@article {1171, title = {Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables.}, journal = {Stat Med}, volume = {29}, year = {2010}, month = {2010 May 30}, pages = {1298-311}, abstract = {

Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.

}, keywords = {Bayes Theorem, Biostatistics, C-Reactive Protein, Fibrinogen, Genetic Markers, Humans, Meta-Analysis as Topic, Models, Statistical, Phenotype, Polymorphism, Single Nucleotide}, issn = {1097-0258}, doi = {10.1002/sim.3843}, author = {Burgess, Stephen and Thompson, Simon G and Burgess, S and Thompson, S G and Andrews, G and Samani, N J and Hall, A and Whincup, P and Morris, R and Lawlor, D A and Davey Smith, G and Timpson, N and Ebrahim, S and Ben-Shlomo, Y and Davey Smith, G and Timpson, N and Brown, M and Ricketts, S and Sandhu, M and Reiner, A and Psaty, B and Lange, L and Cushman, M and Hung, J and Thompson, P and Beilby, J and Warrington, N and Palmer, L J and Nordestgaard, B G and Tybjaerg-Hansen, A and Zacho, J and Wu, C and Lowe, G and Tzoulaki, I and Kumari, M and Sandhu, M and Yamamoto, J F and Chiodini, B and Franzosi, M and Hankey, G J and Jamrozik, K and Palmer, L and Rimm, E and Pai, J and Psaty, B and Heckbert, S and Bis, J and Anand, S and Engert, J and Collins, R and Clarke, R and Melander, O and Berglund, G and Ladenvall, P and Johansson, L and Jansson, J-H and Hallmans, G and Hingorani, A and Humphries, S and Rimm, E and Manson, J and Pai, J and Watkins, H and Clarke, R and Hopewell, J and Saleheen, D and Frossard, R and Danesh, J and Sattar, N and Robertson, M and Shepherd, J and Schaefer, E and Hofman, A and Witteman, J C M and Kardys, I and Ben-Shlomo, Y and Davey Smith, G and Timpson, N and de Faire, U and Bennet, A and Sattar, N and Ford, I and Packard, C and Kumari, M and Manson, J and Lawlor, Debbie A and Davey Smith, George and Anand, S and Collins, R and Casas, J P and Danesh, J and Davey Smith, G and Franzosi, M and Hingorani, A and Lawlor, D A and Manson, J and Nordestgaard, B G and Samani, N J and Sandhu, M and Smeeth, L and Wensley, F and Anand, S and Bowden, J and Burgess, S and Casas, J P and Di Angelantonio, E and Engert, J and Gao, P and Shah, T and Smeeth, L and Thompson, S G and Verzilli, C and Walker, M and Whittaker, J and Hingorani, A and Danesh, J} } @article {6218, title = {Strategy to control type I error increases power to identify genetic variation using the full biological trajectory.}, journal = {Genet Epidemiol}, volume = {37}, year = {2013}, month = {2013 Jul}, pages = {419-30}, abstract = {

Genome-wide association studies have been successful in identifying loci that underlie continuous traits measured at a single time point. To additionally consider continuous traits longitudinally, it is desirable to look at SNP effects at baseline and over time using linear-mixed effects models. Estimation and interpretation of two coefficients in the same model raises concern regarding the optimal control of type I error. To investigate this issue, we calculate type I error and power under an alternative for joint tests, including the two degree of freedom likelihood ratio test, and compare this to single degree of freedom tests for each effect separately at varying alpha levels. We show which joint tests are the optimal way to control the type I error and also illustrate that information can be gained by joint testing in situations where either or both SNP effects are underpowered. We also show that closed form power calculations can approximate simulated power for the case of balanced data, provide reasonable approximations for imbalanced data, but overestimate power for complicated residual error structures. We conclude that a two degree of freedom test is an attractive strategy in a hypothesis-free genome-wide setting and recommend its use for genome-wide studies employing linear-mixed effects models.

}, keywords = {Cohort Studies, Computer Simulation, Genetic Variation, Genome-Wide Association Study, Humans, Linear Models, Models, Genetic, Polymorphism, Single Nucleotide}, issn = {1098-2272}, doi = {10.1002/gepi.21733}, author = {Benke, K S and Wu, Y and Fallin, D M and Maher, B and Palmer, L J} } @article {6851, title = {Association of 25-Hydroxyvitamin D status and genetic variation in the vitamin D metabolic pathway with FEV1 in the Framingham Heart Study.}, journal = {Respir Res}, volume = {16}, year = {2015}, month = {2015}, pages = {81}, abstract = {

BACKGROUND: Vitamin D is associated with lung function in cross-sectional studies, and vitamin D inadequacy is hypothesized to play a role in the pathogenesis of chronic obstructive pulmonary disease. Further data are needed to clarify the relation between vitamin D status, genetic variation in vitamin D metabolic genes, and cross-sectional and longitudinal changes in lung function in healthy adults.

METHODS: We estimated the association between serum 25-hydroxyvitamin D [25(OH)D] and cross-sectional forced expiratory volume in the first second (FEV1) in Framingham Heart Study (FHS) Offspring and Third Generation participants and the association between serum 25(OH)D and longitudinal change in FEV1 in Third Generation participants using linear mixed-effects models. Using a gene-based approach, we investigated the association between 241 SNPs in 6 select vitamin D metabolic genes in relation to longitudinal change in FEV1 in Offspring participants and pursued replication of these findings in a meta-analyzed set of 4 independent cohorts.

RESULTS: We found a positive cross-sectional association between 25(OH)D and FEV1 in FHS Offspring and Third Generation participants (P=0.004). There was little or no association between 25(OH)D and longitudinal change in FEV1 in Third Generation participants (P=0.97). In Offspring participants, the CYP2R1 gene, hypothesized to influence usual serum 25(OH)D status, was associated with longitudinal change in FEV1 (gene-based P<0.05). The most significantly associated SNP from CYP2R1 had a consistent direction of association with FEV1 in the meta-analyzed set of replication cohorts, but the association did not reach statistical significance thresholds (P=0.09).

CONCLUSIONS: Serum 25(OH)D status was associated with cross-sectional FEV1, but not longitudinal change in FEV1. The inconsistent associations may be driven by differences in the groups studied. CYP2R1 demonstrated a gene-based association with longitudinal change in FEV1 and is a promising candidate gene for further studies.

}, keywords = {Adult, Aged, Cohort Studies, Cross-Sectional Studies, DNA-Binding Proteins, Female, Genetic Variation, Humans, Longitudinal Studies, Male, Massachusetts, Metabolic Networks and Pathways, Middle Aged, Nuclear Proteins, Polymorphism, Single Nucleotide, Vitamin D}, issn = {1465-993X}, doi = {10.1186/s12931-015-0238-y}, author = {Hansen, J G and Gao, W and Dupuis, J and O{\textquoteright}Connor, G T and Tang, W and Kowgier, M and Sood, A and Gharib, S A and Palmer, L J and Fornage, M and Heckbert, S R and Psaty, B M and Booth, S L and Cassano, Patricia A} }