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MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization.

Millard LA, Davies NM, Timpson NJ, Tilling K, Flach PA, Davey Smith G - Sci Rep (2015)

Bottom Line: We found 21 of the 172 outcomes were associated with the allele score at an unadjusted p < 0.05 threshold, and use Bonferroni corrections, permutation testing and estimates of the false discovery rate to consider the strength of results given the number of tests performed.The most strongly associated outcomes included leptin, lipid profile, and blood pressure.We also found novel evidence of effects of BMI on a global self-worth score.

View Article: PubMed Central - PubMed

Affiliation: MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, University of Bristol, Bristol.

ABSTRACT
Observational cohort studies can provide rich datasets with a diverse range of phenotypic variables. However, hypothesis-driven epidemiological analyses by definition only test particular hypotheses chosen by researchers. Furthermore, observational analyses may not provide robust evidence of causality, as they are susceptible to confounding, reverse causation and measurement error. Using body mass index (BMI) as an exemplar, we demonstrate a novel extension to the phenome-wide association study (pheWAS) approach, using automated screening with genotypic instruments to screen for causal associations amongst any number of phenotypic outcomes. We used a sample of 8,121 children from the ALSPAC dataset, and tested the linear association of a BMI-associated allele score with 172 phenotypic outcomes (with variable sample sizes). We also performed an instrumental variable analysis to estimate the causal effect of BMI on each phenotype. We found 21 of the 172 outcomes were associated with the allele score at an unadjusted p < 0.05 threshold, and use Bonferroni corrections, permutation testing and estimates of the false discovery rate to consider the strength of results given the number of tests performed. The most strongly associated outcomes included leptin, lipid profile, and blood pressure. We also found novel evidence of effects of BMI on a global self-worth score.

No MeSH data available.


QQ-plot of the associations between the BMI allelic score and the 172 outcomes.Association of log BMI age 8 with outcomes, of the stage 1 tests. Using the original dataset with variable number of individuals for each outcome. Tests performed with the Stata regress command and robust option. Top result leptin is not shown as P value too small. Corrected P = 0.00023 line: The Bonferroni corrected P = 0.05, accounting for the 160 tests (excluding validation set) performed. P expected = actual line: The expected trajectory, assuming the P values are uniformly distributed.
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f1: QQ-plot of the associations between the BMI allelic score and the 172 outcomes.Association of log BMI age 8 with outcomes, of the stage 1 tests. Using the original dataset with variable number of individuals for each outcome. Tests performed with the Stata regress command and robust option. Top result leptin is not shown as P value too small. Corrected P = 0.00023 line: The Bonferroni corrected P = 0.05, accounting for the 160 tests (excluding validation set) performed. P expected = actual line: The expected trajectory, assuming the P values are uniformly distributed.

Mentions: Our stage 1 tests found the BMI allele score was associated with 21 outcomes, using an unadjusted P < 0.05 threshold (Table 3). Of these, 14 outcomes were from the 160 outcomes we randomly included in our dataset (test of proportions P = 0.030), compared to 8 expected by chance alone (160 × 0.05, making the conservative assumption that all outcomes are uncorrelated). Hence we would expect 6 of the 14 identified outcomes to be true associations (false discovery rate of 0.571). We found stronger associations than would be expected by chance, illustrated by the QQ plot in Fig. 1, suggesting that BMI affects many outcomes. After Bonferroni correction only HDL at age 9 was found with a P value below P < 0.05 whereas using the permutation P values we found 8 associations with P <0.05. In comparison, we found 57 stage 1 associations with P < 0.05 using observational BMI at age 8. Of these, 48 were from the 160 randomly included in our dataset (test of proportions P = <0.001), compared to 8 expected by chance alone. The instrumental variable effect estimates (stage 2 results) are given in Table 4 and Figs 2 and 3 (and observational estimates are also provided for comparison).


MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization.

Millard LA, Davies NM, Timpson NJ, Tilling K, Flach PA, Davey Smith G - Sci Rep (2015)

QQ-plot of the associations between the BMI allelic score and the 172 outcomes.Association of log BMI age 8 with outcomes, of the stage 1 tests. Using the original dataset with variable number of individuals for each outcome. Tests performed with the Stata regress command and robust option. Top result leptin is not shown as P value too small. Corrected P = 0.00023 line: The Bonferroni corrected P = 0.05, accounting for the 160 tests (excluding validation set) performed. P expected = actual line: The expected trajectory, assuming the P values are uniformly distributed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644974&req=5

f1: QQ-plot of the associations between the BMI allelic score and the 172 outcomes.Association of log BMI age 8 with outcomes, of the stage 1 tests. Using the original dataset with variable number of individuals for each outcome. Tests performed with the Stata regress command and robust option. Top result leptin is not shown as P value too small. Corrected P = 0.00023 line: The Bonferroni corrected P = 0.05, accounting for the 160 tests (excluding validation set) performed. P expected = actual line: The expected trajectory, assuming the P values are uniformly distributed.
Mentions: Our stage 1 tests found the BMI allele score was associated with 21 outcomes, using an unadjusted P < 0.05 threshold (Table 3). Of these, 14 outcomes were from the 160 outcomes we randomly included in our dataset (test of proportions P = 0.030), compared to 8 expected by chance alone (160 × 0.05, making the conservative assumption that all outcomes are uncorrelated). Hence we would expect 6 of the 14 identified outcomes to be true associations (false discovery rate of 0.571). We found stronger associations than would be expected by chance, illustrated by the QQ plot in Fig. 1, suggesting that BMI affects many outcomes. After Bonferroni correction only HDL at age 9 was found with a P value below P < 0.05 whereas using the permutation P values we found 8 associations with P <0.05. In comparison, we found 57 stage 1 associations with P < 0.05 using observational BMI at age 8. Of these, 48 were from the 160 randomly included in our dataset (test of proportions P = <0.001), compared to 8 expected by chance alone. The instrumental variable effect estimates (stage 2 results) are given in Table 4 and Figs 2 and 3 (and observational estimates are also provided for comparison).

Bottom Line: We found 21 of the 172 outcomes were associated with the allele score at an unadjusted p < 0.05 threshold, and use Bonferroni corrections, permutation testing and estimates of the false discovery rate to consider the strength of results given the number of tests performed.The most strongly associated outcomes included leptin, lipid profile, and blood pressure.We also found novel evidence of effects of BMI on a global self-worth score.

View Article: PubMed Central - PubMed

Affiliation: MRC Integrative Epidemiology Unit (IEU) at the University of Bristol, University of Bristol, Bristol.

ABSTRACT
Observational cohort studies can provide rich datasets with a diverse range of phenotypic variables. However, hypothesis-driven epidemiological analyses by definition only test particular hypotheses chosen by researchers. Furthermore, observational analyses may not provide robust evidence of causality, as they are susceptible to confounding, reverse causation and measurement error. Using body mass index (BMI) as an exemplar, we demonstrate a novel extension to the phenome-wide association study (pheWAS) approach, using automated screening with genotypic instruments to screen for causal associations amongst any number of phenotypic outcomes. We used a sample of 8,121 children from the ALSPAC dataset, and tested the linear association of a BMI-associated allele score with 172 phenotypic outcomes (with variable sample sizes). We also performed an instrumental variable analysis to estimate the causal effect of BMI on each phenotype. We found 21 of the 172 outcomes were associated with the allele score at an unadjusted p < 0.05 threshold, and use Bonferroni corrections, permutation testing and estimates of the false discovery rate to consider the strength of results given the number of tests performed. The most strongly associated outcomes included leptin, lipid profile, and blood pressure. We also found novel evidence of effects of BMI on a global self-worth score.

No MeSH data available.