ArticlePloS one2018
Missing single nucleotide polymorphisms in Genetic Risk Scores: A simulation study.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Neurobiological correlates of schizophrenia-specific and highly pleiotropic genetic risk scores for neuropsychiatric disorders.Translational psychiatry · 2025Article
- The relationship of genetic signature for cardiometabolic risk with biomarkers of inflammatory and oxidative stress in diabetic patients.BMC endocrine disorders · 2025Article
- Physical activity alters the effect of genetic determinants of adiposity on hypertension among individuals of European ancestry in the UKB.Scandinavian journal of medicine & science in sports · 2024Article
- Improved Genomic Prediction of Staphylococcus epidermidis Isolation Sources with a Novel Polygenic Score.Journal of clinical microbiology · 2023Article
- Constructing a polygenic risk score for childhood obesity using functional data analysis.Econometrics and statistics · 2023Article
- Clinical Application of a Novel Genetic Risk Score for Ischemic Stroke in Patients With Cardiometabolic Disease.Circulation · 2021Article
- Genetic and non-genetic factors associated with the phenotype of exceptional longevity & normal cognition.Scientific reports · 2020Article
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Authors and funding
5 authors.
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Abstract
Using a genetic risk score (GRS) to predict a phenotype in a target sample can be complicated by missing data on the single nucleotide polymorphisms (SNPs) that comprise the GRS. This is usually addressed by imputation, omission of the SNPs or by replacing the missing SNPs with proxy SNPs. To assess the impact of the omission and proxy approaches on effect size estimation and predictive ability of weighted and unweighted GRS with small numbers of SNPs, we simulated a dichotomous phenotype conditional on real genotype data. We considered scenarios in which the proportion of missing SNPs ranged from 20-70%. We assessed the impact of omitting or replacing missing SNPs on the association between the GRS and phenotype, the corresponding statistical power and the area under the receiver operating curve. Omission resulted in a larger bias towards the null value of the effect size, a smaller predictive ability and greater loss of statistical power than proxy approaches. The predictive ability of a weighted GRS that includes SNPs with large weights depends of the availability of these large-weight SNPs.
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