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 Follow-Up Household Serosurvey in Northeast Brazil for Zika Virus: Sexual Contacts of Index Patients Have the Highest Risk for Seropositivity

2020 ◽  
Author(s):  
Tereza Magalhaes ◽  
Clarice N L Morais ◽  
Iracema J A A Jacques ◽  
Elisa A N Azevedo ◽  
Ana M Brito ◽  
...  
Keyword(s):  
Zika Virus ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Sexual Transmission ◽  
Northeast Brazil ◽  
Modes Of Transmission ◽  
Key Factor ◽  
Sexual Contacts ◽  
Household Members

Abstract Background Zika virus (ZIKV) is a mosquito-borne virus that is also transmitted sexually; however, the epidemiological relevance of ZIKV sexual transmission in endemic regions is unclear. Methods We performed a household-based serosurvey in Northeast Brazil to evaluate the differential exposure to ZIKV and chikungunya virus (CHIKV) among households. Individuals who participated in our previous arboviral disease cohort (indexes) were recontacted and enrolled, and their household members were newly enrolled. Results The relative risk of sexual partners being ZIKV-seropositive when living with a ZIKV-seropositive index participant was significantly higher, whereas this was not observed among nonsexual partners of the index. For CHIKV, both sexual and nonsexual partner household members living with a CHIKV-seropositive index had a significantly higher risk of being seropositive. In the nonindex-based dyadic and generalized linear mixed model analyses, the odds of sexual dyads having a concordant ZIKV plaque reduction neutralization test result was significantly higher. We have also analyzed retrospective clinical data according to the participants’ exposure to ZIKV and CHIKV. Conclusions Our data suggest that ZIKV sexual transmission may be a key factor for the high ZIKV seroprevalence among households in endemic areas and raises important questions about differential disease from the 2 modes of transmission.

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 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.

scholarly journals The importance of healthcare managers’ organizational preconditions and support resources for their appraisal of planned change and its outcomes

2016 ◽  
Vol 6 (1) ◽  
pp. 25
Author(s):  
Jörgen Andreasson ◽  
Linda Ahlstrom ◽  
Andrea Eriksson ◽  
Lotta Dellve
Keyword(s):  
Organizational Support ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Strong Support ◽  
Planned Change ◽  
Successful Implementation ◽  
Negative Appraisal ◽  
Decision Control ◽  
Healthcare Process ◽  
Managerial Experience

Background: Healthcare managers are expected to lead and manage planned organizational change intended to improve healthcare process quality. However, their complex working conditions offer limited decision control, and healthcare managers often feel ill prepared and inadequately supported to perform their duties. Healthcare managers have previously described their need for organizational support, but we lack knowledge of the preconditions and resources that help managers implement planned change.Methods: This prospective cohort study examined healthcare managers at three Swedish hospitals implementing lean production and two Swedish hospitals implementing their own improvement model. Questionnaire data from 2012, 2103, and 2014 were used in following up. We used t-tests and a linear mixed model design in analysing the data.Results: Healthcare managers who perceived strong support from managers, employees, colleagues, and the organization and managers with the longest managerial experience had the least negative appraisal of change. Managers who perceived strong support from employees, management, and the organizational structure perceived higher levels of healthcare process quality.Conclusions: Long managerial experience and strong support from managers, employees, and the organization are important formanagers’ appraisal of, work on, and successful implementation of planned change. Top management must therefore ensure that the healthcare managers have sufficient managerial experience and support before they delegate to them the responsibility to implement planned change.

Heritability of a resting heart rate in a 20-year follow-up family cohort with GWAS data: Insights from the STANISLAS cohort

2019 ◽  
pp. 204748731989076
Author(s):  
Constance Xhaard ◽  
Claire Dandine-Roulland ◽  
Pierre de Villemereuil ◽  
Edith Le Floch ◽  
Delphine Bacq-Daian ◽  
...  
Keyword(s):  
Heart Rate ◽  
Repeated Measures ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genome Wide Association Study ◽  
Genetic Relatedness ◽  
Coffee Consumption ◽  
Study Data ◽  
Resting Heart Rate ◽  
Genetic Origin

Background The association between resting heart rate (HR) and cardiovascular outcomes, especially heart failure, is now well established. However, whether HR is mainly an integrated marker of risk associated with other features, or rather a genetic origin risk marker, is still a matter for debate. Previous studies reported a heritability ranging from 14% to 65%. Design We assessed HR heritability in the STANISLAS family-study, based on the data of four visits performed over a 20-year period, and adjusted for most known confounding effects. Methods These analyses were conducted using a linear mixed model, adjusted on age, sex, tea or coffee consumption, beta-blocker use, physical activity, tobacco use, and alcohol consumption to estimate the variance captured by additive genetic effects, via average information restricted maximum likelihood analysis, with both self-reported pedigree and genetic relatedness matrix (GRM) calculated from genome-wide association study data. Results Based on the data of all visits, the HR heritability (h2) estimate was 23.2% with GRM and 24.5% with pedigree. However, we found a large heterogeneity of HR heritability estimations when restricting the analysis to each of the four visits (h2 from 19% to 39% using pedigree, and from 14% to 32% using GRM). Moreover, only a little part of variance was explained by the common household effect (<5%), and half of the variance remained unexplained. Conclusion Using a comprehensive analysis based on a family cohort, including the data of multiple visits and GRM, we found that HR variability is about 25% from genetic origin, 25% from repeated measures and 50% remains unexplained.

scholarly journals Plant Variety Selection Using Interaction Classes Derived From Factor Analytic Linear Mixed Models: Models With Independent Variety Effects

2021 ◽  
Vol 12 ◽  
Author(s):  
Alison Smith ◽  
Adam Norman ◽  
Haydn Kuchel ◽  
Brian Cullis
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Subsequent Paper ◽  
Environment Interaction ◽  
Fundamental Factor ◽  
Factor Analytic ◽  
Graphical Tool ◽  
Variety Selection ◽  
Variety Performance

A major challenge in the analysis of plant breeding multi-environment datasets is the provision of meaningful and concise information for variety selection in the presence of variety by environment interaction (VEI). This is addressed in the current paper by fitting a factor analytic linear mixed model (FALMM) then using the fundamental factor analytic parameters to define groups of environments in the dataset within which there is minimal crossover VEI, but between which there may be substantial crossover VEI. These groups are consequently called interaction classes (iClasses). Given that the environments within an iClass exhibit minimal crossover VEI, it is then valid to obtain predictions of overall variety performance (across environments) for each iClass. These predictions can then be used not only to select the best varieties within each iClass but also to match varieties in terms of their patterns of VEI across iClasses. The latter is aided with the use of a new graphical tool called an iClass Interaction Plot. The ideas are introduced in this paper within the framework of FALMMs in which the genetic effects for different varieties are assumed independent. The application to FALMMs which include information on genetic relatedness is the subject of a subsequent paper.

scholarly journals An Efficient Score Test Integrated with Empirical Bayes for Genome-Wide Association Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Jing Xiao ◽  
Yang Zhou ◽  
Shu He ◽  
Wen-Long Ren
Keyword(s):  
Empirical Bayes ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genetic Relatedness ◽  
Association Studies ◽  
Score Test ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
New Genes ◽  
Genome Wide

Many methods used in multi-locus genome-wide association studies (GWAS) have been developed to improve statistical power. However, most existing multi-locus methods are not quicker than single-locus methods. To address this concern, we proposed a fast score test integrated with Empirical Bayes (ScoreEB) for multi-locus GWAS. Firstly, a score test was conducted for each single nucleotide polymorphism (SNP) under a linear mixed model (LMM) framework, taking into account the genetic relatedness and population structure. Then, all of the potentially associated SNPs were selected with a less stringent criterion. Finally, Empirical Bayes in a multi-locus model was performed for all of the selected SNPs to identify the true quantitative trait nucleotide (QTN). Our new method ScoreEB adopts the similar strategy of multi-locus random-SNP-effect mixed linear model (mrMLM) and fast multi-locus random-SNP-effect EMMA (FASTmrEMMA), and the only difference is that we use the score test to select all the potentially associated markers. Monte Carlo simulation studies demonstrate that ScoreEB significantly improved the computational efficiency compared with the popular methods mrMLM, FASTmrEMMA, iterative modified-sure independence screening EM-Bayesian lasso (ISIS EM-BLASSO), hybrid of restricted and penalized maximum likelihood (HRePML) and genome-wide efficient mixed model association (GEMMA). In addition, ScoreEB remained accurate in QTN effect estimation and effectively controlled false positive rate. Subsequently, ScoreEB was applied to re-analyze quantitative traits in plants and animals. The results show that ScoreEB not only can detect previously reported genes, but also can mine new genes.

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.

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