Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Barbeira, Alvaro N.; Dickinson, Scott; Torres, Jason; Bonazzola, Rodrigo; Zheng, Jingwen; Torstenson, Eric S.; Wheeler, Heather; Shah, Kaanan P.; Edwards, Todd L.; Garcia, Tzintzuni; Nicolae, Dan L.; Cox, Nancy J.; Im, Hae Kyung

doi:10.1101/045260

Cited by 368 publications

(621 citation statements)

References 54 publications

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“…We also performed integrated transcriptome-wide analyses using recently-described MetaXcan39 and SMR approaches40. Accounting for 5973 independent expression probes (Bonferroni-corrected P ≤8.37 × 10 −6 for α ≤0.05), and potential coincidental overlap of eQTL signals with GWAS loci, SMR analyses using whole blood transcriptome data41 suggested correlation between higher grip strength and lower expression levels of ERP27 ( P SMR = 2.50 × 10 −9 ) and KANSL1 ( P SMR = 3.05 × 10 −7 ), both of which are implicated genes from our GWAS analysis.…”

Section: Resultsmentioning

confidence: 99%

“…To supplement this variant-centric approach, we additionally took advantage of two new methods for integrating genome wide GWAS summary statistics with expression associations from independent studies: SMR40 and MetaXcan39. By utilizing established eQTL data sets as reference, these approaches are able to effectively model expected variation in the transcriptome of the GWAS sample based on variation in autosomal SNVs across the genome, and then test for independent associations between imputed transcript levels and the phenotype of interest.…”

Section: Methodsmentioning

confidence: 99%

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Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness

Willems¹,

Wright²,

Day³

et al. 2017

Nat Commun

178

158

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Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P<5 × 10−8) in combined analyses. A number of these loci contain genes implicated in structure and function of skeletal muscle fibres (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), or monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1). Mendelian randomization analyses are consistent with a causal effect of higher genetically predicted grip strength on lower fracture risk. In conclusion, our findings provide new biological insight into the mechanistic underpinnings of grip strength and the causal role of muscular strength in age-related morbidities and mortality.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness

Willems¹,

Wright²,

Day³

et al. 2017

Nat Commun

178

158

View full text Add to dashboard Cite

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“…4 The lung eQTL dataset was used as the training set to derive the expression weights. 5 For analysis with FUSION, expression prediction models for each gene were evaluated in cis, using markers within 500 Kb on both sides of the expression probe sets. 9 For analysis with S-PrediXcan, gene expression traits were first trained with elastic net linear models (alpha = 0.5, n_k_folds = 10, window = 500 Kb) using the lung eQTL set.…”

Section: Transcriptome-wide Association Studymentioning

confidence: 99%

“…3 Recent development in bioinformatics now allows transcriptome-wide association study (TWAS), which is a more advanced approach to integrate GWAS and eQTL results and identify candidate causal genes underlying diseases. 4,5 TWAS requires a set of individuals for whom both gene expression and genetic variants have been measured, that is, an eQTL dataset. The part of gene expression that can be explained by cis-acting SNPs can then be modeled in the eQTL dataset and used to impute the genetic component of expression in a second (usually larger) set of individuals with only SNP GWAS data.…”

Section: Introductionmentioning

confidence: 99%

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

Bossé

Xia

et al. 2019

Intl Journal of Cancer

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We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of our study has been to integrate the complete GWAS results with a large‐scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n = 1,038) to identify candidate causal genes for lung cancer. We performed transcriptome‐wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma and small cell lung cancer) and smoking subgroups (never‐ and ever‐smokers). We performed replication analysis using lung data from the Genotype‐Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever‐smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (pTWAS = 1.09E−99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (pTWAS = 3.72E−6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3‐adenocarcinoma on 9p13.3 was replicated using GTEx (pTWAS = 6.55E−5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influences lung cancer risk.

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“…PrediXcan has been used to identify genes which may play causal roles in amyloid deposition and cognitive changes in Alzheimer's disease [100] and genes associated with Asthma [101]. While PrediXcan requires individual level genotype data, the extension MetaXcan requires only summary statistics and promises similar accuracy, if the right reference population is used for LD estimation [102].…”

Section: Identifying Causal Genesmentioning

confidence: 99%

The genetic architecture of psychiatric disorders

Maier¹

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The genetic nature of psychiatric disorders was observed by clinicians long before DNA had been identified as the molecule of inheritance. The greatest identified risk factor of many psychiatric disorders still is a positive family history. Until recently this knowledge has not contributed substantially to treatment efforts or to a better understanding of the disease processes because we lacked the necessary genetic data. Advances in genotyping technologies have brought an end to this data shortage which is leading to a better understanding of the genetic architecture of psychiatric disorders. Two patterns started to emerge which were uncommon in earlier studied Mendelian disorders. (i) most of the genetic part of disease risk is conferred by a large number of genetic loci of small effect, and (ii) genetic loci often influence a large number of traits at the same time. While this is true of many traits ("complex" traits), these two phenomena (polygenicity and pleiotropy) are particularly pronounced in psychiatric disorders. This has wide-reaching consequences for the analysis and interpretation of genetic data and provides challenges as well as opportunities. This thesis focuses on two areas in particular: genetic heterogeneity and genetic risk prediction.Genetic heterogeneity in a phenotypically homogenous group describes a situation where different and distinct genetic risk profiles are causing similar symptoms in different people.This can be easily identified under a Mendelian inheritance pattern, but proves to be challenging under polygenicity. The presence of genetic heterogeneity can limit the accuracy of genetic risk prediction.The aim of genetic risk prediction is to use the information that has been gathered on the effects of genetic loci to estimate the genetic liability of an individual to develop a disease.Here, pleiotropy offers an opportunity to increase the accuracy of genetic prediction by leveraging information from multiple diseases at the same time.The aim in this thesis is to describe several projects which center around the two concepts of genetic risk prediction based on multiple traits and of genetic heterogeneity. Chapter 1 sets the scene with an overview of recently developed polygenic methods. Chapter 2 deals with the effects of genetic heterogeneity on heritability estimates and demonstrates how genetic heterogeneity might contribute to the phenomenon of missing heritability, which is 3 the discrepancy between twin study heritability estimates and the variance explained by the sum of individual genetic loci. Chapter 3 addresses the question of whether genotype clustering can detect groups with different genetic risk profiles. Chapter 4 describes the implementation of a multivariate extension to the univariate Best Linear Unbiased Prediction (BLUP) method and its application to five psychiatric traits. Chapters 4 and 5 investigate whether this multivariate BLUP model can be approximated when only summary statistics, not individual level genotype data, are available for the predi...

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Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics

Cited by 368 publications

References 54 publications

Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness

Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness

Transcriptome‐wide association study reveals candidate causal genes for lung cancer

The genetic architecture of psychiatric disorders

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