Power Calculation of Multi-step Combined Principal Components with Applications to Genetic Association Studies.
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ABSTRACT:
SUBMITTER: Li Z
PROVIDER: S-EPMC4870571 | biostudies-other | 2016
REPOSITORIES: biostudies-other
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Project description:BACKGROUND:Quantitative traits often underlie risk for complex diseases. For example, weight and body mass index (BMI) underlie the human abdominal obesity-metabolic syndrome. Many attempts have been made to identify quantitative trait loci (QTL) over the past decade, including association studies. However, a single QTL is often capable of affecting multiple traits, a quality known as gene pleiotropy. Gene pleiotropy may therefore cause a loss of power in association studies focused only on a single trait, whether based on single or multiple markers. RESULTS:We propose using principal-component-based multivariate regression (PCBMR) to test for gene pleiotropy with comprehensive evaluation. This method generates one or more independent canonical variables based on the principal components of original traits and conducts a multivariate regression to test for association with these new variables. Systematic simulation studies have shown that PCBMR has great power. PCBMR-based pleiotropic association studies of abdominal obesity-metabolic syndrome and its possible linkage to chromosomal band 3q27 identified 11 susceptibility genes with significant associations. Whereas some of these genes had been previously reported to be associated with metabolic traits, others had never been identified as metabolism-associated genes. CONCLUSIONS:PCBMR is a computationally efficient and powerful test for gene pleiotropy. Application of PCBMR to abdominal obesity-metabolic syndrome indicated the existence of gene pleiotropy affecting this syndrome.
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Project description:The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.
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Project description:Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data.
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Project description:MotivationIn large-scale genetic association studies with tens of hundreds of single nucleotide polymorphisms (SNPs) genotyped, the traditional statistical framework of logistic regression using maximum likelihood estimator (MLE) to infer the odds ratios of SNPs may not work appropriately. This is because a large number of odds ratios need to be estimated, and the MLEs may be not stable when some of the SNPs are in high linkage disequilibrium. Under this situation, the P-value combination procedures seem to provide good alternatives as they are constructed on the basis of single-marker analysis.ResultsThe commonly used P-value combination methods (such as the Fisher's combined test, the truncated product method, the truncated tail strength and the adaptive rank truncated product) may lose power when the significance level varies across SNPs. To tackle this problem, a group combined P-value method (GCP) is proposed, where the P-values are divided into multiple groups and then are combined at the group level. With this strategy, the significance values are integrated at different levels, and the power is improved. Simulation shows that the GCP can effectively control the type I error rates and have additional power over the existing methods-the power increase can be as high as over 50% under some situations. The proposed GCP method is applied to data from the Genetic Analysis Workshop 16. Among all the methods, only the GCP and ARTP can give the significance to identify a genomic region covering gene DSC3 being associated with rheumatoid arthritis, but the GCP provides smaller P-value.Availability and implementationhttp://www.statsci.amss.ac.cn/yjscy/yjy/lqz/201510/t20151027_313273.htmlContactliqz@amss.ac.cnSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundConcentrated breeding effort to produce various body structures and behaviors of dogs to suit human demand has inadvertently produced unwanted traits and diseases that accompany the morphological and behavioral phenotypes. We explored the relationship between pelvic conformation and canine hip dysplasia (HD) because purebred dogs which are predisposed, or not, to HD share common morphologic features, respectively. Thirteen unique bilateral anatomical features of the pelvis were measured on 392 dogs of 51 breeds and 95 mixed breed dogs. Principal components (PCs) were derived to describe pelvic morphology. Dogs were genotyped at ~183,000 single nucleotide polymorphisms and their hip conformation was measured by the Norberg angle and angle of inclination between the femoral neck and diaphysis.ResultsNo associations reached genome wide significance for the Norberg angle when averaged over both hips. PC1 was negatively correlated with the Norberg angle (r = -0.31; P < 0.05) but not the angle of inclination (r = -0.08; P > 0.05). PC1, 2, 4, and 5 differed significantly between male and female dogs confirming pelvic sexual dimorphism. With sex as a covariate, the eigenvector contribution to PC1 reflected the overall size of the pelvis and was significantly associated with the IGF-1 locus, a known contributor to canine body size. PC3, which represented a tradeoff between ilial length and ischial length in which a longer ischium is associated with a shorter ilium, was significantly associated with a marker on canine chromosome 16:5181388 bp. The closest candidate gene is TPK1, a thiamine-dependent enzyme and part of the PKA complex. Associations with the remaining PCs did not reach genome wide significance.ConclusionIGF-1 was associated with the overall size of the pelvis and sex is related to pelvic size. Ilial/ischial proportion is genetically controlled and the closest candidate gene is thiamine-dependent and affects birth weight and development of the nervous system. Dogs with larger pelves tend to have smaller NAs consistent with increased tendency toward HD in large breed dogs. Based on the current study, pelvic shape alone was not strongly associated with canine hip dysplasia.
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Project description:Performing linkage and association analyses on a large set of correlated data presents an interesting set of problems. In the current setting, we have 3554 expression levels from lymphoblastoid cell lines in 194 individuals from 14 three-generation Utah CEPH (Centre d'Etude du Polymorphisme Humain) pedigrees. We formed multivariate expression phenotypes from six sets of genes. These consisted of a set of genes identified by the data providers as showing common linkage to a region of chromosome 14, as well as five other sets suggested by ontological evidence. Using principal-component analyses, we generated seven quantitative phenotypes for expression levels from these six sets of genes. We performed quantitative genome linkage screens on these traits using the expression traits from the third generation of each pedigree. As expected, the strongest linkage signal was achieved when the trait under analysis was the composite of the expressions of genes previously showing linkage to chromosome 14. In particular, this trait produced a LOD score of 5.2 on chromosome 14. The trait also produced LOD scores over 3.5 on chromosomes 1, 7, 9, and 11; this suggests that these genes may be controlled by additional genetic factors on the genome. Subsequent association analyses on the first two generations of these pedigrees identified two polymorphisms on chromosome 11 as significant after correcting for multiple tests. These results suggest that principal-component analyses are useful for the analysis of pleiotropic loci. Furthermore, we have identified two single-nucleotide polymorphisms that may influence the expression of multiple genes linked to chromosome 14.
| S-EPMC2367470 | biostudies-literature
Project description:The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies homogeneous variable blocks, and then extracts sPCs, for each omics dataset. The second stage merges sPCs from each omics dataset, and then constructs a prediction model. We also propose a graphical method showing the results of sparse PCA and model fitting, simultaneously. We applied the proposed methodology to glioblastoma multiforme data from The Cancer Genome Atlas. The comparison with other existing approaches showed that our proposed methodology is more easily interpretable than other approaches, and has comparable predictive power, with a much smaller number of variables.
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Project description:BackgroundOur study aimed to assess and compare the accuracy of 8 intraocular lens (IOL) power calculation formulas (Barrett Universal II, EVO 2.0, Haigis, Hoffer Q, Holladay 1, Kane and PEARL-DGS) in patients submitted to combined phacovitrectomy for vitreomacular (VM) interface disorders.MethodsRetrospective chart review study including axial-length matched patients submitted to phacoemulsification alone (Group 1) and combined phacovitrectomy (Group 2). Using optimized constants in both groups, refraction prediction error of each formula was calculated for each eye. The optimised constants from Group 1 were also applied to patients of Group 2 - Group 3. Outcome measures included the mean prediction error (ME) and its standard deviation (SD), mean (MAE) and median (MedAE) absolute errors, in diopters (D), and the percentage of eyes within ± 0.25D, ± 0.50D and ± 1.00D.ResultsA total of 220 eyes were included (Group 1: 100; Group 2: 120). In Group 1, the difference in formulas absolute error was significative (p = 0.005). The Kane Formula had the lowest MAE (0.306) and MedAE (0.264). In Group 2, Kane had the overall best performance, followed by PEARL-DGS, EVO 2.0 and Barrett Universal II. The ME of all formulas in both Groups 1 and 2 were 0.000 (p = 0.934; p = 0.971, respectively). In Group 3, a statistically significant myopic shift was observed for each formula (p < 0.001).ConclusionSurgeons must be careful regarding IOL power selection in phacovitrectomy considering the systematic myopic shift evidenced-constant optimization may help eliminating such error. Moreover, newly introduced formulas and calculation methods may help us achieving increasingly better refractive outcomes both in cataract surgery alone and phacovitrectomy.
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Project description:Estimating the genetic and environmental variances for multivariate and function-valued phenotypes poses problems for estimation and interpretation. Even when the phenotype of interest has a large number of dimensions, most variation is typically associated with a small number of principal components (eigen-vectors or eigenfunctions). We propose an approach that directly estimates these leading principal components; these then give estimates for the covariance matrices (or functions). Direct estimation of the principal components reduces the number of parameters to be estimated, uses the data efficiently, and provides the basis for new estimation algorithms. We develop these concepts for both multivariate and function-valued phenotypes and illustrate their application in the restricted maximum-likelihood framework.
| S-EPMC1448747 | biostudies-other
Project description:To decipher gene regulatory networks, we used systematic suppression of 97 transcription factors (TFs) and 7 other genes with shRNA in mouse embryonic stem (ES) cells, followed by global gene expression profiling. Meta-analysis of these data together with the earlier data obtained by the induction of 50 TFs and the existing genome-wide data on TF binding sites identified the sets of regulated target genes for 23 TFs. Principal component analysis shows different roles of two groups of TFs that are active in ES cells: Pou5f1 and Sox2 support the expression of their target genes and prevent the upregulation of trophectoderm-related genes, whereas Esrrb, Sall4, Nanog, Gbx2, Grhl2, Mtf2, Aff1, Tcfap4, and Cdc5l support the expression of targets of Esrrb, including glycolysis genes, and prevent upregulation of targets of Trp53 and Polycomb TFs. If TFs from the second group are downregulated while Pou5f1 and Sox2 are still active, then the cell state changes towards epiblast lineages. Sequences for shRNA were designed to target 3 untranslated region of genes (Supplementary Table S8). Gene expression change was checked with real time qPCR (Supplementary Figure S7) and Westerm blot. ES cells (ES[MC1R(20)], passage 20) were cultured without feeders and were co-transfected with 1.6 g of shRNA expression vector and 0.4 g of pPyCAG-EGFP-IP carrying expression cassettes for puromycin resistant genes and EGFP using Effectene (Qiagen). Transfected cells were cultured in presence of 1.5 g/ml of puromycin and were harvested at 72 h after transfection. Mock cells were treated with transfection reagent without DNA and cultured in the absence of puromycin. Experiments were done in 3 replications with 2 of them used for gene expression profiling with microarrays. Total RNA was isolated by TRIzol (Invitrogen) after 2 days. Cy3-CTP labeled sample targets were prepared with total RNA by Low RNA Input Fluorescent Linear Amplification Kit (Agilent). Cy5-CTP labeled reference target was Stratagene Universal Mouse Reference RNA. Note that the processed data in the paper, which is also attached as GSE26520_Table_data.txt.gz, is slightly different from the values columns in each sample. The original processed data are normalized with a batch method, as the new batches of arrays added in the set. The value columns in this submission reflect full normalization as described in the data processing fields in each sample.
2013-03-14 | E-GEOD-26520 | biostudies-arrayexpress