scholarly journals The Design of Early-Stage Plant Breeding Trials Using Genetic Relatedness

2020 ◽  
Vol 25 (4) ◽  
pp. 553-578 ◽  
Author(s):  
Brian R. Cullis ◽  
Alison B. Smith ◽  
Nicole A. Cocks ◽  
David G. Butler
Keyword(s):  
Plant Breeding ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Early Stage ◽  
Genetic Effects ◽  
Breeding Programs ◽  
Model Based ◽  
Selection Decisions ◽  
Design Generation

Abstract The use of appropriate statistical methods has a key role in improving the accuracy of selection decisions in a plant breeding program. This is particularly important in the early stages of testing in which selections are based on data from a limited number of field trials that include large numbers of breeding lines with minimal replication. The method of analysis currently recommended for early-stage trials in Australia involves a linear mixed model that includes genetic relatedness via ancestral information: non-genetic effects that reflect the experimental design and a residual model that accommodates spatial dependence. Such analyses have been widely accepted as they have been found to produce accurate predictions of both additive and total genetic effects, the latter providing the basis for selection decisions. In this paper, we present the results of a case study of 34 early-stage trials to demonstrate this type of analysis and to reinforce the importance of including information on genetic relatedness. In addition to the application of a superior method of analysis, it is also critical to ensure the use of sound experimental designs. Recently, model-based designs have become popular in Australian plant breeding programs. Within this paradigm, the design search would ideally be based on a linear mixed model that matches, as closely as possible, the model used for analysis. Therefore, in this paper, we propose the use of models for design generation that include information on genetic relatedness and also include non-genetic and residual models based on the analysis of historic data for individual breeding programs. At present, the most commonly used design generation model omits genetic relatedness information and uses non-genetic and residual models that are supplied as default models in the associated software packages. The major reasons for this are that preexisting software is unacceptably slow for designs incorporating genetic relatedness and the accuracy gains resulting from the use of genetic relatedness have not been quantified. Both of these issues are addressed in the current paper. An updating scheme for calculating the optimality criterion in the design search is presented and is shown to afford prodigious computational savings. An in silico study that compares three types of design function across a range of ancillary treatments shows the gains in accuracy for the prediction of total genetic effects (and thence selection) achieved from model-based designs using genetic relatedness and program specific non-genetic and residual models. Supplementary materials accompanying this paper appear online.

scholarly journals Use of Contemporary Groups in the Construction of Multi-Environment Trial Datasets for Selection in Plant Breeding Programs

2021 ◽  
Vol 11 ◽  
Author(s):  
Alison Smith ◽  
Aanandini Ganesalingam ◽  
Christopher Lisle ◽  
Gururaj Kadkol ◽  
Kristy Hobson ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Genetic Relatedness ◽  
Genotype By Environment Interaction ◽  
Breeding Program ◽  
Environment Interaction ◽  
Breeding Programs ◽  
New Concepts ◽  
Selection Decisions ◽  
Testing Stage ◽  
Selection Decision

Plant breeding programs use multi-environment trial (MET) data to select superior lines, with the ultimate aim of increasing genetic gain. Selection accuracy can be improved with the use of advanced statistical analysis methods that employ informative models for genotype by environment interaction, include information on genetic relatedness and appropriately accommodate within-trial error variation. The gains will only be achieved, however, if the methods are applied to suitable MET datasets. In this paper we present an approach for constructing MET datasets that optimizes the information available for selection decisions. This is based on two new concepts that characterize the structure of a breeding program. The first is that of “contemporary groups,” which are defined to be groups of lines that enter the initial testing stage of the breeding program in the same year. The second is that of “data bands,” which are sequences of trials that correspond to the progression through stages of testing from year to year. MET datasets are then formed by combining bands of data in such a way as to trace the selection histories of lines within contemporary groups. Given a specified dataset, we use the A-optimality criterion from the model-based design literature to quantify the information for any given selection decision. We demonstrate the methods using two motivating examples from a durum and chickpea breeding program. Datasets constructed using contemporary groups and data bands are shown to be superior to other forms, in particular those that relate to a single year alone.

scholarly journals Family ties: the multilevel effects of households and kinship on the networks of individuals

2018 ◽  
Vol 5 (4) ◽  
pp. 172159 ◽  
Author(s):  
Jeremy Koster
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Strong Support ◽  
Group Level ◽  
Family Ties ◽  
Individual Household ◽  
Household Members

Among social mammals, humans uniquely organize themselves into communities of households that are centred around enduring, predominantly monogamous unions of men and women. As a consequence of this social organization, individuals maintain social relationships both within and across households, and potentially there is conflict among household members about which social ties to prioritize or de-emphasize. Extending the logic of structural balance theory, I predict that there will be considerable overlap in the social networks of individual household members, resulting in a pattern of group-level reciprocity. To test this prediction, I advance the Group-Structured Social Relations Model, a generalized linear mixed model that tests for group-level effects in the inter-household social networks of individuals. The empirical data stem from social support interviews conducted in a community of indigenous Nicaraguan horticulturalists, and model results show high group-level reciprocity among households. Although support networks are organized around kinship, covariates that test predictions of kin selection models do not receive strong support, potentially because most kin-directed altruism occurs within households, not between households. In addition, the models show that households with high genetic relatedness in part from children born to adulterous relationships are less likely to assist each other.

scholarly journals Early kidney injury predicts disease progression in patients with COVID-19: a cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tingting Xia ◽  
Wenjing Zhang ◽  
Yu Xu ◽  
Bin Wang ◽  
Zhiquan Yuan ◽  
...  
Keyword(s):  
Cohort Study ◽  
Disease Progression ◽  
Kidney Function ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Early Stage ◽  
Kidney Injury ◽  
Generalized Linear Mixed Model ◽  
Cox Proportional Hazards ◽  
Baseline Serum

Abstract Background The receptor of severe respiratory syndrome coronavirus 2 (SARS-CoV-2), angiotensin-converting enzyme 2, is more abundant in kidney than in lung tissue, suggesting that kidney might be another important target organ for SARS-CoV-2. However, our understanding of kidney injury caused by Coronavirus Disease 2019 (COVID-19) is limited. This study aimed to explore the association between kidney injury and disease progression in patients with COVID-19. Methods A retrospective cohort study was designed by including 2630 patients with confirmed COVID-19 from Huoshenshan Hospital (Wuhan, China) from 1 February to 13 April 2020. Kidney function indexes and other clinical information were extracted from the electronic medical record system. Associations between kidney function indexes and disease progression were analyzed using Cox proportional-hazards regression and generalized linear mixed model. Results We found that estimated glomerular filtration rate (eGFR) and creatinine clearance (Ccr) decreased in 22.0% and 24.0% of patients with COVID-19, respectively. Proteinuria was detected in 15.0% patients and hematuria was detected in 8.1% of patients. Hematuria (HR 2.38, 95% CI 1.50–3.78), proteinuria (HR 2.16, 95% CI 1.33–3.51), elevated baseline serum creatinine (HR 2.84, 95% CI 1.92–4.21) and blood urea nitrogen (HR 3.54, 95% CI 2.36–5.31), and decrease baseline eGFR (HR 1.58, 95% CI 1.07–2.34) were found to be independent risk factors for disease progression after adjusted confounders. Generalized linear mixed model analysis showed that the dynamic trajectories of uric acid was significantly related to disease progression. Conclusion There was a high proportion of early kidney function injury in COVID-19 patients on admission. Early kidney injury could help clinicians to identify patients with poor prognosis at an early stage. Graphic abstract

Development of a Third Trimester Contingent Prognostic Prediction Scheme for Suspected Early-Onset Pre-Eclampsia

2021 ◽  
pp. 1-9
Author(s):  
Enric Sabrià ◽  
Paula Lafuente-Ganuza ◽  
Paloma Lequerica-Fernández ◽  
Ana Isabel Escudero ◽  
Eduardo Martínez-Morillo ◽  
...  
Keyword(s):  
Early Onset ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
False Positive Rate ◽  
Area Under The Curve ◽  
Prediction Tool ◽  
Model Based ◽  
Prognostic Prediction ◽  
Short Term Prediction ◽  
Plgf Ratio

<b><i>Introduction:</i></b> Short-term prediction of pre-eclampsia (PE) using soluble FMS-like tyrosine kinase-1 (sFlt-1)/ placental growth factor (PlGF) ratio has high false-positive rate. Therefore, we developed a prognostic prediction tool that predicts early-onset PE leading to delivery within 1 week on pregnancies with an sFlt-1/PlGF ratio above 38 and compared it with an analogous model based on sFlt-1/PlGF ratio and with the 655 sFlt-1/PlGF ratio cutoff. <b><i>Methods:</i></b> Cohort study of 363 singleton pregnancies with clinical suspicion of PE before 34 weeks of gestation, allowing repeated assessments (522). 213 samples with an sFlt-1/PlGF ratio above 38 were assessed to construct and identify the best-fit linear mixed model. N-terminal pro-B-type natriuretic peptide (NT-proBNP), sFlt-1 MoM, PlGF MoM, and sFlt-1/PlGF ratio combined with gestational age (GA) were assessed. <b><i>Results:</i></b> None of the pregnancies with an sFlt-1/PlGF ratio of 38 or below developed early-onset PE (309 samples from 240 pregnancies). Conversely, 47 women of 213 assessments (22.1%) with an sFlt-1/PlGF ratio above 38 developed the assessed outcome. The selected model included sFlt-1 MoM, NT-proBNP, and GA. Differences in area under the curve were observed between the selected model and the GA + sFlt-1/PlGF model (<i>p</i> = 0.04). At an sFlt-1/PlGF ratio cutoff of 655, detection rate was 31.9% (15/47), while the selected model detection was 55.3% (26/47) (<i>p</i> = 0.008). <b><i>Discussion:</i></b> Considering repeated assessments, the sFlt-1/PlGF ratio of 38 or below adequately ruled out early-onset PE, leading to delivery within 1 week. However, when sFlt-1/PlGF ratio is above 38, the prediction tool derived from linear mixed model based on GA, NT-proBNP, and sFlt-1 MoM, provided a better prognosis prediction than the sFlt-1/PlGF ratio.

A New Possibility for Nondestructive Protein Content Determinations in Viable Seeds

1982 ◽  
Vol 36 (3) ◽  
pp. 259-260 ◽  
Author(s):  
V. Rutar
Keyword(s):  
Plant Breeding ◽  
Protein Content ◽  
Magic Angle ◽  
Breeding Programs ◽  
Potential Value ◽  
Content Determination ◽  
Selection Decisions ◽  
Magic Angle Sample Spinning ◽  
Viable Seeds ◽  
C Nmr

The use of proton-enhanced magic-angle sample spinning 13C NMR for nondestructive protein content determination is discussed and the first spectra of intact viable seeds are shown. The method has great potential value in plant breeding programs, since selection decisions can be based on the properties of individual seeds.

On the analysis of cubic smoothing spline-based stem curve prediction for forest harvesters

2006 ◽  
Vol 36 (11) ◽  
pp. 2909-2919 ◽  
Author(s):  
Laura Koskela ◽  
Tapio Nummi ◽  
Simone Wenzel ◽  
Veli-Pekka Kivinen
Keyword(s):  
Variable Exponent ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Prediction Method ◽  
Smoothing Spline ◽  
Model Based ◽  
Cutting Pattern ◽  
Taper Equation

In the cut-to-length (CTL) harvesting system the felling, delimbing, and bucking processes take place at the harvesting site. The optimal cutting points along the stem can be determined if the whole stem curve is known. In practice, however, it is not economically feasible to measure the whole stem first before crosscutting, and hence the first cutting decisions are usually made when only a short part of the stem is known. Predictions are used to determine the cutting pattern to compensate for the unknown part of the stem. In this paper our interest focuses on stem curve prediction in a harvesting situation and we study a modified version of a cubic smoothing spline-based prediction method devised by Nummi and Mottonen (T. Nummi and J. Mottonen. 2004. J. Appl. Stat. 31: 105–114). The method's performance was assessed in five different final felling stands of spruce and pine, collected by harvesters in southern Finland. The results for the spline approach are very promising and show the superiority of the method over the linear mixed-model-based approach of Liski and Nummi (E. Liski and T. Nummi. 1995. Scand. J. Stat. 22: 255–269) and also over the approach based on the variable-exponent taper equation of Kozak (A. Kozak. 1988. Can. J. For. Res. 18: 1363–1368).

Genomic selection in multi‐environment plant breeding trials using a factor analytic linear mixed model

2019 ◽  
Vol 136 (4) ◽  
pp. 279-300 ◽  
Author(s):  
Daniel J. Tolhurst ◽  
Ky L. Mathews ◽  
Alison B. Smith ◽  
Brian R. Cullis
Keyword(s):  
Plant Breeding ◽  
Genomic Selection ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Factor Analytic

scholarly journals Discrepancy between Lung Function Measurements at Home and in the Hospital in Children with Asthma and CF

2020 ◽  
Vol 9 (6) ◽  
pp. 1617 ◽  
Author(s):  
Frederick L.G.R. Gerzon ◽  
Quirijn Jöbsis ◽  
Michiel A.G.E. Bannier ◽  
Bjorn Winkens ◽  
Edward Dompeling
Keyword(s):  
Lung Function ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Early Stage ◽  
Home Monitoring ◽  
Clinical Situation ◽  
Forced Expiratory Volume ◽  
Mixed Model Analysis ◽  
Children With Asthma ◽  
At Home

The Coronavirus pandemic stresses the importance of eHealth techniques to monitor patients at home. Home monitoring of lung function in asthma and cystic fibrosis (CF) may help to detect deterioration of lung function at an early stage, but the reliability is unclear. We investigated whether lung function measurements at home were comparable to measurements during clinical visits. We analysed prospectively collected data of two one-year observational cohort studies in 117 children (36 with CF and 81 with asthma). All patients performed forced expiratory volume in one second (FEV1) measurements with a monitor at home. Paired FEV1 measurements were included if the measurement on the home monitor was performed on the same day as the FEV1 measurement on the pneumotachometer during a two monthly clinical visit. Bland-Altman plots and linear mixed model analysis were used. The mean difference (home measurement was subtracted from clinical measurement) in FEV1 was 0.18 L in CF (95% confidence interval (CI) 0.08–0.27 L; p < 0.001) and 0.12 L in asthma (95%CI 0.05–0.19 L; p < 0.001). FEV1 measurements at home were significantly lower than clinically obtained FEV1 measurements, which has implications for the application of this technique in the daily clinical situation.

scholarly journals Stable habituation deficits in the early stage of psychosis: a 2-year follow-up study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suzanne N. Avery ◽  
Maureen McHugo ◽  
Kristan Armstrong ◽  
Jennifer Urbano Blackford ◽  
Neil D. Woodward ◽  
...  
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Early Stage ◽  
Occipital Cortex ◽  
Brain Regions ◽  
Early Psychosis ◽  
Brain Response ◽  
Face Area ◽  
Healthy Control

AbstractNeural habituation, the decrease in brain response to repeated stimuli, is a fundamental, highly conserved mechanism that acts as an essential filter for our complex sensory environment. Convergent evidence indicates neural habituation is disrupted in both early and chronic stages of schizophrenia, with deficits co-occurring in brain regions that show inhibitory dysfunction. As inhibitory deficits have been proposed to contribute to the onset and progression of illness, habituation may be an important treatment target. However, a crucial first step is clarifying whether habituation deficits progress with illness. In the present study, we measured neural habituation in 138 participants (70 early psychosis patients (<2 years of illness), 68 healthy controls), with 108 participants assessed longitudinally at both baseline and 2-year follow-up. At follow-up, all early psychosis patients met criteria for a schizophrenia spectrum disorder (i.e., schizophreniform disorder, schizophrenia, schizoaffective disorder). Habituation slopes (i.e., rate of fMRI signal change) to repeated images were computed for the anterior hippocampus, occipital cortex, and the fusiform face area. Habituation slopes were entered into a linear mixed model to test for effects of group and time by region. We found that early psychosis patients showed habituation deficits relative to healthy control participants across brain regions, and that these deficits were maintained, but did not worsen, over two years. These results suggest a stable period of habituation deficits in the early stage of schizophrenia.

scholarly journals Common bean SNP alleles and candidate genes affecting photosynthesis under contrasting water regimes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Susana Trindade Leitão ◽  
Maria Catarina Bicho ◽  
Priscila Pereira ◽  
Maria João Paulo ◽  
Marcos Malosetti ◽  
...  
Keyword(s):  
Common Bean ◽  
Candidate Genes ◽  
Water Deficit ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Protein Translocation ◽  
Leaf Gas Exchange ◽  
Gas Exchange Parameters ◽  
Trait Associations

AbstractWater deficit is a major worldwide constraint to common bean (Phaseolus vulgaris L.) production, being photosynthesis one of the most affected physiological processes. To gain insights into the genetic basis of the photosynthetic response of common bean under water-limited conditions, a collection of 158 Portuguese accessions was grown under both well-watered and water-deficit regimes. Leaf gas-exchange parameters were measured and photosynthetic pigments quantified. The same collection was genotyped using SNP arrays, and SNP-trait associations tested considering a linear mixed model accounting for the genetic relatedness among accessions. A total of 133 SNP-trait associations were identified for net CO2 assimilation rate, transpiration rate, stomatal conductance, and chlorophylls a and b, carotenes, and xanthophyll contents. Ninety of these associations were detected under water-deficit and 43 under well-watered conditions, with only two associations common to both treatments. Identified candidate genes revealed that stomatal regulation, protein translocation across membranes, redox mechanisms, hormone, and osmotic stress signaling were the most relevant processes involved in common bean response to water-limited conditions. These candidates are now preferential targets for common bean water-deficit-tolerance breeding. Additionally, new sources of water-deficit tolerance of Andean, Mesoamerican, and admixed origin were detected as accessions valuable for breeding, and not yet explored.

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