Genetic Prediction of Serum 25-Hydroxyvitamin D, Calcium, and Parathyroid Hormone Levels in Relation to Development of Type 2 Diabetes: A Mendelian Randomization Study

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Abstract

OBJECTIVE

We conducted a Mendelian randomization study to investigate the associations of genetically predicted serum 25-hydroxyvitamin D (S-25OHD), calcium (S-Ca), and parathyroid hormone (S-PTH) levels with type 2 diabetes (T2DM).

RESEARCH DESIGN AND METHODS

Seven, six, and five single nucleotide polymorphisms (SNPs) associated with S-25OHD, S-Ca, and S-PTH levels, respectively, were used as instrumental variables. Data on T2DM were available for 74,124 case subjects with T2DM and 824,006 control subjects. The inverse variance–weighted method was used for the primary analyses, and the weighted median and Mendelian randomization (MR)–Egger methods were used for supplementary analyses.

RESULTS

Genetically predicted S-25OHD but not S-Ca and S-PTH levels were associated with T2DM in the primary analyses. For 1 SD increment of S-25OHD levels, the odds ratio (OR) of T2DM was 0.94 (95% CI 0.88–0.99; P = 0.029) in an analysis based on all seven SNPs and 0.90 (95% CI 0.83–0.98; P = 0.011) in an analysis based on three SNPs within or near genes involved in vitamin D synthesis. Only the association based on the SNPs involved in vitamin D synthesis remained in the weighted median analysis, and no pleiotropy was detected (P = 0.153). Pleiotropy was detected in the analysis of S-Ca (P = 0.013). After correcting for this bias using MR-Egger regression, the OR of T2DM per 1 SD increment of S-Ca levels was 1.41 (95% CI 1.12–1.77; P = 0.003).

CONCLUSIONS

Modest lifelong higher S-25OHD levels were associated with reduced odds of T2DM, but the association was only robust for SNPs in the vitamin D synthesis pathway. The possible role of S-Ca levels for T2DM development requires further research.

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Jack Bowden, George Davey Smith, Stephen Burgess (2016)

Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy). Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables. Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples. Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator

Jack Bowden, George Davey Smith, Philip C. Haycock … (2017)

ABSTRACT Developments in genome‐wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse‐variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite‐sample Type 1 error rates than the inverse‐variance weighted method, and is complementary to the recently proposed MR‐Egger (Mendelian randomization‐Egger) regression method. In analyses of the causal effects of low‐density lipoprotein cholesterol and high‐density lipoprotein cholesterol on coronary artery disease risk, the inverse‐variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR‐Egger regression methods suggest a null effect of high‐density lipoprotein cholesterol that corresponds with the experimental evidence. Both median‐based and MR‐Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants.