1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
|
# GEMMA: Genome-wide Efficient Mixed Model Association
GEMMA is a software toolkit for fast application of linear mixed
models (LMMs) and related models to genome-wide association studies
(GWAS) and other large-scale data sets.
*Add note here about posting questions, comments or bug reports to
Issues.*
*Note: The image above summarizes physiological and behavioral trait
loci in CFW mice identified using GEMMA, from [Parker et al, Nature
Genetics, 2006](https://doi.org/10.1038/ng.3609).*
## Key features
1. Fast assocation tests implemented using the univariate linear mixed
model (LMM). In GWAS, this can correct for population structure and
sample nonexchangeability. It also provides estimates of the
proportion of variance in phenotypes explained by available genotypes
(PVE), often called "chip heritability" or "SNP heritability".
2. Fast association tests for multiple phenotypes implemented using a
multivariate linear mixed model (mvLMM). In GWAS, this can correct for
populations tructure and sample nonexchangeability jointly in multiple
complex phenotypes.
3. Bayesian sparse linear mixed model (BSLMM) for estimating PVE,
phenotype prediction, and multi-marker modeling in GWAS.
4. Estimation of variance components ("chip heritability") partitioned
by different SNP functional categories from raw (individual-level)
data or summary data. For raw data, HE regression or the REML AI
algorithm can be used to estimate variance components when
individual-level data are available. For summary data, GEMMA uses the
MQS algorithm to estimate variance components.
## Citing GEMMA
If you use GEMMA for published work, please cite our paper:
Xiang Zhou and Matthew Stephens (2012). [Genome-wide efficient
mixed-model analysis for association studies.](http://doi.org/10.1038/ng.2310)
*Nature Genetics* **44**, 821–824.
If you use the multivariate linear mixed model (mvLMM) in your
research, please cite:
Xiang Zhou and Matthew Stephens (2014). [Efficient multivariate linear
mixed model algorithms for genome-wide association
studies.](http://doi.org/10.1038/nmeth.2848)
*Nature Methods* **11**, 407–409.
If you use the Bayesian sparse linear mixed model (BSLMM), please cite:
Xiang Zhou, Peter Carbonetto and Matthew Stephens (2013). [Polygenic
modeling with bayesian sparse linear mixed
models.](http://doi.org/10.1371/journal.pgen.1003264) *PLoS Genetics*
**9**, e1003264.
And if you use of the variance component estimation using summary
statistics, please cite:
Xiang Zhou (2016). [A unified framework for variance component
estimation with summary statistics in genome-wide association
studies.](https://doi.org/10.1101/042846) *bioRxiv* 042846.
## License
Copyright (C) 2012–2017, Xiang Zhou.
## Quick start
1. Download and install the software. *Give more details here.*
2. Work through the tutorial. *Give more details here.*
3. Read the manual. *Give more details.*
## Setup
### Using precompiled executables
### Building from source
## Credits
*Add text here.*
|
