An assessment of the performance of the logistic mixed model for analyzing binary traits in maize and sorghum diversity panels

Abstract Maximum likelihood methodology was applied to determine the mode of inheritance of rare binary traits with data structures typical for swine populations. The genetic models considered included a monogenic, a digenic, a polygenic, and three mixed polygenic and major gene models. The main emphasis was on the detection of major genes acting on a polygenic background. Deterministic algorithms were employed to integrate and maximize likelihoods. A simulation study was conducted to evaluate model selection and parameter estimation. Three designs were simulated that differed in the number of sires/number of dams within sires (10/10, 30/30, 100/30). Major gene effects of at least one SD of the liability were detected with satisfactory power under the mixed model of inheritance, except for the smallest design. Parameter estimates were empirically unbiased with acceptable standard errors, except for the smallest design, and allowed to distinguish clearly between the genetic models. Distributions of the likelihood ratio statistic were evaluated empirically, because asymptotic theory did not hold. For each simulation model, the Average Information Criterion was computed for all models of analysis. The model with the smallest value was chosen as the best model and was equal to the true model in almost every case studied.

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Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2018.12.012 ◽

2019 ◽

Vol 104 (2) ◽

pp. 260-274 ◽

Cited By ~ 26

Author(s):

Han Chen ◽

Jennifer E. Huffman ◽

Jennifer A. Brody ◽

Chaolong Wang ◽

Seunggeun Lee ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Large Scale ◽

Mixed Model ◽

Whole Genome ◽

Association Tests ◽

Binary Traits ◽

Sequencing Studies

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FastGWA-GLMM: a generalized linear mixed model association tool for biobank-scale data

10.21203/rs.3.rs-128758/v1 ◽

2021 ◽

Author(s):

Jian Yang ◽

Longda Jiang ◽

Zhili Zheng

Keyword(s):

Complex Traits ◽

Mixed Model ◽

Linear Mixed Model ◽

Rare Variants ◽

Sparse Matrix ◽

Extreme Case ◽

Generalized Linear Mixed Model ◽

Binary Complex ◽

Binary Traits ◽

The Uk

Abstract Compared to linear mixed model-based genome-wide association (GWA) methods, generalized linear mixed model (GLMM)-based methods have better statistical properties when applied to binary traits but are computationally much slower. Here, leveraging efficient sparse matrix-based algorithms, we developed a GLMM-based GWA tool (called fastGWA-GLMM) that is orders of magnitude faster than the state-of-the-art tool (e.g., ~37 times faster when n=400,000) with more scalable memory usage. We show by simulation that the fastGWA-GLMM test-statistics of both common and rare variants are well-calibrated under the null, even for traits with an extreme case-control ratio (e.g., 0.1%). We applied fastGWA-GLMM to the UK Biobank data of 456,348 individuals, 11,842,647 variants and 2,989 binary traits (full summary statistics available at http://fastgwa.info/ukbimpbin) and identified 259 rare variants associated with 75 traits, demonstrating the use of imputed genotype data in a large cohort to discover rare variants for binary complex traits.

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Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole genome sequencing studies

10.1101/395046 ◽

2018 ◽

Cited By ~ 2

Author(s):

Han Chen ◽

Jennifer E. Huffman ◽

Jennifer A. Brody ◽

Chaolong Wang ◽

Seunggeun Lee ◽

...

Keyword(s):

Population Structure ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Large Scale ◽

Mixed Model ◽

Null Model ◽

Type I ◽

Whole Genome ◽

Association Tests ◽

Binary Traits

ABSTRACTWith advances in Whole Genome Sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and Sequence Kernel Association Test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally-efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-Set Mixed Model Association Tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program. SMMAT tests share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be only fit once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMAT tests correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

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Effects of Aging on the Subcortical Encoding of Stop Consonants

American Journal of Audiology ◽

10.1044/2020_aja-19-00044 ◽

2020 ◽

Vol 29 (3) ◽

pp. 391-403

Author(s):

Dania Rishiq ◽

Ashley Harkrider ◽

Cary Springer ◽

Mark Hedrick

Keyword(s):

Older Adults ◽

Repeated Measures ◽

Mixed Model ◽

Auditory Brainstem ◽

Auditory Brainstem Responses ◽

Aging Effects ◽

Formant Frequency ◽

Place Of Articulation ◽

Second Formant ◽

Effects Of Aging

Purpose The main purpose of this study was to evaluate aging effects on the predominantly subcortical (brainstem) encoding of the second-formant frequency transition, an essential acoustic cue for perceiving place of articulation. Method Synthetic consonant–vowel syllables varying in second-formant onset frequency (i.e., /ba/, /da/, and /ga/ stimuli) were used to elicit speech-evoked auditory brainstem responses (speech-ABRs) in 16 young adults ( M age = 21 years) and 11 older adults ( M age = 59 years). Repeated-measures mixed-model analyses of variance were performed on the latencies and amplitudes of the speech-ABR peaks. Fixed factors were phoneme (repeated measures on three levels: /b/ vs. /d/ vs. /g/) and age (two levels: young vs. older). Results Speech-ABR differences were observed between the two groups (young vs. older adults). Specifically, older listeners showed generalized amplitude reductions for onset and major peaks. Significant Phoneme × Group interactions were not observed. Conclusions Results showed aging effects in speech-ABR amplitudes that may reflect diminished subcortical encoding of consonants in older listeners. These aging effects were not phoneme dependent as observed using the statistical methods of this study.

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Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽

10.1027/1614-2241/a000124 ◽

2017 ◽

Vol 13 (1) ◽

pp. 9-22 ◽

Cited By ~ 1

Author(s):

Pablo Livacic-Rojas ◽

Guillermo Vallejo ◽

Paula Fernández ◽

Ellián Tuero-Herrero

Keyword(s):

Repeated Measures ◽

Statistical Power ◽

Mixed Model ◽

Covariance Structure ◽

Simulation Method ◽

Future Research ◽

Repeated Measures Design ◽

Fixed And Random Effects ◽

Split Plot ◽

High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

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