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On the Adjustment for Covariates in Genetic Association Analysis: A Novel, Simple Principle to Infer Direct Causal Effects

(2009) GENETIC EPIDEMIOLOGY. 33(5). p.394-405
Author
Organization
Abstract
In genetic association studies, different complex phenotypes are often associated with the same marker. Such associations can be indicative of pleiotropy (i.e. common genetic causes), of indirect genetic effects via one of these phenotypes, or can be solely attributable to non-genetic/environmental links between the traits. To identify the phenotypes with the inducing genetic association, statistical methodology is needed that is able to distinguish between the different causes of the genetic associations. Here, we propose a simple, general adjustment principle that can be incorporated into many standard genetic association tests which are then able to infer whether an SNP has a direct biological influence on a given trait other than through the SNP's influence on another correlated phenotype. Using simulation studies, we show that, in the presence of a non-marker related link between phenotypes, standard association tests without the proposed adjustment can be biased. In contrast to that, the proposed methodology remains unbiased. Its achieved power levels are identical to those of standard adjustment methods, making the adjustment principle universally applicable in genetic association studies. The principle is illustrated by an application to three genome-wide association analyses.
Keywords
POPULATION-BASED COHORT, FAMILY-BASED ASSOCIATION, LUNG-CANCER, SUSCEPTIBILITY LOCUS, NICOTINE DEPENDENCE, BIRTH-WEIGHT, CHILDHOOD ASTHMA, UNIFIED APPROACH, GENOME-WIDE ASSOCIATION, OBESITY

Citation

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MLA
Vansteelandt, Stijn et al. “On the Adjustment for Covariates in Genetic Association Analysis: A Novel, Simple Principle to Infer Direct Causal Effects.” GENETIC EPIDEMIOLOGY 33.5 (2009): 394–405. Print.
APA
Vansteelandt, S., Goetgeluk, S., Lutz, S., Waldman, I., Lyon, H., Schadt, E., Weiss, S., et al. (2009). On the Adjustment for Covariates in Genetic Association Analysis: A Novel, Simple Principle to Infer Direct Causal Effects. GENETIC EPIDEMIOLOGY, 33(5), 394–405.
Chicago author-date
Vansteelandt, Stijn, Sylvie Goetgeluk, S Lutz, I Waldman, H Lyon, EE Schadt, ST Weiss, and C Lange. 2009. “On the Adjustment for Covariates in Genetic Association Analysis: A Novel, Simple Principle to Infer Direct Causal Effects.” Genetic Epidemiology 33 (5): 394–405.
Chicago author-date (all authors)
Vansteelandt, Stijn, Sylvie Goetgeluk, S Lutz, I Waldman, H Lyon, EE Schadt, ST Weiss, and C Lange. 2009. “On the Adjustment for Covariates in Genetic Association Analysis: A Novel, Simple Principle to Infer Direct Causal Effects.” Genetic Epidemiology 33 (5): 394–405.
Vancouver
1.
Vansteelandt S, Goetgeluk S, Lutz S, Waldman I, Lyon H, Schadt E, et al. On the Adjustment for Covariates in Genetic Association Analysis: A Novel, Simple Principle to Infer Direct Causal Effects. GENETIC EPIDEMIOLOGY. HOBOKEN: WILEY-LISS; 2009;33(5):394–405.
IEEE
[1]
S. Vansteelandt et al., “On the Adjustment for Covariates in Genetic Association Analysis: A Novel, Simple Principle to Infer Direct Causal Effects,” GENETIC EPIDEMIOLOGY, vol. 33, no. 5, pp. 394–405, 2009.
@article{791154,
  abstract     = {In genetic association studies, different complex phenotypes are often associated with the same marker. Such associations can be indicative of pleiotropy (i.e. common genetic causes), of indirect genetic effects via one of these phenotypes, or can be solely attributable to non-genetic/environmental links between the traits. To identify the phenotypes with the inducing genetic association, statistical methodology is needed that is able to distinguish between the different causes of the genetic associations. Here, we propose a simple, general adjustment principle that can be incorporated into many standard genetic association tests which are then able to infer whether an SNP has a direct biological influence on a given trait other than through the SNP's influence on another correlated phenotype. Using simulation studies, we show that, in the presence of a non-marker related link between phenotypes, standard association tests without the proposed adjustment can be biased. In contrast to that, the proposed methodology remains unbiased. Its achieved power levels are identical to those of standard adjustment methods, making the adjustment principle universally applicable in genetic association studies. The principle is illustrated by an application to three genome-wide association analyses.},
  author       = {Vansteelandt, Stijn and Goetgeluk, Sylvie and Lutz, S and Waldman, I and Lyon, H and Schadt, EE and Weiss, ST and Lange, C},
  issn         = {0741-0395},
  journal      = {GENETIC EPIDEMIOLOGY},
  keywords     = {POPULATION-BASED COHORT,FAMILY-BASED ASSOCIATION,LUNG-CANCER,SUSCEPTIBILITY LOCUS,NICOTINE DEPENDENCE,BIRTH-WEIGHT,CHILDHOOD ASTHMA,UNIFIED APPROACH,GENOME-WIDE ASSOCIATION,OBESITY},
  language     = {eng},
  number       = {5},
  pages        = {394--405},
  publisher    = {WILEY-LISS},
  title        = {On the Adjustment for Covariates in Genetic Association Analysis: A Novel, Simple Principle to Infer Direct Causal Effects},
  url          = {http://dx.doi.org/10.1002/gepi.20393},
  volume       = {33},
  year         = {2009},
}

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