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Predictive Ability of Statistical Genomic Prediction Methods When Underlying Genetic Architecture of Trait Is Purely Additive
Published in Iranian Journal of Applied Animal Science, 2016
The objective of this study was to compare different genomic prediction models includes parametric and non-parametric, and assess their abilities toward a specific trait with only additive architecture. We hypothesize that with further exploration of predictive ability of GS models toward the purely genomic architectures, we will clearly find the true accuracy of genomic prediction models under a simple genetic architecture.
Recommended citation: Momen, M., A. Ayatollahi Mehrgardi, A. Sheikhy, A. K. Esmailizadeh, and M. Assadi Foozi. "Predictive Ability of Statistical Genomic Prediction Methods When Underlying Genetic Architecture of Trait Is Purely Additive." Iranian Journal of Applied Animal Science 6, no. 4 (2016): 815-822. http://ijas.iaurasht.ac.ir/article_526626.html
Published in Genetics Selection Evolution, 2017
Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations.
Recommended citation: Momen M.,Mehrgardi, Mehrgardi A. A., Sheikhy A., Esmailizadeh A., Fozi M. A.,Kranis A., Valente B. D.,Rosa, Rosa G. J. M., Gianola D. (2017) A predictive assessment of genetic correlations between traits in chickens using markers https://www.ncbi.nlm.nih.gov/pubmed/28148241
Published in Scientific Reports, 2018
Recent work has suggested that the performance of prediction models for complex traits may depend on the architecture of the target traits. Here we compared several prediction models with respect to their ability of predicting phenotypes under various statistical architectures of gene action.
Recommended citation: Momen M , Ayatollahi Mehrgardi A, Kranis A, Tusell L, Morota G, Rosa G J M, Gianola D. (2018). Predictive ability of genome-assisted predic-tion machines under various statistical genetic architectures.Scientific Reports.8:12309. https://www.nature.com/articles/s41598-018-30089-2
Quantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions
Published in Genetics Selection Evolution, 2018
We investigated the relationship between the estimated level of connectedness and prediction accuracy in the presence of non-additive genetic variation.
Recommended citation: Momen, Mehdi, and Gota Morota. "Quantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions." Genetics Selection Evolution 50, no. 1 (2018): 45. https://gsejournal.biomedcentral.com/articles/10.1186/s12711-018-0415-9
Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models
Published in Frontiers in Genetics, 2018
Network based statistical models accounting for putative causal relationships among multiple phenotypes can be used to infer single-nucleotide polymorphism (SNP) effect which transmitting through a given causal path in genome-wide association studies (GWAS). In GWAS with multiple phenotypes, reconstructing underlying causal structures among traits and SNPs using a single statistical framework is essential for understanding the entirety of genotype-phenotype maps.
Recommended citation: Momen Mehdi, Ayatollahi Mehrgardi Ahmad, Amiri Roudbar Mahmoud, Kranis Andreas, Mercuri Pinto Renan, Valente Bruno D., Morota Gota, Rosa Guilherme J. M., Gianola Daniel 2018.Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models. https://www.frontiersin.org/articles/10.3389/fgene.2018.00455/full
Importance of genotype by environment interaction on genetic analysis of milk yield in Iranian Holstein cows using random regression model.
Published in Animal Production Science, 2018
Changes in the relative performance of genotypes (sires) across different environments, which are referred to as genotype–environment interactions, play an important role in dairy production systems, especially in countries that rely on imported genetic material. Importance of genotype by environment interaction on genetic analysis of milk yield was investigated in Holstein cows by using random regression model.
Recommended citation: Fazel Y., Esmailizadeh A., Momen M., Fozi M. Asadi (2018) Importance of genotype by environment interaction on genetic analysis of milk yield in Iranian Holstein cows using a random regression model. http://www.publish.csiro.au/AN/AN17714
Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits
Published in The Plant Genome, 2019
This study builds on the random regression genomic prediction approach described in Campbell et al 2018, and used the derived breeding values for genomic inferenece across time points.
Recommended citation: Campbell M.T., Momen M., Walia H., Morota G. (2019) Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits. The Plant Genome. https://www.biorxiv.org/content/10.1101/435685v2
Harnessing phenotypic networks and structural equation models to improve genome-wide association analysis
Published in bioRxiv, 2019
We extended the scope of MTM-GWAS by incorporating phenotypic network structures into GWAS using structural equation models (SEM-GWAS). In this network GWAS model, one or more phenotypes appear in the equations for other phenotypes as explanatory variables. A salient feature of SEM-GWAS is that it can partition the total single nucleotide polymorphism (SNP) effects into direct and indirect effects.
Recommended citation: Momen, Mehdi and Campbell, Malachy T. and Walia, Harkamal and Morota, Gota. 2019. Harnessing phenotypic networks and structural equation models to improve genome-wide association analysis.bioRxiv. https://www.biorxiv.org/content/10.1101/553008v1?rss=1/
Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines
Published in bioRxiv, 2019
Here, we sought to apply random regression model(RRM) to forecast the rice projected shoot erea(PSA) in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories.
Recommended citation: Momen M., Campbell T.M., Walia H., Morota G. (2019) Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines https://www.biorxiv.org/content/10.1101/632117v1
Gain obtained by enriching whole genome prediction machines using genotypic data and bioinformatics initiative tools
Graduate courses, Department of Animal Science, SBUK University, Kerman, Iran, 2018
Genomic prediction in animal breeding programs, Statistical analysis of large genomic data and advanced genetic prediction models.