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The use of imputed sibling genotypes in sibship-based association analysis: on modeling alternatives, power and model misspecification.
Behav Genet. 2013 May; 43(3):254-66.BG

Abstract

When phenotypic, but no genotypic data are available for relatives of participants in genetic association studies, previous research has shown that family-based imputed genotypes can boost the statistical power when included in such studies. Here, using simulations, we compared the performance of two statistical approaches suitable to model imputed genotype data: the mixture approach, which involves the full distribution of the imputed genotypes and the dosage approach, where the mean of the conditional distribution features as the imputed genotype. Simulations were run by varying sibship size, size of the phenotypic correlations among siblings, imputation accuracy and minor allele frequency of the causal SNP. Furthermore, as imputing sibling data and extending the model to include sibships of size two or greater requires modeling the familial covariance matrix, we inquired whether model misspecification affects power. Finally, the results obtained via simulations were empirically verified in two datasets with continuous phenotype data (height) and with a dichotomous phenotype (smoking initiation). Across the settings considered, the mixture and the dosage approach are equally powerful and both produce unbiased parameter estimates. In addition, the likelihood-ratio test in the linear mixed model appears to be robust to the considered misspecification in the background covariance structure, given low to moderate phenotypic correlations among siblings. Empirical results show that the inclusion in association analysis of imputed sibling genotypes does not always result in larger test statistic. The actual test statistic may drop in value due to small effect sizes. That is, if the power benefit is small, that the change in distribution of the test statistic under the alternative is relatively small, the probability is greater of obtaining a smaller test statistic. As the genetic effects are typically hypothesized to be small, in practice, the decision on whether family-based imputation could be used as a means to increase power should be informed by prior power calculations and by the consideration of the background correlation.

Authors+Show Affiliations

Department of Biological Psychology, VU University Amsterdam, Van der Boechorststraat 1, Amsterdam, The Netherlands. c.c.minica@vu.nlNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23519635

Citation

Minică, Camelia C., et al. "The Use of Imputed Sibling Genotypes in Sibship-based Association Analysis: On Modeling Alternatives, Power and Model Misspecification." Behavior Genetics, vol. 43, no. 3, 2013, pp. 254-66.
Minică CC, Dolan CV, Hottenga JJ, et al. The use of imputed sibling genotypes in sibship-based association analysis: on modeling alternatives, power and model misspecification. Behav Genet. 2013;43(3):254-66.
Minică, C. C., Dolan, C. V., Hottenga, J. J., Willemsen, G., Vink, J. M., & Boomsma, D. I. (2013). The use of imputed sibling genotypes in sibship-based association analysis: on modeling alternatives, power and model misspecification. Behavior Genetics, 43(3), 254-66. https://doi.org/10.1007/s10519-013-9590-1
Minică CC, et al. The Use of Imputed Sibling Genotypes in Sibship-based Association Analysis: On Modeling Alternatives, Power and Model Misspecification. Behav Genet. 2013;43(3):254-66. PubMed PMID: 23519635.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - The use of imputed sibling genotypes in sibship-based association analysis: on modeling alternatives, power and model misspecification. AU - Minică,Camelia C, AU - Dolan,Conor V, AU - Hottenga,Jouke-Jan, AU - Willemsen,Gonneke, AU - Vink,Jacqueline M, AU - Boomsma,Dorret I, Y1 - 2013/03/22/ PY - 2012/09/12/received PY - 2013/02/18/accepted PY - 2013/3/23/entrez PY - 2013/3/23/pubmed PY - 2013/11/20/medline SP - 254 EP - 66 JF - Behavior genetics JO - Behav. Genet. VL - 43 IS - 3 N2 - When phenotypic, but no genotypic data are available for relatives of participants in genetic association studies, previous research has shown that family-based imputed genotypes can boost the statistical power when included in such studies. Here, using simulations, we compared the performance of two statistical approaches suitable to model imputed genotype data: the mixture approach, which involves the full distribution of the imputed genotypes and the dosage approach, where the mean of the conditional distribution features as the imputed genotype. Simulations were run by varying sibship size, size of the phenotypic correlations among siblings, imputation accuracy and minor allele frequency of the causal SNP. Furthermore, as imputing sibling data and extending the model to include sibships of size two or greater requires modeling the familial covariance matrix, we inquired whether model misspecification affects power. Finally, the results obtained via simulations were empirically verified in two datasets with continuous phenotype data (height) and with a dichotomous phenotype (smoking initiation). Across the settings considered, the mixture and the dosage approach are equally powerful and both produce unbiased parameter estimates. In addition, the likelihood-ratio test in the linear mixed model appears to be robust to the considered misspecification in the background covariance structure, given low to moderate phenotypic correlations among siblings. Empirical results show that the inclusion in association analysis of imputed sibling genotypes does not always result in larger test statistic. The actual test statistic may drop in value due to small effect sizes. That is, if the power benefit is small, that the change in distribution of the test statistic under the alternative is relatively small, the probability is greater of obtaining a smaller test statistic. As the genetic effects are typically hypothesized to be small, in practice, the decision on whether family-based imputation could be used as a means to increase power should be informed by prior power calculations and by the consideration of the background correlation. SN - 1573-3297 UR - https://www.unboundmedicine.com/medline/citation/23519635/The_use_of_imputed_sibling_genotypes_in_sibship_based_association_analysis:_on_modeling_alternatives_power_and_model_misspecification_ L2 - https://doi.org/10.1007/s10519-013-9590-1 DB - PRIME DP - Unbound Medicine ER -