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Objective: To design and establish a prospective biospecimen repository that integrates multi-omics assays with clinical data to study mechanisms of controlled injury and healing., Background: Elective surgery is an opportunity to understand both the systemic and focal responses accompanying controlled and well-characterized injury to the human body. The overarching goal of this ongoing project is to define stereotypical responses to surgical injury, with the translational purpose of identifying targetable pathways involved in healing and resilience, and variations indicative of aberrant peri-operative outcomes., Methods: Clinical data from the electronic medical record combined with large-scale biological data sets derived from blood, urine, fecal matter, and tissue samples are collected prospectively through the peri-operative period on patients undergoing 14 surgeries chosen to represent a range of injury locations and intensities. Specimens are subjected to genomic, transcriptomic, proteomic, and metabolomic assays to describe their genetic, metabolic, immunologic, and microbiome profiles, providing a multidimensional landscape of the human response to injury., Results: The highly multiplexed data generated includes changes in over 28,000 mRNA transcripts, 100 plasma metabolites, 200 urine metabolites, and 400 proteins over the longitudinal course of surgery and recovery. In our initial pilot dataset, we demonstrate the feasibility of collecting high quality multi-omic data at pre- and postoperative time points and are already seeing evidence of physiologic perturbation between timepoints., Conclusions: This repository allows for longitudinal, state-of-the-art geno-mic, transcriptomic, proteomic, metabolomic, immunologic, and clinical data collection and provides a rich and stable infrastructure on which to fuel further biomedical discovery., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Colorectal cancer (CRC) is a biologically heterogeneous disease. To characterize its mutational profile, we conduct targeted sequencing of 205 genes for 2,105 CRC cases with survival data. Our data shows several findings in addition to enhancing the existing knowledge of CRC. We identify PRKCI, SPZ1, MUTYH, MAP2K4, FETUB, and TGFBR2 as additional genes significantly mutated in CRC. We find that among hypermutated tumors, an increased mutation burden is associated with improved CRC-specific survival (HR = 0.42, 95% CI: 0.21-0.82). Mutations in TP53 are associated with poorer CRC-specific survival, which is most pronounced in cases carrying TP53 mutations with predicted 0% transcriptional activity (HR = 1.53, 95% CI: 1.21-1.94). Furthermore, we observe differences in mutational frequency of several genes and pathways by tumor location, stage, and sex. Overall, this large study provides deep insights into somatic mutations in CRC, and their potential relationships with survival and tumor features.

The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10 -6 , including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.

DNA methylation is an epigenetic regulator of gene transcription, which has been found to be both metastable and variable within human cohort studies. Currently, few studies have been done to identify metastable DNA methylation biomarkers associated with longitudinal lung function decline in humans. The identification of such biomarkers is important for screening vulnerable populations. We hypothesized that quantifiable blood-based DNA methylation alterations would serve as metastable biomarkers of lung function decline and aging, which may help to discover new pathways and/or mechanisms related to pulmonary pathogenesis. Using linear mixed models, we performed an Epigenome Wide Association Study (EWAS) between DNA methylation at CpG dinucleotides and longitudinal lung function (FVC, FEV 1 , FEF 25-75% ) decline and aging with initial discovery in the Normative Aging Study, and replication in the Cooperative Health Research in the Region of Augsburg cohort. We identified two metastable epigenetic loci associated with either poor lung function and aging, cg05575921 (AHRR gene), or lung function independently of aging, cg06126421 (IER3 gene). These loci may inform basic mechanisms associated with pulmonary function, pathogenesis, and aging. Human epigenomic variation, may help explain features of lung function decline and related pathophysiology not attributable to DNA sequence alone, such as accelerated pulmonary decline in smokers, former smokers, and perhaps non-smokers. Our EWAS across two cohorts, therefore, will likely have implications for the human population, not just the elderly.

DNA methylation is an important epigenetic mechanism that has been linked to complex diseases and is of great interest to researchers as a potential link between genome, environment, and disease. As the scale of DNA methylation association studies approaches that of genome-wide association studies, issues such as population stratification will need to be addressed. It is well-documented that failure to adjust for population stratification can lead to false positives in genetic association studies, but population stratification is often unaccounted for in DNA methylation studies. Here, we propose several approaches to correct for population stratification using principal components (PCs) from different subsets of genome-wide methylation data. We first illustrate the potential for confounding due to population stratification by demonstrating widespread associations between DNA methylation and race in 388 individuals (365 African American and 23 Caucasian). We subsequently evaluate the performance of our PC-based approaches and other methods in adjusting for confounding due to population stratification. Our simulations show that (1) all of the methods considered are effective at removing inflation due to population stratification, and (2) maximum power can be obtained with single-nucleotide polymorphism (SNP)-based PCs, followed by methylation-based PCs, which outperform both surrogate variable analysis and genomic control. Among our different approaches to computing methylation-based PCs, we find that PCs based on CpG sites chosen for their potential to proxy nearby SNPs can provide a powerful and computationally efficient approach to adjust for population stratification in DNA methylation studies when genome-wide SNP data are unavailable., (© 2014 WILEY PERIODICALS, INC.)

Obesity is a highly heritable condition and a risk factor for other diseases, including type 2 diabetes, cardiovascular disease, hypertension, and cancer. Recently, genomic copy number variation (CNV) has been implicated in cases of early onset obesity that may be comorbid with intellectual disability. Here, we describe a recurrent CNV that causes a syndrome associated with intellectual disability, seizures, macrocephaly, and obesity. This unbalanced chromosome translocation leads to duplication of over 100 genes on chromosome 12, including the obesity candidate gene G protein β3 (GNB3). We generated a transgenic mouse model that carries an extra copy of GNB3, weighs significantly more than its wild-type littermates, and has excess intraabdominal fat accumulation. GNB3 is highly expressed in the brain, consistent with G-protein signaling involved in satiety and/or metabolism. These functional data connect GNB3 duplication and overexpression to elevated body mass index and provide evidence for a genetic syndrome caused by a recurrent CNV.

Summary: With the increasing availability of high-density methylation microarrays, there has been growing interest in analysis of DNA methylation data. We have developed CpGassoc, an R package that can efficiently perform the statistical analysis needed for increasingly large methylation datasets. CpGassoc is a modular, expandable package with functions to perform rapid analyses of DNA methylation data via fixed or mixed effects models, to perform basic quality control, to carry out permutation tests, and to display results via an array of publication-quality plots., Availability and Implementation: CpGassoc is implemented in R and is freely available at http://genetics.emory.edu/conneely; we are in the process of submitting it to CRAN.

Recent evidence suggests that DNA methylation changes may underlie numerous complex traits and diseases. The advent of commercial, array-based methods to interrogate DNA methylation has led to a profusion of epigenetic studies in the literature. Array-based methods, such as the popular Illumina GoldenGate and Infinium platforms, estimate the proportion of DNA methylated at single-base resolution for thousands of CpG sites across the genome. These arrays generate enormous amounts of data, but few software resources exist for efficient and flexible analysis of these data. We developed a software package called MethLAB (http://genetics.emory.edu/conneely/MethLAB) using R, an open source statistical language that can be edited to suit the needs of the user. MethLAB features a graphical user interface (GUI) with a menu-driven format designed to efficiently read in and manipulate array-based methylation data in a user-friendly manner. MethLAB tests for association between methylation and relevant phenotypes by fitting a separate linear model for each CpG site. These models can incorporate both continuous and categorical phenotypes and covariates, as well as fixed or random batch or chip effects. MethLAB accounts for multiple testing by controlling the false discovery rate (FDR) at a user-specified level. Standard output includes a spreadsheet-ready text file and an array of publication-quality figures. Considering the growing interest in and availability of DNA methylation data, there is a great need for user-friendly open source analytical tools. With MethLAB, we present a timely resource that will allow users with no programming experience to implement flexible and powerful analyses of DNA methylation data.

Context: The serotonin transporter (SLC6A4) has been associated with several stress-related syndromes including posttraumatic stress disorder (PTSD). The ability to detect meaningful associations is largely dependent on reliable measures of preexisting trauma., Objective: To study the association of genetic variants within SLC6A4 with acute and posttraumatic stress symptoms in a civilian cohort with known levels of preexisting trauma and PTSD symptoms collected prior to a shared index traumatic event., Design: Ongoing longitudinal study., Setting: On February 14, 2008, a lone gunman shot multiple people on the campus of Northern Illinois University in DeKalb, Illinois, killing 5 and wounding 21. As part of an ongoing longitudinal study on that campus, a cohort of female undergraduate students, interviewed prior to the shooting, completed follow-up trauma-related measures including PTSD symptom severity (follow-up survey was launched 17 days postshooting; n = 691). To obtain DNA, salivary samples were collected from a subset of the original study population based on willingness to participate (n = 276)., Participants: Two hundred four undergraduate women., Main Outcome Measures: SLC6A4 polymorphisms STin2, 5-HTTLPR, and rs25531 were genotyped in 235 individuals., Results: We found that although the STin2 variant and 5-HTTLPR alone did not associate with increased PTSD symptoms, rs25531 and the 5-HTTLPR multimarker genotype (combined 5-HTTLPR and rs25531) were associated with significantly increased acute stress disorder symptoms at 2 to 4 weeks postshooting (n = 161; P < .05). This association remained significant when controlling for race and for level of shooting exposure (n = 123; P < .007). The association was most robust with the 5-HTTLPR multimarker genotype and avoidance symptoms (P = .003)., Conclusion: These data suggest that differential function of the serotonin transporter may mediate differential response to a severe trauma. When examined in a relatively homogenous sample with shared trauma and known prior levels of child and adult trauma, the 5-HTTLPR multimarker genotype may serve as a useful predictor of risk for PTSD-related symptoms in the weeks and months following the trauma.