GitHub - izabelcavassim/Quantitative_genomics: The data used in this Project was originated from a M16 x ICR F_2 population of 943 mice in half-sib families with records for Body Weight (BW) and Food Intake (FI). Genomic analysis were executed based on a total of 1,821 SNP markers distributed across the chromosomes. It was first decided to treat the genetic effects as fixed factor in the regression model and fit each marker individually to compute the p-values of association. In order to compare the prediction of SMA approach with other models, it was decided to apply Bayesian Shrinkage Models, where the genomic variants were considered as random factors with prior distributions on marker effects. The models analyzed were: Bayes A, Bayes B, Bayes C, BayesRR and Bayes Lasso and Ridge Regression BLUP. In all models, variables of sex and batch were included as fixed factors.

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Name		Name	Last commit message	Last commit date
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.gitattributes		.gitattributes
.gitignore		.gitignore
Models_of_prediction_body_weight.R		Models_of_prediction_body_weight.R
Models_of_prediction_food_intake.R		Models_of_prediction_food_intake.R
RMARKDOWN_PROJECT.Rmd		RMARKDOWN_PROJECT.Rmd

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The data used in this Project was originated from a M16 x ICR F_2 population of 943 mice in half-sib families with records for Body Weight (BW) and Food Intake (FI). Genomic analysis were executed based on a total of 1,821 SNP markers distributed across the chromosomes. It was first decided to treat the genetic effects as fixed factor in the reg…