Neonatal Blood Methylation Marks Associated with Obstetric Pain Relief

The placenta, responsible for intrauterine development, can facilitate modifications within the placental epigenome in response to changes in the mother. In turn these changes have the potential to also influence the neonate1. Pain relief during delivery is widely used and frequently involves the use of nitrous oxide (N2O, commonly referred to as laughing gas), and pudendal blocks. These treatments, alone or in combination, are generally accepted as safe methods of providing pain relief to mothers. However, laughing gas and local anesthetics such as the ones used during pudendal blocks have been known to cross the placental barrier from mother to child2,3. Furthermore, although current literature about the effects of laughing gas and pudendal blocks on the epigenome, when used as maternal pain relief, is very limited, some evidence implicates effects of obstetric anesthesia on the neonatal methylome2,4,5. Thus, it is reasonable to hypothesize that obstetric pain relief administered to the mother during childbirth may affect the methylome of the child. In conclusion, we detected methylome-wide significantly associated loci for laughing gas and pudendal block treatment when studied in combination, but not for either of the treatments separately.https://scholarscompass.vcu.edu/uresposters/1421/thumbnail.jp

Post-Mortem Brain Nuclei Isolation for Single Nucleus RNA Sequencing

Abstract Post-Mortem Brain Nuclei Isolation for Single Nucleus RNA Sequencing Charles Tran, Dept. of Biology, with Dr. Karolina Aberg, VCU School of Pharmacy When tissue samples are studied in bulk without consideration for different cell proportions and types, results can be biased due to the attenuation of unique cellular expressions. In order to study cell type specific RNA expression profiles within tissue, single cell RNA sequencing (scRNA-seq) is used. For scRNA-seq studies it is critical to have intact cells. However, when investigating frozen post-mortem brain tissue, it is often challenging to isolate intact whole cells. An alternative solution is to instead isolate nuclei (which have similar, but not identical, transcriptomes to cells) and then perform single-nucleus RNA sequencing (snRNA-seq). In this study we have carefully optimized a protocol for nuclei extraction from post-mortem brain cells suitable for downstream snRNA-seq analysis. We found that adjusting our protocol to include less aggressive methods of tissue homogenization and sample-retaining lab techniques has resulted in the successful removal of cell debris and myelin alongside providing a workable sample size. In conclusion we have successfully evaluated and prepared enough high-quality nuclei for downstream scRNA-seq using our optimized protocol.https://scholarscompass.vcu.edu/uresposters/1398/thumbnail.jp

Estimation of CpG Coverage in Whole Methylome Next-Generation Sequencing Studies

Background Methylation studies are a promising complement to genetic studies of DNA sequence. However, detailed prior biological knowledge is typically lacking, so methylome-wide association studies (MWAS) will be critical to detect disease relevant sites. A cost-effective approach involves the next-generation sequencing (NGS) of single-end libraries created from samples that are enriched for methylated DNA fragments. A limitation of single-end libraries is that the fragment size distribution is not observed. This hampers several aspects of the data analysis such as the calculation of enrichment measures that are based on the number of fragments covering the CpGs. Results We developed a non-parametric method that uses isolated CpGs to estimate sample-specific fragment size distributions from the empirical sequencing data. Through simulations we show that our method is highly accurate. While the traditional (extended) read count methods resulted in severely biased coverage estimates and introduces artificial inter-individual differences, through the use of the estimated fragment size distributions we could remove these biases almost entirely. Furthermore, we found correlations of 0.999 between coverage estimates obtained using fragment size distributions that were estimated with our method versus those that were “observed” in paired-end sequencing data. Conclusions We propose a non-parametric method for estimating fragment size distributions that is highly precise and can improve the analysis of cost-effective MWAS studies that sequence single-end libraries created from samples that are enriched for methylated DNA fragments

Suggestive Linkage Detected for Blood Pressure Related Traits on 2q and 22q in the Population on the Samoan Islands

Background High blood pressure or hypertension is a major risk factor involved in the development of cardiovascular diseases. We conducted genome-wide variance component linkage analyses to search for loci influencing five blood pressure related traits including the quantitative traits systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP), the dichotomous trait hypertension (HT) and the bivariate quantitative trait SBP-DBP in families residing in American Samoa and Samoa, as well as in the combined sample from the two polities. We adjusted the traits for a number of environmental covariates such as smoking, alcohol consumption, physical activity and material life style. Results We found suggestive univariate linkage for SBP on chromosome 2q35-q37 (LOD 2.4) and for PP on chromosome 22q13 (LOD 2.2), two chromosomal regions that recently have been associated with SBP and PP, respectively. Conclusion We have detected additional evidence for a recently reported locus associated with SBP on chromosome 2q and a susceptibility locus for PP on chromosome 22q. However, differences observed between the results from our three partly overlapping genetically homogenous study samples from the Samoan islands suggest that additional studies should be performed in order to verify these results

MethylPCA: a toolkit to control for confounders in methylome-wide association studies

Background In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. Result We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. Conclusions MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS

Evaluation of Methyl-Binding Domain Based Enrichment Approaches Revisited

Methyl-binding domain (MBD) enrichment followed by deep sequencing (MBD-seq), is a robust and cost efficient approach for methylome-wide association studies (MWAS). MBD-seq has been demonstrated to be capable of identifying differentially methylated regions, detecting previously reported robust associations and producing findings that replicate with other technologies such as targeted pyrosequencing of bisulfite converted DNA. There are several kits commercially available that can be used for MBD enrichment. Our previous work has involved MethylMiner (Life Technologies, Foster City, CA, USA) that we chose after careful investigation of its properties. However, in a recent evaluation of five commercially available MBD-enrichment kits the performance of the MethylMiner was deemed poor. Given our positive experience with MethylMiner, we were surprised by this report. In an attempt to reproduce these findings we here have performed a direct comparison of MethylMiner with MethylCap (Diagenode Inc, Denville, NJ, USA), the best performing kit in that study. We find that both MethylMiner and MethylCap are two well performing MBD-enrichment kits. However, MethylMiner shows somewhat better enrichment efficiency and lower levels of background “noise”. In addition, for the purpose of MWAS where we want to investigate the majority of CpGs, we find MethylMiner to be superior as it allows tailoring the enrichment to the regions where most CpGs are located. Using targeted bisulfite sequencing we confirmed that sites where methylation was detected by either MethylMiner or by MethylCap indeed were methylated

MethylPCA: a toolkit to control for confounders in methylome-wide association studies

Abstract Background In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. Result We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. Conclusions MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS

Deep Sequencing of Three Loci Implicated in Large-Scale Genome-Wide Association Study Smoking Meta-Analyses

Genome-wide association study meta-analyses have robustly implicated three loci that affect susceptibility for smoking: CHRNA5\CHRNA3\CHRNB4, CHRNB3\CHRNA6 and EGLN2\CYP2A6. Functional follow-up studies of these loci are needed to provide insight into biological mechanisms. However, these efforts have been hampered by a lack of knowledge about the specific causal variant(s) involved. In this study, we prioritized variants in terms of the likelihood they account for the reported associations. We employed targeted capture of the CHRNA5\CHRNA3\CHRNB4, CHRNB3\CHRNA6, and EGLN2\CYP2A6 loci and flanking regions followed by next-generation deep sequencing (mean coverage 78×) to capture genomic variation in 363 individuals. We performed single locus tests to determine if any single variant accounts for the association, and examined if sets of (rare) variants that overlapped with biologically meaningful annotations account for the associations. In total, we investigated 963 variants, of which 71.1% were rare (minor allele frequency < 0.01), 6.02% were insertion/deletions, and 51.7% were catalogued in dbSNP141. The single variant results showed that no variant fully accounts for the association in any region. In the variant set results, CHRNB4 accounts for most of the signal with significant sets consisting of directly damaging variants. CHRNA6 explains most of the signal in the CHRNB3\CHRNA6 locus with significant sets indicating a regulatory role for CHRNA6. Significant sets in CYP2A6 involved directly damaging variants while the significant variant sets suggested a regulatory role for EGLN2. We found that multiple variants implicating multiple processes explain the signal. Some variants can be prioritized for functional follow-up. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: [email protected]

Systematic Integration of Brain eQTL and GWAS Identifies ZNF323 as a Novel Schizophrenia Risk Gene and Suggests Recent Positive Selection Based on Compensatory Advantage on Pulmonary Function

Genome-wide association studies have identified multiple risk variants and loci that show robust association with schizophrenia. Nevertheless, it remains unclear how these variants confer risk to schizophrenia. In addition, the driving force that maintains the schizophrenia risk variants in human gene pool is poorly understood. To investigate whether expression-associated genetic variants contribute to schizophrenia susceptibility, we systematically integrated brain expression quantitative trait loci and genome-wide association data of schizophrenia using Sherlock, a Bayesian statistical framework. Our analyses identified ZNF323 as a schizophrenia risk gene (P = 2.22×10-6). Subsequent analyses confirmed the association of the ZNF323 and its expression-associated single nucleotide polymorphism rs1150711 in independent samples (gene-expression: P = 1.40×10-6; single-marker meta-analysis in the combined discovery and replication sample comprising 44123 individuals: P = 6.85×10−10). We found that the ZNF323 was significantly downregulated in hippocampus and frontal cortex of schizophrenia patients (P = .0038 and P = .0233, respectively). Evidence for pleiotropic effects was detected (association of rs1150711 with lung function and gene expression of ZNF323 in lung: P = 6.62×10-5 and P = 9.00×10-5, respectively) with the risk allele (T allele) for schizophrenia acting as protective allele for lung function. Subsequent population genetics analyses suggest that the risk allele (T) of rs1150711 might have undergone recent positive selection in human population. Our findings suggest that the ZNF323 is a schizophrenia susceptibility gene whose expression may influence schizophrenia risk. Our study also illustrates a possible mechanism for maintaining schizophrenia risk variants in the human gene poo