A gene-based association method for mapping traits using reference transcriptome data, 2015, Gamazon et al

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A gene-based association method for mapping traits using reference transcriptome data

Spoiler: Authors

Gamazon, Eric R; Wheeler, Heather E; Shah, Kaanan P; Mozaffari, Sahar V; Aquino-Michaels, Keston; Carroll, Robert J; Eyler, Anne E; Denny, Joshua C; Nicolae, Dan L; Cox, Nancy J; Im, Hae Kyung

Abstract
Genome-wide association studies (GWAS) have identified thousands of variants robustly associated with complex traits. However, the biological mechanisms underlying these associations are, in general, not well understood.

We propose a gene-based association method called PrediXcan that directly tests the molecular mechanisms through which genetic variation affects phenotype. The approach estimates the component of gene expression determined by an individual’s genetic profile and correlates the “imputed” gene expression with the phenotype under investigation to identify genes involved in the etiology of the phenotype.

The genetically regulated gene expression is estimated using whole-genome tissue-dependent prediction models trained with reference transcriptome datasets. PrediXcan enjoys the benefits of gene-based approaches such as reduced multiple testing burden and a principled approach to the design of follow-up experiments.

Our results demonstrate that PrediXcan can detect known and novel genes associated with disease traits and provide insights into the mechanism of these associations.

Web | DOI | PMC | PDF | Nature Genetics | Open Access on PMC

 

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The results reported from doing a transcriptome-wide association study (TWAS) based on depression GWAS data made me interested in learning more about TWAS.

The abstract above is from the paper that introduced the first form of TWAS, PrediXcan. The first paper below introduced S-PrediXcan, a version of TWAS that can use summary-level genetic information, instead of individual data. The second paper below reviews various TWAS methods.

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

Spoiler: Authors

Barbeira, Alvaro N.; Dickinson, Scott P.; Bonazzola, Rodrigo; Zheng, Jiamao; Wheeler, Heather E.; Torres, Jason M.; Torstenson, Eric S.; Shah, Kaanan P.; Garcia, Tzintzuni; Edwards, Todd L.; Stahl, Eli A.; Huckins, Laura M.; Aguet, François; Ardlie, Kristin G.; Cummings, Beryl B.; Gelfand, Ellen T.; Getz, Gad; Hadley, Kane; Handsaker, Robert E.; Huang, Katherine H.; Kashin, Seva; Karczewski, Konrad J.; Lek, Monkol; Li, Xiao; MacArthur, Daniel G.; Nedzel, Jared L.; Nguyen, Duyen T.; Noble, Michael S.; Segrè, Ayellet V.; Trowbridge, Casandra A.; Tukiainen, Taru; Abell, Nathan S.; Balliu, Brunilda; Barshir, Ruth; Basha, Omer; Battle, Alexis; Bogu, Gireesh K.; Brown, Andrew; Brown, Christopher D.; Castel, Stephane E.; Chen, Lin S.; Chiang, Colby; Conrad, Donald F.; Damani, Farhan N.; Davis, Joe R.; Delaneau, Olivier; Dermitzakis, Emmanouil T.; Engelhardt, Barbara E.; Eskin, Eleazar; Ferreira, Pedro G.; Frésard, Laure; Gamazon, Eric R.; Garrido-Martín, Diego; Gewirtz, Ariel D. H.; Gliner, Genna; Gloudemans, Michael J.; Guigo, Roderic; Hall, Ira M.; Han, Buhm; He, Yuan; Hormozdiari, Farhad; Howald, Cedric; Jo, Brian; Kang, Eun Yong; Kim, Yungil; Kim-Hellmuth, Sarah; Lappalainen, Tuuli; Li, Gen; Li, Xin; Liu, Boxiang; Mangul, Serghei; McCarthy, Mark I.; McDowell, Ian C.; Mohammadi, Pejman; Monlong, Jean; Montgomery, Stephen B.; Muñoz-Aguirre, Manuel; Ndungu, Anne W.; Nobel, Andrew B.; Oliva, Meritxell; Ongen, Halit; Palowitch, John J.; Panousis, Nikolaos; Papasaikas, Panagiotis; Park, YoSon; Parsana, Princy; Payne, Anthony J.; Peterson, Christine B.; Quan, Jie; Reverter, Ferran; Sabatti, Chiara; Saha, Ashis; Sammeth, Michael; Scott, Alexandra J.; Shabalin, Andrey A.; Sodaei, Reza; Stephens, Matthew; Stranger, Barbara E.; Strober, Benjamin J.; Sul, Jae Hoon; Tsang, Emily K.; Urbut, Sarah; van de Bunt, Martijn; Wang, Gao; Wen, Xiaoquan; Wright, Fred A.; Xi, Hualin S.; Yeger-Lotem, Esti; Zappala, Zachary; Zaugg, Judith B.; Zhou, Yi-Hui; Akey, Joshua M.; Bates, Daniel; Chan, Joanne; Chen, Lin S.; Claussnitzer, Melina; Demanelis, Kathryn; Diegel, Morgan; Doherty, Jennifer A.; Feinberg, Andrew P.; Fernando, Marian S.; Halow, Jessica; Hansen, Kasper D.; Haugen, Eric; Hickey, Peter F.; Hou, Lei; Jasmine, Farzana; Jian, Ruiqi; Jiang, Lihua; Johnson, Audra; Kaul, Rajinder; Kellis, Manolis; Kibriya, Muhammad G.; Lee, Kristen; Li, Jin Billy; Li, Qin; Li, Xiao; Lin, Jessica; Lin, Shin; Linder, Sandra; Linke, Caroline; Liu, Yaping; Maurano, Matthew T.; Molinie, Benoit; Montgomery, Stephen B.; Nelson, Jemma; Neri, Fidencio J.; Oliva, Meritxell; Park, Yongjin; Pierce, Brandon L.; Rinaldi, Nicola J.; Rizzardi, Lindsay F.; Sandstrom, Richard; Skol, Andrew; Smith, Kevin S.; Snyder, Michael P.; Stamatoyannopoulos, John; Stranger, Barbara E.; Tang, Hua; Tsang, Emily K.; Wang, Li; Wang, Meng; Van Wittenberghe, Nicholas; Wu, Fan; Zhang, Rui; Nierras, Concepcion R.; Branton, Philip A.; Carithers, Latarsha J.; Guan, Ping; Moore, Helen M.; Rao, Abhi; Vaught, Jimmie B.; Gould, Sarah E.; Lockart, Nicole C.; Martin, Casey; Struewing, Jeffery P.; Volpi, Simona; Addington, Anjene M.; Koester, Susan E.; Little, A. Roger; Brigham, Lori E.; Hasz, Richard; Hunter, Marcus; Johns, Christopher; Johnson, Mark; Kopen, Gene; Leinweber, William F.; Lonsdale, John T.; McDonald, Alisa; Mestichelli, Bernadette; Myer, Kevin; Roe, Brian; Salvatore, Michael; Shad, Saboor; Thomas, Jeffrey A.; Walters, Gary; Washington, Michael; Wheeler, Joseph; Bridge, Jason; Foster, Barbara A.; Gillard, Bryan M.; Karasik, Ellen; Kumar, Rachna; Miklos, Mark; Moser, Michael T.; Jewell, Scott D.; Montroy, Robert G.; Rohrer, Daniel C.; Valley, Dana R.; Davis, David A.; Mash, Deborah C.; Undale, Anita H.; Smith, Anna M.; Tabor, David E.; Roche, Nancy V.; McLean, Jeffrey A.; Vatanian, Negin; Robinson, Karna L.; Sobin, Leslie; Barcus, Mary E.; Valentino, Kimberly M.; Qi, Liqun; Hunter, Steven; Hariharan, Pushpa; Singh, Shilpi; Um, Ki Sung; Matose, Takunda; Tomaszewski, Maria M.; Barker, Laura K.; Mosavel, Maghboeba; Siminoff, Laura A.; Traino, Heather M.; Flicek, Paul; Juettemann, Thomas; Ruffier, Magali; Sheppard, Dan; Taylor, Kieron; Trevanion, Stephen J.; Zerbino, Daniel R.; Craft, Brian; Goldman, Mary; Haeussler, Maximilian; Kent, W. James; Lee, Christopher M.; Paten, Benedict; Rosenbloom, Kate R.; Vivian, John; Zhu, Jingchun; Nicolae, Dan L.; Cox, Nancy J.; Im, Hae Kyung

Spoiler: Abstract

Scalable, integrative methods to understand mechanisms that link genetic variants with phenotypes are needed. Here we derive a mathematical expression to compute PrediXcan (a gene mapping approach) results using summary data (S-PrediXcan) and show its accuracy and general robustness to misspecified reference sets.

We apply this framework to 44 GTEx tissues and 100+ phenotypes from GWAS and meta-analysis studies, creating a growing public catalog of associations that seeks to capture the effects of gene expression variation on human phenotypes. Replication in an independent cohort is shown.

Most of the associations are tissue specific, suggesting context specificity of the trait etiology. Colocalized significant associations in unexpected tissues underscore the need for an agnostic scanning of multiple contexts to improve our ability to detect causal regulatory mechanisms.

Monogenic disease genes are enriched among significant associations for related traits, suggesting that smaller alterations of these genes may cause a spectrum of milder phenotypes.

Web | DOI | PMC | PDF | Nature Communications | Open Access on PMC | 2018

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Transcriptome‐Wide Association Studies (TWAS): Methodologies, Applications, and Challenges

Spoiler: Authors

Evans, Patrick; Nagai, Taylor; Konkashbaev, Anuar; Zhou, Dan; Knapik, Ela W.; Gamazon, Eric R.

Spoiler: Abstract

Transcriptome‐wide association study (TWAS) methodologies aim to identify genetic effects on phenotypes through the mediation of gene transcription.

In TWAS, in silico models of gene expression are trained as functions of genetic variants and then applied to genome‐wide association study (GWAS) data. This post‐GWAS analysis identifies gene‐trait associations with high interpretability, enabling follow‐up functional genomics studies and the development of genetics‐anchored resources.

We provide an overview of commonly used TWAS approaches, their advantages and limitations, and some widely used applications.

Web | DOI | PMC | PDF | Current Protocols | Open Access on PMC | 2024

 

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The aim of TWAS is to help identify the consequences of genetic variation in a GWAS on gene expression, which could help identify genes and tissues that may causally influence the disease.

The basic steps of TWAS
1. First, a prediction model is created using data from large reference datasets which combine genetic and expression data (such as the GTEx database). The model is trained to predict the magnitude of a given gene's expression based on an individual's pattern of genetic variants (generally limited to variants around the gene, but it can be extended to far away variants).

(The training of the model only needs to be done once, and multiple GWAS can then use the same model.)​

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2. A GWAS is performed on a trait of interest. At this point, we have genetic information about individuals in the GWAS study, but not expression information.

3. The TWAS prediction model from step 1 is applied to the subjects in the GWAS study, and this produces data for predicted gene expression in these new individuals. One can then test to see if there is an association between predicted expression of a given gene in a given tissue and the trait under study.

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In essence, TWAS is testing whether the cases and controls have differential expression of a gene without actually testing expression in these individuals (which would be practically impossible for some tissues, such as brain). A further benefit is that the predicted expression being tested is only based on genetic, not environmental, regulation of gene expression, limiting the confounding effect of environment which is present in regular case-control studies of gene expression.

Also, if only variants near a gene are used to predict gene expression, this limits the possibility of reverse causation (i.e. variants cause a trait which in turn cause changes in expression), because an association of variants with the expression of a nearby gene is likely to be due to relatively direct genetic regulation of expression, instead of round-about pathways that influence expression through a trait. Still, TWAS doesn't prove causation, since, for example, variants could theoretically affect both expression and the trait through different pathways. It just produces gene candidates for further validation.

Several TWAS methods have been developed, such as S-PrediXcan, described in the second paper above, which can test the association of predicted gene expression and phenotype using only GWAS summary statistics, instead of individual genetic data. Fusion is another software/approach for performing TWAS based on summary stats.

Limitations exist, such as linkage disequilibrium, which may lead to genes being predicted to be differentially expressed only due to their LD with other genes. Also, it may be difficult to determine which specific tissue is interesting for the trait, if expression in multiple tissues is similarly genetically regulated.

The review linked above provides some examples of when TWAS results have stood up to validation through other methods:

Gusev et al. validated gene-level associations with schizophrenia using data on physical chromatin interactions during brain development (Gusev et al., 2018).

Applying CRISPR/Cas9 gene editing at the TWAS locus 5q13.2 in CD34+ hematopoietic and progenitor cells, Yao et al. identified the causal gene in the locus for neurophil count (Yao et al., 2020).

Unlu et al. (Unlu et al., 2019) showed that GRIK5 contributes to the polygenic liability to develop eye diseases in humans through its GReX [genetically regulated gene expression], which was further mechanistically investigated via depletion of its ortholog in zebrafish.

GReX analysis of COVID-19 severity led to the inclusion of the repurposing candidate baricitinib in a large clinical trial (Pairo-Castineira et al., 2021, 2023). The drug is now the first FDA-approved immunomodulatory treatment for COVID-19 after clinical trials showed therapeutic benefit (Rubin, 2022; Kalil et al., 2021; RECOVERY Collaborative Group, 2022).