@article {1335,
	title = {The association of genetic variants in interleukin-1 genes with cognition: findings from the cardiovascular health study.},
	journal = {Exp Gerontol},
	volume = {46},
	year = {2011},
	month = {2011 Dec},
	pages = {1010-9},
	abstract = {<p>The inflammatory cytokine interleukin-1 (IL1) potentially plays a role in cognitive deterioration through pathology due to a dementing disorder or due to an aging process. Study of genetic variants in the IL1 genes has been mostly limited to diseases such as Alzheimer{\textquoteright}s, however, there may be benefit to studying a continuous measure of cognition. Using data from the Cardiovascular Health Study, we evaluate genetic variation in the genes encoding inflammatory agonists IL1A and IL1B, and the antagonist IL1RN, with repeated measures of global cognition (3MS) and processing speed (DSST), using mixed effects models. We found statistically significant minor allele SNP associations with baseline performance on the 3MS in the IL1RN gene for Caucasians (rs17042917: beta=0.47, 95\%CI=0.09, 0.85, p=0.016; rs4251961: beta=-0.36, 95\%CI=-0.13,-0.60, p=0.0027; rs931471: beta=0.39, 95\%CI=0.13, 0.65, p=0.0032), and the IL1B gene for African Americans (rs1143627: beta=1.6, 95\%CI=0.48, 2.8; p=0.006 and rs1143634: beta=2.09, 95\%CI=0.39, 3.8; p=0.016). Associations appear to be weaker in a subgroup with higher education level. Upon removing those diagnosed with dementia, effect sizes and statistical significance attenuated. These results provide supporting evidence that genetic variants in IL1 genes may be involved in inflammatory-related lowered cognition, that higher education may modify genetic predisposition, and that these associations may be driven by a dementia process.</p>},
	keywords = {African Americans, Aged, Aged, 80 and over, Cognition, Cognition Disorders, Dementia, Educational Status, European Continental Ancestry Group, Female, Genetic Predisposition to Disease, Genetic Variation, Genotype, Humans, Interleukin 1 Receptor Antagonist Protein, Interleukin-1alpha, Interleukin-1beta, Linkage Disequilibrium, Longitudinal Studies, Male, Polymorphism, Single Nucleotide, Prospective Studies, Risk Factors, United States},
	issn = {1873-6815},
	doi = {10.1016/j.exger.2011.09.005},
	author = {Benke, K S and Carlson, M C and Doan, B Q and Walston, J D and Xue, Q L and Reiner, A P and Fried, L P and Arking, D E and Chakravarti, A and Fallin, M D}
}
@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 = {<p>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.</p>},
	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}
}
