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Autoencoder-Transformed Transcriptome Improves Genotype-Phenotype Association Studies.
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本文引用的文献

1
A Powerful Framework for Integrating eQTL and GWAS Summary Data.
Genetics. 2017 Nov;207(3):893-902. doi: 10.1534/genetics.117.300270. Epub 2017 Sep 11.
2
Bayesian longitudinal low-rank regression models for imaging genetic data from longitudinal studies.
Neuroimage. 2017 Apr 1;149:305-322. doi: 10.1016/j.neuroimage.2017.01.052. Epub 2017 Jan 29.
3
Gene- and pathway-based association tests for multiple traits with GWAS summary statistics.
Bioinformatics. 2017 Jan 1;33(1):64-71. doi: 10.1093/bioinformatics/btw577. Epub 2016 Sep 4.
4
Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data.
Genetics. 2016 Jun;203(2):715-31. doi: 10.1534/genetics.115.186502. Epub 2016 Apr 13.
5
Integrative approaches for large-scale transcriptome-wide association studies.
Nat Genet. 2016 Mar;48(3):245-52. doi: 10.1038/ng.3506. Epub 2016 Feb 8.
6
Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept.
Nat Neurosci. 2016 Mar;19(3):420-431. doi: 10.1038/nn.4228. Epub 2016 Feb 1.
7
Common and Rare Genetic Variants Associated With Alzheimer's Disease.
J Cell Physiol. 2016 Jul;231(7):1432-7. doi: 10.1002/jcp.25225. Epub 2015 Dec 17.
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A gene-based association method for mapping traits using reference transcriptome data.
Nat Genet. 2015 Sep;47(9):1091-8. doi: 10.1038/ng.3367. Epub 2015 Aug 10.
9
Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans.
Alzheimers Dement. 2015 Jul;11(7):792-814. doi: 10.1016/j.jalz.2015.05.009.
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A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants.
Am J Hum Genet. 2015 Jul 2;97(1):86-98. doi: 10.1016/j.ajhg.2015.05.018. Epub 2015 Jun 25.

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