Year: 2013
Type: article
Abstract: BACKGROUND: An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome. METHODS: Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate 'weak instrument' bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed. RESULTS: Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases. CONCLUSIONS: Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score.
DOI: 10.1093/ije/dyt093
Authors: Stephen Burgess, Simon G Thompson
Institution: University of Cambridge
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,514
Awards: NIHR Senior Investigator Award, Collaborative pooled analysis of data on C-reactive protein gene variants and coronary disease, Causal inference in large datasets using genetic instrumental variables: extending Mendelian randomization techniques., RENEWAL: Systematic approaches to the evaluation of emerging coronary risk markers. Large-scale epidemiological analyses of existing data and stored biological samples, Large-scale integrative studies of risk factors in coronary heart disease: from discovery to application
OpenAlex ID: W2162204400