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Modern Programming in Julia for Genomic Prediction

Preface

Introduction

1. Introduction to Julia
2. Introduction to Bayesian Inference
3. Introduction to JWAS

Additive Relationship Matrix

4. Algorithm to Invert Additive Relationship Matrix
5. Pedigree Module of JWAS
6. Exercise: Matrix calculations in Julia
7. Exercise: Algorithm to invert matrix

Mixed Effects Model (Part 1)

8. Normal Equations
9. Exercise: Computing full-stored or sparse-stored X’X without matrix multiplication
10. Iterative methods for solving linear systems
11. Exercise: The Conjugate Gradient Method
13. Design Matrices from Data and Model
14. Mixed Model Equations (MME) from Data and Model (known variance components)

Bayesian Inference via Gibbs Sampling by Application to Linear Regression

15. Simple Linear Regression
16. Multiple Linear Regression

Mixed Effects Model (Part 2, Bayesian Approach)

17. Mixed Effects Model (Single-trait)
18. Extension to Multi-trait Models
19. Extensions for Models with SNP Covariates

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By Hao Cheng and Rohan Fernando
© Copyright 2022.