Abstract
Estimated genetic associations with prognosis, or conditional on a phenotype (e.g. disease incidence), may be affected by collider bias, whereby conditioning on the phenotype induces associations between causes of the phenotype and prognosis. We propose a method, ‘Slope-Hunter’, that uses model-based clustering to identify and utilise the class of variants only affecting the phenotype to estimate the adjustment factor, assuming this class explains more variation in the phenotype than any other variant classes. Simulation studies show that our approach eliminates the bias and outperforms alternatives even in the presence of genetic correlation. In a study of fasting blood insulin levels (FI) conditional on body mass index, we eliminate paradoxical associations of the underweight loci: COBLLI; PPARG with increased FI, and reveal an association for the locus rs1421085 (FTO). In an analysis of a case-only study for breast cancer mortality, a single region remains associated with more pronounced results.
Highlights
Estimated genetic associations with prognosis, or conditional on a phenotype, may be affected by collider bias, whereby conditioning on the phenotype induces associations between causes of the phenotype and prognosis
An example is the ‘paradox of glucose6-phosphate dehydrogenase (G6PD) deficiency’ whereby among individuals selected according to their status of severe malarial anaemia (SMA), higher levels of G6PD deficiency appear to protect against cerebral malaria (CM)[10,11]
We have proposed an approach that overcomes a major disadvantage of previous methods, and showed that it provides unbiased estimates of single nucleotide polymorphism (SNP)-outcome associations in a variety of situations, including in the presence of genetic correlations between I and outcome
Summary
Estimated genetic associations with prognosis, or conditional on a phenotype (e.g. disease incidence), may be affected by collider bias, whereby conditioning on the phenotype induces associations between causes of the phenotype and prognosis. Such an analysis is referred to as ‘conditional analysis’ throughout this manuscript This leads to a type of selection bias—termed index event bias or collider bias— whereby uncorrelated causes of the disease appear correlated when carrying out a conditional analysis, or studying only cases[2,3,7,8]. This means that if there is unmeasured confounding between incidence and prognosis, any cause of incidence will appear to cause prognosis. This assumption may be incompatible with most genetic studies where shared pathways have been observed for many traits including psychiatric[12], metabolites[13] and phenotypes related to cumulative effects of long-term exposures[14]
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