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

@article{Tang2016GAPITV2,
  title={GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction},
  author={You Tang and Xiaolei Liu and Jiabo Wang and Meng Li and Qishan Wang and Feng Tian and Zhongbin Su and Yuchun Pan and Di Liu and Alexander E. Lipka and Edward S. Buckler and Zhiwu Zhang},
  journal={The Plant Genome},
  year={2016},
  volume={9}
}
Most human diseases and agriculturally important traits are complex. [...] Key Result These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER.Expand
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References

SHOWING 1-10 OF 30 REFERENCES
GAPIT: genome association and prediction integrated tool
TLDR
An R package called GAPIT is developed that implements advanced statistical methods including the compressed mixed linear model (CMLM) and CMLM-based genomic prediction and selection and can handle large datasets in excess of 10 000 individuals and 1 million single-nucleotide polymorphisms with minimal computational time.
Towards sequence-based genomic selection of cattle
TLDR
An international effort to resequence the genomes of a large number of key ancestor bulls of the most important domestic cattle breeds based on the analysis of the first 234 bovine whole-genome sequences reports on the first results.
TASSEL: software for association mapping of complex traits in diverse samples
TLDR
TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure and allows for linkage disequilibrium statistics to be calculated and visualized graphically.
An efficient multi-locus mixed model approach for genome-wide association studies in structured populations
TLDR
Simulations suggest that the proposed multi-locus mixed model as a general method for mapping complex traits in structured populations outperforms existing methods in terms of power as well as false discovery rate.
A mixed-model approach for genome-wide association studies of correlated traits in structured populations
TLDR
This work extends this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-Trait mixed model (MTMM) that considers both the within-trait and between-traits variance components simultaneously for multiple traits.
Prioritizing GWAS results: A review of statistical methods and recommendations for their application.
TLDR
This review is written from the viewpoint that findings from the GWAS provide preliminary genetic information that is available for additional analysis by statistical procedures that accumulate evidence, and that these secondary analyses are very likely to provide valuable information that will help prioritize the strongest constellations of results.
Using Whole-Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster
TLDR
It is hypothesized that predictive power in this population stems from the SNP–based modeling of the subtle relationship structure caused by long-range linkage disequilibrium and not from population structure or SNPs in linkage diseqilibrium with causal variants.
Mixed linear model approach adapted for genome-wide association studies
TLDR
A compression approach is reported, called 'compressed MLM', that decreases the effective sample size of such datasets by clustering individuals into groups and a complementary approach, 'population parameters previously determined' (P3D), that eliminates the need to re-compute variance components.
Variance component model to account for sample structure in genome-wide association studies
TLDR
A variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours is reported.
A SUPER Powerful Method for Genome Wide Association Study
TLDR
A method to extract a small subset of SNPs and use them in FaST-LMM, which not only retains the computational advantage of FaST, but also remarkably increases statistical power even when compared to using the entire set ofSNPs.
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