Collider scope: when selection bias can substantially influence observed associations.
paper
Cited
Public
- Authors
- MunafΓ², Marcus R; Tilling, Kate; Taylor, Amy E; Evans, David M; Davey Smith, George
- Year
- 2018
- Journal
- International journal of epidemiology
- PMID
- 29040562
- DOI
- 10.1093/ije/dyx206
- PMCID
- PMC5837306
Large-scale cross-sectional and cohort studies have transformed our understanding of the genetic and environmental determinants of health outcomes. However, the representativeness of these samples may be limited-either through selection into studies, or by attrition from studies over time. Here we explore the potential impact of this selection bias on results obtained from these studies, from the perspective that this amounts to conditioning on a collider (i.e. a form of collider bias). Whereas it is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates, it is often assumed that it should not have a strong impact on estimates of associations. We argue that because selection can induce collider bias (which occurs when two variables independently influence a third variable, and that third variable is conditioned upon), selection can lead to substantially biased estimates of associations. In particular, selection related to phenotypes can bias associations with genetic variants associated with those phenotypes. In simulations, we show that even modest influences on selection into, or attrition from, a study can generate biased and potentially misleading estimates of both phenotypic and genotypic associations. Our results highlight the value of knowing which population your study sample is representative of. If the factors influencing selection and attrition are known, they can be adjusted for. For example, having DNA available on most participants in a birth cohort study offers the possibility of investigating the extent to which polygenic scores predict subsequent participation, which in turn would enable sensitivity analyses of the extent to which bias might distort estimates.
Illustration of collider bias. The basic premise of collider bias is shown. In this example, a bell is sounded whenever either coin come up βheadsβ. The result of one coin toss is independent of the other. However, if we hear the bell ring (i.e. we condition on the bell ringing), then if we see a tail on one coin we know there must be a head on the otherβthe two coin results are no longer independent and a spurious inverse correlation has been induced. Reproduced from Gage SH, Davey Smith G, Ware JJ, Flint J, MunafΓ² MR. G = E: What GWAS can tell us about the environment. PLoS Genet 2016;12: e1005765.
Illustration of selection bias simulation. In the intended study population there is no association between allele score and outcome. Selection into the study (either through voluntary participation at baseline, or attrition over time) induces an association between allele score and outcome (collider bias).
Scenarios where selection bias would occur. A. In truth, the SNP is not causally associated with the outcome; selection will induce an association (which could be positive or negative). B. In truth, the SNP is not causally associated with the outcome; selection will induce an association (which could be positive or negative). C. In truth, the SNP is causally associated with the outcome; selection could make this larger or attenuate it. D. In truth, the SNP is causally associated with the outcome; selection could make this larger or attenuate it. E. In truth, the SNP is causally associated with the outcome; selection will bias this association (which could be positive or negative). F. Note that the association between P and O is biased in the selected sample; however, the association between SNP and O is unbiased in the selected sample. P, Phenotype; O, Outcome; S, Selection; U, Other variables.
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| Name | Type |
|---|---|
| 5-year mortality local | phenotype |
| actual study population local | cohort |
| alcohol | phenotype |
| allele score local | drug |
| allele score local | variant |
| Allele score local | drug |
| ALSPAC | cohort |
| ALSPAC participants local | cohort |
| Alzheimer's disease | phenotype |
| ARIES local | cohort |
| Avon Longitudinal Study of Parents and Children | cohort |
| binary phenotype | phenotype |
| birth cohort | cohort |
| BMI | phenotype |
| body mass index | phenotype |
| British Doctors Study local | cohort |
| case/control outcome local | phenotype |
| case-control sample | cohort |
| case/control status | phenotype |
| Case-control study local | phenotype |
| childhood socioeconomic position local | phenotype |
| chronotype | phenotype |
| cigarettes | phenotype |
| cognition | phenotype |
| cognitive ability | phenotype |
| Cohort study local | cohort |
| common variants | cohort |
| Continuing participation local | phenotype |
| current smoking | phenotype |
| disease | phenotype |
| DNA | drug |
| educational or vocational qualifications local | phenotype |
| Educational qualifications local | phenotype |
| environmental factors | drug |
| Environmental factor U local | variant |
| ever smoking | phenotype |
| ever vs never smoking local | phenotype |
| general population | cohort |
| General population (UK) local | cohort |
| genetic factors | cohort |
| Genetic factor U local | variant |
| genetic risk score for smoking local | variant |
| genetic risk scores local | variant |
| genetic variants | cohort |
| Genome-wide Allelic Score local | drug |
| Gx local | gene |
| Gy local | gene |
| health status | phenotype |
| height | phenotype |
| intended study population local | cohort |
| large-scale cross-sectional studies local | cohort |
| Maternal retention local | phenotype |
| maternal smoking | phenotype |
| mental health outcomes | phenotype |
| Missing individuals local | cohort |
| missingness local | drug |
| Missingness local | drug |
| mortality | phenotype |
| Non-attendance at data collection clinics local | phenotype |
| non-smokers | phenotype |
| no qualifications local | phenotype |
| Offspring retention local | phenotype |
| outcome | phenotype |
| parental education | phenotype |
| participants | cohort |
| participation local | phenotype |
| Participation local | phenotype |
| personality traits | phenotype |
| p-factor | phenotype |
| phenotype | phenotype |
| phenotypic associations local | phenotype |
| physical health outcomes local | phenotype |
| Physical health outcomes local | phenotype |
| polygenic risk score | cohort |
| polygenic risk score for height local | variant |
| Polygenic risk score for schizophrenia local | other |
| Polygenic score local | other |
| population | cohort |
| Premature mortality local | other |
| Prospective study local | phenotype |
| prostate cancer | phenotype |
| psychiatric disorders | phenotype |
| psychotic experiences | phenotype |
| rs16969968 | variant |
| schizophrenia | phenotype |
| secondary phenotype | phenotype |
| selection bias | phenotype |
| selection into the study local | phenotype |
| sex | phenotype |
| single genetic variants local | variant |
| smoking | phenotype |
| smoking genetic risk score local | variant |
| Smoking genetic risk score local | phenotype |
| SNP | cohort |
| SNPs related to Phenotype local | variant |
| social position local | phenotype |
| Socioeconomic inequalities local | phenotype |
| study cohort | cohort |
| Tobacco and Genetics Consortium | cohort |
| tobacco use | phenotype |
| UK BiLEVE | cohort |
| UK Biobank | cohort |
| Whole-genome Genetic Correlation local | drug |
| X local | phenotype |
| years of education | phenotype |
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| Mendelian randomisation analyses find pulmonary factors mediate the effect of height on coronary artery disease. | Marouli E et al. | β | 2019 | β |
| Mendelian randomisation analysis of the effect of educational attainment and cognitive ability on smoking behaviour. | Sanderson E et al. | β | 2019 | β |
| Mendelian randomisation and the goal of inferring causation from observational studies in the vision sciences. | Plotnikov D et al. | β | 2019 | β |
| Meta-analysis of genome-wide association studies for body fat distribution in 694Β 649 individuals of European ancestry. | Pulit SL et al. | β | 2019 | β |
| MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging. | Millard LAC et al. | β | 2019 | β |
| No Support for Historical Candidate Gene or Candidate Gene-by-Interaction Hypotheses for Major Depression Across Multiple Large Samples. | Border R et al. | β | 2019 | β |
| Phenotypic and genetic relationship between BMI and cigarette smoking in a sample of UK adults. | Wills AG et al. | β | 2019 | β |
| Schizophrenia risk and reproductive success: a Mendelian randomization study. | Lawn RB et al. | β | 2019 | β |
| Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization. | Millard LAC et al. | β | 2019 | β |
| Sexual Activity and Sexual Satisfaction Among Older Adults in Four European Countries. | TrΓ¦en B et al. | β | 2019 | β |
| Socioeconomic position during pregnancy and DNA methylation signatures at three stages across early life: epigenome-wide association studies in the ALSPAC birth cohort. | Alfano R et al. | β | 2019 | β |
| Survival Bias in Mendelian Randomization Studies: A Threat to Causal Inference. | Smit RAJ et al. | β | 2019 | β |
| Telomere length and aging-related outcomes in humans: A Mendelian randomization study in 261,000 older participants. | Kuo CL et al. | β | 2019 | β |
| The Association Between Adiposity and Inpatient Hospital Costs in the UK Biobank Cohort. | Dixon P et al. | β | 2019 | β |
| The associations between self-reported depression, self-reported chronic inflammatory conditions and cognitive abilities in UK Biobank. | Lyall LM et al. | β | 2019 | β |
| The Genetic Relationship Between Alcohol Consumption and Aspects of Problem Drinking in an Ascertained Sample. | Johnson EC et al. | β | 2019 | β |
| The second generation of The Avon Longitudinal Study of Parents and Children (ALSPAC-G2): a cohort profile. | Lawlor DA et al. | β | 2019 | β |
| Understanding cognitive impairment in mood disorders: mediation analyses in the UK Biobank cohort. | Cullen B et al. | β | 2019 | β |
| Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 associate with gestational duration. | Liu X et al. | β | 2019 | β |
| Within family Mendelian randomization studies. | Davies NM et al. | β | 2019 | β |
| Women Outperform Men in Verbal Episodic Memory Even in Oldest-Old Age: 13-Year Longitudinal Results of the AgeCoDe/AgeQualiDe Study. | Golchert J et al. | β | 2019 | β |
| A 22-Year Follow-Up (Range 16 to 23) of Original Subjects with Baseline Alcohol Use Disorders from the Collaborative Study on Genetics of Alcoholism. | Schuckit MA et al. | β | 2018 | β |
| Assessing causal relationships using genetic proxies for exposures: an introduction to Mendelian randomization. | Katikireddi SV et al. | β | 2018 | β |
| Assessment of the genetic and clinical determinants of fracture risk: genome wide association and mendelian randomisation study. | Trajanoska K et al. | β | 2018 | β |
| Association of copy number variation across the genome with neuropsychiatric traits in the general population. | Guyatt AL et al. | β | 2018 | β |
| Association of Polygenic Risk for Attention-Deficit/Hyperactivity Disorder With Co-occurring Traits and Disorders. | Du Rietz E et al. | β | 2018 | β |
| Associations of coffee genetic risk scores with consumption of coffee, tea and other beverages in the UK Biobank. | Taylor AE et al. | β | 2018 | β |
| Bias in Mendelian randomization due to assortative mating. | Hartwig FP et al. | β | 2018 | β |
| Can Survival Bias Explain the Age Attenuation of Racial Inequalities in Stroke Incidence?: A Simulation Study. | Mayeda ER et al. | β | 2018 | β |
| Cross-sectional and longitudinal analyses of outdoor air pollution exposure and cognitive function in UK Biobank. | Cullen B et al. | β | 2018 | β |
| Exploring causality in the association between circulating 25-hydroxyvitamin D and colorectal cancer risk: a large Mendelian randomisation study. | He Y et al. | β | 2018 | β |
| Exploring the association of genetic factors with participation in the Avon Longitudinal Study of Parents and Children. | Taylor AE et al. | β | 2018 | β |
| Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. | Evangelou E et al. | β | 2018 | β |
| How Healthy Are Survey Respondents Compared with the General Population?: Using Survey-linked Death Records to Compare Mortality Outcomes. | Keyes KM et al. | β | 2018 | β |
| Investigating causality in associations between education and smoking: a two-sample Mendelian randomization study. | Gage SH et al. | β | 2018 | β |
| Is smoking heaviness causally associated with alcohol use? A Mendelian randomization study in four European cohorts. | Taylor M et al. | β | 2018 | β |
| Mendelian Randomization in Case Only Studies: A Promising Approach to be Applied With Caution. | Mitchell RE et al. | β | 2018 | β |
| Mendelian Randomization Studies of Coffee and Caffeine Consumption. | Cornelis MC et al. | β | 2018 | β |
| Paternal and maternal obesity but not gestational weight gain is associated with type 1 diabetes. | Magnus MC et al. | β | 2018 | β |
| Physical activity and longevity: how to move closer to causal inference. | Wade KH et al. | β | 2018 | β |
| Precision Medicine for ProstheticΒ ValveΒ Deterioration: A Glimpse Into the Future? | Thanassoulis G | β | 2018 | β |
| Quantitative Bias Analysis for Collaborative Science. | Weuve J et al. | β | 2018 | β |
| Relationships between estimated autozygosity and complex traits in the UK Biobank. | Johnson EC et al. | β | 2018 | β |
| Reply to Pearl: Algorithm of the truth vs real-world science. | Krieger N et al. | β | 2018 | β |
| Risk factors of hippocampal sclerosis in the oldest old: The 90+ Study. | Trieu T et al. | β | 2018 | β |
| Selection bias in population-representative studies? A commentary on Deaton and Cartwright. | Schooling CM | β | 2018 | β |
| Shared Genetic Contribution of Type 2 Diabetes and Cardiovascular Disease: Implications for Prognosis and Treatment. | Strawbridge RJ et al. | β | 2018 | β |
| Testing theΒ validity of value-added measures of educational progress withΒ genetic data. | Morris TT et al. | β | 2018 | β |
| Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders. | Walters RK et al. | β | 2018 | β |
| Understanding the role of bitter taste perception in coffee, tea and alcohol consumption through Mendelian randomization. | Ong JS et al. | β | 2018 | β |
| Genetic epidemiology and Mendelian randomization for informing disease therapeutics: Conceptual and methodological challenges. | Paternoster L et al. | β | 2017 | β |
| Heavier smoking increases coffee consumption: findings from a Mendelian randomization analysis. | BjΓΈrngaard JH et al. | β | 2017 | β |
| Mortality selection in a genetic sample and implications for association studies. | Domingue BW et al. | β | 2017 | β |
| Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. | Weiner DJ et al. | β | 2017 | β |
| Recent Developments in Mendelian Randomization Studies. | Zheng J et al. | β | 2017 | β |