly associate them. With the availability of other omics data such as gene expression, the field is stepping into an exciting era of multi-scale omics integration. An emerging technique is transcriptome-wide association study (TWAS) that conducts association mapping by utilizing gene expression data from a separate reference dataset based on which a model predicting expression by genotype is trained. Despite its success in practice, two fundamental questions have been unaddressed yet. First, in practice, the accuracy of predicting expression by genotype is generally low, which is bounded by the expression heritability. So, the question is whether such a low accuracy may impact the power of TWAS, and what level of accuracy is sufficient. Second, since predicting expression is a critical step in TWAS, one may ask what if we have actual expression assessed by a real experiment, and whether that will improve or deteriorate power. Answering these questions will bring thorough understanding of TWAS and practical guidelines in association mapping. To address the above questions, we conducted power analysis for GWAS, TWAS, and expression medicated GWAS (emGWAS). Specifically, we derived non-centrality parameters (NCPs), enabling closed-form derivation of statistical power to facilitate a thorough power analysis without relying on particular implementations. We assessed the power of the three protocols with respect to two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression). For both scenarios, we tested various properties including expression heritability. Our analysis led to two main outcomes: (1) TWAS utilizing predicted expression enjoys higher power than emGWAS that has actual expressions in the pleiotropy scenario, revealing a deep insight into TWAS models as well as a practical guideline of applying TWAS even in cases when expressions are available in a GWAS dataset. (2) TWAS is suboptimal compared to GWAS when expression heritability is too low. The superiority ordering of TWAS and GWAS disclosed a turn-point in each of the causality and pleiotropy scenarios. Analysis of published discoveries shows the selection of protocols might be improved based on the identified turn-points.

/pdf/power-analysis-of-transcriptome-wide-association-study-vebyqlbvgu.pdf

Power analysis of transcriptome-wide association study

Background: Standard Genome-wide association study (GWAS) discovers genetic variants explaining phenotypic variance by directly associate them. With the availability of other omics data such as gene expression, the field is stepping into an exciting era of multi-scale omics integration. An emerging technique is transcriptome-wide association study (TWAS) that conducts association mapping by utilizing gene expression data from a separate reference dataset based on which a model predicting expression by genotype is trained. Despite its success in practice, two fundamental questions have been unaddressed yet. First, in practice, the accuracy of predicting expression by genotype is generally low, which is bounded by the expression heritability. So, the question is whether such a low accuracy may impact the power of TWAS, and what level of accuracy is sufficient. Second, since predicting expression is a critical step in TWAS, one may ask what if we have actual expression assessed by a real experiment, and whether that will improve or deteriorate power. Answering these questions will bring thorough understanding of TWAS and practical guidelines in association mapping. Results: To address the above questions, we conducted power analysis for GWAS, TWAS, and expression medicated GWAS (emGWAS). Specifically, we derived non-centrality parameters (NCPs), enabling closed-form derivation of statistical power to facilitate a thorough power analysis without relying on particular implementations. We assessed the power of the three protocols with respect to two representative scenarios: causality (genotype contributes to phenotype through expression) and pleiotropy (genotype contributes directly to both phenotype and expression). For both scenarios, we tested various properties including expression heritability. Conclusions: (1) TWAS utilizing predicted expression enjoys higher power than emGWAS that has actual expressions in the pleiotropy scenario, revealing a deep insight into TWAS models as well as a practical guideline of applying TWAS even in cases when expressions are available in a GWAS dataset. (2) TWAS is suboptimal compared to GWAS when expression heritability is too low. The superiority ordering of TWAS and GWAS disclosed a turn-point in each of the causality and pleiotropy scenarios. Analysis of published discoveries shows the selection of protocols might be questionable based on the identified turn-points.

/pdf/power-analysis-of-transcriptome-wide-association-study-1hcsg812ut.pdf

Power analysis of transcriptome-wide association study: implications for practical protocol choice

The success of transcriptome-wide association studies (TWAS) has led to substantial research towards improving its core component of genetically regulated expression (GReX). GReX links expression information with phenotype by serving as both the outcome of genotype-based expression models and the predictor for downstream association testing. In this work, we demonstrate that current linear models of GReX inadvertently combine two separable steps of machine learning - feature selection and aggregation - which can be independently replaced to improve overall power. We show that the monolithic approach of GReX limits the adaptability of TWAS methodology and practice, especially given low expression heritability.

https://www.biorxiv.org/content/biorxiv/early/2020/11/20/2020.11.19.390617.full.pdf

Qing Li

Papers

Power analysis of transcriptome-wide association study: Implications for practical protocol choice

Power analysis of transcriptome-wide association study

Power analysis of transcriptome-wide association study: implications for practical protocol choice

Disentangling genetic feature selection and aggregation in transcriptome-wide association studies