Statistical Methods for Aggregation of Sequence Data and Multiple Testing Correction in Common and Rare Variant Analysis

Over the last fifteen years, there have been substantial improvements in how we study the association between trait and genetic variations in the human genome. Genome-wide association studies (GWAS) now routinely test millions of variants in hundreds of thousands of individuals and the advance of genome sequencing technology allows us to examine the role of genetic variants across the full allele-frequency spectrum. However, with these changes come new challenges in analyzing and interpreting genetic results. In this dissertation, we present methods to aggregate sequence data and identify significant associations in common and rare variant analysis. In chapter two, we compare two strategies to aggregate sequence data from multiple studies: joint variant calling of all samples together versus calling each study individually and then combining the results using meta-analysis. Although joint calling is the gold standard, single-study calling can be more appealing due to fewer privacy restrictions and smaller computational burden. We use deep- and low-coverage sequence data on 2,250 samples from the GoT2D study to compare the two strategies in terms of variant detection sensitivity, genotype accuracy, and association power. We show single-study calling to be a viable alternative to joint calling for deep-coverage sequence data but show them to have noticeable discrepancies in rare variant calling and association results for low-coverage sequence data. In chapter three, we revisit the common variant P-value significance threshold of 5e-8 and explore the rates of true and false discoveries that can be expected using less restrictive P-value thresholds and three other multiple testing procedures: Benjamini-Hochberg (BH) and Benjamini-Yekutieli (BY) for controlling false discovery rate (FDR), and Bayesian false discovery probability for controlling Bayesian FDR. Using data from the Global Lipids and GIANT consortia, we show for large sample common variant GWAS that using a less stringent P-value threshold of 5e-7 or use of the BH procedure at target FDR threshold of 5% substantially increases the number of true positive discoveries while only modestly increasing false positive discoveries compared with the 5e-8 threshold. The latter threshold remains appropriate for modest-sized studies or for resource-intensive follow-ups such as constructing animal models where a stringently curated list of significant loci is desired from GWAS. In the chapter four, we propose a Bayesian method for multiple testing correction in rare variant studies that calculates the posterior probabilities using an approximation of the Bayes factor and estimates prior parameters from summary statistics using an Expectation-Maximization algorithm. Using simulations analyses of ~400,000 individuals and ~107 million variants from the TOPMed-imputed UK Biobank study, we show that our Bayesian method discovers more true positive loci than P-value-based methods such as the P-value threshold, BH, and BY procedures at equivalent false positive rates. In addition, we show that the Bayesian method controls empirical FDR among discovered loci. Finally, we estimate the genome-wide significant P-value threshold for testing ~107 million variants from the TOPMed imputation reference panel to be 1e-9.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162936/1/zhongshc_1.pd

Combining sequence data from multiple studies: Impact of analysis strategies on rare variant calling and association results

Individual sequencing studies often have limited sample sizes and so limited power to detect trait associations with rare variants. A common strategy is to aggregate data from multiple studies. For studying rare variants, jointly calling all samples together is the gold standard strategy but can be difficult to implement due to privacy restrictions and computational burden. Here, we compare joint calling to the alternative of single‐study calling in terms of variant detection sensitivity and genotype accuracy as a function of sequencing coverage and assess their impact on downstream association analysis. To do so, we analyze deep‐coverage (~82×) exome and low‐coverage (~5×) genome sequence data on 2,250 individuals from the Genetics of Type 2 Diabetes study jointly and separately within five geographic cohorts.For rare single nucleotide variants (SNVs): (a) ≥97% of discovered SNVs are found by both calling strategies; (b) nonreference concordance with a set of highly accurate genotypes is ≥99% for both calling strategies; (c) meta‐analysis has similar power to joint analysis in deep‐coverage sequence data but can be less powerful in low‐coverage sequence data. Given similar data processing and quality control steps, we recommend single‐study calling as a viable alternative to joint calling for analyzing SNVs of all minor allele frequency in deep‐coverage data.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153654/1/gepi22261_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153654/2/gepi22261.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153654/3/gepi22261-sup-0002-final_revised_supp_figures_7_19_2019.pd

THE MODERATING EFFECTS OF CONTEXTUAL FACTORS ON A BUYER’S TRUST IN E-COMMERCE PLATFORMS AND SELLERS

Drawing on trust transfer theory and signal theory, we investigate how perceived effectiveness of e-commerce institutional mechanisms (PEEIM) and perceived website quality of the seller (PWQS) moderate the relationships between trust in platform, trust in seller and purchase intention in the context of Consumer to Consumer (C2C) platforms. To test our proposed model, we surveyed 224 buyers of TaoBao, a major Chinese C2C portal. The results indicate that PEEIM has no effect on the relationship between trust in platform and trust in seller, yet it positively moderates the relationship between trust in seller and purchase intention. In addition, PWQS positively moderates the relationship between trust in platform and trust in seller, but negatively moderates the relationship between trust in seller and purchase intention. The theoretical and practical implications are discussed

System-level coupled modeling of piezoelectric vibration energy harvesting systems by joint finite element and circuit analysis

A practical piezoelectric vibration energy harvesting (PVEH) system is usually composed of two coupled parts: a harvesting structure and an interface circuit. Thus, it is much necessary to build system-level coupled models for analyzing PVEH systems, so that the whole PVEH system can be optimized to obtain a high overall efficiency. In this paper, two classes of coupled models are proposed by joint finite element and circuit analysis. The first one is to integrate the equivalent circuit model of the harvesting structure with the interface circuit and the second one is to integrate the equivalent electrical impedance of the interface circuit into the finite element model of the harvesting structure. Then equivalent circuit model parameters of the harvesting structure are estimated by finite element analysis and the equivalent electrical impedance of the interface circuit is derived by circuit analysis. In the end, simulations are done to validate and compare the proposed two classes of system-level coupled models. The results demonstrate that harvested powers from the two classes of coupled models approximate to theoretic values. Thus, the proposed coupled models can be used for system-level optimizations in engineering applications

Nonlinear dynamic behaviors of rotated blades with small breathing cracks based on vibration power flow analysis

Rotated blades are key mechanical components in turbomachinery and high cycle fatigues often induce blade cracks. Accurate detection of small cracks in rotated blades is very significant for safety, reliability, and availability. In nature, a breathing crack model is fit for a small crack in a rotated blade rather than other models. However, traditional vibration displacements-based methods are less sensitive to nonlinear characteristics due to small breathing cracks. In order to solve this problem, vibration power flow analysis (VPFA) is proposed to analyze nonlinear dynamic behaviors of rotated blades with small breathing cracks in this paper. Firstly, local flexibility due to a crack is derived and then time-varying dynamic model of the rotated blade with a small breathing crack is built. Based on it, the corresponding vibration power flow model is presented. Finally, VPFA-based numerical simulations are done to validate nonlinear behaviors of the cracked blade. The results demonstrate that nonlinear behaviors of a crack can be enhanced by power flow analysis and VPFA is more sensitive to a small breathing crack than displacements-based vibration analysis. Bifurcations will occur due to breathing cracks and subharmonic resonance factors can be defined to identify breathing cracks. Thus the proposed method can provide a promising way for detecting and predicting small breathing cracks in rotated blades

Characterization of the Small RNA Transcriptomes of Androgen Dependent and Independent Prostate Cancer Cell Line by Deep Sequencing

Given the important roles of miRNA in post-transcriptional regulation and its implications for cancer, characterization of miRNA facilitates us to uncover molecular mechanisms underlying the progression of androgen-independent prostate cancer (PCa). The emergence of next-generation sequencing technologies has dramatically changed the speed of all aspects of sequencing in a rapid and cost-effective fashion, which can permit an unbiased, quantitive and in-depth investigation of small RNA transcriptome. In this study, we used high-throughput Illumina sequencing to comprehensively represent the full complement of individual small RNA and to characterize miRNA expression profiles in both the androgen dependent and independent Pca cell line. At least 83 miRNAs are significantly differentially expressed, of which 41 are up-regulated and 42 are down-regulated, indicating these miRNAs may be involved in the transition of LNCaP to an androgen-independent phenotype. In addition, we have identified 43 novel miRNAs from the androgen dependent and independent PCa library and 3 of them are specific to the androgen-independent PCa. Function annotation of target genes indicated that most of these differentially expressed miRNAs tend to target genes involved in signal transduction and cell communication, epically the MAPK signaling pathway. The small RNA transcriptomes obtained in this study provide considerable insights into a better understanding of the expression and function of small RNAs in the development of androgen-independent prostate cancer

Epigenetic Activation of ASCT2 in the Hippocampus Contributes to Depression-Like Behavior by Regulating D-Serine in Mice

The roles of D-serine in depression are raised concerned recently as an intrinsic co-agonist for the NMDA receptor. However, the mechanisms underlying its regulation are not fully elucidated. ASCT2 is a Na+-dependent D-serine transporter. We found that decreased D-serine and increased hippocampal ASCT2 levels correlated with chronic social defeat stress (CSDS) in mice. Lentivirus-mediated shRNA-mediated knockdown of ASCT2 and the administration of exogenous D-serine in the hippocampus alleviated CSDS-induced social avoidance and immobility. In vivo and in vitro experiments revealed that upregulation of ASCT2 expression in CSDS was regulated through histone hyper-acetylation, not DNA methylation in its promoter region. Immunohistochemistry demonstrated the co-localization of ASCT2 and D-serine. Uptake of D-serine by ASCT2 was demonstrated by in vivo and in vitro experiments. Our results indicate that CSDS induces ASCT2 expression through epigenetic activation and decreases hippocampal D-serine levels, leading to social avoidance, and immobility. Thus, targeting D-serine transport represents an attractive new strategy for treating depression

Assignment of Reference 5’-end 16S rDNA Sequences and Species-Specific Sequence Polymorphisms Improves Species Identification of Nocardia

16S rDNA sequence analysis is the most accurate method for definitive species identification of nocardiae. However, conflicting results can be found due to sequence errors in gene databases. This study tested the feasibility of species identification of Nocardia by partial (5’-end 606-bp) 16S rDNA sequencing, based on sequence comparison with “reference” sequences of well-annotated strains. This new approach was evaluated using 96 American Type Culture Collection (n=6), and clinical (n=90) Nocardia isolates. Nucleotide sequence-based polymorphisms within species were indicative of “sequence types” for that species. Sequences were compared with those in the GenBank, Bioinformatics Bacteria Identification and Ribosomal Database Project databases. Compared with the reference sequence set, all 96 isolates were correctly identified using the criterion of ≥99% sequence similarity. Seventy-eight (81.3%) were speciated by database comparison; alignment with reference sequences resolved the identity of 14 (15%) isolates whose sequences yielded 100% similarity to sequences in GenBank under >1 species designation. Of 90 clinical isolates, the commonest species was Nocardia nova (33.3%) followed by Nocardia cyriacigeorgica (26.7%). Recently-described or uncommon species included Nocardia veterana (4.4%), Nocarida bejingensis (2.2%) and, Nocardia abscessus and Nocardia arthriditis (each n=1). Nocardia asteroides sensu stricto was rare (n=1). There were nine sequence types of N. nova, three of Nocardia brasiliensis with two each of N. cyriacigeorgica and Nocardia farcinica. Thirteen novel sequences were identified. Alignment of sequences with reference sequences facilitated species identification of Nocardia and allowed delineation of sequence types within species, suggesting that such a barcoding approach can be clinically useful for identification of bacteria