Funmap2: an R package for QTL mapping using longitudinal phenotypes

AbstractQTL mapping is a powerful tool to infer the complexity of the genetic architecture underlying phenotypic traits, and has been extended to include longitudinal traits measured at multiple temporal/spatial points. Here, we introduce the R package Funmap2 based on the functional mapping framework, which integrates biological prior knowledge into the statistical model. Specifically, the functional mapping framework is engineered to include longitudinal curves that describes the genetic effects, and the covariance matrix of the trait of interest. Funmap2 may automatically choose the type of longitudinal curve and covariance matrix by information criterion. Funmap2 is available for download at https://github.com/wzhy2000/Funmap2.

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Funmap2: an R package for QTL mapping using longitudinal phenotypes

PeerJ ◽

10.7717/peerj.7008 ◽

2019 ◽

Vol 7 ◽

pp. e7008

Author(s):

Nating Wang ◽

Tinyi Chu ◽

Jiangtao Luo ◽

Rongling Wu ◽

Zhong Wang

Keyword(s):

Qtl Mapping ◽

Covariance Matrix ◽

Complex Traits ◽

Genetic Architecture ◽

R Package ◽

Information Criteria ◽

Functional Mapping ◽

Biological Knowledge ◽

Phenotypic Traits ◽

Trait Locus

Quantitative trait locus (QTL) mapping has been used as a powerful tool for inferring the complexity of the genetic architecture that underlies phenotypic traits. This approach has shown its unique power to map the developmental genetic architecture of complex traits by implementing longitudinal data analysis. Here, we introduce the R package Funmap2 based on the functional mapping framework, which integrates prior biological knowledge into the statistical model. Specifically, the functional mapping framework is engineered to include longitudinal curves that describe the genetic effects and the covariance matrix of the trait of interest. Funmap2 chooses the type of longitudinal curve and covariance matrix automatically using information criteria. Funmap2 is available for download at https://github.com/wzhy2000/Funmap2.

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The genetics of reproductive behavior

10.1093/oso/9780198797500.003.0002 ◽

2018 ◽

Author(s):

John Hunt ◽

James Rapkin ◽

Clarissa House

Keyword(s):

Reproductive Behavior ◽

Genetic Architecture ◽

Genetic Effects ◽

Future Research ◽

Phenotypic Traits ◽

Social Environments ◽

Reproductive Behaviors

Genes play a fundamental role in the regulation and evolution of most phenotypic traits, including behavior. This chapter focuses on the genetics of reproductive behavior in insects.More specifically, the distribution of genetic effects for reproductive behavior in insects (many genes of small effect or few genes of large effect) is examined, as well as how these genes interact with each other, with genes for other important traits, and with the abiotic and social environments. The chapter concludes by discussing the wider implications of this complex genetic architecture to the evolution of reproductive behaviors in insects and outline some key directions for future research on this topic.

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A statistical model for functional mapping of quantitative trait loci regulating drug response

The Pharmacogenomics Journal ◽

10.1038/sj.tpj.6500262 ◽

2004 ◽

Vol 4 (5) ◽

pp. 315-321 ◽

Cited By ~ 7

Author(s):

Y Gong ◽

Z Wang ◽

T Liu ◽

W Zhao ◽

Y Zhu ◽

...

Keyword(s):

Quantitative Trait Loci ◽

Statistical Model ◽

Quantitative Trait ◽

Drug Response ◽

Functional Mapping ◽

Trait Loci

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fullsibQTL: an R package for QTL mapping in biparental populations of outcrossing species

10.1101/2020.12.04.412262 ◽

2020 ◽

Author(s):

Rodrigo Gazaffi ◽

Rodrigo R. Amadeu ◽

Marcelo Mollinari ◽

João R. B. F. Rosa ◽

Cristiane H. Taniguti ◽

...

Keyword(s):

Qtl Mapping ◽

Open Source ◽

Qtl Analysis ◽

Source Code ◽

R Package ◽

Genetic Maps ◽

Linkage Phase ◽

Position Effects ◽

Genetic Features ◽

Outcrossing Species

ABSTRACTAccurate QTL mapping in outcrossing species requires software programs which consider genetic features of these populations, such as markers with different segregation patterns and different level of information. Although the available mapping procedures to date allow inferring QTL position and effects, they are mostly not based on multilocus genetic maps. Having a QTL analysis based in such maps is crucial since they allow informative markers to propagate their information to less informative intervals of the map. We developed fullsibQTL, a novel and freely available R package to perform composite interval QTL mapping considering outcrossing populations and markers with different segregation patterns. It allows to estimate QTL position, effects, segregation patterns, and linkage phase with flanking markers. Additionally, several statistical and graphical tools are implemented, for straightforward analysis and interpretations. fullsibQTL is an R open source package with C and R source code (GPLv3). It is multiplatform and can be installed from https://github.com/augusto-garcia/fullsibQTL.

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Deviance information criterion (DIC) in Bayesian multiple QTL mapping

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2008.01.016 ◽

2009 ◽

Vol 53 (5) ◽

pp. 1850-1860 ◽

Cited By ~ 10

Author(s):

Daniel Shriner ◽

Nengjun Yi

Keyword(s):

Qtl Mapping ◽

Deviance Information Criterion ◽

Information Criterion ◽

Multiple Qtl ◽

Multiple Qtl Mapping

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Flexible modelling of spatial variation in agricultural field trials with the R package INLA

Theoretical and Applied Genetics ◽

10.1007/s00122-019-03424-y ◽

2019 ◽

Vol 132 (12) ◽

pp. 3277-3293 ◽

Cited By ~ 6

Author(s):

Maria Lie Selle ◽

Ingelin Steinsland ◽

John M. Hickey ◽

Gregor Gorjanc

Keyword(s):

Spatial Variation ◽

Open Source ◽

Field Trial ◽

Spatial Models ◽

R Package ◽

Field Trials ◽

Wheat Breeding ◽

Genetic Effects ◽

Agricultural Field ◽

Combining Data

Abstract Key message Established spatial models improve the analysis of agricultural field trials with or without genomic data and can be fitted with the open-source R package INLA. Abstract The objective of this paper was to fit different established spatial models for analysing agricultural field trials using the open-source R package INLA. Spatial variation is common in field trials, and accounting for it increases the accuracy of estimated genetic effects. However, this is still hindered by the lack of available software implementations. We compare some established spatial models and show possibilities for flexible modelling with respect to field trial design and joint modelling over multiple years and locations. We use a Bayesian framework and for statistical inference the integrated nested Laplace approximations (INLA) implemented in the R package INLA. The spatial models we use are the well-known independent row and column effects, separable first-order autoregressive ($$\mathrm{AR1} \otimes \mathrm{AR1}$$ AR 1 ⊗ AR 1 ) models and a Gaussian random field (Matérn) model that is approximated via the stochastic partial differential equation approach. The Matérn model can accommodate flexible field trial designs and yields interpretable parameters. We test the models in a simulation study imitating a wheat breeding programme with different levels of spatial variation, with and without genome-wide markers and with combining data over two locations, modelling spatial and genetic effects jointly. The results show comparable predictive performance for both the $$\mathrm{AR1} \otimes \mathrm{AR1}$$ AR 1 ⊗ AR 1 and the Matérn models. We also present an example of fitting the models to a real wheat breeding data and simulated tree breeding data with the Nelder wheel design to show the flexibility of the Matérn model and the R package INLA.

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BIC Extensions for Order-constrained Model Selection

Sociological Methods & Research ◽

10.1177/0049124119882459 ◽

2019 ◽

pp. 004912411988245 ◽

Cited By ~ 2

Author(s):

J. Mulder ◽

A. E. Raftery

Keyword(s):

Model Selection ◽

Model Comparison ◽

Social Science Research ◽

Information Criterion ◽

Science Research ◽

R Package ◽

Local Unit ◽

European Values ◽

Constrained Model ◽

Constrained Models

The Schwarz or Bayesian information criterion (BIC) is one of the most widely used tools for model comparison in social science research. The BIC, however, is not suitable for evaluating models with order constraints on the parameters of interest. This article explores two extensions of the BIC for evaluating order-constrained models, one where a truncated unit information prior is used under the order-constrained model and the other where a truncated local unit information prior is used. The first prior is centered on the maximum likelihood estimate, and the latter prior is centered on a null value. Several analyses show that the order-constrained BIC based on the local unit information prior works better as an Occam’s razor for evaluating order-constrained models and results in lower error probabilities. The methodology based on the local unit information prior is implemented in the R package “BICpack” which allows researchers to easily apply the method for order-constrained model selection. The usefulness of the methodology is illustrated using data from the European Values Study.

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QTL Analysis for Downy Mildew Resistance in Cucumber Inbred Line PI 197088

Plant Disease ◽

10.1094/pdis-04-17-0491-re ◽

2018 ◽

Vol 102 (7) ◽

pp. 1240-1245 ◽

Cited By ~ 5

Author(s):

Lixia Li ◽

Huiqiang He ◽

Zhirong Zou ◽

Yuhong Li

Keyword(s):

Qtl Mapping ◽

Downy Mildew ◽

Genetic Architecture ◽

Plant Introduction ◽

Minor Effect ◽

Phenotypic Variance ◽

Chromosome 4 ◽

Phenotypic Data ◽

Trait Locus ◽

Simple Sequence

Downy mildew (DM), caused by Pseudoperonospora cubensis, is one of the major foliar diseases prevailing in cucumber-growing areas. The mechanism of DM resistance in cucumber, particularly the plant introduction (PI) 197088 from India, is presently unclear. Quantitative trait locus (QTL) mapping is an efficient approach to studying DM resistance genes in cucumber. In this study, we performed QTL mapping for DM resistance in PI 197088 with 183 F2-derived F3 (F2:3) families from the cross between PI 197088 (DM resistant) and Changchunmici (DM susceptible). A linkage map was constructed using 141 simple sequence repeat markers. Phenotypic data were collected from seven independent experiments. In total, five QTL were detected on chromosomes 1, 3, 4, and 5 with DM resistance contributed by PI 197088. The QTL on chromosome 4, dm4.1, was reproducibly detected in all indoor experiments, which could explain 27% of the phenotypic variance detected. Additionally, dm1.1 and dm5.2 showed moderate effects, while dm3.1 and dm5.1 were minor-effect QTL. This study revealed the unique genetic architecture of DM resistance in PI 197088, which may provide important guidance for efficient use in cucumber breeding for DM resistance.

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