KAML <img alt="Hits" src="https://hits.sh/github.com/YinLiLin/KAML.svg?view=today-total&label=Today%2FTotal%20Visitors&extraCount=4872"/>

Kinship Adjusted Multiple Loci Best Linear Unbiased Prediction

Contents

• OVERVIEW
• CITATION
• GETTING STARTED<img src="https://raw.githubusercontent.com/YinLiLin/R-KAML/master/figures/KAML_log.png" height="250" align="right" />
  • Installation
  • Test Datasets
• INPUT
  • Phenotype file/Covariate file/Kinship file
  • Genotype file
    • Hapmap/VCF/PLINK Binary/Numeric
• USAGE
  • Basic
  • Advanced
• OUTPUT
• FAQ AND HINTS

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OVERVIEW

KAML is designed to predict genetic values using genome-wide or chromosome-wide SNPs for either simple traits that controlled by a limited number of major genes or complex traits that influenced by many polygenes with minor effects. In brief, KAML provides a flexible assumption to accommodate traits of various genetic architectures and incorporates pseudo-QTNs as fixed effect terms and a trait-specific random effect term under the LMM framework. The model parameters are optimized using the information of bootstrap strategy based GWAS results in a parallel accelerated machine learning procedure combing cross-validation, grid search and bisection algorithms. For more statistical methods under the BLUP Framework please see our developed package HIBLUP (https://www.hiblup.com).

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KAML is developed by Lilin Yin with the help of Haohao Zhang, and Xiaolei Liu* at the Huazhong(Central China) Agricultural University.

If you have any bug reports or questions, please feed back :point_right:here:point_left:, or send email to xiaoleiliu@mail.hzau.edu.cn

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CITATION

Yin, L., Zhang, H., Zhou, X. et al. KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters. Genome Biol 21, 146 (2020). https://doi.org/10.1186/s13059-020-02052-w

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:toolbox: RELEVANT SOFTWARE TOOLS FOR GENETIC ANALYSES AND GENOMIC BREEDING

<table> <tr> <td><g-emoji class="g-emoji" alias="mailbox" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f4eb.png">📫</g-emoji> <strong><a href="https://www.hiblup.com/" rel="nofollow">HIBLUP</a></strong>: Versatile and easy-to-use GS toolbox.</td> <td><g-emoji class="g-emoji" alias="four_leaf_clover" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f340.png">🍀</g-emoji> <strong><a href="https://github.com/xiaolei-lab/SIMER">SIMER</a></strong>: data simulation for life science and breeding.</td> </tr> <tr> <td><g-emoji class="g-emoji" alias="swimmer" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f3ca.png">🏊</g-emoji> <strong><a href="https://github.com/YinLiLin/hibayes">hibayes</a></strong>: A Bayesian-based GWAS and GS tool.</td> <td><g-emoji class="g-emoji" alias="mountain_snow" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f3d4.png">🏔️</g-emoji> <strong><a href="http://ianimal.pro/" rel="nofollow">IAnimal</a></strong>: an omics knowledgebase for animals.</td> </tr> <tr> <td><g-emoji class="g-emoji" alias="postbox" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f4ee.png">📮</g-emoji> <strong><a href="https://github.com/xiaolei-lab/rMVP">rMVP</a></strong>: Efficient and easy-to-use GWAS tool.</td> <td><g-emoji class="g-emoji" alias="bar_chart" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/1f4ca.png">📊</g-emoji> <strong><a href="https://github.com/YinLiLin/CMplot">CMplot</a></strong>: A drawing tool for genetic analyses.</td> </tr> </table>

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GETTING STARTED

KAML is written in R language, it is recommended to link MKL (Math Kernel Library) or OpenBLAS with R for fast computing with big data (see how to link MKL with R), because the BLAS/LAPACK library can be accelerated automatically in multi-threads, which would significantly reduce computational time.

Installation

(1) KAML can be installed with "devtools" by using the following R codes:

#if "devtools" isn't installed, please "install.packages(devtools)" first.
devtools::install_github("YinLiLin/KAML")

(2) If you get trouble in installing "devtools", try to install it locally as following:

# install required packages first
R> pkg <- c("RcppArmadillo", "RcppEigen", "RcppProgress", "bigmemory", "gaston", "RhpcBLASctl")
R> new.pkg <- pkg[!(pkg %in% installed.packages()[,"Package"])]
R> if(length(new.pkg)) install.packages(new.pkg)

shell$ git clone https://github.com/YinLiLin/KAML
shell$ R CMD INSTALL KAML

After installed successfully, the KAML package can be loaded by typing

library(KAML)

Typing ?KAML could get the details of all parameter