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FaST-LMM specifications

Information


Unique identifier OMICS_05009
Name FaST-LMM
Alternative name Factored Spectrally Transformed Linear Mixed Models
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Windows
Programming languages Python
License Apache License version 2.0
Computer skills Advanced
Version 0.2.31
Stability Stable
Requirements
Numpy, Scipy, Matplotlib, Pandas, Sklearn, Cython, Pysnptools
Maintained Yes

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Documentation


Maintainer


  • person_outline David Heckerman

Publications for Factored Spectrally Transformed Linear Mixed Models

FaST-LMM citations

 (68)
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Genome wide association studies reveal that members of bHLH subfamily 16 share a conserved function in regulating flag leaf angle in rice (Oryza sativa)

2018
PLoS Genet
PMCID: 5902044
PMID: 29617374
DOI: 10.1371/journal.pgen.1007323
call_split See protocol

[…] ponica subpopulation, including Tej, Trj and japonica intermediate. To control spurious associations, population structure and kinship were regarded as cofactors when performing GWAS using LMM by the FaST-LMM program [, ]. Kinship was calculated as a realized relationship matrix using FaST-LMM program. Population structure was calculated as Q matrix base on the admixture model[]. A total of 3,916, […]

library_books

A Genomic Reference Panel for Drosophila serrata

2018
PMCID: 5873922
PMID: 29487184
DOI: 10.1534/g3.117.300487

[…] ternational) for a total of 3,318,503 SNPs. There are a couple of differences between the approach outlined above and other mixed modeling approaches to GWAS implemented in programs such as GEMMA (), FaST-LMM (), and GCTA (). First, the use of individual-level opposed to line mean level observations, allows for estimation of the genomic heritability (). Although mapping power is unlikely to be sig […]

library_books

RNA sequencing provides insights into the evolution of lettuce and the regulation of flavonoid biosynthesis

2017
Nat Commun
PMCID: 5741661
PMID: 29273740
DOI: 10.1038/s41467-017-02445-9

[…] and high LD make GWAS in lettuce difficult. However, GWAS for leaf color is predicted not to be affected considerably by population structure since red cultivars are found in all horticultural types. FaST-LMM was used to identify association signals for leaf color. Six significant loci controlling leaf color were identified at the suggestive threshold (−log10(P) = 5.02, α = 1) (Fig. , Table , Supp […]

library_books

Dissecting the Genetic Basis of Local Adaptation in Soybean

2017
Sci Rep
PMCID: 5722827
PMID: 29222468
DOI: 10.1038/s41598-017-17342-w

[…] Mixed-model association as implemented in the Factored Spectrally Transformed Linear Mixed Models (FaST-LMM) was used to test for associations between individual SNPs and bioclimatic and biophysical variables. The following models were explored: […]

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Differentially evolved glucosyltransferases determine natural variation of rice flavone accumulation and UV tolerance

2017
Nat Commun
PMCID: 5719032
PMID: 29213047
DOI: 10.1038/s41467-017-02168-x
call_split See protocol

[…] the minor allele ≥6 in a (sub) population were used to carry out GWAS. Population structure was modeled as a random effect in LMM using the kinship (K) matrix. We performed GWAS using LMM provided by FaST-LMM program. Instead of calculating the P value for the large number of SNPs, a test of association between the trait and each of the available SNPs within a gene was carried out and the resultin […]

library_books

Genome wide linkage and association study implicates the 10q26 region as a major genetic contributor to primary nonsyndromic vesicoureteric reflux

2017
Sci Rep
PMCID: 5668427
PMID: 29097723
DOI: 10.1038/s41598-017-15062-9

[…] e Control Consortium controls), at 108,134 autosomal SNPs passing QC in all case and control cohorts. This analysis was carried out using a linear mixed modelling approach implemented in the software FaST-LMM in order to adjust for both relatedness between cases and population differences between cases and controls–. […]

Citations

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FaST-LMM institution(s)
eScience Group, Microsoft Research, Los Angeles, CA, USA
FaST-LMM funding source(s)
Supported by Microsoft Research; by National Human Genome Research Institute grant U01HG004402 (E. Boerwinkle); by the Division of Aging Biology and the Division of Geriatrics and Clinical Gerontology, NIA; and by Heath ABC Study Investigators.

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