FaST linear mixed models for genome-wide association studies

By Christoph Lippert, Jennifer Listgarten, Ying Liu, Carl Myers Kadie, R. Davidson, David E. Heckerman. Nature Methods, 2011

1,189 citations recorded by the source corpus.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/2107add4b612fc9bd0260387b96d5f2f9b56bfe4

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In Genetics

Citations in the corpus

A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness (2014, methodology): “Methods for carrying out association studies using related individuals have recently been re-discovered in the literature as a powerful approach, with the additional benefit of implicitly avoiding confounding due to population structure [14–16].”
Fast Principal Component Analysis of Large-Scale Genome-Wide Data (2014, methodology): “More generally, the approach behind flashpca could prove useful for accelerating other methods that depend on performing a large number of eigen-decompositions across many samples, such as varLD [13] which assesses local differences in SNP LD between populations or FaST-LMM which implements linear m”
Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (2015, methodology): “…for modeling variability in bulk mRNA expression studies 21,22 and to methods used in genome-wide association studies in which the relatedness between individuals is inferred from genotype 29 and/or expression levels 30 and then accounted for in downstream analyses using linear mixed models.”
Efficient Bayesian mixed model analysis increases association power in large cohorts (2014, background): “The first perspective is motivated by the observation that in human genetics applications, the denominator of the prospective statistic in equation (5), xtest'Vxtest, is nearly independent of the SNP xtest Loh et al.”
GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction (2020, methodology): “Many of the software package developments accompanied GWAS and GS method developments so that the methods and the software were given the same name, such as EMMA[5], EMMAx[22], FaST-LMM[8], FarmCPU [15], and BLINK[16].”
BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions (2018, neutral): “Thefourthmilestonewasamethodcalledfactoredspectrally transformed linear mixed model (FaST-LMM) [23], which uses a rank-reduced kinship.”
Modelling local gene networks increases power to detect trans-acting genetic effects on gene expression (2016, methodology): “We use the fastLMM algorithm [60] as implemented in LIMIX [64] for all model fits and association tests.”
Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies (2017, methodology): “δ = σ(2) e σ2 g to replace σ(2) g for more efficient optimization [16].”
Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines (2014, methodology): “We fitted this linear mixed model using the FastLMM program (version 1.09; Lippert et al. 2011).”
Marker-Assisted Plant Breeding: Principles and Practices (2015, methodology): “FaST-LMM is faster than EMMA as it can analyze in few hours the phenotype data from several thousand individuals along with the genotype data for a reasonable number of markers (Lippert et al. 2011).”
rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study (2020, neutral): “4 Mixed-Model Association eXpedited) / P3D (Population Parameters Previously 107 Determined)[8, 16], FaST-LMM (Factored Spectrally Transformed Linear Mixed Model) 108 algorithms[9] and HE regression (Haseman-Elston regression)[27].”