Association of polygenic risk scores for coronary artery disease with subsequent events amongst established cases

Dudbridge, Frank; Schmidt, Amand F.; Finan, Chris; Denaxas, Spiros; Asselbergs, Folkert W.; Hingorani, Aroon D.; Patel, Riyaz

doi:10.1101/19009431

Cited by 4 publications

(6 citation statements)

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“…However, restricting prognostic study samples to individuals with cancer can cause "collider bias", which can potentially induce an attenuation, reversal or overestimation of associations between otherwise independent exposures for cancer progression when adjusting or selecting for a common consequence of those two exposures [124][125][126]. When restricting the study sample to those who have cancer in prognostic studies, all independent risk factors for cancer incidence become associated with each other.…”

Section: Evaluating Risk Factors For Cancer Progression and The Poten...mentioning

confidence: 99%

“…Conditioning analyses on colorectal cancer incidence (i.e., case status, a collider in this scenario) could generate a spurious association between two causes of colorectal cancer incidence (i.e., body mass index and cigarette smoking). This then induces an association between body mass index and colorectal cancer survival (via cigarette smoking) even in the absence of a true causal relationship between these two traits in the target population genetic effects of cancer incidence to eliminate (when the genetic effects on disease incidence and progression are independent) or, more realistically, reduce this bias [126,130,131].…”

Section: Evaluating Risk Factors For Cancer Progression and The Poten...mentioning

confidence: 99%

“…However, these methods require certain population-level parameters, such as cancer prevalence and the effects of genetic variation and confounders on cancer incidence, to be quantified. Thirdly, Dudbridge et al recently proposed a method that uses the residuals from the regression of genetic effects of cancer prognosis on the genetic effects of cancer incidence to eliminate (when the genetic effects on disease incidence and progression are independent) or, more realistically, reduce this bias [ 126 , 130 , 131 ].…”

Section: Developments To Mitigate the Challenges Of Mr Of Nutrition I...mentioning

confidence: 99%

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Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Wade

Yarmolinsky

Giovannucci

et al. 2022

Cancer Causes Control

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Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner. Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.

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Section: Evaluating Risk Factors For Cancer Progression and The Poten...mentioning

confidence: 99%

Section: Evaluating Risk Factors For Cancer Progression and The Poten...mentioning

confidence: 99%

Section: Developments To Mitigate the Challenges Of Mr Of Nutrition I...mentioning

confidence: 99%

See 1 more Smart Citation

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Wade

Yarmolinsky

Giovannucci

et al. 2022

Cancer Causes Control

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Evaluating Risk Factors For Cancer Progression and The Poten...mentioning

confidence: 99%

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Wade¹,

Yarmolinsky²,

Giovannucci³

et al. 2021

Preprint

Self Cite

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Dietary factors are assumed to play an important role in cancer risk, apparent in consensus recommendations for cancer prevention that promote nutritional changes. However, the evidence in this field has been generated predominantly through observational studies, which may result in biased effect estimates because of confounding, exposure misclassification, and reverse causality. With major geographical differences and rapid changes in cancer incidence over time, it is crucial to establish which of the observational associations reflect causality and to identify novel risk factors as these may be modified to prevent the onset of cancer and reduce its progression. Mendelian randomization (MR) uses the special properties of germline genetic variation to strengthen causal inference regarding potentially modifiable exposures and disease risk. MR can be implemented through instrumental variable (IV) analysis and, when robustly performed, is generally less prone to confounding, reverse causation and measurement error than conventional observational methods and has different sources of bias (discussed in detail below). It is increasingly used to facilitate causal inference in epidemiology and provides an opportunity to explore the effects of nutritional exposures on cancer incidence and progression in a cost-effective and timely manner.Here, we introduce the concept of MR and discuss its current application in understanding the impact of nutritional factors (e.g., any measure of diet and nutritional intake, circulating biomarkers, patterns, preference or behaviour) on cancer aetiology and, thus, opportunities for MR to contribute to the development of nutritional recommendations and policies for cancer prevention. We provide applied examples of MR studies examining the role of nutritional factors in cancer to illustrate how this method can be used to help prioritise or deprioritise the evaluation of specific nutritional factors as intervention targets in randomised controlled trials. We describe possible biases when using MR, and methodological developments aimed at investigating and potentially overcoming these biases when present. Lastly, we consider the use of MR in identifying causally relevant nutritional risk factors for various cancers in different regions across the world, given notable geographical differences in some cancers. We also discuss how MR results could be translated into further research and policy. We conclude that findings from MR studies, which corroborate those from other well-conducted studies with different and orthogonal biases, are poised to substantially improve our understanding of nutritional influences on cancer. For such corroboration, there is a requirement for an interdisciplinary and collaborative approach to investigate risk factors for cancer incidence and progression.

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“…Indeed, a study investigating the association of known common type 2 diabetes variants with BMI (a strong risk factor for type 2 diabetes) found three overestimated and one underestimated associations among 11 type 2 diabetes risk alleles when comparing to a non-diabetic population (16). Another example uses a polygenic risk score to examine associations between CHD genetic risk variants and cardiovascular outcomes and found that these differ when examined in those with and without prior CHD (18). These studies highlight the need to address this bias by detecting and accounting for its presence in caseonly studies.…”

Section: Introductionmentioning

confidence: 99%

Strategies to investigate and mitigate collider bias in genetic and Mendelian randomization studies of disease progression

Mitchell

Hartley

Walker

et al. 2022

Preprint

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Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors which influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as “index event”) bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomization analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and Mendelian Randomization studies using both individual and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-hunter and Dudbridge’s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, whilst our second example investigates genetic associations with breast cancer mortality.

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Association of polygenic risk scores for coronary artery disease with subsequent events amongst established cases

Abstract: Background: There is growing evidence that polygenic risk scores (PRS) can be used to identify individuals at high lifetime risk of coronary artery disease (CAD).Whether they can also be used to stratify risk of subsequent events among those surviving a first CAD event remains uncertain.

Cited by 4 publications

References 44 publications

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Applying Mendelian randomization to appraise causality in relationships between nutrition and cancer

Strategies to investigate and mitigate collider bias in genetic and Mendelian randomization studies of disease progression

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