In longitudinal genetic studies, investigators collect repeated measurements on a trait that changes with time along with genetic markers. For family-based longitudinal studies, since repeated measurements are nested within subjects and subjects are nested within families, both the subject level and the measurement level correlations must be taken into account in the statistical analysis to achieve more accurate estimation. In such studies, the primary interests include testing for a quantitative trait locus effect, and estimating the age-specific quantitative trait locus effect and residual polygenic heritability function. We propose flexible semiparametric models and their statistical estimation and hypothesis testing procedures for longitudinal genetic data. We employ penalized splines to estimate non-parametric functions in the model. We find that misspecifying the baseline function or the genetic effect function in a parametric analysis may lead to a substantially inflated or highly conservative type I error rate on testing and large mean-squared error on estimation. We apply the proposed approaches to examine age-specific effects of genetic variants reported in a recent genomewide association study of blood pressure collected in the Framingham Heart Study.
Merging multiple datasets collected from studies with identical or similar scientific objectives is often undertaken in practice to increase statistical power. This article concerns the development of an effective statistical method that enables to merge multiple longitudinal datasets subject to various heterogeneous characteristics, such as different follow-up schedules and study-specific missing covariates (e.g., covariates observed in some studies but missing in other studies). The presence of study-specific missing covariates presents great statistical methodology challenge in data merging and analysis. We propose a joint estimating function approach to addressing this challenge, in which a novel nonparametric estimating function constructed via splines-based sieve approximation is utilized to bridge estimating equations from studies with missing covariates to those with fully observed covariates. Under mild regularity conditions, we show that the proposed estimator is consistent and asymptotically normal. We evaluate finite-sample performances of the proposed method through simulation studies. In comparison to the conventional multiple imputation approach, our method exhibits smaller estimation bias. We provide an illustrative data analysis using longitudinal cohorts collected in Mexico City to assess the effect of lead exposures on children's somatic growth.
more » « less- NSF-PAR ID:
- 10485055
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Biometrics
- Volume:
- 71
- Issue:
- 4
- ISSN:
- 0006-341X
- Format(s):
- Medium: X Size: p. 929-940
- Size(s):
- ["p. 929-940"]
- Sponsoring Org:
- National Science Foundation
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Results Here, we propose a gene-level meta-analysis framework that overcomes these limitations and identifies a gene signature that is reliable and reproducible across multiple independent studies of a given disease. The approach provides a comprehensive global signature that can be used to understand the underlying biological phenomena, and a smaller test signature that can be used to classify future samples of a given disease. We demonstrate the utility of the framework by constructing disease signatures for influenza and Alzheimer’s disease using nine datasets including 1108 individuals. These signatures are then validated on 12 independent datasets including 912 individuals. The results indicate that the proposed approach performs better than the majority of the existing meta-analysis approaches in terms of both sensitivity as well as specificity. The proposed signatures could be further used in diagnosis, prognosis and identification of therapeutic targets.
Supplementary information Supplementary data are available at Bioinformatics online.
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