Fine-mapping and QTL tissue-sharing information improve causal gene identification and transcriptome prediction performance

Barbeira, Alvaro N.; Liang, Yanyu; Bonazzola, Rodrigo; Wang, Gao; Wheeler, Heather E.; Melia, Owen; Aguet, François; Consortiumt, GTEx; Ardlie, Kristin; Wen, Xiaoquan; Im, Hae Kyung

doi:10.1101/2020.03.19.997213

Cited by 7 publications

(3 citation statements)

References 43 publications

(100 reference statements)

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“…In the end, we collected 266 trios. We applied the PRS-based imputation scheme to these trios where the predicted expression was obtained from elastic net and DAPG weighted elastic net models trained on GTEx V8 whole blood European samples Barbeira et al (2020a,b). In particular, we ran the imputation for each chromosome, And to examine the power of the imputation, within each chromosome, we downsampled the genes to a fraction of the original number and re-ran the imputation to see how the imputation quality depends on the downsampling fraction.…”

Section: Methodsmentioning

confidence: 99%

Imputing the parental origin of the sibling’s haplotype from parental phenotypes

Liang

2022

Preprint

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To recruit cases for late-onset disease study is challenging since these diseases occur in elder people. Moreover, typically we have a very limited number of late-onset disease cases in Biobank data. But, on the other hand, the parental disease status may be available by questionnaire. Because of this, methods have been developed to utilize parental disease status instead Liu et al. (2017); Hujoel et al. (2020). In these approaches, the late-onset phenotype of the participant is imputed from parental statuses. And, downstream, a genome-wide association study (GWAS) is performed using the participant's genotype and imputed phenotype. In this paper, we take another view on utilizing parental phenotypes. We treat this problem as missing parental genotype rather than missing participant's phenotype. First, we propose an imputation scheme to infer the parental origin of the participant's genotype from a collection of extra parental phenotypes (non-focal phenotypes) and the participant's genotype. Second, we propose a computationally efficient approach to incorporate the imputed parental origin information into the downstream GWAS. We explore the feasibility of the proposed two-step approach on simulated and real data. And we derive the power increase of GWAS as a function of imputation quality. These results indicate that the imputation scheme needs about 100 non-focal phenotypes to achieve enough accuracy to facilitate the GWAS downstream.

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Section: Methodsmentioning

confidence: 99%

Imputing the parental origin of the sibling’s haplotype from parental phenotypes

Liang

2022

Preprint

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“…Expression counts were transformed to trimmed mean of M-values (TMMs). Gene expression prediction models of GTEx v8 were obtained from elsewhere [1,7] (see data availability statement). Gene expression prediction models of BarcUVa-Seq were generated for the whole sample size and for subsets of the data according to the anatomic location where the biopsies were collected (ascending, transverse and descending colon).…”

Section: Methodsmentioning

confidence: 99%

“…Gene expression prediction models of BarcUVa-Seq were generated for the whole sample size and for subsets of the data according to the anatomic location where the biopsies were collected (ascending, transverse and descending colon). The elastic net-based models were generated following the Pre-dictDB pipeline, which was the one used for GTEx v8 data [7]. Following this pipeline, we considered significant gene models those with a predictive performance P < 0.05 and R2 > 0.1.…”

Section: Methodsmentioning

confidence: 99%

The Colon Transcriptome Explorer (CoTrEx) 2.0: a Reference Web-Based Resource for Exploring Population-Based Normal Colon Gene Expression

Díez-Obrero¹,

Moratalla-Navarro²,

Dampier³

et al. 2021

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Gene expression data is key for the functional annotation of single nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS). Expression and splicing quantitative trait loci (e/sQTLs) in normal colon tissue, such as those from the University of Barcelona and University of Virginia RNA sequencing project (BarcUVa-Seq) and the Genotype-Tissue Expression project (GTEx), are required to gain biological insight of colon-related diseases risk loci. Moreover, transcriptome-wide association studies (TWAS) rely on reference gene expression imputation panels in the tissue of interest to nominate susceptibility genes. Also, it is of high interest to study the relationships between genes in a network framework. For facilitating these analyses, we have updated and expanded the scope of the Colon Transcriptome Explorer (CoTrEx) to the version 2.0. This web-based resource provides exhaustive visualization and analysis of transcriptome-wide gene expression profiles of normal colon tissue from BarcUVa-Seq and GTEx. In addition to the integration of new datasets, CoTrEx 2.0 provides additional e/sQTLs sets, as well as gene expression prediction models and regulatory and co-expression networks. It is freely available at https://barcuvaseq.org/cotrex/. Overall, it is of high interest for researchers aiming to investigate the genetic susceptibility to colon-related complex traits and diseases.

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Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries

Liang

Pividori

Manichaikul

et al. 2020

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Polygenic risk scores (PRS) are on course to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry, meaning that the utility of PRS for non-European populations is limited because SNP effects and LD patterns may not be conserved across populations. We hypothesized that cross population prediction at the level of genes rather than SNPs would be more effective, since the effect of genes on traits is likely to be more highly conserved. Therefore, we developed a framework to convert effect sizes at SNPs into effect sizes for genetically predicted transcript abundance, which we used for prediction in non-European populations. We compared this approach, which we call polygenic transcriptome risk scores (PTRS), to PRS, using data from 17 quantitative traits that were measured in multiple ancestries (European, African, East Asian, and South Asian) by UK Biobank. On average, PTRS using whole blood predicted transcriptome had lower absolute prediction accuracy than PRS, as we expected since not all regulatory processes were captured by a single tissue. However, as hypothesized, we found that in the African target set, the portability (prediction accuracy relative to the European reference set) was significantly higher for PTRS than PRS (p=0.03) with additional gain when transcriptomic prediction models ancestry matched the target population (p=0.021). Taken together, our results suggest that using PTRS can improve prediction in underrepresented populations and that increasing the diversity of transcriptomic data may be an effective way to improve portability of GWAS results between populations and help reduce health disparities.

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Fine-mapping and QTL tissue-sharing information improve causal gene identification and transcriptome prediction performance

Cited by 7 publications

References 43 publications

Imputing the parental origin of the sibling’s haplotype from parental phenotypes

Imputing the parental origin of the sibling’s haplotype from parental phenotypes

The Colon Transcriptome Explorer (CoTrEx) 2.0: a Reference Web-Based Resource for Exploring Population-Based Normal Colon Gene Expression

Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries

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