GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction

Genome‐wide association study Genomic prediction Simulation and experimental design

[1]  Zhiwu Zhang,et al.  Mixed linear model approach adapted for genome-wide association studies , 2010, Nature Genetics.

[2]  Edward S. Buckler,et al.  TASSEL: software for association mapping of complex traits in diverse samples , 2007, Bioinform..

[3]  I Misztal,et al.  Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information. , 2009, Journal of dairy science.

[4]  J. Shendure,et al.  Advanced sequencing technologies: methods and goals , 2004, Nature Reviews Genetics.

[5]  José Crossa,et al.  Genomic Selection in Wheat Breeding using Genotyping‐by‐Sequencing , 2012 .

[6]  Eleazar Eskin,et al.  Improved linear mixed models for genome-wide association studies , 2012, Nature Methods.

[7]  P. VanRaden,et al.  Invited review: reliability of genomic predictions for North American Holstein bulls. , 2009, Journal of dairy science.

[8]  Edward S. Buckler,et al.  A SUPER Powerful Method for Genome Wide Association Study , 2014, PloS one.

[9]  M. Stephens,et al.  Genome-wide Efficient Mixed Model Analysis for Association Studies , 2012, Nature Genetics.

[10]  Jeffrey B. Endelman,et al.  Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP , 2011 .

[11]  Tatiana I Axenovich,et al.  Rapid variance components–based method for whole-genome association analysis , 2012, Nature Genetics.

[12]  Bjarni J. Vilhjálmsson,et al.  A mixed-model approach for genome-wide association studies of correlated traits in structured populations , 2012, Nature Genetics.

[13]  P. Visscher,et al.  Advantages and pitfalls in the application of mixed-model association methods , 2014, Nature Genetics.

[14]  Bjarni J. Vilhjálmsson,et al.  An efficient multi-locus mixed model approach for genome-wide association studies in structured populations , 2012, Nature Genetics.

[15]  Bjarni J. Vilhjálmsson,et al.  Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines , 2010 .

[16]  D. Heckerman,et al.  Efficient Control of Population Structure in Model Organism Association Mapping , 2008, Genetics.

[17]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[18]  Ying Liu,et al.  FaST linear mixed models for genome-wide association studies , 2011, Nature Methods.

[19]  M. McMullen,et al.  A unified mixed-model method for association mapping that accounts for multiple levels of relatedness , 2006, Nature Genetics.

[20]  Meng Li,et al.  Genetics and population analysis Advance Access publication July 13, 2012 , 2012 .

[21]  H. Kang,et al.  Variance component model to account for sample structure in genome-wide association studies , 2010, Nature Genetics.

[22]  Daniel Gianola,et al.  Using Whole-Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster , 2012, PLoS genetics.

[23]  K. Lange,et al.  Prioritizing GWAS results: A review of statistical methods and recommendations for their application. , 2010, American journal of human genetics.

[24]  Michel Georges.  Towards sequence-based genomic selection of cattle , 2014, Nature Genetics.

[25]  Zhiwu Zhang,et al.  Enrichment of statistical power for genome-wide association studies , 2014, BMC Biology.

[26]  M. Stephens,et al.  Inference of population structure using multilocus genotype data: dominant markers and null alleles , 2007, Molecular ecology notes.

[27]  J. A. López del Val,et al.  Principal Components Analysis , 2018, Applied Univariate, Bivariate, and Multivariate Statistics Using Python.