scholarly journals Principal Component Regression and Linear Mixed Model in Association Analysis of Structured Samples: Competitors or Complements?

2014 ◽  
Vol 39 (3) ◽  
pp. 149-155 ◽  
Author(s):  
Yiwei Zhang ◽  
Wei Pan
Keyword(s):  
Association Analysis ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Principal Component Regression ◽  
Principal Component

Modeling relevance of mobile communication services by social setting dimensions

2016 ◽  
Vol 20 (1) ◽  
pp. 311-331
Author(s):  
Elena Menichelli ◽  
Richard Ling
Keyword(s):  
Mobile Communication ◽  
Communication Channel ◽  
Mixed Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Communication Services ◽  
Channel Choice ◽  
Social Settings ◽  
Negative Messages ◽  
The Relationship

There is little research examining the confluence of what communication channel is used for which purpose with which person. This study examines the “setting” for communication that includes what is communicated (e.g. positive or negative messages), the nature of the relationship (close versus distant), and the information channel. The respondents to a web-based questionnaire ( n = 627) were Norwegian smartphone users aged 16–35 years. Respondents evaluated mobile communication services that they used in specific social settings by “checking off” all that apply. Two methods of analysis are used to examine the material. First, a Principal Component Regression validated the main method, namely a mixed model for the Analysis of Variance. Results show the probability of using a mobile communication service is based on the effects of social group, communication purpose, communication channel, and their interaction. The relationship to the interlocutor was found to have the strongest effect on channel choice.

scholarly journals Stability of Rice Mutant Lines By Linear Mixed Model And Suggestion A New Index Based For Yield Performance And Stability

2021 ◽  
Author(s):  
Peyman Sharifi ◽  
Ali Akbar Ebadi ◽  
Mohammad Taher Hallajian
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Block Design ◽  
Weighted Average ◽  
Principal Component ◽  
Fars Province ◽  
Yield Performance ◽  
Rice Mutant ◽  
Superior Genotypes ◽  
Mutant Lines

Abstract Fourteen rice mutant lines with four cultivars were evaluated in a randomized complete block design with three replications in three locations in Iran (Rasht, ChaparSar and Fars province) during two growing seasons (2014-2016). In addition, a new index namely as equivalent index of stability and performance (EISP) is suggested for simultaneous evaluation of yield performance and stability. The heat map of yield performance and WAASB (weighted average of absolute scores based on BLUP (best linear unbiased prediction)) identified G3, G9, G6, G12 and G5 as highly productive and stable genotypes. Based on the analysis by multi-trait stability index (MTSI) G7, G5 and G1 were selected as superior genotypes. The top five superior genotypes based on harmonic mean and of the relative performance of genotypic values (HMRPGV) were G5, G12, G7, G2 and G1. For verification of EISP, its value was calculated for some of multi and univariate stability indices and identified genotypes G5 and G12 as the best ones. Principal component analysis indicated yield positively correlated with HMGV, RPGV, HMRPGV, EIS2P EIbP and EIPiP. In conclusion, G12, G5 and G9 had a significant advantage over all genotypes and could undergo selection or cultivar introduction processes.

Genetic Association Analysis of Lipid Profiles Using Linear Mixed Model

2006 ◽  
Vol 36 (3) ◽  
pp. 229
Author(s):  
Kijun Song ◽  
Chan Mi Park ◽  
Kil Seob Lim ◽  
Yang Soo Jang ◽  
Dong Kee Kim
Keyword(s):  
Genetic Association ◽  
Association Analysis ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Lipid Profiles ◽  
Genetic Association Analysis

scholarly journals FFselect: An improved linear mixed model for genome-wide association study in populations featuring shared environments confounded by relatedness

2020 ◽  
Author(s):  
Nichol Schultz ◽  
Kent Weigel
Keyword(s):  
Association Analysis ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
False Discovery ◽  
Genome Wide ◽  
Novel Method ◽  
Cryptic Relatedness

AbstractLinear mixed models are effective tools to identify genetic loci contributing to phenotypic variation while handling confounding due to population structure and cryptic relatedness. Recent improvements of the linear mixed model for genome-wide association analysis have been directed at more accurately modeling loci of large effect. We describe FFselect (https://github.com/NicholSchultz/FFselect), a novel method that both builds upon recent advances and further extends the linear mixed model for genome-wide association analysis to allow modeling of shared environmental effects. FFselect improves power, controls false discovery rate, and simultaneously corrects for environmental confounding to improve the utility of GWAS.

scholarly journals Generalized Hierarchical Mixed Model Association Analysis

2021 ◽  
Author(s):  
Runqing Yang ◽  
Yuxin Song ◽  
Zhiyu Hao ◽  
Zhonghua Liu
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Least Square ◽  
Linear Unbiased Prediction ◽  
Genomic Breeding ◽  
Genome Wide ◽  
Complex Population ◽  
Best Linear Unbiased

AbstractIn genome-wide association analysis for complex diseases, we partitioned the genomic generalized linear mixed model (GLMM) into two hierarchies—the GLMM regarding genomic breeding values (GBVs) and a generalized linear regression of the GBVs to the tested marker effects. In the first hierarchy, the GBVs were predicted by solving for the genomic best linear unbiased prediction for GLMM, and in the second hierarchy, association tests were performed using the generalized least square (GLS) method. The so-called Hi-GLMM method exhibited advantages over existing methods in terms of both genomic control for complex population structure and statistical power to detect quantitative trait nucleotides (QTNs), especially when the GBVs were estimated precisely, and using joint association analysis for QTN candidates obtained from a test at once.

scholarly journals Efficient Genomic Control for Mixed Model Associations in Large-scale Population

2021 ◽  
Author(s):  
Zhiyu Hao ◽  
Jin Gao ◽  
Yuxin Song ◽  
Runqing Yang ◽  
Di Liu
Keyword(s):  
Association Analysis ◽  
Statistical Power ◽  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
False Negative ◽  
False Negative Rate ◽  
Genomic Control ◽  
Genomic Heritability ◽  
Complex Population

AbstractAmong linear mixed model-based association methods, GRAMMAR has the lowest computing complexity for association tests, but it produces a high false-negative rate due to the deflation of test statistics for complex population structure. Here, we present an optimized GRAMMAR method by efficient genomic control, Optim-GRAMMAR, that estimates the phenotype residuals by regulating downward genomic heritability in the genomic best linear unbiased prediction. Even though using the fewer sampling markers to evaluate genomic relationship matrices and genomic controls, Optim-GRAMMAR retains a similar statistical power to the exact mixed model association analysis, which infers an extremely efficient approach to handle large-scale data. Moreover, joint association analysis significantly improved statistical power over existing methods.

scholarly journals Background ozone over Canada and the United States

2009 ◽  
Vol 9 (5) ◽  
pp. 21111-21164 ◽  
Author(s):  
E. Chan ◽  
R. J. Vet
Keyword(s):  
United States ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Principal Component ◽  
The United States ◽  
Eastern Canada ◽  
Mixing Ratios ◽  
Background Ozone ◽  
The Pacific ◽  
Decadal Scale

Abstract. Planetary boundary layer (PBL) ozone temporal variations were investigated on diurnal, seasonal and decadal scales in various regions across Canada and the United States for the period 1997–2006. Background ozone is difficult to quantify and define through observations. In light of the importance of its estimates for achievable policy targets, evaluation of health impacts and relationship with climate, background ozone mixing ratios were estimated. Principal Component Analyses (PCA) were performed using 97 non-urban ozone sites for each season to define contiguous regions. Backward air parcel trajectories were used to systematically select the cleanest background air cluster associated with the lowest May–September 95th percentile for each site. Decadal ozone trends were estimated by season for each PCA-derived region using a~generalized linear mixed model (GLMM). Background ozone mixing ratios were variable geographically and seasonally. For example, the mixing ratios annually ranged from 21 to 38, and 23 to 38 ppb for the continental Eastern Canada and Eastern US. The Pacific and Atlantic coastal regions typically had relatively low background levels ranging from 14 to 24, and 17 to 36 ppb, respectively. On the decadal scale, the direction and magnitude of trends are different in all seasons across the regions (−1.56 to +0.93 ppb/a). Trends increased in the Pacific region for all seasons. Background ozone decadal changes are shown to be masked by the much stronger regional signals in areas that have seen substantial reductions of ozone precursors since the early 2000s.

scholarly journals Testing for genetic associations in arbitrarily structured populations

2014 ◽  
Author(s):  
Minsun Song ◽  
Wei Hao ◽  
John D. Storey
Keyword(s):  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Principal Component ◽  
Statistical Test ◽  
Structured Populations ◽  
Birth Cohort Study ◽  
Genome Wide Association Studies ◽  
Genetic Associations

We present a new statistical test of association between a trait and genetic markers, which we theoretically and practically prove to be robust to arbitrarily complex population structure. The statistical test involves a set of parameters that can be directly estimated from large-scale genotyping data, such as that measured in genome-wide association studies (GWAS). We also derive a new set of methodologies, called a genotype-conditional association test (GCAT), shown to provide accurate association tests in populations with complex structures, manifested in both the genetic and environmental contributions to the trait. We demonstrate the proposed method on a large simulation study and on the Northern Finland Birth Cohort study. In the Finland study, we identify several new significant loci that other methods do not detect. Our proposed framework provides a substantially different approach to the problem from existing methods, such as the linear mixed model and principal component approaches.

Sign in / Sign up

Export Citation Format

Share Document