scholarly journals Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

2021 ◽  
Author(s):  
Anna L Tyler ◽  
Baha El Kassaby ◽  
Georgi Kolishovski ◽  
Jake Emerson ◽  
Ann E Wells ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Interaction ◽  
Recombinant Inbred Lines ◽  
Test Statistics ◽  
Test Statistic ◽  
Kinship Matrix ◽  
Main Effect ◽  
Main Effects ◽  
Interaction Test

Abstract It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

scholarly journals Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

2021 ◽  
Author(s):  
Anna L. Tyler ◽  
Baha El Kassaby ◽  
Georgi Kolishovski ◽  
Jake Emerson ◽  
Ann Wells ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Interaction ◽  
Recombinant Inbred Lines ◽  
Test Statistics ◽  
Test Statistic ◽  
Kinship Matrix ◽  
Main Effect ◽  
Main Effects ◽  
Interaction Test

AbstractIt is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied, are the effects of kinship on genetic interaction test statistics. Here we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using a LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

scholarly journals Permutation Testing in the Presence of Polygenic Variation

2015 ◽  
Author(s):  
Mark Abney
Keyword(s):  
Quantitative Trait ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Permutation Test ◽  
Statistical Significance ◽  
Null Distribution ◽  
Polygenic Effect ◽  
Permutation Testing ◽  
Test Statistic ◽  
Omnibus Test

This article discusses problems with and solutions to performing valid permutation tests for quantitative trait loci in the presence of polygenic effects. Although permutation testing is a popular approach for determining statistical significance of a test statistic with an unknown distribution--for instance, the maximum of multiple correlated statistics or some omnibus test statistic for a gene, gene-set or pathway--naive application of permutations may result in an invalid test. The risk of performing an invalid permutation test is particularly acute in complex trait mapping where polygenicity may combine with a structured population resulting from the presence of families, cryptic relatedness, admixture or population stratification. I give both analytical derivations and a conceptual understanding of why typical permutation procedures fail and suggest an alternative permutation based algorithm, MVNpermute, that succeeds. In particular, I examine the case where a linear mixed model is used to analyze a quantitative trait and show that both phenotype and genotype permutations may result in an invalid permutation test. I provide a formula that predicts the amount of inflation of the type 1 error rate depending on the degree of misspecification of the covariance structure of the polygenic effect and the heritability of the trait. I validate this formula by doing simulations, showing that the permutation distribution matches the theoretical expectation, and that my suggested permutation based test obtains the correct null distribution. Finally, I discuss situations where naive permutations of the phenotype or genotype are valid and the applicability of the results to other test statistics.

scholarly journals The Effect of Test, Electrode, and Rate on Electrocochleography Measures

2019 ◽  
Vol 30 (01) ◽  
pp. 041-053 ◽  
Author(s):  
Alyson Butler Lake ◽  
Andrew Stuart
Keyword(s):  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Amplitude Ratio ◽  
Area Ratio ◽  
Electrode Placement ◽  
Stimulus Rate ◽  
Summating Potential ◽  
Main Effect ◽  
Test Electrode

AbstractElectrocochleography (ECochG) is the measurement of stimulus-related cochlear potentials and the compound action potential (AP). Its primary clinical application is with the assessment of inner ear disorders. There are few studies examining the variability of ECochG measures.The objective of the study was to examine the effect of test (i.e., initial versus retest), electrode (i.e., extratympanic versus tympanic), and stimulus rate (i.e., 7.7 versus 77.7/sec) on ECochG indices (i.e., summating potential [SP] amplitude, AP latency, AP amplitude, SP/AP amplitude ratio, and SP/AP area ratio).Correlational and three-factor repeated measures designs were employed.Eighteen normal-hearing young adults participated.ECochG responses were obtained with 90 dB nHL click stimuli for an initial test and retest at two stimulus rates with a commercially available extratympanic (TIPtrode™) and tympanic (Lilly TM-Wick) electrode. Separate repeated measures linear mixed-model analysis of variance examined the effect of test, electrode, and rate for all ECochG indices. Test–retest variability was also examined with correlation analyses; an examination of mean test–retest differences and their 95% confidence intervals (CI); and construction of Bland-Altman plots.The presence of SP and AP responses varied across experimental conditions. Electrode and rate were statistically significant predictors (p < 0.05) of SP and AP responses: SP and AP responses were more likely to be present with the tympanic electrode and at the slow rate. Statistically significant correlations (p < 0.05) were found between initial tests and retests with all ECochG indices with both electrodes with the exception of SP amplitude with the TIPtrode™ electrode. There were no significant main effects of test (initial versus retest) or interactions of test and electrode or rate for any of the ECochG indices (p > 0.05). The 95% CI of the mean test–retest differences contained 0 confirming that the effect of test was not statistically significant. There was a statistically significant main effect of electrode (p < 0.05) on three ECochG measures. The Lilly TM-Wick electrode produced larger SP amplitudes, AP amplitudes, and SP/AP area ratios than TIPtrode™ electrodes. A statistically significant main effect of rate (p < 0.05) was identified for all ECochG measures. The effect of rate on AP latency and amplitude was expected. Increasing the stimulus rate prolonged the AP latency and decreased AP amplitude. SP amplitude was larger for the faster rate.There was no difference between electrodes with regard to test–retest measures. However, considering the higher likelihood of ECochG SP and AP responses and larger SP amplitude, SP/AP amplitude ratio, and SP/AP area ratio indices, the tympanic electrode placement is recommended for clinical practice. The addition of a fast stimulus rate may be considered for enhanced SP amplitude, SP/AP amplitude ratio, and SP/AP area ratio albeit with the consideration of the loss of SP and AP responses in some individuals.

Estimation of Genetic Variance Contributed by a Quantitative Trait Locus — Correcting the Bias Associated with Significance Tests

Genetics ◽  
2021 ◽  
Author(s):  
Fangjie Xie ◽  
Shibo Wang ◽  
William D Beavis ◽  
Shizhong Xu
Keyword(s):  
Qtl Mapping ◽  
Effect Size ◽  
Bias Correction ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Chromosome 9 ◽  
Genome Wide Association Studies ◽  
Test Statistic ◽  
Chi Square

Abstract The Beavis effect in QTL mapping describes a phenomenon that the estimated effect size of a statistically significant QTL (measured by the QTL variance) is greater than the true effect size of the QTL if the sample size is not sufficiently large. This is a typical example of the Winners’ curse applied to molecular quantitative genetics. Theoretical evaluation and correction for the Winners’ curse have been studied for interval mapping. However, similar technologies have not been available for current models of QTL mapping and genome-wide association studies where a polygene is often included in the linear mixed models to control the genetic background effect. In this study, we developed the theory of the Beavis effect in a linear mixed model using a truncated non-central Chi-square distribution. We equated the observed Wald test statistic of a significant QTL to the expectation of a truncated non-central Chi-square distribution to obtain a bias-corrected estimate of the QTL variance. The results are validated from replicated Monte Carlo simulation experiments. We applied the new method to the grain width (GW) trait of a rice population consisting of 524 homozygous varieties with over 300k single nucleotide polymorphism (SNPs) markers. Two loci were identified and the estimated QTL heritability were corrected for the Beavis effect. Bias correction for the larger QTL on chromosome 5 (GW5) with an estimated heritability of 12% did not change the QTL heritability due to the extremely large test score and estimated QTL effect. The smaller QTL on chromosome 9 (GW9) had an estimated QTL heritability of 9% reduced to 6% after the bias-correction.

scholarly journals Can Special Light Glasses Reduce Sleepiness and Improve Sleep of Nightshift Workers? A Placebo-Controlled Explorative Field Study

2020 ◽  
Vol 2 (2) ◽  
pp. 225-245 ◽  
Author(s):  
Mariëlle P. J. Aarts ◽  
Steffen L. Hartmeyer ◽  
Kars Morsink ◽  
Helianthe S. M. Kort ◽  
Yvonne A. W. de Kort
Keyword(s):  
Field Study ◽  
Interaction Effect ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Subjective Sleep Quality ◽  
Placebo Condition ◽  
Significant Interaction Effect ◽  
Sleep Complaints ◽  
Main Effect ◽  
Driver Sleepiness

Nightshift workers go against the natural sleep–wake rhythm. Light can shift the circadian clock but can also induce acute alertness. This placebo-controlled exploratory field study examined the effectiveness of light glasses to improve alertness while reducing the sleep complaints of hospital nurses working nightshifts. In a crossover within-subjects design, 23 nurses participated, using treatment glasses and placebo glasses. Sleepiness and sleep parameters were measured. A linear mixed model analysis on sleepiness revealed no significant main effect of the light intervention. An interaction effect was found indicating that under the placebo condition, sleepiness was significantly higher on the first nightshift than on the last night, while under the treatment condition, sleepiness remained stable across nightshift sessions. Sleepiness during the commute home also showed a significant interaction effect, demonstrating that after the first nightshift, driver sleepiness was higher for placebo than for treatment. Subjective sleep quality showed a negative main effect of treatment vs. placebo, particularly after the first nightshift. In retrospect, both types of light glasses were self-rated as effective. The use of light glasses during the nightshift may help to reduce driver sleepiness during the commute home, which is relevant, as all participants drove home by car or (motor) bike.

Multiple Group Comparisons of the Fixed and Random Effects From the Generalized Linear Mixed Model

2021 ◽  
pp. 004912412098618
Author(s):  
Daniel Kasper ◽  
Katrin Schulz-Heidorf ◽  
Knut Schwippert
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Asymptotic Properties ◽  
Generalized Linear Mixed Model ◽  
Test Statistic ◽  
Multiple Group ◽  
Fixed And Random Effects ◽  
Group Comparisons

In this article, we extend Liao’s test for across-group comparisons of the fixed effects from the generalized linear model to the fixed and random effects of the generalized linear mixed model (GLMM). Using as our basis the Wald statistic, we developed an asymptotic test statistic for across-group comparisons of these effects. The test can be applied when the fixed and random effects are multivariate normally distributed, and it works well for any link function and conditional distribution of the dependent variable of the GLMM. We also derived the asymptotic properties of this test, and because power information does not exist for either our new test statistic or Liao’s test, we implemented a power study to demonstrate the superiority of these tests over the alternatively proposed F test. Using an example, we show the application of the test and then discuss its possible restrictions with respect to the distribution of the random effects.

scholarly journals Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongdong Li ◽  
Zhiqiang Zhou ◽  
Xiaohuan Lu ◽  
Yong Jiang ◽  
Guoliang Li ◽  
...  
Keyword(s):  
Grain Yield ◽  
Recombinant Inbred ◽  
Fixed Effects ◽  
Prediction Accuracy ◽  
Genetic Architecture ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Hybrid Performance

Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.

scholarly journals Symbiotic interactions between chickpea (Cicer arietinum L.) genotypes and Mesorhizobium strains

Symbiosis ◽  
2020 ◽  
Vol 82 (3) ◽  
pp. 235-248
Author(s):  
A. H. Gunnabo ◽  
J. van Heerwaarden ◽  
R. Geurts ◽  
E. Wolde-meskel ◽  
T. Degefu ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Pot Experiment ◽  
Negative Interaction ◽  
Mesorhizobium Ciceri ◽  
Genetic Groups ◽  
Symbiotic Interactions ◽  
Rhizobium Strains ◽  
Ammi Model ◽  
Main Effects

AbstractLegume genotype (GL) x rhizobium genotype (GR) interaction in chickpea was studied using a genetically diverse set of accessions and rhizobium strains in modified Leonard Jars. A subset of effective GL x GR combinations was subsequently evaluated in a pot experiment to identify combinations of chickpea genotypes and rhizobium strains with stable and superior symbiotic performance. A linear mixed model was employed to analyse the occurrence of GL x GR interaction and an additive main effects and multiplicative interaction (AMMI) model was used to study patterns in the performance of genotype-strain combinations. We found statistically significant interaction in jars in terms of symbiotic effectiveness that was entirely due to the inclusion of one of the genotypes, ICC6263. No interaction was found in a subsequent pot experiment. The presence of two genetic groups (Kabuli and Desi genepools) did not affect interaction with Mesorhizobium strains. With the exception of a negative interaction with genotype ICC6263 in the jar experiment, the type strain Mesorhizobium ciceri LMG 14989 outperformed or equalled other strains on all chickpea genotypes in both jar and pot experiments. Similar to earlier reports in common bean, our results suggest that efforts to find more effective strains may be more rewarding than aiming for identification of superior combinations of strains and genotypes.

Overdominant Epistatic Loci Are the Primary Genetic Basis of Inbreeding Depression and Heterosis in Rice. I. Biomass and Grain Yield

Genetics ◽  
2001 ◽  
Vol 158 (4) ◽  
pp. 1737-1753 ◽  
Author(s):  
Zhi-Kang Li ◽  
L J Luo ◽  
H W Mei ◽  
D L Wang ◽  
Q Y Shu ◽  
...  
Keyword(s):  
Grain Yield ◽  
Inbreeding Depression ◽  
Genetic Basis ◽  
Mixed Model ◽  
Recombinant Inbred Lines ◽  
Hybrid Breakdown ◽  
Mixed Model Approach ◽  
Epistatic Qtl ◽  
Main Effect ◽  
Mapping Populations

AbstractTo understand the genetic basis of inbreeding depression and heterosis in rice, main-effect and epistatic QTL associated with inbreeding depression and heterosis for grain yield and biomass in five related rice mapping populations were investigated using a complete RFLP linkage map of 182 markers, replicated phenotyping experiments, and the mixed model approach. The mapping populations included 254 F10 recombinant inbred lines derived from a cross between Lemont (japonica) and Teqing (indica) and two BC and two testcross hybrid populations derived from crosses between the RILs and their parents plus two testers (Zhong 413 and IR64). For both BY and GY, there was significant inbreeding depression detected in the RI population and a high level of heterosis in each of the BC and testcross hybrid populations. The mean performance of the BC or testcross hybrids was largely determined by their heterosis measurements. The hybrid breakdown (part of inbreeding depression) values of individual RILs were negatively associated with the heterosis measurements of their BC or testcross hybrids, indicating the partial genetic overlap of genes causing hybrid breakdown and heterosis in rice. A large number of epistatic QTL pairs and a few main-effect QTL were identified, which were responsible for &gt;65% of the phenotypic variation of BY and GY in each of the populations with the former explaining a much greater portion of the variation. Two conclusions concerning the loci associated with inbreeding depression and heterosis in rice were reached from our results. First, most QTL associated with inbreeding depression and heterosis in rice appeared to be involved in epistasis. Second, most (~90%) QTL contributing to heterosis appeared to be overdominant. These observations tend to implicate epistasis and overdominance, rather than dominance, as the major genetic basis of heterosis in rice. The implications of our results in rice evolution and improvement are discussed.

The effect of laryngeal elevation training on swallowing function in patients with dysphagia

2021 ◽  
pp. 1-5
Author(s):  
N Sugaya ◽  
F Goto ◽  
Y Seino ◽  
K Nishiyama ◽  
K Okami
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Strength Test ◽  
Visit Time ◽  
Scoring Method ◽  
Laryngeal Elevation ◽  
Swallowing Function ◽  
Hand Grip ◽  
Flow Test ◽  
Main Effect

Abstract Objective To investigate the effect of laryngeal elevation training without highly loaded head lifting on swallowing function in patients with dysphagia. Methods Fifty-seven patients with dysphagia (36 men; mean age, 78.5 ± 11.4 years) were included. All participants performed the swallowing forehead exercise and the chin push–pull manoeuvre for two months. Videoendoscopy to assess swallowing function, the peak expiratory flow test and the hand grip strength test were performed at the initial visit (time 1) and two months after the start of the intervention (time 2). We used the Hyodo score, a scoring method for videoendoscopic assessment, for evaluation of swallowing function. Results The linear mixed model showed a significant main effect of time (the Hyodo score at time 1 was greater than the score at time 2). The effects of the co-variates were not significant. Conclusion The present study demonstrated the significant effect of laryngeal elevation training without head lifting on the Hyodo score.

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