Gene-environment interaction testing in family-based association studies with phenotypically ascertained samples: a causal inference approach.
ABSTRACT We propose a method for testing gene-environment (G × E) interactions on a complex trait in family-based studies in which a phenotypic ascertainment criterion has been imposed. This novel approach employs G-estimation, a semiparametric estimation technique from the causal inference literature, to avoid modeling of the association between the environmental exposure and the phenotype, to gain robustness against unmeasured confounding due to population substructure, and to acknowledge the ascertainment conditions. The proposed test allows for incomplete parental genotypes. It is compared by simulation studies to an analogous conditional likelihood-based approach and to the QBAT-I test, which also invokes the G-estimation principle but ignores ascertainment. We apply our approach to a study of chronic obstructive pulmonary disorder.
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ABSTRACT: We propose a simple and general resampling strategy to estimate variances for parameter estimators derived from nonsmooth estimating functions. This approach applies to a wide variety of semiparametric and nonparametric problems in biostatistics. It does not require solving estimating equations and is thus much faster than the existing resampling procedures. Its usefulness is illustrated with heteroscedastic quantile regression and censored data rank regression. Numerical results based on simulated and real data are provided.Biostatistics 05/2008; 9(2):355-63. · 2.43 Impact Factor
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ABSTRACT: only slightly a gene's expression or a protein's function. The gene's pathway, however, may be deci sive for a particular condition, or pharmacologic action on the same protein may produce much larger effects in controlling disease. These arguments are reasonable, as far as they go, and there are supporting examples, such as a polymorphism of modest effect in PPARG, a gene that encodes a drug target for diabetes. But the arguments hold only if common genetic variation im plicates a manageable number of genes. If effect sizes were so small as to require a large chunk of the genome to explain the genet ic component of a disorder, then no guidance would be provided: in pointing at everything, genet ics would point at nothing. To assess whether effect sizes are too small in this sense, consider two examples of complex human traits — type 2 diabetes and height. In their recent review,New England Journal of Medicine 05/2009; 360(17):1696-8. · 51.66 Impact Factor
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ABSTRACT: Missing data occur in genetic association studies for several reasons including missing family members and uncertain haplotype phase. Maximum likelihood is a commonly used approach to accommodate missing data, but it can be difficult to apply to family-based association studies, because of possible loss of robustness to confounding by population stratification. Here a novel likelihood for nuclear families is proposed, in which distinct sets of association parameters are used to model the parental genotypes and the offspring genotypes. This approach is robust to population structure when the data are complete, and has only minor loss of robustness when there are missing data. It also allows a novel conditioning step that gives valid analysis for multiple offspring in the presence of linkage. Unrelated subjects are included by regarding them as the children of two missing parents. Simulations and theory indicate similar operating characteristics to TRANSMIT, but with no bias with missing data in the presence of linkage. In comparison with FBAT and PCPH, the proposed model is slightly less robust to population structure but has greater power to detect strong effects. In comparison to APL and MITDT, the model is more robust to stratification and can accommodate sibships of any size. The methods are implemented for binary and continuous traits in software, UNPHASED, available from the author.Human Heredity 02/2008; 66(2):87-98. · 1.57 Impact Factor