Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang D
PROVIDER: S-EPMC2795880 | biostudies-literature | 2009 Dec
REPOSITORIES: biostudies-literature
Publications
Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling.
Wang Dai D Sun Yu Y Stang Paul P Berlin Jesse A JA Wilcox Marsha A MA Li Qingqin Q
BMC proceedings 20091215
Population stratification (PS) represents a major challenge in genome-wide association studies. Using the Genetic Analysis Workshop 16 Problem 1 data, which include samples of rheumatoid arthritis patients and healthy controls, we compared two methods that can be used to evaluate population structure and correct PS in genome-wide association studies: the principal-component analysis method and the multidimensional-scaling method. While both methods identified similar population structures in thi ...[more]
Similar Datasets
Project description:BackgroundPopulation stratification is a known confounder of genome-wide association studies, as it can lead to false positive results. Principal component analysis (PCA) method is widely applied in the analysis of population structure with common variants. However, it is still unclear about the analysis performance when rare variants are used.ResultsWe derive a mathematical expectation of the genetic relationship matrix. Variance and covariance elements of the expected matrix depend explicitly on allele frequencies of the genetic markers used in the PCA analysis. We show that inter-population variance is solely contained in K principal components (PCs) and mostly in the largest K-1 PCs, where K is the number of populations in the samples. We propose FPC, ratio of the inter-population variance to the intra-population variance in the K population informative PCs, and d2, sum of squared distances among populations, as measures of population divergence. We show analytically that when allele frequencies become small, the ratio FPC abates, the population distance d2 decreases, and portion of variance explained by the K PCs diminishes. The results are validated in the analysis of the 1000 Genomes Project data. The ratio FPC is 93.85, population distance d2 is 444.38, and variance explained by the largest five PCs is 17.09% when using with common variants with allele frequencies between 0.4 and 0.5. However, the ratio, distance and percentage decrease to 1.83, 17.83 and 0.74%, respectively, with rare variants of frequencies between 0.0001 and 0.01.ConclusionsThe PCA of population stratification performs worse with rare variants than with common ones. It is necessary to restrict the selection to only the common variants when analyzing population stratification with sequencing data.
| S-EPMC7077175 | biostudies-literature
Project description:The concept of similarity, or a sense of 'sameness' among things, is pivotal to theories in the cognitive sciences and beyond. Similarity, however, is a difficult thing to measure. Multidimensional scaling (MDS) is a tool by which researchers can obtain quantitative estimates of similarity among groups of items. More formally, MDS refers to a set of statistical techniques that are used to reduce the complexity of a data set, permitting visual appreciation of the underlying relational structures contained therein. The current paper provides an overview of MDS. We discuss key aspects of performing this technique, such as methods that can be used to collect similarity estimates, analytic techniques for treating proximity data, and various concerns regarding interpretation of the MDS output. MDS analyses of two novel data sets are also included, highlighting in step-by-step fashion how MDS is performed, and key issues that may arise during analysis. WIREs Cogn Sci 2013, 4:93-103. doi: 10.1002/wcs.1203 This article is categorized under: Psychology > Perception and Psychophysics.
| S-EPMC3555222 | biostudies-literature
Project description:Although inherited mitochondrial genetic variation can cause human disease, no validated methods exist for control of confounding due to mitochondrial population stratification (PS). We sought to identify a reliable method for PS assessment in mitochondrial medical genetics. We analyzed mitochondrial SNP data from 1513 European American individuals concomitantly genotyped with the use of a previously validated panel of 144 mitochondrial markers as well as the Affymetrix 6.0 (n = 432), Illumina 610-Quad (n = 458), or Illumina 660 (n = 623) platforms. Additional analyses were performed in 938 participants in the Human Genome Diversity Panel (HGDP) (Illumina 650). We compared the following methods for controlling for PS: haplogroup-stratified analyses, mitochondrial principal-component analysis (PCA), and combined autosomal-mitochondrial PCA. We computed mitochondrial genomic inflation factors (mtGIFs) and test statistics for simulated case-control and continuous phenotypes (10,000 simulations each) with varying degrees of correlation with mitochondrial ancestry. Results were then compared across adjustment methods. We also calculated power for discovery of true associations under each method, using a simulation approach. Mitochondrial PCA recapitulated haplogroup information, but haplogroup-stratified analyses were inferior to mitochondrial PCA in controlling for PS. Correlation between nuclear and mitochondrial principal components (PCs) was very limited. Adjustment for nuclear PCs had no effect on mitochondrial analysis of simulated phenotypes. Mitochondrial PCA performed with the use of data from commercially available genome-wide arrays correlated strongly with PCA performed with the use of an exhaustive mitochondrial marker panel. Finally, we demonstrate, through simulation, no loss in power for detection of true associations with the use of mitochondrial PCA.
| S-EPMC3032075 | biostudies-literature
Project description:An important tool in information analysis is dimensionality reduction. There are various approaches for large data simplification by scaling its dimensions down that play a significant role in recognition and classification tasks. The efficiency of dimension reduction tools is measured in terms of memory and computational complexity, which are usually a function of the number of the given data points. Sparse local operators that involve substantially less than quadratic complexity at one end, and faithful multiscale models with quadratic cost at the other end, make the design of dimension reduction procedure a delicate balance between modeling accuracy and efficiency. Here, we combine the benefits of both and propose a low-dimensional multiscale modeling of the data, at a modest computational cost. The idea is to project the classical multidimensional scaling problem into the data spectral domain extracted from its Laplace-Beltrami operator. There, embedding into a small dimensional Euclidean space is accomplished while optimizing for a small number of coefficients. We provide a theoretical support and demonstrate that working in the natural eigenspace of the data, one could reduce the process complexity while maintaining the model fidelity. As examples, we efficiently canonize nonrigid shapes by embedding their intrinsic metric into , a method often used for matching and classifying almost isometric articulated objects. Finally, we demonstrate the method by exposing the style in which handwritten digits appear in a large collection of images. We also visualize clustering of digits by treating images as feature points that we map to a plane.
| S-EPMC3831466 | biostudies-other
Project description:Rheumatoid arthritis (RA) is a chronic autoimmune, inflammatory disease, characterized by synovitis in small- and medium-sized joints and, if not treated early and efficiently, joint damage, and destruction. RA is a heterogeneous disease with a plethora of treatment options. The pro-inflammatory cytokine tumor necrosis factor (TNF) plays a central role in the pathogenesis of RA, and TNF inhibitors effectively repress inflammatory activity in RA. Currently, treatment decisions are primarily based on empirics and economic considerations. However, the considerable interpatient variability in response to treatment is a challenge. Markers for a more exact patient classification and stratification are lacking. The objective of this study was to identify markers in immune cell populations that distinguish RA patients from healthy donors with an emphasis on TNF signaling. We employed mass cytometry (CyTOF) with a panel of 13 phenotyping and 10 functional markers to explore signaling in unstimulated and TNF-stimulated peripheral blood mononuclear cells from 20 newly diagnosed, untreated RA patients and 20 healthy donors. The resulting high-dimensional data were analyzed in three independent analysis pipelines, characterized by differences in both data clean-up, identification of cell subsets/clustering and statistical approaches. All three analysis pipelines identified p-p38, IkBa, p-cJun, p-NFkB, and CD86 in cells of both the innate arm (myeloid dendritic cells and classical monocytes) and the adaptive arm (memory CD4+ T cells) of the immune system as markers for differentiation between RA patients and healthy donors. Inclusion of the markers p-Akt and CD120b resulted in the correct classification of 18 of 20 RA patients and 17 of 20 healthy donors in regression modeling based on a combined model of basal and TNF-induced signal. Expression patterns in a set of functional markers and specific immune cell subsets were distinct in RA patients compared to healthy individuals. These signatures may support studies of disease pathogenesis, provide candidate markers for response, and non-response to TNF inhibitor treatment, and aid the identification of future therapeutic targets.
| S-EPMC6626904 | biostudies-literature
Project description:Population structure occurs when a sample is composed of individuals with different ancestries and can result in excess type I error in genome-wide association studies. Genome-wide principal-component analysis (PCA) has become a popular method for identifying and adjusting for subtle population structure in association studies. Using the Genetic Analysis Workshop 16 (GAW16) NARAC data, we explore two unresolved issues concerning the use of genome-wide PCA to account for population structure in genetic associations studies: the choice of single-nucleotide polymorphism (SNP) subset and the choice of adjustment model. We computed PCs for subsets of genome-wide SNPs with varying levels of LD. The first two PCs were similar for all subsets and the first three PCs were associated with case status for all subsets. When the PCs associated with case status were included as covariates in an association model, the reduction in genomic inflation factor was similar for all SNP sets. Several models have been proposed to account for structure using PCs, but it is not yet clear whether the different methods will result in substantively different results for association studies with individuals of European descent. We compared genome-wide association p-values and results for two positive-control SNPs previously associated with rheumatoid arthritis using four PC adjustment methods as well as no adjustment and genomic control. We found that in this sample, adjusting for the continuous PCs or adjusting for discrete clusters identified using the PCs adequately accounts for the case-control population structure, but that a recently proposed randomization test performs poorly.
| S-EPMC2795879 | biostudies-literature
Project description:Although principal component analysis is frequently used in multivariate/ analysis, it has disadvantages when applied to experimental or diagnostic data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the set. Second, the method is sensitive to experimental noise and bias between sample groups, since it cannot reflect the design of experiments; rather, it estimates the same weight and independence of all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. The resulting components were scaled to unify their size unit. Also, the principal axes were identified using training data sets and shared among experiments. This training data reflects the design of experiments, and its preparation allows noise to be reduced and group bias to be removed. The effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. Additionally, unknown samples were appropriately classified using pre-arranged axes, and principal axes well reflected the characteristics of groups in the experiments. This SuperSeries is composed of the SubSeries listed below.
2011-08-15 | GSE31375 | GEO
Project description:OBJECTIVE:To determine the performance of 3 circulating markers for the diagnosis and the progression of interstitial lung disease (ILD) associated with rheumatoid arthritis (RA). METHODS:Serum concentrations of 3 circulating markers, lung epithelial-derived surfactant protein D (SPD), chemokine CCL-18 and Krebs von den Lungen-6 glycoprotein (KL-6), were measured by ELISA in consecutive patients with established RA. These patients were recruited from 3 tertiary centers and they all had been investigated by chest high-resolution computed tomography (HRCT). For a subset of French patients, a follow-up HRCT was available (mean interval between HRCT: 3±1.5 years). RESULTS:Among the 147 included patients (age: 66 ± 12 years, 69% women, disease duration 11 ± 10 years), 40 (27%) had RA-ILD on chest HRCT. SPD, CCL18 and KL-6 concentrations were significantly higher in patients with RA-ILD. ROC curve analysis to assess the diagnostic abilities of the three markers for the diagnosis of RA-ILD showed a superiority of KL-6 (Area under the curve, AUC: 0.79 95% CI 0.72-0.86) compared to SPD (AUC: 0.66 95% CI 0.58-0.74) and CCL18 (AUC: 0.62, 95% CI 0.53-0.70). The sensitivity of KL-6 for the diagnosis of RA-ILD was 68% with a specificity of 83%. The combination of KL-6 with SPD and CCL18 improved its diagnostic ability, with increased sensitivity from 68% to 77%, specificity from 83% to 97%. Increased KL-6 levels were independently associated with the presence of RA-ILD after the adjustment on other RA-ILD risk factors. In the French subset with longitudinal data, baseline KL-6 serum levels were predictive of ILD progression and the degree of ILD progression on HRCT was proportional to baseline KL-6 concentrations. CONCLUSION:These results show that KL-6 is a relevant circulating marker for the diagnosis and might be an interesting marker for the progression of RA-ILD.
| S-EPMC7209254 | biostudies-literature
Project description:ObjectiveMultidimensional computerized adaptive testing enables precise measurements of patient-reported outcomes at an individual level across different dimensions. This study examined the construct validity of a multidimensional computerized adaptive test (CAT) for fatigue in rheumatoid arthritis (RA).MethodsThe 'CAT Fatigue RA' was constructed based on a previously calibrated item bank. It contains 196 items and three dimensions: 'severity', 'impact' and 'variability' of fatigue. The CAT was administered to 166 patients with RA. They also completed a traditional, multidimensional fatigue questionnaire (BRAF-MDQ) and the SF-36 in order to examine the CAT's construct validity. A priori criterion for construct validity was that 75% of the correlations between the CAT dimensions and the subscales of the other questionnaires were as expected. Furthermore, comprehensive use of the item bank, measurement precision and score distribution were investigated.ResultsThe a priori criterion for construct validity was supported for two of the three CAT dimensions (severity and impact but not for variability). For severity and impact, 87% of the correlations with the subscales of the well-established questionnaires were as expected but for variability, 53% of the hypothesised relations were found. Eighty-nine percent of the items were selected between one and 137 times for CAT administrations. Measurement precision was excellent for the severity and impact dimensions, with more than 90% of the CAT administrations reaching a standard error below 0.32. The variability dimension showed good measurement precision with 90% of the CAT administrations reaching a standard error below 0.44. No floor- or ceiling-effects were found for the three dimensions.ConclusionThe CAT Fatigue RA showed good construct validity and excellent measurement precision on the dimensions severity and impact. The dimension variability had less ideal measurement characteristics, pointing to the need to recalibrate the CAT item bank with a two-dimensional model, solely consisting of severity and impact.
| S-EPMC4692469 | biostudies-literature
Project description:When two or more populations have been separated by geographic or cultural boundaries for many generations, drift, spontaneous mutations, differential selection pressures and other factors may lead to allele frequency differences among populations. If these 'parental' populations subsequently come together and begin inter-mating, disequilibrium among linked markers may span a greater genetic distance than it typically does among populations under panmixia [see glossary]. This extended disequilibrium can make association studies highly effective and more economical than disequilibrium mapping in panmictic populations since less marker loci are needed to detect regions of the genome that harbor phenotype-influencing loci. However, under some circumstances, this process of intermating (as well as other processes) can produce disequilibrium between pairs of unlinked loci and thus create the possibility of confounding or spurious associations due to this population stratification. Accordingly, researchers are advised to employ valid statistical tests for linkage disequilibrium mapping allowing conduct of genetic association studies that control for such confounding. Many recent papers have addressed this need. We provide a comprehensive review of advances made in recent years in correcting for population stratification and then evaluate and synthesize these methods based on statistical principles such as (1) randomization, (2) conditioning on sufficient statistics, and (3) identifying whether the method is based on testing the genotype-phenotype covariance (conditional upon familial information) and/or testing departures of the marginal distribution from the expected genotypic frequencies.
| S-EPMC2803696 | biostudies-literature