@article{luo_song_styner_gilmore_zhu_2018, title={FSEM: Functional Structural Equation Models for Twin Functional Data}, volume={114}, ISSN={0162-1459 1537-274X}, url={http://dx.doi.org/10.1080/01621459.2017.1407773}, DOI={10.1080/01621459.2017.1407773}, abstractNote={ABSTRACT The aim of this article is to develop a novel class of functional structural equation models (FSEMs) for dissecting functional genetic and environmental effects on twin functional data, while characterizing the varying association between functional data and covariates of interest. We propose a three-stage estimation procedure to estimate varying coefficient functions for various covariates (e.g., gender) as well as three covariance operators for the genetic and environmental effects. We develop an inference procedure based on weighted likelihood ratio statistics to test the genetic/environmental effect at either a fixed location or a compact region. We also systematically carry out the theoretical analysis of the estimated varying functions, the weighted likelihood ratio statistics, and the estimated covariance operators. We conduct extensive Monte Carlo simulations to examine the finite-sample performance of the estimation and inference procedures. We apply the proposed FSEM to quantify the degree of genetic and environmental effects on twin white matter tracts obtained from the UNC early brain development study. Supplementary materials for this article are available online.}, number={525}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Luo, S. and Song, R. and Styner, M. and Gilmore, J. H. and Zhu, H.}, year={2018}, month={Jul}, pages={344–357} }
 @article{kaddurah-daouk_yuan_boyle_matson_wang_zeng_zhu_dougherty_yao_chen_et al._2012, title={Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile}, volume={2}, journal={Scientific Reports}, author={Kaddurah-Daouk, R. and Yuan, P. X. and Boyle, S. H. and Matson, W. and Wang, Z. and Zeng, Z. B. and Zhu, H. J. and Dougherty, G. G. and Yao, J. K. and Chen, G. and et al.}, year={2012} }
 @article{kaddurah-daouk_mcevoy_baillie_zhu_yao_nimgaonkar_buckley_keshavan_georgiades_nasrallah_2012, title={Impaired plasmalogens in patients with schizophrenia}, volume={198}, number={3}, journal={Psychiatry Research}, author={Kaddurah-Daouk, R. and McEvoy, J. and Baillie, R. and Zhu, H. J. and Yao, J. K. and Nimgaonkar, V. L. and Buckley, P. F. and Keshavan, M. S. and Georgiades, A. and Nasrallah, H. A.}, year={2012}, pages={347–352} }
 @article{abo_hebbring_ji_zhu_zeng_batzler_jenkins_biernacka_snyder_drews_et al._2012, title={Merging pharmacometabolomics with pharmacogenomics using ‘1000 Genomes’ single-nucleotide polymorphism imputation}, volume={22}, ISSN={1744-6872}, url={http://dx.doi.org/10.1097/FPC.0b013e32835001c9}, DOI={10.1097/fpc.0b013e32835001c9}, abstractNote={Objective We set out to test the hypothesis that pharmacometabolomic data could be efficiently merged with pharmacogenomic data by single-nucleotide polymorphism (SNP) imputation of metabolomic-derived pathway data on a ‘scaffolding’ of genome-wide association (GWAS) SNP data to broaden and accelerate ‘pharmacometabolomics-informed pharmacogenomic’ studies by eliminating the need for initial genotyping and by making broader SNP association testing possible. Methods We previously genotyped 131 tag SNPs for six genes encoding enzymes in the glycine synthesis and degradation pathway using DNA from 529 depressed patients treated with citalopram/escitalopram to pursue a glycine metabolomics ‘signal’ associated with selective serotonine reuptake inhibitor response. We identified a significant SNP in the glycine dehydrogenase gene. Subsequently, GWAS SNP data were generated for the same patients. In this study, we compared SNP imputation within 200 kb of these same six genes with the results of the previous tag SNP strategy as a rapid strategy for merging pharmacometabolomic and pharmacogenomic data. Results Imputed genotype data provided greater coverage and higher resolution than did tag SNP genotyping, with a higher average genotype concordance between genotyped and imputed SNP data for ‘1000 Genomes’ (96.4%) than HapMap 2 (93.2%) imputation. Many low P-value SNPs with novel locations within genes were observed for imputed compared with tag SNPs, thus altering the focus for subsequent functional genomic studies. Conclusion These results indicate that the use of GWAS data to impute SNPs for genes in pathways identified by other ‘omics’ approaches makes it possible to rapidly and cost efficiently identify SNP markers to ‘broaden’ and accelerate pharmacogenomic studies.}, number={4}, journal={Pharmacogenetics and Genomics}, publisher={Ovid Technologies (Wolters Kluwer Health)}, author={Abo, Ryan and Hebbring, Scott and Ji, Yuan and Zhu, Hongjie and Zeng, Zhao-Bang and Batzler, Anthony and Jenkins, Gregory D. and Biernacka, Joanna and Snyder, Karen and Drews, Maureen and et al.}, year={2012}, month={Apr}, pages={247–253} }
 @article{zhu_li_2011, title={Biological pathway selection through nonlinear dimension reduction}, volume={12}, ISSN={["1468-4357"]}, DOI={10.1093/biostatistics/kxq081}, abstractNote={In the analysis of high-throughput biological data, it is often believed that the biological units such as genes behave interactively by groups, that is, pathways in our context. It is conceivable that utilization of priorly available pathway knowledge would greatly facilitate both interpretation and estimation in statistical analysis of such high-dimensional biological data. In this article, we propose a 2-step procedure for the purpose of identifying pathways that are related to and influence the clinical phenotype. In the first step, a nonlinear dimension reduction method is proposed, which permits flexible within-pathway gene interactions as well as nonlinear pathway effects on the response. In the second step, a regularized model-based pathway ranking and selection procedure is developed that is built upon the summary features extracted from the first step. Simulations suggest that the new method performs favorably compared to the existing solutions. An analysis of a glioblastoma microarray data finds 4 pathways that have evidence of support from the biological literature.}, number={3}, journal={BIOSTATISTICS}, author={Zhu, Hongjie and Li, Lexin}, year={2011}, month={Jul}, pages={429–444} }
 @article{kaddurah-daouk_baillie_zhu_zeng_wiest_nguyen_wojnoonski_watkins_trupp_krauss_2011, title={Enteric Microbiome Metabolites Correlate with Response to Simvastatin Treatment}, volume={6}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0025482}, abstractNote={Although statins are widely prescribed medications, there remains considerable variability in therapeutic response. Genetics can explain only part of this variability. Metabolomics is a global biochemical approach that provides powerful tools for mapping pathways implicated in disease and in response to treatment. Metabolomics captures net interactions between genome, microbiome and the environment. In this study, we used a targeted GC-MS metabolomics platform to measure a panel of metabolites within cholesterol synthesis, dietary sterol absorption, and bile acid formation to determine metabolite signatures that may predict variation in statin LDL-C lowering efficacy. Measurements were performed in two subsets of the total study population in the Cholesterol and Pharmacogenetics (CAP) study: Full Range of Response (FR), and Good and Poor Responders (GPR) were 100 individuals randomly selected from across the entire range of LDL-C responses in CAP. GPR were 48 individuals, 24 each from the top and bottom 10% of the LDL-C response distribution matched for body mass index, race, and gender. We identified three secondary, bacterial-derived bile acids that contribute to predicting the magnitude of statin-induced LDL-C lowering in good responders. Bile acids and statins share transporters in the liver and intestine; we observed that increased plasma concentration of simvastatin positively correlates with higher levels of several secondary bile acids. Genetic analysis of these subjects identified associations between levels of seven bile acids and a single nucleotide polymorphism (SNP), rs4149056, in the gene encoding the organic anion transporter SLCO1B1. These findings, along with recently published results that the gut microbiome plays an important role in cardiovascular disease, indicate that interactions between genome, gut microbiome and environmental influences should be considered in the study and management of cardiovascular disease. Metabolic profiles could provide valuable information about treatment outcomes and could contribute to a more personalized approach to therapy.}, number={10}, journal={PLOS ONE}, author={Kaddurah-Daouk, Rima and Baillie, Rebecca A. and Zhu, Hongjie and Zeng, Zhao-Bang and Wiest, Michelle M. and Nguyen, Uyen Thao and Wojnoonski, Katie and Watkins, Steven M. and Trupp, Miles and Krauss, Ronald M.}, year={2011}, month={Oct} }