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.

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 The Effect of Compositional Changes Due to Seasonal Variation on Milk Density and the Determination of Season-Based Density Conversion Factors for Use in the Dairy Industry

Foods ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1004 ◽  
Author(s):  
Puneet Parmar ◽  
Nicolas Lopez-Villalobos ◽  
John T. Tobin ◽  
Eoin Murphy ◽  
Arleen McDonagh ◽  
...  
Keyword(s):  
Seasonal Variation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Milk Composition ◽  
Milk Samples ◽  
Genetic Groups ◽  
Holstein Friesian ◽  
Significant Difference ◽  
Density Values ◽  
Compositional Variations

The objective of this study was to determine the effect of seasonal variation on milk composition and establish an algorithm to predict density based on milk composition to enable the calculation of season-based density conversion calculations. A total of 1035 raw whole milk samples were collected from morning and evening milking of 60 spring-calving individual cows of different genetic groups, namely Jersey, Elite HF (Holstein–Friesian) and National Average HF, once every two weeks for a period of 9 months (March–November, 2018). The average mean and standard deviation for milk compositional traits were 4.72 ± 1.30% fat, 3.85 ± 0.61% protein and 4.69 ± 0.30% lactose and density was estimated at 1.0308 ± 0.002 g/cm3. The density of the milk samples was evaluated using three methods: a portable density meter, DMA 35; a standard desktop version, DMA 4500M; and an Association of Official Agricultural Chemists (AOAC) method using 100-mL glass pycnometers. Statistical analysis using a linear mixed model showed a significant difference in density of milk samples (p < 0.05) across seasonal and compositional variations adjusted for the effects of days in milk, parity, the feeding treatment, the genetic group and the measurement technique. The mean density values and standard error of mean estimated for milk samples in each season, i.e., spring, summer and autumn were 1.0304 ± 0.00008 g/cm3, 1.0314 ± 0.00005 g/cm3 and 1.0309 ± 0.00007 g/cm3, respectively.

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 Efficient gene-environment interaction tests for large biobank-scale sequencing studies

2020 ◽  
Author(s):  
Xinyu Wang ◽  
Elise Lim ◽  
Ching-Ti Liu ◽  
Yun Ju Sung ◽  
Dabeeru C. Rao ◽  
...  
Keyword(s):  
Large Scale ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Environment Interaction ◽  
Whole Genome ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Sequencing Studies ◽  
Related Individuals ◽  
Main Effects

ABSTRACTComplex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

2020 ◽  
Vol 641 ◽  
pp. 159-175
Author(s):  
J Runnebaum ◽  
KR Tanaka ◽  
L Guan ◽  
J Cao ◽  
L O’Brien ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
Management Tool ◽  
American Lobster ◽  
Suitable Habitat ◽  
Climate Conditions ◽  
Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

Long-term seizure outcomes after pediatric temporal lobectomy: does brain MRI lesion matter?

2019 ◽  
Vol 24 (2) ◽  
pp. 200-208
Author(s):  
Ravindra Arya ◽  
Francesco T. Mangano ◽  
Paul S. Horn ◽  
Sabrina K. Kaul ◽  
Serena K. Kaul ◽  
...  
Keyword(s):  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Hippocampal Sclerosis ◽  
Brain Mri ◽  
Temporal Lobectomy ◽  
Seizure Freedom ◽  
Seizure Onset

OBJECTIVEThere is emerging data that adults with temporal lobe epilepsy (TLE) without a discrete lesion on brain MRI have surgical outcomes comparable to those with hippocampal sclerosis (HS). However, pediatric TLE is different from its adult counterpart. In this study, the authors investigated if the presence of a potentially epileptogenic lesion on presurgical brain MRI influences the long-term seizure outcomes after pediatric temporal lobectomy.METHODSChildren who underwent temporal lobectomy between 2007 and 2015 and had at least 1 year of seizure outcomes data were identified. These were classified into lesional and MRI-negative groups based on whether an epilepsy-protocol brain MRI showed a lesion sufficiently specific to guide surgical decisions. These patients were also categorized into pure TLE and temporal plus epilepsies based on the neurophysiological localization of the seizure-onset zone. Seizure outcomes at each follow-up visit were incorporated into a repeated-measures generalized linear mixed model (GLMM) with MRI status as a grouping variable. Clinical variables were incorporated into GLMM as covariates.RESULTSOne hundred nine patients (44 females) were included, aged 5 to 21 years, and were classified as lesional (73%), MRI negative (27%), pure TLE (56%), and temporal plus (44%). After a mean follow-up of 3.2 years (range 1.2–8.8 years), 66% of the patients were seizure free for ≥ 1 year at last follow-up. GLMM analysis revealed that lesional patients were more likely to be seizure free over the long term compared to MRI-negative patients for the overall cohort (OR 2.58, p < 0.0001) and for temporal plus epilepsies (OR 1.85, p = 0.0052). The effect of MRI lesion was not significant for pure TLE (OR 2.64, p = 0.0635). Concordance of ictal electroencephalography (OR 3.46, p < 0.0001), magnetoencephalography (OR 4.26, p < 0.0001), and later age of seizure onset (OR 1.05, p = 0.0091) were associated with a higher likelihood of seizure freedom. The most common histological findings included cortical dysplasia types 1B and 2A, HS (40% with dual pathology), and tuberous sclerosis.CONCLUSIONSA lesion on presurgical brain MRI is an important determinant of long-term seizure freedom after pediatric temporal lobectomy. Pediatric TLE is heterogeneous regarding etiologies and organization of seizure-onset zones with many patients qualifying for temporal plus nosology. The presence of an MRI lesion determined seizure outcomes in patients with temporal plus epilepsies. However, pure TLE had comparable surgical seizure outcomes for lesional and MRI-negative groups.

scholarly journals Reducing Aggression by Developing Emotional and Inhibitory Control

2021 ◽  
Vol 18 (10) ◽  
pp. 5263
Author(s):  
Miriam Romero-López ◽  
María Carmen Pichardo ◽  
Ana Justicia-Arráez ◽  
Judit Bembibre-Serrano
Keyword(s):  
Psychosocial Development ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Emotional Control ◽  
Control Group ◽  
Post Intervention ◽  
Mixed Model Analysis ◽  
Intervention Measures ◽  
Experimental Group

The objective of this study is to measure the effectiveness of a program on improving inhibitory and emotional control among children. In addition, it is assessed whether the improvement of these skills has an effect on the reduction of aggressive behavior in pre-school children. The participants were 100 children, 50 belonging to the control group and 50 to the experimental group, aged between 5 and 6 years. Pre-intervention and post-intervention measures of inhibitory and emotional control (BRIEF-P) and aggression (BASC) were taken. A Generalized Linear Mixed Model analysis (GLMM) was performed and found that children in the experimental group scored higher on inhibitory and emotional control compared to their peers in the control group. In addition, these improvements have an effect on the decrease in aggressiveness. In conclusion, preventive research should have among its priorities the design of such program given their implications for psychosocial development.

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