Integrative approaches for large-scale transcriptome-wide association studies

Gusev, Alexander; Ko, Arthur; Shi, Huwenbo; Bhatia, Gaurav; Chung, Wonil; Penninx, Brenda W. J. H.; Jansen, Ritsert C.; Geus, Eco J. C. de; Boomsma, Dorret I.; Wright, Fred A.; Sullivan, Patrick F.; Nikkola, Elina; Alvarez, Marcus; Civelek, Mete; Lusis, Aldons J.; Lehtimäki, Terho; Raitoharju, Emma; Kähönen, Mika; Seppälä, Ilkka; Raitakari, Olli T.; Kuusisto, Johanna; Laakso, Markku; Price, Alkes L.; Pajukanta, Päivi; Paşaniuc, Bogdan

doi:10.1038/ng.3506

Cited by 1,907 publications

(2,449 citation statements)

References 61 publications

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“…The same method has been used in a variety of contexts including discovery genetics (Zhu et al., 2016), and prediction and model selection (Chen et al., 2015; Benner et al., 2016; Newcombe, Conti, & Richardson, 2016). A number of different solutions have been proposed to the problem of highly correlated variants, including pruning and clumping at a threshold correlation, and adding a small positive number to the diagonal of the correlation matrix (Gusev et al., 2016). In the applied example of the paper at a correlation threshold of

ρ = 0.8

, adding 0.1 to the diagonal of the correlation matrix changed the causal estimate from −0.137 (SE 0.031) to −0.065 (0.057).…”

Section: Discussionmentioning

confidence: 99%

Mendelian randomization with fine‐mapped genetic data: Choosing from large numbers of correlated instrumental variables

Burgess

Zuber

Valdès-Márquez

et al. 2017

Genetic Epidemiology

165

189

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Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine‐mapped genetic data, there may be hundreds of genetic variants in a single gene region any of which could be used to assess this causal relationship. However, using too many genetic variants in the analysis can lead to spurious estimates and inflated Type 1 error rates. But if only a few genetic variants are used, then the majority of the data is ignored and estimates are highly sensitive to the particular choice of variants. We propose an approach based on summarized data only (genetic association and correlation estimates) that uses principal components analysis to form instruments. This approach has desirable theoretical properties: it takes the totality of data into account and does not suffer from numerical instabilities. It also has good properties in simulation studies: it is not particularly sensitive to varying the genetic variants included in the analysis or the genetic correlation matrix, and it does not have greatly inflated Type 1 error rates. Overall, the method gives estimates that are less precise than those from variable selection approaches (such as using a conditional analysis or pruning approach to select variants), but are more robust to seemingly arbitrary choices in the variable selection step. Methods are illustrated by an example using genetic associations with testosterone for 320 genetic variants to assess the effect of sex hormone related pathways on coronary artery disease risk, in which variable selection approaches give inconsistent inferences.

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ρ = 0.8

, adding 0.1 to the diagonal of the correlation matrix changed the causal estimate from −0.137 (SE 0.031) to −0.065 (0.057).…”

Section: Discussionmentioning

confidence: 99%

Mendelian randomization with fine‐mapped genetic data: Choosing from large numbers of correlated instrumental variables

Burgess

Zuber

Valdès-Márquez

et al. 2017

Genetic Epidemiology

165

189

View full text Add to dashboard Cite

show abstract

“…In addition, due to the high cost and logistic difficulties, despite recent efforts in forming large consortia for imaging studies (Thompson et al 2013), the sample size of a typical GWAS with imaging traits is still much smaller than those of other GWAS with clinical traits, hindering the discovery of SNPs associated with imaging endophenotypes, as shown by the ADNI data (Shen et al 2014; Saykin et al 2015). Alternatively, we extend the idea of transcriptome-wide association study (TWAS) (Gamazon et al 2015; Gusev et al 2016) to imaging-wide association study (IWAS) : instead of using gene expression as an endophenotype, we use an imaging endophenotype to construct weights for a weighted gene-based GWAS test. TWAS is motivated by possible regulatory roles of eQTL or expression SNPs which are more likely to be disease-associated (Nicolae et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

Pan

2017

NeuroImage

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A new and powerful approach, called imaging-wide association study (IWAS), is proposed to integrate imaging endophenotypes with GWAS to boost statistical power and enhance biological interpretation for GWAS discoveries. IWAS extends the promising transcriptome-wide association study (TWAS) from using gene expression endophenotypes to using imaging and other endophenotypes with a much wider range of possible applications. As illustration, we use gray-matter volumes of several brain regions of interest (ROIs) drawn from the ADNI-1 structural MRI data as imaging endophenotypes, which are then applied to the individual-level GWAS data of ADNI-GO/2 and a large meta-analyzed GWAS summary statistics dataset (based on about 74000 individuals), uncovering some novel genes significantly associated with Alzheimer’s disease (AD). We also compare the performance of IWAS with TWAS, showing much larger numbers of significant AD-associated genes discovered by IWAS, presumably due to the stronger link between brain atrophy and AD than that between gene expression of normal individuals and the risk for AD. The proposed IWAS is general and can be applied to other imaging endophenotypes, and GWAS individual-level or summary association data.

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“…In this regard, computational strategies integrating gene expression measurements with summary GWAS data have been recently developed to identify genes whose cis-regulated expression is associated with complex traits, an approach called transcriptome-wide association study (TWAS) (48,49). In addition, transcriptomic studies in relevant tissue samples from MS patients can also help identifying specific genetic signatures associated with disease susceptibility or progression.…”

Section: Resultsmentioning

confidence: 99%

The Genetics of Multiple Sclerosis

Didonna

Oksenberg

2017

Multiple Sclerosis: Perspectives in Treatment and Pathogenesis

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Multiple sclerosis (MS) is an autoimmune disease of the central nervous system, characterized by focal inflammation, demyelination, and axonal injury. The etiology of MS is still uncertain, but the most updated working model for disease pathogenesis proposes the interplay between genetic and environmental factors as necessary for MS manifestation. With the notable exception of the major histocompatibility complex (MHC), the identity of MS genetic determinants has been elusive for decades. In recent years, the advent of genome-wide association studies (GWAS) and collaborative efforts among international centers have fueled the characterization of several non-MHC loci associated with MS susceptibility. To date, after a number of GWAS screenings, 110 MS risk variants have been discovered outside the MHC locus in European populations. In the future, functional studies will be required to define the biological pathways and cellular activities connected to these variants.

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Integrative approaches for large-scale transcriptome-wide association studies

Cited by 1,907 publications

References 61 publications

Mendelian randomization with fine‐mapped genetic data: Choosing from large numbers of correlated instrumental variables

Mendelian randomization with fine‐mapped genetic data: Choosing from large numbers of correlated instrumental variables

Imaging-wide association study: Integrating imaging endophenotypes in GWAS

The Genetics of Multiple Sclerosis

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