PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs.

Estimation of the skeleton of a directed acyclic graph (DAG) is of great importance for understanding the underlying DAG and causal effects can be assessed from the skeleton when the DAG is not identifiable. We propose a novel method named PenPC to estimate the skeleton of a high-dimensional DAG by a two-step approach. We first estimate the nonzero entries of a concentration matrix using penalized regression, and then fix the difference between the concentration matrix and the skeleton by evaluating a set of conditional independence hypotheses. For high-dimensional problems where the number of vertices p is in polynomial or exponential scale of sample size n, we study the asymptotic property of PenPC on two types of graphs: traditional random graphs where all the vertices have the same expected number of neighbors, and scale-free graphs where a few vertices may have a large number of neighbors. As illustrated by extensive simulations and applications on gene expression data of cancer patients, PenPC has higher sensitivity and specificity than the state-of-the-art method, the PC-stable algorithm.

Do debit cards increase household spending? Evidence from a semiparametric causal analysis of a survey

© Institute of Mathematical Statistics, 2014.Motivated by recent findings in the field of consumer science, this paper evaluates the causal effect of debit cards on household consumption using population-based data from the Italy Survey on Household Income and Wealth (SHIW). Within the Rubin Causal Model, we focus on the estimand of population average treatment effect for the treated (PATT). We consider three existing estimators, based on regression, mixed matching and regression, propensity score weighting, and propose a new doubly-robust estimator. Semiparametric specification based on power series for the potential outcomes and the propensity score is adopted. Cross-validation is used to select the order of the power series. We conduct a simulation study to compare the performance of the estimators. The key assumptions, overlap and unconfoundedness, are systematically assessed and validated in the application. Our empirical results suggest statistically significant positive effects of debit cards on the monthly household spending in Italy.

Functional Evaluation of Causal Mutations Identified in Human Genetic Studies

Human genetics has been experiencing a wave of genetic discoveries thanks to the development of several technologies, such as genome-wide association studies (GWAS), whole-exome sequencing, and whole genome sequencing. Despite the massive genetic discoveries of new variants associated with human diseases, several key challenges emerge following the genetic discovery. GWAS is known to be good at identifying the locus associated with the patient phenotype. However, the actually causal variants responsible for the phenotype are often elusive. Another challenge in human genetics is that even the causal mutations are already known, the underlying biological effect might remain largely ambiguous. Functional evaluation plays a key role to solve these key challenges in human genetics both to identify causal variants responsible for the phenotype, and to further develop the biological insights from the disease-causing mutations.

We adopted various methods to characterize the effects of variants identified in human genetic studies, including patient genetic and phenotypic data, RNA chemistry, molecular biology, virology, and multi-electrode array and primary neuronal culture systems. Chapter 1 is a broader introduction for the motivation and challenges for functional evaluation in human genetic studies, and the background of several genetics discoveries, such as hepatitis C treatment response, in which we performed functional characterization.

Chapter 2 focuses on the characterization of causal variants following the GWAS study for hepatitis C treatment response. We characterized a non-coding SNP (rs4803217) of IL28B (IFNL3) in high linkage disequilibrium (LD) with the discovery SNP identified in the GWAS. In this chapter, we used inter-disciplinary approaches to characterize rs4803217 on RNA structure, disease association, and protein translation.

Chapter 3 describes another avenue of functional characterization following GWAS focusing on the novel transcripts and proteins identified near the IL28B (IFNL3) locus. It has been recently speculated that this novel protein, which was named IFNL4, may affect the HCV treatment response and clearance. In this chapter, we used molecular biology, virology, and patient genetic and phenotypic data to further characterize and understand the biology of IFNL4. The efforts in chapter 2 and 3 provided new insights to the candidate causal variant(s) responsible for the GWAS for HCV treatment response, however, more evidence is still required to make claims for the exact causal roles of these variants for the GWAS association.

Chapter 4 aims to characterize a mutation already known to cause a disease (seizure) in a mouse model. We demonstrate the potential use of multi-electrode array (MEA) system for the functional characterization and drug testing on mutations found in neurological diseases, such as seizure. Functional characterization in neurological diseases is relatively challenging and available systematic tools are relatively limited. This chapter shows an exploratory research and example to establish a system for the broader use for functional characterization and translational opportunities for mutations found in neurological diseases.

Overall, this dissertation spans a range of challenges of functional evaluations in human genetics. It is expected that the functional characterization to understand human mutations will become more central in human genetics, because there are still many biological questions remaining to be answered after the explosion of human genetic discoveries. The recent advance in several technologies, including genome editing and pluripotent stem cells, is also expected to make new tools available for functional studies in human diseases.