Predicting drug repurposing opportunities �for Alzheimer’s disease prevention �using genome-wide association study data��Venexia Walker
Why use genome-wide association study data?
Methods using genome-wide association study data
Predicting drug repurposing opportunities for Alzheimer’s Disease prevention
A note on triangulation of evidence
Future directions
Why use genome-wide association study data?
Methods using genome-wide association study data
Predicting drug repurposing opportunities for Alzheimer’s Disease prevention
A note on triangulation of evidence
Future directions
Source: Calcoen et al. (2015) What does it take to produce a breakthrough drug? Nature Reviews Drug Discovery
“We found that, among well-studied indications, the proportion of �drug mechanisms with direct genetic support increases significantly �across the drug development pipeline, from 2.0% at the preclinical stage �to 8.2% among mechanisms for approved drugs, and varies �dramatically among disease areas.”
Source: Nelson et al. (2015) The support of human genetic evidence for approved drug indications. Nature Genetics.
Source: https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data
Source: Lambert et al. (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nature Genetics.
Source: Elsworth et al. (2020) The MRC IEU OpenGWAS data infrastructure. bioRxiv
Source: Elsworth et al. (2020) The MRC IEU OpenGWAS data infrastructure. bioRxiv
Why use genome-wide association study data?
Methods using genome-wide association study data
Predicting drug repurposing opportunities for Alzheimer’s Disease prevention
A note on triangulation of evidence
Future directions
Source: Evans et al. (2015) Mendelian Randomization: New Applications in the Coming Age of Hypothesis-Free Causality. Annual Reviews Genomics Hum Genetics.
Instrument
SNP(s) selected to proxy a drug target
Exposure
Drug target
Outcome
Alzheimer’s disease
(Un)measured confounders
Socioeconomic position, BMI, smoking, …
Source: Hemani et al. (2018) The MR-Base platform supports systematic causal inference across the human phenome. eLife.
Source: Walker et al (2017) Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities. International Journal of Epidemiology.
Strengths of Mendelian randomization in this setting include:
Source: Walker et al (2017) Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities. International Journal of Epidemiology.
Limitations of Mendelian randomization in this setting include:
Why use genome-wide association study data?
Methods using genome-wide association study data
Predicting drug repurposing opportunities for Alzheimer’s Disease prevention
A note on triangulation of evidence
Future directions
Source: Giambartolomei et al. (2014) Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics. PLOS Genetics.
Colocalization quantifies the likelihood of a shared causal variant by calculating the posterior support for five hypotheses:
• H0 : No association with phenotype A or phenotype B
• H1 : Association with phenotype A but not phenotype B
• H2 : Association with phenotype B but not phenotype A
• H3 : Association with phenotypes A and B, different causal variants
• H4 : Association with phenotypes A and B, same causal variant
Source: Giambartolomei et al. (2014) Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics. PLOS Genetics.
Why use genome-wide association study data?
Methods using genome-wide association study data
Predicting drug repurposing opportunities for Alzheimer’s Disease prevention
A note on triangulation of evidence
Future directions
Step 1
Identify drugs of interest, their protein targets
and corresponding genes
Step 2
Select instruments for each protein target �using eQTL data
Step 3
Validate against the existing indication of the drug and retain SNPs with evidence of an effect
Step 4
Perform two-sample Mendelian randomization analysis of interest
Step 1
Identify drugs of interest, their protein targets
and corresponding genes
Step 2
Select instruments for each protein target �using eQTL data
Step 3
Validate against the existing indication of the drug and retain SNPs with evidence of an effect
Step 4
Perform two-sample Mendelian randomization analysis of interest
Step 1
Identify drugs of interest, their protein targets
and corresponding genes
Step 2
Select instruments for each protein target �using eQTL data
Step 3
Validate against the existing indication of the drug and retain SNPs with evidence of an effect
Step 4
Perform two-sample Mendelian randomization analysis of interest
SNPs to represent each target were obtained by extracting the ‘best SNP’ for the corresponding gene in each tissue from GTEx, where the ‘best SNP’ was defined by GTEx as the variant with the smallest nominal p-value for a variant-gene pair.
Source: GTEx consortium. (2015) Genetic effects on gene expression across human tissues. Nature.
Step 1
Identify drugs of interest, their protein targets
and corresponding genes
Step 2
Select instruments for each protein target �using eQTL data
Step 3
Validate against the existing indication of the drug and retain SNPs with evidence of an effect
Step 4
Perform two-sample Mendelian randomization analysis of interest
Two-sample Mendelian randomization using data from GTEx for the instrument-exposure associations and a UK Biobank GWAS of systolic blood pressure for the instrument-outcome associations.
Source: Walker et al. (2019) Repurposing antihypertensive drugs for the prevention of Alzheimer’s disease: a Mendelian randomization study. International Journal of Epidemiology.
Step 1
Identify drugs of interest, their protein targets
and corresponding genes
Step 2
Select instruments for each protein target �using eQTL data
Step 3
Validate against the existing indication of the drug and retain SNPs with evidence of an effect
Step 4
Perform two-sample Mendelian randomization analysis of interest
Instrument
SNP(s) selected to proxy antihypertensive drug targets
Exposure
Systolic blood pressure
Outcome
Alzheimer’s disease
(Un)measured confounders
Socioeconomic position, BMI, smoking, …
GWAS based on 317,754
UK Biobank participants
IGAP GWAS Stage 1 results
Twelve drug classes identified in the BNF
Source: Walker et al. (2019) Repurposing antihypertensive drugs for the prevention of Alzheimer’s disease: a Mendelian randomization study. International Journal of Epidemiology.
Source: Walker et al. (2019) Repurposing antihypertensive drugs for the prevention of Alzheimer’s disease: a Mendelian randomization study. International Journal of Epidemiology.
Sources: Walker et al. (2019) Repurposing antihypertensive drugs for the prevention of Alzheimer’s disease: a Mendelian randomization study. International Journal of Epidemiology. � Gill et al. (2019) Use of Genetic Variants Related to Antihypertensive Drugs to Inform on Efficacy and Side Effects. Hypertension.
Source: Gill et al. (2019) Comparison with randomized controlled trials as a strategy for evaluating instruments in Mendelian randomization. International Journal of Epidemiology.
Why use genome-wide association study data?
Methods using genome-wide association study data
Predicting drug repurposing opportunities for Alzheimer’s Disease prevention
A note on triangulation of evidence
Future directions
Source: Zheng et al. (2021) Evaluating the impact of metformin, a multiple targets drugs, on risk of complex diseases: a phenome-wide Mendelian randomisation study. Submitted.
Source: Zheng et al. (2021) Evaluating the impact of metformin, a multiple targets drugs, on risk of complex diseases: a phenome-wide Mendelian randomisation study. Submitted.
Source: Zheng et al. (2021) Evaluating the impact of metformin, a multiple targets drugs, on risk of complex diseases: a phenome-wide Mendelian randomisation study. Submitted.
Source: Zheng et al. (2021) Evaluating the impact of metformin, a multiple targets drugs, on risk of complex diseases: a phenome-wide Mendelian randomisation study. Submitted.
Source: Zheng et al. (2021) Evaluating the impact of metformin, a multiple targets drugs, on risk of complex diseases: a phenome-wide Mendelian randomisation study. Submitted.
Why use genome-wide association study data?
Methods using genome-wide association study data
Predicting drug repurposing opportunities for Alzheimer’s Disease prevention
A note on triangulation of evidence
Future directions
“Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding.”
Source: Lawlor et al. (2016) Triangulation in aetiological epidemiology. International Journal of Epidemiology.
Why use genome-wide association study data?
Methods using genome-wide association study data
Predicting drug repurposing opportunities for Alzheimer’s Disease prevention
A note on triangulation of evidence
Future directions
(1) What is the optimal approach for deriving a drug target instrument?
(2) What disease and/or populations are not covered by genome-wide association studies?
(3) How do estimates compare with other forms of evidence, particularly randomized controlled trials, and how can these different forms of evidence be used together?
Predicting drug repurposing opportunities �for Alzheimer’s disease prevention �using genome-wide association study data��Venexia Walker