Human Genomics and Drug Development

Schmidt, Amand F.; Hingorani, Aroon D.; Finan, Chris

doi:10.1101/cshperspect.a039230

Cited by 25 publications

(33 citation statements)

References 107 publications

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“…Genotyping may also be of value in blood and tissue matching. However, perhaps counterintuitively, the main healthcare benefit of common disease genomics may come from understanding the causes of disease and drug target discovery 29 rather than disease prediction.…”

Section: Discussionmentioning

confidence: 99%

Performance of polygenic risk scores in screening, prediction, and risk stratification

Hingorani

Gratton

Finan

et al. 2022

Preprint

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We examine the performance of polygenic scores in screening and disease prediction using metrics which are firmly established for non-genetic tests but surprisingly, rarely used in evaluations of polygenic scores. Using reported metrics from the Polygenic Score Catalog (odds and hazard ratios, area under the receiver operating characteristic curve or C-index), we calculate the sensitivity or detection rate (DR5) for polygenic score cut-offs that define a 5% false positive rate (FPR). To examine clinical and public health relevance, we use information on disease incidence to calculate the odds of being affected given a positive result (OAPR; the ratio of true to false positives) both for an individual and a population. Polygenic scores typically detect 7.6-16.4% (and therefore miss 83.6-92.4%) of affected individuals with a 5% FPR. For a polygenic score for coronary artery disease (CAD) with a DR5 of 12% (88% of cases missed), the OAPR is 1:3.75 if used in a population with an average 10-year CAD risk of 10%, and 1:41 in a population with an average 10-year CAD risk of 1%. For a polygenic score for breast cancer also with a DR5 of 12% and a population average 10-year risk of 1 in 65 (odds =1:64), odds are reduced to 1:91 for a woman with a polygenic score at the 25th centile and increased to 1:53 for a woman with a result at the 75th centile. Analysis using the relevant metrics reveals the weak predictive performance of polygenic risk scores, which limits their effectiveness in screening and disease prevention.

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Section: Discussionmentioning

confidence: 99%

Performance of polygenic risk scores in screening, prediction, and risk stratification

Hingorani

Gratton

Finan

et al. 2022

Preprint

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“…Instruments that are associated with the target of that drug should be used, rather than those associated with the risk factor that the drug acts on. 42,152,153…”

Section: [H2] Testing Causation Across the Life Coursementioning

confidence: 99%

Mendelian randomization

Sanderson

Glymour

Holmes

et al. 2022

Nat Rev Methods Primers

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655

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“…However, formal causal inference languages subsume both under the same theoretical frameworks and notations and, in practice, they can and should be complementary. Ference et al (2021) show how MR can be used to improve the design of randomized control trials and Schmidt et al (2021) how genetics can be used to prioritize drug targets for trials.…”

Section: Contributionsmentioning

confidence: 99%

Causal Inference with Genetic Data: Past, Present, and Future

Pingault

Richmond

Smith

2021

Cold Spring Harb Perspect Med

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The set of methods discussed in this collection has emerged from the convergence of two scientific fields-genetics and causal inference. In this introduction, we discuss relevant aspects of each field and show how their convergence arises from the natural experiments that genetics offer. We present introductory concepts useful to readers unfamiliar with genetically informed methods for causal inference. We conclude that existing applications and foreseeable developments should ensure that we rapidly reap the rewards of this relatively new field, not only in terms of our understanding of human disease and development, but also in terms of tangible translational applications.

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Human Genomics and Drug Development

Cited by 25 publications

References 107 publications

Performance of polygenic risk scores in screening, prediction, and risk stratification

Performance of polygenic risk scores in screening, prediction, and risk stratification

Mendelian randomization

Causal Inference with Genetic Data: Past, Present, and Future

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