Soumya Shraddhya Paul, Amity University, Noida
In most plant and animal breeding, as well as human genetics, large-scale phenotypic data can improve the accuracy of genomic prediction. However, the multivariate linear mixed effect model, which is notorious for its fragility when applied to more than a handful of traits, serves as the statistical foundation of multi-trait genomic prediction. A study conducted by Daniel E. Runcie et al. showed that MegaLMM (Mega-scale linear mixed models) is a software program and can be used as the statistical framework for mixed model analysis of almost any number of characteristics. MegaLMM can use hundreds of characteristics at once to greatly increase genetic value prediction accuracy, as demonstrated by three instances using real plant data.
New high-throughput phenotyping tools have the potential to revolutionize data-driven breeding decisions in plants and animals. Breeders might use these high-dimensional characteristics to measure many aspects of performance more precisely and sooner in development than they could be using existing methods. By enhancing selection precision, increasing selection intensity, and shortening breeding cycle lengths, can enhance the rate of growth of desired characteristics.
The multivariate linear mixed model (MvLMM) is a commonly used statistical technique for separating genetic and non-genetic components in phenotypic relationships. The MvLMM is a multi-outcome extension of the univariate linear mixed model (LMM), which is used in most quantitative genetics techniques.
A Study on MegaLMM:
Researchers who worked on MegaLMM belonged to Microsoft Research New England and UC, Davis. They mostly focused on the plant breeding applications since the MegaLMM is a novel statistical method fitting massive-scale MvLMMs to large-scale phenotypic datasets using a computational algorithm. Therefore, this method can be used wherever multi-trait linear mixed models are being used like in linguistics, human genetics, industrial experiments, psychology etc. MegaLMM vastly outperforms previous low-rank MvLMM fitting techniques, allowing for many random effects and unbalanced research designs with significant quantities of missing data.
Combining strong, but biologically grounded, Bayesian priors for statistical regularization–analogous to the p>>n approach of genomic prediction methods–with algorithmic improvements recently established for LMMs, the researchers were able to achieve both scalability and statistical robustness. The studies showed when applied to data from real breeding operations, the system retains excellent predictive accuracy for tens of thousands of characteristics and substantially improves the prediction of genetic values over previous techniques.
Significance of the study:
Novel statistical approaches may be able to aid in the optimization of plant and animal breeding programs to satisfy future food security requirements. Incorporating high-throughput phenotyping data from remote sensors and synthesizing data on gene-environment interactions across large-scale multi-environment trials are two areas where large-scale phenotype data might increase the accuracy of genomic prediction in realistic plant breeding scenarios.
To efficiently incorporate all available genotype and phenotypic data into genetic value predictions, researchers used high-dimensional multivariate linear mixed models. MegaLMM is a tool that is scalable and increases the range of data that can be used in multivariate linear mixed models by at least two orders of magnitude when compared to prior methods, while also allowing it to be integrated into existing breeding programs.
Also read: Reverse optogenetics tool using zebrafish
About Author:
Soumya Shraddhya Paul is an undergrad biotechnology student who worked in building 3D prosthetics in Base Hospital Delhi Cantt, and holds a key interest in nutraceuticals and enzymology.
Publications:
- https://bioxone.in/news/worldnews/understanding-b-cell-genomics-to-fight-against-covid-19/
- https://bioxone.in/news/worldnews/the-current-ebola-epidemic-comes-to-an-end/
- https://bioxone.in/news/worldnews/crispr-act-3-0-a-revolution-in-plant-gene-technology/
Social Media Info: www.linkedin.com/in/soumya-shraddhya-paul-858229203
References:
- Runcie, D. E., Qu, J., Cheng, H., & Crawford, L. (2021). MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits. Genome Biology, 22(1), 213. https://doi.org/10.1186/s13059-021-02416-w
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