Risk Prediction Modeling on Family-Based Sequencing Data Using a Random Field Method

Bayesian linear mixed model with multiple random effects for family-based genetic studies

Motivation: Family-based study design is one of the popular designs used in genetic research, and the whole-genome sequencing data obtained from family-based studies offer many unique features for risk prediction studies. They can not only provide a more comprehensive view of many complex diseases, but also utilize information in the design to further improve the prediction accuracy. While promising, existing analytical methods often ignore the information embedded in the study design and overlook the predictive effects of rare variants, leading to a prediction model with sub-optimal performance. Results: We proposed a Bayesian linear mixed model for the prediction analysis of sequencing data obtained from family-based studies. Our method can not only capture predictive effects from both common and rare variants, but also easily accommodate various disease model assumptions. It uses information embedded in the study design to form surrogates, where the predictive effects from unmeasured/unknown genetic and environmental risk factors can be modelled. Through extensive simulation studies and the analysis of sequencing data obtained from the Michigan State University Twin Registry study, we have demonstrated that the proposed method outperforms commonly adopted techniques. Availability: R package is available at https://github.com/yhai943/FBLMM.

[Family history of affective disorders, the gender factor and clinical characteristics of depression]

OBJECTIVE

Analysis of clinical features of development and course of depression in patients with FH of mood disorders taking into account sex differences.

MATERIAL AND METHODS

This multicenter cross-sectional study included patients over 18 years of age with depressive episode/recurrent depressive disorder. Clinical characteristics of depression, presence of comorbid mental illness and family history (FH) information were obtained in a structured clinical interview.

RESULTS

One hundred and seventy-one patients (mean age (M (SD)) 40.87 (15.86) y.o.), including 64.5% of women, were enrolled in the study. FH was revealed in 30.2% of patients. The proportion of FH did not differ in men and women (p=0.375). Generalized anxiety disorder (GAD) was more frequent in FH positive patients (p=0.016). Logistic regression also revealed that FH is a risk factor for concomitant GAD (p=0.019, OR=2.4). The GLM demonstrated a significant joint effect of FH and sex on the maximum duration of a depressive episode (p=0.044), as well on the number of suicide attempts (p=0.055) and the number of depressive episodes as a trend (p=0.072).

CONCLUSION

We have demonstrated the specific interaction of FH of mood disorders with sex on clinical course of depression. Thus, the manifestation of a genetic influence on the clinical phenotype of depression can be significantly moderated by sex.

Exploring gene-gene interaction in family-based data with an unsupervised machine learning method: EPISFA

Gene-gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were often developed for case-control designs with less considerations for their applications in families. Family-based studies are robust against bias led from population stratification in genetic studies and helpful in understanding G × G. We proposed a new algorithm epistasis sparse factor analysis (EPISFA) and epistasis sparse factor analysis for linkage disequilibrium (EPISFA-LD) based on unsupervised machine learning to screen G × G. Extensive simulations were performed to compare EPISFA/EPISFA-LD with a classical family-based algorithm FAM-MDR (family-based multifactor dimensionality reduction). The results showed that EPISFA/EPISFA-LD is a tool of both high power and computational efficiency that could be applied in family designs and is applicable within high-dimensionality datasets. Finally, we applied EPISFA/EPISFA-LD to a real dataset drawn from the Fangshan/family-based Ischemic Stroke Study in China. Five pairs of G × G were discovered by EPISFA/EPISFA-LD, including three pairs verified by other algorithms (FAM-MDR and logistic), and an additional two pairs uniquely identified by EPISFA/EPISFA-LD only. The results from EPISFA might offer new insights for understanding the genetic etiology of complex diseases. EPISFA/EPISFA-LD was implemented in R. All relevant source code as well as simulated data could be freely downloaded from https://github.com/doublexism/episfa.

Weighted Transmission Disequilibrium Test for Family Trio Association Design

BACKGROUND

Family-based design is one of the most popular designs in genetic studies. Transmission disequilibrium test (TDT) for family trio design is optimal only under the additive trait model and may lose power under the other trait models. The TDT-type tests are powerful only when the underlying trait model is correctly specified. Usually, the true trait model is unknown, and the selection of the TDT-type test is problematic. Several methods, which are robust against the mis-specification of the trait model, have been proposed. In this paper, we propose a new efficiency robust procedure for family trio design, namely, the weighted TDT (WTDT) test.

METHODS

We combine information of the largest two TDT-type tests by using weights related to the three TDT-type tests and take the weighted sum as the test statistic.

RESULTS

Simulation results demonstrate that WTDT has power close to, but much more robust than, the optimal TDT-type test based on a single trait model. WTDT also outperforms other efficiency robust methods in terms of power. Applications to real and simulated data from Genetic Analysis Workshop (GAW15) illustrate the practical application of the WTDT method.

CONCLUSION

WTDT is not only efficiency robust to model mis-specifications but also efficiency robust against mis-specifications of risk allele.

An ensemble-based likelihood ratio approach for family-based genomic risk prediction

OBJECTIVE

As one of the most popular designs used in genetic research, family-based design has been well recognized for its advantages, such as robustness against population stratification and admixture. With vast amounts of genetic data collected from family-based studies, there is a great interest in studying the role of genetic markers from the aspect of risk prediction. This study aims to develop a new statistical approach for family-based risk prediction analysis with an improved prediction accuracy compared with existing methods based on family history.

METHODS

In this study, we propose an ensemble-based likelihood ratio (ELR) approach, Fam-ELR, for family-based genomic risk prediction. Fam-ELR incorporates a clustered receiver operating characteristic (ROC) curve method to consider correlations among family samples, and uses a computationally efficient tree-assembling procedure for variable selection and model building.

RESULTS

Through simulations, Fam-ELR shows its robustness in various underlying disease models and pedigree structures, and attains better performance than two existing family-based risk prediction methods. In a real-data application to a family-based genome-wide dataset of conduct disorder, Fam-ELR demonstrates its ability to integrate potential risk predictors and interactions into the model for improved accuracy, especially on a genome-wide level.

CONCLUSIONS

By comparing existing approaches, such as genetic risk-score approach, Fam-ELR has the capacity of incorporating genetic variants with small or moderate marginal effects and their interactions into an improved risk prediction model. Therefore, it is a robust and useful approach for high-dimensional family-based risk prediction, especially on complex disease with unknown or less known disease etiology.