Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me
About me
Page not in menu
This is a page not in th emain menu
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
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
A predictive assessment of genetic correlations between traits in chickens using markers
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
Predictive ability of genome-assisted statistical models under various forms of gene action
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.ncbi.nlm.nih.gov/pubmed/31290928
Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies
Published in Plant methods, 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://link.springer.com/article/10.1186/s13007-019-0493-x
Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines
Published in G3: Genes, Genomes, Genetics, 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.g3journal.org/content/9/10/3369
Performance of pedigree and various forms of marker‐derived relationship coefficients in genomic prediction and their correlations
Published in Journal of Animal Breeding and Genetics, 2020
The objective of this work was to compare estimates of genetic and genomic parameters between several types of similarity matrices as well as their predictive performance under a Bayesian multiple‐trait genomic prediction context.
Recommended citation: Solaymani, Samaneh, Ahmad Ayatollahi Mehrgardi, Ali Esmailizadeh, Llibertat Tusell, and Mehdi Momen. "Performance of pedigree and various forms of marker‐derived relationship coefficients in genomic prediction and their correlations." Journal of Animal Breeding and Genetics (2020). https://www.g3journal.org/content/9/10/3369
Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping
Published in PLOS ONE , 2020
However, given the capability of HTP to collect multiple temporal phenotypes, one unresolved question in plant breeding is how to jointly model multiple temporal phenotypes. To address this, we aimed to integrate the RRM framework for temporal traits into a MT model.
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://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0228118&type=printable
Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls
Published in Heredity, 2020
This study evaluated the use of multiomics data for classification accuracy of rheumatoid arthritis (RA). Three approaches were used and compared in terms of prediction accuracy:(1) whole-genome prediction (WGP) using SNP marker information only,(2) whole-methylome prediction (WMP) using methylation profiles only, and (3) whole-genome/methylome prediction (WGMP) with combining both omics layers.
Recommended citation: Roudbar, Mahmoud Amiri, Mohammad Reza Mohammadabadi, Ahmad Ayatollahi Mehrgardi, Rostam Abdollahi-Arpanahi, Mehdi Momen, Gota Morota, Fernando Brito Lopes, Daniel Gianola, and Guilherme JM Rosa. "Integration of single nucleotide variants and whole-genome DNA methylation profiles for classification of rheumatoid arthritis cases from controls." Heredity (2020): 1-17. https://www.nature.com/articles/s41437-020-0301-4
talks
Network-Based Genome-Wide Association Analysis Using Structural Equation Models (SEM-GWAS)
Published:
teaching
Biostatistics, Quantitative Genetics
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.