scholarly journals A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation

2021 ◽  
Vol 12 ◽  
Author(s):  
Victoria L. Arthur ◽  
Zhengbang Li ◽  
Rui Cao ◽  
William S. Oetting ◽  
Ajay K. Israni ◽  
...  
Keyword(s):  
Statistical Power ◽  
Degrees Of Freedom ◽  
Score Test ◽  
Human Leukocyte ◽  
Genetic Data ◽  
Matching Effect ◽  
Transplant Outcomes ◽  
Genotype Effect ◽  
Eigen Decomposition ◽  
Matching Score

Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.

scholarly journals A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS

Biostatistics ◽  
2017 ◽  
Vol 18 (3) ◽  
pp. 477-494 ◽  
Author(s):  
Jakub Pecanka ◽  
Marianne A. Jonker ◽  
Zoltan Bochdanovits ◽  
Aad W. Van Der Vaart ◽  
Keyword(s):  
Complex Traits ◽  
Multiple Testing ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Score Test ◽  
Interaction Model ◽  
Type I ◽  
Two Stage ◽  
Genome Wide ◽  
Strong Control

Summary For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the “missing heritability” of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson’s disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.

Unified Rotational and Translational Stiffness Characterization of Compliant Shell Mechanisms

2018 ◽  
Author(s):  
Joost R. Leemans ◽  
Charles J. Kim ◽  
Werner W. P. J. van de Sande ◽  
Just L. Herder
Keyword(s):  
Degrees Of Freedom ◽  
Compliant Mechanisms ◽  
Screw Theory ◽  
Characterization Methods ◽  
Six Degrees Of Freedom ◽  
Thin Walled Structures ◽  
Spatial Mechanisms ◽  
Eigen Decomposition ◽  
Comprehensive Characterization

Compliant shell mechanisms utilize spatially curved thin-walled structures to transfer or transmit force, motion or energy through elastic deformation. To design with spatial mechanisms designers need comprehensive characterization methods, while existing methods fall short of meaningful comparisons between rotational and translational degrees of freedom. This paper presents two approaches, both of which are based on the principle of virtual loads and potential energy, utilizing properties of screw theory, Plücker coordinates and an eigen-decomposition, leading to two unification lengths that can be used to compare and visualize all six degrees of freedom directions and magnitudes of compliant mechanisms in a non-arbitrary physically meaningful manner.

scholarly journals Powerful Tukey's One Degree-of-Freedom Test for Detecting Gene-Gene and Gene-Environment Interactions

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17305 ◽  
Author(s):  
Yaping Wang ◽  
Donghui Li ◽  
Peng Wei
Keyword(s):  
Statistical Power ◽  
Association Studies ◽  
Score Test ◽  
Principal Component ◽  
Case Control ◽  
Degree Of Freedom ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Missing Heritability ◽  
Gene Environment

Genome-wide association studies (GWASs) have identified thousands of single nucleotide polymorphisms (SNPs) robustly associated with hundreds of complex human diseases including cancers. However, the large number of G WAS-identified genetic loci only explains a small proportion of the disease heritability. This “missing heritability” problem has been partly attributed to the yet-to-be-identified gene-gene (G × G) and gene-environment (G × E) interactions. In spite of the important roles of G × G and G × E interactions in understanding disease mechanisms and filling in the missing heritability, straightforward GWAS scanning for such interactions has very limited statistical power, leading to few successes. Here we propose a two-step statistical approach to test G × G/G × E interactions: the first step is to perform principal component analysis (PCA) on the multiple SNPs within a gene region, and the second step is to perform Tukey's one degree-of-freedom (1-df) test on the leading PCs. We derive a score test that is computationally fast and numerically stable for the proposed Tukey's 1-df interaction test. Using extensive simulations we show that the proposed approach, which combines the two parsimonious models, namely, the PCA and Tukey's 1-df form of interaction, outperforms other state-of-the-art methods. We also demonstrate the utility and efficiency gains of the proposed method with applications to testing G × G interactions for Crohn's disease using the Wellcome Trust Case Control Consortium (WTCCC) GWAS data and testing G × E interaction using data from a case-control study of pancreatic cancer.

scholarly journals Statistical model specification and power: recommendations on the use of test-qualified pooling in analysis of experimental data

2017 ◽  
Vol 284 (1851) ◽  
pp. 20161850 ◽  
Author(s):  
Nick Colegrave ◽  
Graeme D. Ruxton
Keyword(s):  
Experimental Data ◽  
Statistical Model ◽  
Statistical Power ◽  
Error Term ◽  
Degrees Of Freedom ◽  
Type I Error ◽  
Error Rates ◽  
Statistical Testing ◽  
Model Specification ◽  
Type I

A common approach to the analysis of experimental data across much of the biological sciences is test-qualified pooling. Here non-significant terms are dropped from a statistical model, effectively pooling the variation associated with each removed term with the error term used to test hypotheses (or estimate effect sizes). This pooling is only carried out if statistical testing on the basis of applying that data to a previous more complicated model provides motivation for this model simplification; hence the pooling is test-qualified. In pooling, the researcher increases the degrees of freedom of the error term with the aim of increasing statistical power to test their hypotheses of interest. Despite this approach being widely adopted and explicitly recommended by some of the most widely cited statistical textbooks aimed at biologists, here we argue that (except in highly specialized circumstances that we can identify) the hoped-for improvement in statistical power will be small or non-existent, and there is likely to be much reduced reliability of the statistical procedures through deviation of type I error rates from nominal levels. We thus call for greatly reduced use of test-qualified pooling across experimental biology, more careful justification of any use that continues, and a different philosophy for initial selection of statistical models in the light of this change in procedure.

scholarly journals Renal transplant outcomes and de novo donor-specific anti-human leukocyte antigen antibodies: a systematic review

2018 ◽  
Vol 33 (8) ◽  
pp. 1472-1480 ◽  
Author(s):  
Ankit Sharma ◽  
Joshua R Lewis ◽  
Wai H Lim ◽  
Suetonia Palmer ◽  
Giovanni Strippoli ◽  
...  
Keyword(s):  
Systematic Review ◽  
Renal Transplant ◽  
Human Leukocyte Antigen ◽  
De Novo ◽  
Human Leukocyte ◽  
Leukocyte Antigen ◽  
Transplant Outcomes

scholarly journals Efficient approaches for large scale GWAS studies with genotype uncertainty

2019 ◽  
Author(s):  
Emil Jørsboe ◽  
Anders Albrechtsen
Keyword(s):  
Population Structure ◽  
Allele Frequency ◽  
Statistical Power ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Data ◽  
Data Sets ◽  
Sequencing Data ◽  
The Individual ◽  
Genotype Probabilities

1AbstractIntroductionAssociation studies using genetic data from SNP-chip based imputation or low depth sequencing data provide a cost efficient design for large scale studies. However, these approaches provide genetic data with uncertainty of the observed genotypes. Here we explore association methods that can be applied to data where the genotype is not directly observed. We investigate how using different priors when estimating genotype probabilities affects the association results in different scenarios such as studies with population structure and varying depth sequencing data. We also suggest a method (ANGSD-asso) that is computational feasible for analysing large scale low depth sequencing data sets, such as can be generated by the non-invasive prenatal testing (NIPT) with low-pass sequencing.MethodsANGSD-asso’s EM model works by modelling the unobserved genotype as a latent variable in a generalised linear model framework. The software is implemented in C/C++ and can be run multi-threaded enabling the analysis of big data sets. ANGSD-asso is based on genotype probabilities, they can be estimated in various ways, such as using the sample allele frequency as a prior, using the individual allele frequencies as a prior or using haplotype frequencies from haplotype imputation. Using simulations of sequencing data we explore how genotype probability based method compares to using genetic dosages in large association studies with genotype uncertainty.Results & DiscussionOur simulations show that in a structured population using the individual allele frequency prior has better power than the sample allele frequency. If there is a correlation between genotype uncertainty and phenotype, then the individual allele frequency prior also helps control the false positive rate. In the absence of population structure the sample allele frequency prior and the individual allele frequency prior perform similarly. In scenarios with sequencing depth and phenotype correlation ANGSD-asso’s EM model has better statistical power and less bias compared to using dosages. Lastly when adding additional covariates to the linear model ANGSD-asso’s EM model has more statistical power and provides less biased effect sizes than other methods that accommodate genotype uncertainly, while also being much faster. This makes it possible to properly account for genotype uncertainty in large scale association studies.

scholarly journals Factors affecting employee engagement: using sem model for study case of social insurance in Ba Ria - Vung Tau province

2021 ◽  
Vol 5 (3) ◽  
pp. first
Author(s):  
Trần Quang Cảnh ◽  
Vũ Trực Phức ◽  
Hồ Ngọc Minh
Keyword(s):  
Factor Analysis ◽  
Organizational Behavior ◽  
Employee Engagement ◽  
Leadership Style ◽  
Statistical Power ◽  
Social Insurance ◽  
Degrees Of Freedom ◽  
Structural Model ◽  
Empirical Formulas ◽  
Factors Affecting

The employee engagement is an approach in the study of organizational behavior. There have been many studies done to find out the factors affecting the employee engagement to the organization. Limitation of previous studies is that, when choosing the number of factors to be retained, authors based only on the Eigienvalues (eigenvalue-one criterion). They did not take into account the cumulative percentage, screening test, percentage. The variance is calculated for each factor and the interpretability of each factor (The Interpretability Criterion). When doing confirmatory factor analysis (CFA), previous studies also did not test the statistical power of the studies. Samples of those studies, have often been taken according to empirical formulas that did not take into account the required statistical power and degrees of freedom of the study. This study was conducted at the social insurance agency of Ba Ria - Vung Tau province, from January 2019 to May 2019, with the aims to finding out and identify the factors that affect to employee engagement, with the analysis has supplementing and overcoming the shortcomings as mentioned above. In this paper, We use statistical software SAS to perform steps key component analysis (CPA), assess the reliability of the scale by Cronbach's Alpha index, exploratory factor analysis (EFA), Confirmation factor analysis (CFA) and Linear structural model analysis (SEM). The analysis results show that employee engagement with the organization is positively affected by 5 factors. The order of impact level are: Salary, bonus and welfare; Training and development opportunities; Organizational culture; Relationships with colleagues and Organizational Leadership Style.

Sign in / Sign up

Export Citation Format

Share Document