Phenome-wide association studies (PheWAS) across large “real-world data” population cohorts support drug target validation

Diogo, Dorothée; Tian, Chao; Franklin, Christopher S.; Alanne-Kinnunen, Mervi; March, Michael; Spencer, Chris C. A.; Vangjeli, Ciara; Weale, Michael E.; Mattsson, Hannele; Kilpeläinen, Elina; Sleiman, Patrick; Reilly, Dermot F.; McElwee, Joshua; Maranville, Joseph C.; Chatterjee, Arnaub K.; Bhandari, Aman; Reeve, Mary-Pat; Hutz, Janna; Bing, Nan; John, Sally; MacArthur, Daniel G.; Salomaa, Veikko; Ripatti, Samuli; Hákonarson, Hákon; Daly, Mark J.; Palotie, Aarno; Hinds, David A.; Donnelly, Peter; Fox, Caroline S.; Day‐Williams, Aaron G.; Plenge, Robert M.; Runz, Heiko

doi:10.1101/218875

Cited by 8 publications

(10 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent MR studies highlight the value of hypothesis-free (“phenome-wide”) MR in building a comprehensive picture of the causal effects of risk factors on disease outcomes 8 34 35 . Given that human proteins represent the major source of therapeutic targets, we sought to mine our results for targets of molecules already approved as treatments or in ongoing clinical development, and which might represent promising candidates for repositioning.…”

Section: Resultsmentioning

confidence: 99%

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

Zheng

Haberland

Baird

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

The human proteome is a major source of therapeutic targets. Recent genetic association analyses of the plasma proteome enable systematic evaluation of the causal consequences of variation in plasma protein levels. Here, we estimated the effects of 1002 proteins on 225 phenotypes using two-sample Mendelian randomization (MR) and colocalization. Of 413 associations supported by evidence from MR, 130 (31.5%) were not supported by results of colocalization analyses, suggesting that genetic confounding due to linkage disequilibrium (LD) is widespread in naive phenome-wide association studies of proteins. Combining MR and colocalization evidence in cis-only analyses, we identified 111 putatively causal effects between 65 proteins and 52 disease-related phenotypes (www.epigraphdb.org/pqtl/). Evaluation of data from historic drug development programmes showed that target-indication pairs with MR and colocalization support were more likely to be approved, evidencing the value of our approach in identifying and prioritising potential therapeutic targets.

show abstract

Section: Resultsmentioning

confidence: 99%

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

Zheng

Haberland

Baird

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nonetheless, even before 24 the objective of personalized medicine can be achieved, PGS can be used for studying the genetic 25 influence of different phenotypes. By examining the correlation between PGS and various phenotypes, 26 researchers can gather evidence for whether the genetic influence on certain traits were pleiotropic or 27 specific [20,21,22,23,11,6,7]. For example, using PGS, Power et al [11] showed that genetic tendency 28 for schizophrenia and bipolar disorder were predictive of creativity, supporting earlier suggestions that 29 creativity and tendency towards major psychotic illnesses may share some common roots.…”

Section: Introduction 15mentioning

confidence: 79%

“…36 Recently, cohorts with genotype data have become very large. Examples of such cohorts include 37 the UK Biobank [25] (n ≈ 500,000), the 23andMe cohort [26] (n ≈ 600,000), and the deCode cohort 38…”

Section: Introduction 15mentioning

confidence: 99%

Polygenic scores for UK Biobank scale data

Tsh

Porsch

Choi

et al. 2018

Preprint

View full text Add to dashboard Cite

Polygenic scores (PGS) are estimated scores representing the genetic tendency of an individual for a disease or trait and have become an indispensible tool in a variety of analyses. Typically they are linear combination of the genotypes of a large number of SNPs, with the weights calculated from an external source, such as summary statistics from large meta-analyses. Recently cohorts with genetic data have become very large, rendering external summary statistics superfluous. Making use of raw data in calculating PGS, however, presents us with problems of overfitting. Here we discuss the essence of overfitting as applied to PGS calculations, with one of the consequences being the conflation of genetic correlation with environmental correlation. Our simulations show that the impact of overfitting due to the overlap between the Target and the Validation data (OTV) is much less than overfitting due to the overlap between the Target and the Discovery data (OTD), and that a large sample size can vastly reduce OTD in terms of correlation. However, tests of genetic correlations will still be affected by OTD due to increased power. A proposal called cross prediction is offered whereby both OTD and OTV can be avoided when calculating PGS without external summary statistics. Software is made available for implementation of the methods.2

show abstract

“…In this study we show that human genetic studies of drug target proteins can predict not only therapeutic efficacy of drugs, but also can provide evidence about the likelihood of side effects. Although this approach has been proposed and used to make predictions about individual drug targets 39 , it had not been systematically validated. This finding has a number of applications for drug discovery and could ultimately be used to help make safer therapeutics.…”

Section: Discussionmentioning

confidence: 99%

“…An extension of our work is to understand how the genetics of a drug's "off-target" proteins also contribute to its side effect profile. Human genetics data are becoming increasingly rich, especially with comprehensive efforts to deep-phenotype complete human knockouts [57][58][59] and to perform phenome-wide association studies (PheWAS) across electronic medical records to detect pleiotropic effects of genes 39,60 . This analysis underscores the importance of comprehensively characterizing human knockouts and people affected by Mendelian syndromes, because in some cases their biology can help anticipate drug safety issues before they occur, while in other cases their lack of concerning phenotypes can help build conviction that certain proteins are intrinsically safe to drug.…”

Section: Discussionmentioning

confidence: 99%

Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects

Nguyen

Deaton

Nioi

et al. 2018

Preprint

View full text Add to dashboard Cite

Biomedical scientists face major challenges in developing novel drugs for unmet medical needs. Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict drug and target safety as early as possible using a variety of assays in vitro and in preclinical species. In principle, characterizing the effect of natural variation in the genes encoding drug targets should present a powerful alternate approach to predict not only whether a protein will be an effective drug target, but also whether a protein will be an inherently safe drug target, while avoiding the challenges of translating biology from experiments in non-human species. We have embarked on a retrospective analysis, demonstrating for the first time a statistical link between the organ systems involved in genetic syndromes of drug target genes and the organ systems in which side effects are observed clinically. Across 1,819 drugs and 21 organ system phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0% vs 19.2%; OR = 1.80). Conversely, we find that having genetic evidence that a drug target is associated with diseases in which a certain organ system is unaffected decreases the likelihood that side effects will manifest in that organ system, relative to drug targets for which there is no published gene-phenotype information (18.5% vs 20.0%; OR = 0.90). We find a relationship between genetics and side effects even when controlling for known confounders such as drug delivery route and indication, and we find that this relationship replicates in an independent data set of adverse event reports for marketed drugs. We highlight examples where genetics of drug targets could have anticipated side effects observed during clinical trials. This result suggests that human genetic data should be routinely used to predict potential safety issues associated with novel drug targets. This may lead to selection of better targets, appropriate monitoring of putative side effects early in development, reduction of the use of preclinical animal experiments, and ultimately increased success of molecules. Furthermore, deeply phenotyping human knockouts will be critically important to understand the full spectrum of effects that a new drug may elicit.

show abstract

Phenome-wide association studies (PheWAS) across large “real-world data” population cohorts support drug target validation

Cited by 8 publications

References 61 publications

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases

Polygenic scores for UK Biobank scale data

Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects

Contact Info

Product

Resources

About