MethylSeqDesign: a framework for Methyl-Seq genome-wide power calculation and study design issues.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu P
PROVIDER: S-EPMC7846147 | biostudies-literature | 2019 May
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Massively parallel sequencing (a.k.a. next-generation sequencing, NGS) technology has emerged as a powerful tool in characterizing genomic profiles. Among many NGS applications, RNA sequencing (RNA-Seq) has gradually become a standard tool for global transcriptomic monitoring. Although the cost of NGS experiments has dropped constantly, the high sequencing cost and bioinformatic complexity are still obstacles for many biomedical projects. Unlike earlier fluorescence-based technologies such as microarray, modelling of NGS data should consider discrete count data. In addition to sample size, sequencing depth also directly relates to the experimental cost. Consequently, given total budget and pre-specified unit experimental cost, the study design issue in RNA-Seq is conceptually a more complex multi-dimensional constrained optimization problem rather than one-dimensional sample size calculation in traditional hypothesis setting. In this paper, we propose a statistical framework, namely "RNASeqDesign", to utilize pilot data for power calculation and study design of RNA-Seq experiments. The approach is based on mixture model fitting of p-value distribution from pilot data and a parametric bootstrap procedure based on approximated Wald test statistics to infer genome-wide power for optimal sample size and sequencing depth. We further illustrate five practical study design tasks for practitioners. We perform simulations and three real applications to evaluate the performance and compare to existing methods.
| S-EPMC7941184 | biostudies-literature
Project description:The case-cohort (CC) study design usually has been used for risk factor assessment in epidemiologic studies or disease prevention trials for rare diseases. The sample size/power calculation for a stratified CC (SCC) design has not been addressed before. This article derives such result based on a stratified test statistic. Simulation studies show that the proposed test for the SCC design utilizing small sub-cohort sampling fractions is valid and efficient for situations where the disease rate is low. Furthermore, optimization of sampling in the SCC design is discussed and compared with proportional and balanced sampling techniques. An epidemiological study is provided to illustrate the sample size calculation under the SCC design.
| S-EPMC4159408 | biostudies-other
Project description:Chronic exposure to glucocorticoids (GCs) can lead to psychiatric complications through epigenetic mechanisms such as DNA methylation (DNAm). We sought to determine whether epigenetic changes in a peripheral tissue can serve as a surrogate for those in a relatively inaccessible tissue such as the brain. DNA extracted from the hippocampus and blood of mice treated with GCs or vehicle solution was assayed using a genome-wide DNAm platform (Methyl-Seq) to identify differentially methylated regions (DMRs) induced by GC treatment. We observed that ∼70% of the DMRs in both tissues lost methylation following GC treatment. Of the 3,095 DMRs that mapped to the same genes in both tissues, 1,853 DMRs underwent DNAm changes in the same direction. Interestingly, only 209 DMRs (<7%) overlapped in genomic coordinates between the 2 tissues, suggesting tissue-specific differences in GC-targeted loci. Pathway analysis showed that the DMR-associated genes were members of pathways involved in metabolism, immune function, and neurodevelopment. Also, changes in cell type composition of blood and brain were examined by fluorescence-activated cell sorting. Separation of the cortex into neuronal and non-neuronal fractions and the leukocytes into T-cells, B-cells, and neutrophils showed that GC-induced methylation changes primarily occurred in neurons and T-cells, with the blood tissue also undergoing a shift in the proportion of constituent cell types while the proportion of neurons and glia in the brain remained stable. From the current pilot study, we found that despite tissue-specific epigenetic changes and cellular heterogeneity, blood can serve as a surrogate for GC-induced changes in the brain.
| S-EPMC5687336 | biostudies-literature
Project description:BACKGROUND: We are moving to second-wave analysis of genome-wide association studies (GWAS), characterized by comprehensive bioinformatical and statistical evaluation of genetic associations. Existing biological knowledge is very valuable for GWAS, which may help improve their detection power particularly for disease susceptibility loci of moderate effect size. However, a challenging question is how to utilize available resources that are very heterogeneous to quantitatively evaluate the statistic significances. METHODOLOGY/PRINCIPAL FINDINGS: We present a novel knowledge-based weighting framework to boost power of the GWAS and insightfully strengthen their explorative performance for follow-up replication and deep sequencing. Built upon diverse integrated biological knowledge, this framework directly models both the prior functional information and the association significances emerging from GWAS to optimally highlight single nucleotide polymorphisms (SNPs) for subsequent replication. In the theoretical calculation and computer simulation, it shows great potential to achieve extra over 15% power to identify an association signal of moderate strength or to use hundreds of whole-genome subjects fewer to approach similar power. In a case study on late-onset Alzheimer disease (LOAD) for a proof of principle, it highlighted some genes, which showed positive association with LOAD in previous independent studies, and two important LOAD related pathways. These genes and pathways could be originally ignored due to involved SNPs only having moderate association significance. CONCLUSIONS/SIGNIFICANCE: With user-friendly implementation in an open-source Java package, this powerful framework will provide an important complementary solution to identify more true susceptibility loci with modest or even small effect size in current GWAS for complex diseases.
| S-EPMC3013112 | biostudies-literature
Project description:MotivationChIP-seq technology enables investigators to study genome-wide binding of transcription factors and mapping of epigenomic marks. Although the availability of basic analysis tools for ChIP-seq data is rapidly increasing, there has not been much progress on the related design issues. A challenging question for designing a ChIP-seq experiment is how deeply should the ChIP and the control samples be sequenced? The answer depends on multiple factors some of which can be set by the experimenter based on pilot/preliminary data. The sequencing depth of a ChIP-seq experiment is one of the key factors that determine whether all the underlying targets (e.g. binding locations or epigenomic profiles) can be identified with a targeted power.ResultsWe developed a statistical framework named CSSP (ChIP-seq Statistical Power) for power calculations in ChIP-seq experiments by considering a local Poisson model, which is commonly adopted by many peak callers. Evaluations with simulations and data-driven computational experiments demonstrate that this framework can reliably estimate the power of a ChIP-seq experiment at different sequencing depths based on pilot data. Furthermore, it provides an analytical approach for calculating the required depth for a targeted power while controlling the false discovery rate at a user-specified level. Hence, our results enable researchers to use their own or publicly available data for determining required sequencing depths of their ChIP-seq experiments and potentially make better use of the multiplexing functionality of the sequencers. Evaluation of power for multiple public ChIP-seq datasets indicate that, currently, typical ChIP-seq studies are powered well for detecting large fold changes of ChIP enrichment over the control sample, but they have considerably less power for detecting smaller fold changes.AvailabilityAvailable at www.stat.wisc.edu/~zuo/CSSP.Contactkeles@stat.wisc.eduSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC3957067 | biostudies-literature
Project description:Breast and ovarian cancer susceptibility genes, BRCA1 and PALB2 have enigmatic roles in cellular growth and mammalian development. While these genes are essential for growth during early developmental programs, inactivation later in adulthood leads to increased growth and formation of tumors, leading to their designation as tumor suppressors. We performed genome-wide analysis assessing their chromatin residence and gene expression responsiveness using high throughput sequencing in breast epithelial cells. These experiments revealed a critical role for BRCA1 and PALB2 in transcriptional responsiveness to NF-kB, a crucial mediator of growth and inflammatory response during development and cancer. Importantly, we also uncovered a vital role for these proteins in response to retinoic acid (RA), a growth inhibitory signal in breast cancer cells, which may constitute the basis for their tumor suppressor activity. Comparison of the genome wide profiles of the BRCA protein complex (BRCA1 and PALB2) and phosphorylated RNAPII (P-Ser2) in MCF10A cells by ChIP-seq. Effect of BRCA1 and PALB2 knockdown (shRNAs) on transcription was assessed by RNA-seq.
| S-ECPF-GEOD-40591 | biostudies-other
Project description:When planning most scientific studies, one of the first steps is to carry out a power analysis to define a design and sample size that will result in a well-powered study. There are limited resources for calculating power for group fMRI studies due to the complexity of the model. Previous approaches for group fMRI power calculation simplify the study design and/or the variance structure in order to make the calculation possible. These approaches limit the designs that can be studied and may result in inaccurate power calculations. We introduce a flexible power calculation model that makes fewer simplifying assumptions, leading to a more accurate power analysis that can be used on a wide variety of study designs. Our power calculation model can be used to obtain region of interest (ROI) summaries of the mean parameters and variance parameters, which can be use to increase understanding of the data as well as calculate power for a future study. Our example illustrates that minimizing cost to achieve 80% power is not as simple as finding the smallest sample size capable of achieving 80% power, since smaller sample sizes require each subject to be scanned longer.
| S-EPMC2423281 | biostudies-literature
Project description:BackgroundGenome-wide association studies are a promising new tool for deciphering the genetics of complex diseases. To choose the proper sample size and genotyping platform for such studies, power calculations that take into account genetic model, tag SNP selection, and the population of interest are required.ResultsThe power of genome-wide association studies can be computed using a set of tag SNPs and a large number of genotyped SNPs in a representative population, such as available through the HapMap project. As expected, power increases with increasing sample size and effect size. Power also depends on the tag SNPs selected. In some cases, more power is obtained by genotyping more individuals at fewer SNPs than fewer individuals at more SNPs.ConclusionGenome-wide association studies should be designed thoughtfully, with the choice of genotyping platform and sample size being determined from careful power calculations.
| S-EPMC2042984 | biostudies-literature
Project description:Inbred mice are a useful tool for studying the in vivo functions of platelets. Nonetheless, the mRNA signature of mouse platelets is not known. Here, we use paired-end next-generation RNA sequencing (RNA-seq) to characterize the polyadenylated transcriptomes of human and mouse platelets. We report that RNA-seq provides unprecedented resolution of mRNAs that are expressed across the entire human and mouse genomes. Transcript expression and abundance are often conserved between the 2 species. Several mRNAs, however, are differentially expressed in human and mouse platelets. Moreover, previously described functional disparities between mouse and human platelets are reflected in differences at the transcript level, including protease activated receptor-1, protease activated receptor-3, platelet activating factor receptor, and factor V. This suggests that RNA-seq is a useful tool for predicting differences in platelet function between mice and humans. Our next-generation sequencing analysis provides new insights into the human and murine platelet transcriptomes. The sequencing dataset will be useful in the design of mouse models of hemostasis and a catalyst for discovery of new functions of platelets. Access to the dataset is found in the "Introduction."
| S-EPMC3193274 | biostudies-other
Project description:Genetic interaction is a crucial issue in the understanding of functional pathways underlying complex diseases. However, detecting such interaction effects is challenging in terms of both methodology and statistical power. We address this issue by introducing a disease-concordant twin-case-only design, which applies to both monozygotic and dizygotic twins. To investigate the power, we conducted a computer simulation study by setting a series of parameter schemes with different minor allele frequencies and relative risks. Results from the simulation study reveals that the disease-concordant twin-case-only design largely reduces sample size required for sufficient power compared to the ordinary case-only design for detecting gene-gene interaction using unrelated individuals. Sample sizes for dizygotic and monozygotic twins were roughly 1/2 and 1/4 of sample sizes in the ordinary case-only design. Since dizygotic twins are genetically similar as siblings, the enriched power for dizygotic twins also applies to affected siblings, which could help to largely extend the application of the powerful twin-case-only design. In summary, our simulation reveals high value of disease-concordant twins and siblings in efficiently detecting gene-by-gene interactions.
| S-EPMC6460588 | biostudies-literature