scholarly journals An IBD-based mixed model approach for QTL mapping in multiparental populations

2021 ◽  
Author(s):  
Wenhao Li ◽  
Martin P. Boer ◽  
Chaozhi Zheng ◽  
Ronny V.L. Joosen ◽  
Fred A. van Eeuwijk
Keyword(s):  
Qtl Mapping ◽  
Association Mapping ◽  
Mixed Model ◽  
Real Data ◽  
Mapping Method ◽  
Polygenic Effect ◽  
Model Framework ◽  
Mixed Model Approach ◽  
Nested Association Mapping ◽  
Model Approach

Abstract Multiparental populations (MPPs) have become popular for QTL detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercrosses (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach. The estimated parameters are random allelic effects associated with the parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the IBD-based mixed model approach could increase true positive rates and mapping resolutions of QTLs in comparison to a widely used benchmark association mapping method. Successful analyses of various real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

scholarly journals An IBD-based mixed model approach for QTL mapping in multiparental populations

2021 ◽  
Author(s):  
Wenhao Li ◽  
Martin P. Boer ◽  
Chaozhi Zheng ◽  
Ronny V. L. Joosen ◽  
Fred A. van Eeuwijk
Keyword(s):  
Quantitative Trait Loci ◽  
Qtl Mapping ◽  
Association Mapping ◽  
Quantitative Trait ◽  
Mixed Model ◽  
Genetic Effects ◽  
Identity By Descent ◽  
Mixed Model Approach ◽  
Kinship Relations ◽  
Model Approach

Abstract Key message The identity-by-descent (IBD)-based mixed model approach introduced in this study can detect quantitative trait loci (QTLs) referring to the parental origin and simultaneously account for multilevel relatedness of individuals within and across families. This unified approach is proved to be a powerful approach for all kinds of multiparental population (MPP) designs. Abstract Multiparental populations (MPPs) have become popular for quantitative trait loci (QTL) detection. Tools for QTL mapping in MPPs are mostly developed for specific MPPs and do not generalize well to other MPPs. We present an IBD-based mixed model approach for QTL mapping in all kinds of MPP designs, e.g., diallel, Nested Association Mapping (NAM), and Multiparental Advanced Generation Intercross (MAGIC) designs. The first step is to compute identity-by-descent (IBD) probabilities using a general Hidden Markov model framework, called reconstructing ancestry blocks bit by bit (RABBIT). Next, functions of IBD information are used as design matrices, or genetic predictors, in a mixed model approach to estimate variance components for multiallelic genetic effects associated with parents. Family-specific residual genetic effects are added, and a polygenic effect is structured by kinship relations between individuals. Case studies of simulated diallel, NAM, and MAGIC designs proved that the advanced IBD-based multi-QTL mixed model approach incorporating both kinship relations and family-specific residual variances (IBD.MQMkin_F) is robust across a variety of MPP designs and allele segregation patterns in comparison to a widely used benchmark association mapping method, and in most cases, outperformed or behaved at least as well as other tools developed for specific MPP designs in terms of mapping power and resolution. Successful analyses of real data cases confirmed the wide applicability of our IBD-based mixed model methodology.

scholarly journals Mixed model approach for IBD-based QTL mapping in a complex oil palm pedigree

BMC Genomics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Sébastien Tisné ◽  
Marie Denis ◽  
David Cros ◽  
Virginie Pomiès ◽  
Virginie Riou ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Oil Palm ◽  
Mixed Model ◽  
Mixed Model Approach ◽  
Model Approach

scholarly journals A Fumonisin Prevention Tool for Targeting and Ranking Agroclimatic Conditions Favoring Exposure in French Maize-Growing Areas

Toxins ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 214
Author(s):  
Agathe Roucou ◽  
Christophe Bergez ◽  
Benoît Méléard ◽  
Béatrice Orlando
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Fusarium Verticillioides ◽  
Agricultural Practices ◽  
Climatic Conditions ◽  
Agricultural Cooperatives ◽  
Mixed Model Approach ◽  
Food And Feed ◽  
Model Approach

The levels of fumonisins (FUMO)—mycotoxins produced by Fusarium verticillioides—in maize for food and feed are subject to European Union regulations. Compliance with the regulations requires the targeting of, among others, the agroclimatic factors influencing fungal contamination and FUMO production. Arvalis-Institut du végétal has created a national, multiyear database for maize, based on field survey data collected since 2003. This database contains information about agricultural practices, climatic conditions and FUMO concentrations at harvest for 738 maize fields distributed throughout French maize-growing regions. A linear mixed model approach highlights the presence of borers and the use of a late variety, high temperatures in July and October, and a water deficit during the maize cycle as creating conditions favoring maize contamination with Fusarium verticillioides. It is thus possible to target a combination of risk factors, consisting of this climatic sequence associated with agricultural practices of interest. The effects of the various possible agroclimatic combinations can be compared, grouped and classified as promoting very low to high FUMO concentrations, possibly exceeding the regulatory threshold. These findings should facilitate the creation of a national, informative and easy-to-use prevention tool for producers and agricultural cooperatives to manage the sanitary quality of their harvest.

scholarly journals Male and female fertility and hatchability in chickens: a longitudinal mixed model approach

2004 ◽  
Vol 83 (8) ◽  
pp. 1253-1259 ◽  
Author(s):  
R.L. Sapp ◽  
R. Rekaya ◽  
I. Misztal ◽  
T. Wing
Keyword(s):  
Mixed Model ◽  
Female Fertility ◽  
Male And Female ◽  
Mixed Model Approach ◽  
Model Approach

Effects of a dance program on psychophysiological variables in hospitalized patients with depression: A mixed model approach

2021 ◽  
pp. 101857
Author(s):  
Karen Melissa Polanco Zuleta ◽  
Marina Medina-Corrales ◽  
Franciso Javier Mendoza-Farías ◽  
Claudia Cristina Santos Lozano ◽  
José Tristán ◽  
...  
Keyword(s):  
Mixed Model ◽  
Hospitalized Patients ◽  
Mixed Model Approach ◽  
Model Approach

scholarly journals Analyzing Child Malnutrition in Bangladesh: Generalized Linear Mixed Model Approach

2016 ◽  
Vol 64 (2) ◽  
pp. 163-167
Author(s):  
Tahmidul Islam ◽  
Md Golam Rabbani ◽  
Wasimul Bari
Keyword(s):  
Mixed Model ◽  
Sampling Design ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Child Malnutrition ◽  
Demographic Health Survey ◽  
Factors Affecting ◽  
Mixed Model Approach ◽  
Potential Factors ◽  
Model Approach

Child malnutrition is a serious issue for overall child health and future development. Stunting is a key anthropometric indicator of child malnutrition. Because of the nature of sampling design used in Bangladesh Demographic Health Survey, 2011, responses obtained from children under same family might be correlated. Again, children residing in same cluster may also be correlated. To tackle this problem, generalized linear mixed model (GLMM), instead of usual fixed effect logistic regression model, has been utilized in this paper to find out potential factors affecting child malnutrition. Model performances have also been compared. Dhaka Univ. J. Sci. 64(2): 163-167, 2016 (July)

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