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| author | Peter Carbonetto | 2017-05-23 22:36:54 -0500 |
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| committer | Peter Carbonetto | 2017-05-23 22:36:54 -0500 |
| commit | 77332754b8d8bc34a7f3796e154c3d194a25864f (patch) | |
| tree | 2cbff4881b57a3975c0883855ae004aea6955310 | |
| parent | fe2812653048cf4b048b890b5225075550336164 (diff) | |
| download | pangemma-77332754b8d8bc34a7f3796e154c3d194a25864f.tar.gz | |
Adjusted formatting in README.
| -rw-r--r-- | README.md | 8 |
1 files changed, 4 insertions, 4 deletions
@@ -41,21 +41,21 @@ MQS algorithm to estimate variance components. If you use GEMMA for published work, please cite our paper: -Xiang Zhou and Matthew Stephens (2012). [Genome-wide efficient ++ 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 ++ 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 ++ 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. @@ -63,7 +63,7 @@ models.](http://doi.org/10.1371/journal.pgen.1003264) *PLoS Genetics* And if you use of the variance component estimation using summary statistics, please cite: -Xiang Zhou (2016). [A unified framework for variance component ++ 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. |