基于统计学习的影像遗传学方法综述

郝小可; 李蝉秀; 严景文; 沈理; 张道强

doi:10.16383/j.aas.2018.c160696

基于统计学习的影像遗传学方法综述

doi: 10.16383/j.aas.2018.c160696

郝小可^1,,
李蝉秀^1,,
严景文^2,,
沈理^2,,
张道强^1, ,

1.
南京航空航天大学计算机科学与技术学院南京 211106 中国
2.
印第安纳大学医学院印第安纳波利斯 46202 美国

基金项目:

国家自然科学基金 61422204

国家自然科学基金 61732006

国家自然科学基金 61473149

详细信息

作者简介:
郝小可河北工业大学计算机科学与软件学院讲师.于2017年在南京航空航天大学计算机科学与技术学院获得博士学位.分别于2009年和2012年在南京信息工程大学计算机与软件学院获得学士学位和硕士学位.主要研究方向为机器学习, 影像遗传学.E-mail:robinhc@163.com

李蝉秀南京航空航天大学计算机科学与技术学院硕士研究生.2015年在南京航空航天大学计算机科学与技术学院获得学士学位.主要研究方向为机器学习, 影像遗传学.E-mail:lcx_show@nuaa.edu.cn

严景文印第安纳大学普渡大学印第安纳波利斯联合分校信息学与计算学院生物健康信息学系助理教授.曾分别在南京航空航天大学和华中科技大学获得学士学位和硕士学位.2015年获得印第安纳大学信息学与计算学院博士学位.主要研究方向为机器学习, 影像遗传学.E-mail:jingyan@iupui.edu

沈理印第安纳大学医学院放射学与影像科学系副教授.曾分别在西安交通大学和上海交通大学获得学士和硕士学位, 在达特茅斯学院获得博士学位, 专业均为计算机科学.主要研究方向为医学影像计算, 信息生物学, 影像遗传学, 脑连接组.E-mail:shenli@iu.edu

通讯作者:
张道强南京航空航天大学计算机科学与技术学院教授.分别于1999年和2004年在南京航空航天大学获得学士学位和博士学位.主要研究方向为机器学习, 模式识别, 数据挖掘以及医学影像分析.本文通信作者.E-mail:dqzhang@nuaa.edu.cn

计量
- 文章访问数: 2809
- HTML全文浏览量: 795
- PDF下载量: 1064
- 被引次数: 0
出版历程
- 收稿日期: 2016-09-30
- 录用日期: 2017-04-10
- 刊出日期: 2018-01-01

A Review of Statistical-learning Imaging Genetics

1.
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
School of Medicine, Indiana University, Indianapolis, IN 46202, USA

Funds:

National Natural Science Foundation of China 61422204

National Natural Science Foundation of China 61732006

National Natural Science Foundation of China 61473149

More Information

Author Bio:
Lecturer at the School of Computer Science and Engineering, Hebei University of Technology. He received his Ph. D. degree from the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics in 2017. He received his bachelor degree and master degree from the School of Computer and Software, Nanjing University of Information Science and Technology in 2009 and 2012, respectively. His research interest covers machine learning and imaging genetics

Master student at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. She received her bachelor degree from the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics in 2015. Her research interest covers machine learning and imaging genetics

Assistant professor in the Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, USA. She received her bachelor degree from Nanjing University of Aeronautics and Astronautics. She received her master degree from Huazhong University of Science and Technology. She received her Ph. D. degree from the School of Informatics and Computing, Indiana University. Her research interest covers machine learning and imaging genetics

Associate professor of Radiology and Imaging Sciences at Indiana University School of Medicine. He received his bachelor degree from Xi0an Jiao Tong University, master degree from Shanghai Jiao Tong University, and Ph. D. degree from Dartmouth College, all in computer science. His research interest covers medical image computing, bioinformatics, imaging genomics, and brain connectomics

Corresponding author: ZHANG Dao-Qiang Professor at the School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics. He received his bachelor degree and Ph. D. degree in computer science from Nanjing University of Aeronautics and Astronautics, in 1999 and 2004, respectively. His research interest covers machine learning, pattern recognition, data mining, and medical image analysis. Corresponding author of this paper

摘要

摘要: 近年来随着多模态神经影像技术和基因检测技术的发展，影像遗传学这一交叉学科的研究能够运用脑影像技术将人类大脑的结构与功能作为表型来评价基因对个体的影响，使得人们可以在脑的宏观结构上以更客观的测量手段理解基因对行为或精神疾病的影响.而统计学习方法作为基于数据驱动的关联分析强有力工具，能够充分利用生物标志数据内在的结构信息构建模型来分析易感基因与大脑结构或者功能的相关性，从而更好地揭示脑认知行为或者相关疾病的产生机制.本文首先简要介绍了影像遗传学的研究背景和基本原理，然后回顾了单变量方法在影像遗传学研究中的应用，随后对基于多变量统计学习的基因-影像关联的研究思路和建模方法进行了归纳总结，最后对遗传影像学的未来研究发展方向进行了分析和展望.
- 影像遗传学 /
- 统计学习 /
- 结构化稀疏学习 /
- 多变量分析 /
- 关联分析
Abstract: The past decade has witnessed the increasing development of multimodal neuroimaging and genomic techniques. Imaging genetics, an interdisciplinary field, aims to evaluate and characterize genetic variants in individuals that influence phenotypic measures derived from structural and functional brain images. This strategy is able to reveal the complex mechanisms via macroscopic intermediates from genetic level to cognition and psychiatric disorders in humans. On the other hand, statistical learning methods, as a powerful tool in the data-driven based association study, can make full use of priori-knowledge (inter correlated structure information among imaging and genetic data) for correlation modelling. Therefore, the association study can address the correlations between risk gene and brain structure or function, so as to help explore a better mechanistic understanding of behaviors or disordered brain functions. This paper firstly reviews the related background and fundamental work in imaging genetics and then shows the univariate statistical learning approaches for correlation analysis. Subsequently, it summarizes the main idea and modeling in gene-imaging association studies based on multivariate statistical learning. Finally, this paper presents some prospects of future work.
- Imaging genetics /
- statistical learning /
- structured sparse learning /
- multivariate analysis /
- association analysis
注释:

1) 本文责任编委朱朝喆

HTML全文

图 1 基于统计学习的影像遗传学关联分析研究方法

Fig. 1 Association analysis in imaging genetics based on statistical learning

下载: 全尺寸图片幻灯片

图 2 树型结构引导稀疏回归模型^[54]

Fig. 2 Tree-guided sparse regression model ^[54]

下载: 全尺寸图片幻灯片

图 3 任务相关的纵向稀疏回归模型^[56]

Fig. 3 Task-correlated longitudinal sparse regression model ^[56]

下载: 全尺寸图片幻灯片

图 4 多模态关联模型^[57]

Fig. 4 Multi-modality association model ^[57]

下载: 全尺寸图片幻灯片

图 5 组稀疏多任务回归和特征选择模型^[58]

Fig. 5 Group-sparse multi-task regression and feature selection model ^[58]

下载: 全尺寸图片幻灯片

图 6 稀疏低秩回归模型^[59-60]

Fig. 6 Sparse reduced rank regression model ^[59-60]

下载: 全尺寸图片幻灯片

图 7 稀结构化稀疏的双多变量关联模型^[73]

Fig. 7 Structured sparse bi-multivariate correlation model ^[73]

下载: 全尺寸图片幻灯片

参考文献(85)

[1]	Hariri A R, Weinberger D R. Imaging genomics. British Medical Bulletin, 2003, 65(1):259-270 doi: 10.1093/bmb/65.1.259
[2]	Thompson P M, Martin N G, Wright M J. Imaging genomics. Current Opinion in Neurology, 2010, 23(4):368-373 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2927195
[3]	Glahn D C, Thompson P M, Blangero J. Neuroimaging endophenotypes:strategies for finding genes influencing brain structure and function. Human Brain Mapping, 2007, 28(6):488-501 doi: 10.1002/(ISSN)1097-0193
[4]	Gottesman I I, Gould T D. The endophenotype concept in psychiatry:etymology and strategic intentions. The American Journal of Psychiatry, 2003, 160(4):636-645 doi: 10.1176/appi.ajp.160.4.636
[5]	Meyer-Lindenberg A, Weinberger D R. Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nature Reviews Neuroscience, 2006, 7(10):818-827 doi: 10.1038/nrn1993
[6]	Ge T, Schumann G, Feng J F. Imaging genetics-towards discovery neuroscience. Quantitative Biology, 2013, 1(4):227-245 doi: 10.1007/s40484-013-0023-1
[7]	Winkler A M, Kochunov P, Blangero J, Almasy L, Zilles K, Fox P T, Duggirala R, Glahn D C. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage, 2010, 53(3):1135-1146 doi: 10.1016/j.neuroimage.2009.12.028
[8]	Smith S M, Fox P T, Miller K L, Glahn D C, Fox P M, Mackay C E, Filippini N, Watkins K E, Toro R, Laird A R, Beckmann C F. Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(31):13040-13045 doi: 10.1073/pnas.0905267106
[9]	Tost H, Bilek E, Meyer-Lindenberg A. Brain connectivity in psychiatric imaging genetics. NeuroImage, 2012, 62(4):2250-2260 doi: 10.1016/j.neuroimage.2011.11.007
[10]	Rubinov M, Sporns O. Complex network measures of brain connectivity:uses and interpretations. NeuroImage, 2010, 52(3):1059-1069 doi: 10.1016/j.neuroimage.2009.10.003
[11]	Hardy J, Singleton A. Genomewide association studies and human disease. The New England Journal of Medicine, 2009, 360(17):1759-1768 doi: 10.1056/NEJMra0808700
[12]	Klein R J, Zeiss C, Chew E Y, Tsai J Y, Sackler R S, Haynes C, Henning A K, SanGiovanni J P, Mane S M, Mayne S T, Bracken M B, Ferris F L, Ott J, Barnstable C, Hoh J. Complement factor H polymorphism in age-related macular degeneration. Science, 2005, 308(5720):385-389 doi: 10.1126/science.1109557
[13]	Esslinger C, Walter H, Kirsch P, Erk S, Schnell K, Arnold C, Haddad L, Mier D, von Boberfeld C O, Raab K, Witt S H, Rietschel M, Cichon S, Meyer-Lindenberg A. Neural mechanisms of a genome-wide supported psychosis variant. Science, 2009, 324(5927):605 doi: 10.1126/science.1167768
[14]	Medland S E, Jahanshad N, Neale B M, Thompson P M. Whole-genome analyses of whole-brain data:working within an expanded search space. Nature Neuroscience, 2014, 17(6):791-800 doi: 10.1038/nn.3718
[15]	Liu J Y, Calhoun V D. A review of multivariate analyses in imaging genetics. Frontiers in Neuroinformatics, 2014, 8:Article No.29 https://www.researchgate.net/profile/Vince_Calhoun/publication/261605474_A_review_of_multivariate_analyses_in_imaging_genetics/links/54ee6c520cf2e2830864d17b/A-review-of-multivariate-analyses-in-imaging-genetics.pdf
[16]	Thompson P M, Ge T, Glahn D C, Jahanshad N, Nichols T E. Genetics of the connectome. NeuroImage, 2013, 80:475-488 doi: 10.1016/j.neuroimage.2013.05.013
[17]	Daniel W W, Cross C L. Biostatistics:A Foundation for Analysis in the Health Sciences (Tenth Edition). New York:Wiley, 2013.
[18]	Potkin S G, Guffanti G, Lakatos A, Turner J A, Kruggel F, Fallon J H, Saykin A J, Orro A, Lupoli S, Salvi E, Weiner M, Macciardi F, The Alzheimer's Disease Neuroimaging Initiative. Hippocampal atrophy as a quantitative trait in a genome-wide association study identifying novel susceptibility genes for Alzheimer's disease. PLoS One, 2009, 4(8):Article No.e6501 doi: 10.1371/journal.pone.0006501
[19]	Shen L, Thompson P M, Potkin S G, Bertram L, Farrer L A, Foroud T M, Green R C, Hu X L, Huentelman M J, Kim S, Kauwe J S K, Li Q Q, Liu E C, Macciardi F, Moore J H, Munsie L, Nho K, Ramanan V K, Risacher S L, Stone D J, Swaminathan S, Toga A W, Weiner M W, Saykin A J. Genetic analysis of quantitative phenotypes in AD and MCI:imaging, cognition and biomarkers. Brain Imaging and Behavior, 2014, 8(2):183-207 doi: 10.1007/s11682-013-9262-z
[20]	Risacher S L, Shen L, West J D, Kim S, McDonald B C, Beckett L A, Harvey D J, Jack Jr C R, Weiner M W, Saykin A J. Longitudinal MRI atrophy biomarkers:relationship to conversion in the ADNI cohort. Neurobiology of Aging, 2010, 31(8):1401-1418 doi: 10.1016/j.neurobiolaging.2010.04.029
[21]	Risacher S L, Kim S, Shen L, Nho K, Foroud T, Green R C, Petersen R C, Jack Jr C R, Aisen P S, Koeppe R A, Jagust W J, Shaw L M, Trojanowski J Q, Weiner M W, Saykin A J. The role of apolipoprotein E (APOE) genotype in early mild cognitive impairment (E-MCI). Frontiers in Aging Neuroscience, 2013, 5:Article No.11 https://www.sciencedirect.com/science/article/pii/S0197458005001053
[22]	Ho A J, Stein J L, Hua X, Lee S, Hibar D P, Leow A D, Dinov I D, Toga A W, Saykin A J, Shen L, Foroud T, Pankratz N, Huentelman M J, Craig D W, Gerber J D, Allen A N, Corneveaux J J, Stephan D A, DeCarlig C S, DeChairo B M, Potkin S G, Jack Jr C R, Weiner M W, Raji C A, Lopez O L, Becker J T, Carmichael O T, Thompson P M. A commonly carried allele of the obesity-related FTO gene is associated with reduced brain volume in the healthy elderly. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(18):8404-8409 doi: 10.1073/pnas.0910878107
[23]	Reiman E M, Chen K W, Liu X F, Bandy D, Yu M X, Lee D, Ayutyanont N, Keppler J, Reeder S A, Langbaum J B S, Alexander G E, Klunk W E, Mathis C A, Price J C, Aizenstein H J, DeKosky S T, Caselli R J. Fibrillar amyloid-β burden in cognitively normal people at 3 levels of genetic risk for Alzheimer's disease. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(16):6820-6825 doi: 10.1073/pnas.0900345106
[24]	Sloan C D, Shen L, West J D, Wishart H A, Flashman L A, Rabin L A, Santulli R B, Guerin S J, Rhodes C H, Tsongalis G J, McAllister T W, Ahles T A, Lee S L, Moore J H, Saykin A J. Genetic pathway-based hierarchical clustering analysis of older adults with cognitive complaints and amnestic mild cognitive impairment using clinical and neuroimaging phenotypes. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 2010, 153B(5):1060-1069 doi: 10.1002/ajmg.b.v153b:5
[25]	Swaminathan S, Shen L, Risacher S L, Yoder K K, West J D, Kim S, Nho K, Foroud T, Inlow M, Potkin S G, Huentelman M J, Craig D W, Jagust W J, Koeppe R A, Mathis C A, Jack Jr C R, Weiner M W, Saykin A J. Amyloid pathway-based candidate gene analysis of[¹¹C]PiB-PET in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Brain Imaging and Behavior, 2012, 6(1):1-15 doi: 10.1007/s11682-011-9136-1
[26]	Chiang M C, Barysheva M, McMahon K L, de Zubicaray G I, Johnson K, Montgomery G W, Martin N G, Toga A W, Wright M J, Shapshak P, Thompson P M. Gene network effects on brain microstructure and intellectual performance identified in 472 twins. Journal of Neuroscience, 2012, 32(25):8732-8745 doi: 10.1523/JNEUROSCI.5993-11.2012
[27]	Saykin A J, Shen L, Foroud T M, Potkin S G, Swaminathan S, Kim S, Risacher S L, Nho K, Huentelman M J, Craig D W, Thompson P M, Stein J L, Moore J H, Farrer L A, Green R C, Bertram L, Jack Jr C R, Weiner M W. Alzheimer's Disease Neuroimaging Initiative biomarkers as quantitative phenotypes:genetics core aims, progress, and plans. Alzheimer's & Dementia, 2010, 6(3):265-273 https://www.sciencedirect.com/science/article/pii/S1552526010000828
[28]	Potkin S G, Turner J A, Fallon J A, Lakatos A, Keator D B, Guffanti G, Macciardi F. Gene discovery through imaging genetics:identification of two novel genes associated with schizophrenia. Molecular Psychiatry, 2009, 14(4):416-428 doi: 10.1038/mp.2008.127
[29]	Shen L, Kim S, Risacher S L, Nho K, Swaminathan S, West J D, Foroud T, Pankratz N, Moore J H, Sloan C D, Huentelman M J, Craig D W, DeChairo B M, Potkin S G, Jack Jr C R, Weiner M W, Saykin A J. Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD:a study of the ADNI cohort. NeuroImage, 2010, 53(3):1051-1063 doi: 10.1016/j.neuroimage.2010.01.042
[30]	Stein J L, Hua X, Lee S, Ho A J, Leow A D, Toga A W, Saykin A J, Shen L, Foroud T, Pankratz N, Huentelman M J, Craig D W, Gerber J D, Allen A N, Corneveaux J J, DeChairo B M, Potkin S G, Weiner M W, Thompson P M. Voxelwise genome-wide association study (vGWAS). NeuroImage, 2010, 53(3):1160-1174 doi: 10.1016/j.neuroimage.2010.02.032
[31]	Biffi A, Anderson C D, Desikan R S, Sabuncu M, Cortellini L, Schmansky N, Salat D, Rosand J, Alzheimer's Disease Neuroimaging Initiative (ADNI). Genetic variation and neuroimaging measures in Alzheimer disease. Archives of Neurology, 2010, 67(6):677-685 doi: 10.1001/archneurol.2010.108
[32]	Kauwe J S K, Bertelsen S, Mayo K, Cruchaga C, Abraham R, Hollingworth P, Harold D, Owen M J, Williams J, Lovestone S, Morris J C, Goate A M. Suggestive synergy between genetic variants in TF and HFE as risk factors for Alzheimer's disease. American Journal of Medical Genetics Part B-Neuropsychiatric Genetics, 2010, 153B(4):955-959 https://ncrad.iu.edu/docs/Publications/237_Kauwe_2010.pdf
[33]	Dickerson B C, Wolk D A. Dysexecutive versus amnesic phenotypes of very mild Alzheimer's disease are associated with distinct clinical, genetic and cortical thinning characteristics. Journal of Neurology, Neurosurgery & Psychiatry, 2011, 82(1):45-51 http://jnnp.bmj.com/content/jnnp/82/1/45.full.pdf?legid=jnnp;82/1/45
[34]	Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M A R, Bender D, Maller J, Sklar P, de Bakker P I W, Daly M J, Sham P C. PLINK:a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics, 2007, 81(3):559-575 doi: 10.1086/519795
[35]	Gombar S, Jung H J, Dong F, Calder B, Atzmon G, Barzilai N, Tian X L, Pothof J, Hoeijmakers J H J, Campisi J, Vijg J, Suh Y. Comprehensive microRNA profiling in B-cells of human centenarians by massively parallel sequencing. BMC Genomics, 2012, 13(1):Article No.353 doi: 10.1186/1471-2164-13-353
[36]	Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics, 2001, 29(4):1165-1188 doi: 10.1214/aos/1013699998
[37]	Hibar D P, Stein J L, Kohannim O, Jahanshad N, Saykin A J, Shen L, Kim S, Pankratz N, Foroud T, Huentelman M J, Potkin S G, Jack Jr C R, Weiner M W, Toga A W, Thompson P M. Voxelwise gene-wide association study (vGeneWAS):multivariate gene-based association testing in 731 elderly subjects. NeuroImage, 2011, 56(4):1875-1891 doi: 10.1016/j.neuroimage.2011.03.077
[38]	Hibar D P, Stein J L, Kohannim O, Jahanshad N, Jack C R, Weiner M W, Toga A W, Thompson P M. Principal components regression:multivariate, gene-based tests in imaging genomics. In:Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging:from Nano to Macro. Chicago, IL, USA:IEEE, 2011. 289-293
[39]	Hibar D P, Kohannim O, Stein J L, Chiang M C, Thompson P M. Multilocus genetic analysis of brain images. Frontiers in Genetics, 2011, 2:Article No.73 doi: 10.3389/fgene.2011.00073/full
[40]	Ye J P, Liu J. Sparse Methods for Biomedical Data. ACM Sigkdd Explorations Newsletter, 2012, 14(1):4-15 doi: 10.1145/2408736
[41]	Wang J, Yang T, Thompson P, Ye J. Sparse models for imaging genetics. Machine Learning and Medical Imaging. New York:Academic Press, 2016. 129-151
[42]	Lin D D, Cao H B, Calhoun V D, Wang Y P. Sparse models for correlative and integrative analysis of imaging and genetic data. Journal of Neuroscience Methods, 2014, 237:69-78 doi: 10.1016/j.jneumeth.2014.09.001
[43]	Yan J, Du L, Yao X, Shen L. Machine learning in brain imaging genomics. Machine Learning and Medical Imaging. New York:Academic Press, 2016. 411-434
[44]	Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306 doi: 10.1109/TIT.2006.871582
[45]	Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society:Series B (Methodological), 1996, 58(1):267-288 http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.7574
[46]	Kohannim O, Hibar D P, Stein J L, Jahanshad N, Jack C R, Weiner M W, Toga A W, Thompson P M. Boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression. In:Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging:from Nano to Macro. Chicago, IL, USA:IEEE, 2011. 1855-1859
[47]	Kohannim O, Hibar D P, Jahanshad N, Stein J L, Hua X, Toga A W, Jack C R, Weinen M W, Thompson P M. Predicting temporal lobe volume on MRI from Genotypes Using L¹-L² regularized regression. In:Proceedings of the 9th IEEE International Symposium on Biomedical Imaging (ISBI). Barcelona, Spain:IEEE, 2012. 1160-1163
[48]	Kohannim O, Hibar D P, Stein J L, Jahanshad N, Hua X, Rajagopalan P, Toga A W, Jack Jr C R, Weiner M W, de Zubicaray G I, McMahon K L, Hansell N K, Martin N G, Wright M J, Thompson P M, The Alzheimer's Disease Neuroimaging Initiative. Discovery and replication of gene influences on brain structure using LASSO regression. Frontiers in Neuroscience, 2012, 6:Article No.115
[49]	Yang T, Wang J, Sun Q, Hibar D P, Jahanshad N, Liu L, Wang Y L, Zhan L, Thompson P M, Ye J P. Detecting genetic risk factors for Alzheimer's disease in whole genome sequence data via lasso screening. In:Proceedings of the 12th International Symposium on Biomedical Imaging (ISBI). New York, USA:IEEE, 2015. 985-989
[50]	Silver M, Montana G. Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps. Statistical Applications in Genetics and Molecular Biology, 2012, 11(1):Article No.7 https://www.researchgate.net/publication/227379253_Fast_Identification_of_Biological_Pathways_Associated_with_a_Quantitative_Trait_Using_Group_Lasso_with_Overlaps
[51]	Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society:Series B-Statistical Methodology, 2006, 68(1):49-67 doi: 10.1111/rssb.2006.68.issue-1
[52]	Silver M, Chen P, Li R Y, Cheng C Y, Wong T Y, Tai E S, Teo Y Y. Pathways-driven sparse regression identifies pathways and genes associated with high-density lipoprotein cholesterol in two Asian cohorts. PLoS Genetics, 2013, 9(11):Article No.e1003939 doi: 10.1371/journal.pgen.1003939
[53]	Barrett J C, Fry B, Maller J, Daly M J. Haploview:analysis and visualization of LD and haplotype maps. Bioinformatics, 2005, 21(2):263-265 https://www.researchgate.net/publication/8414403_HAPLOVIEW_analysis_and_visualization_of_LD_and_haplotype_maps
[54]	Hao X K, Yu J T, Zhang D Q. Identifying genetic associations with MRI-derived measures via tree-guided sparse learning. In:Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention. Boston, USA:Springer, 2014. 757-764
[55]	Wang J, Ye J P. Multi-layer feature reduction for tree structured group lasso via hierarchical projection. In:Proceedings of the 28th International Conference on Neural Information Processing Systems. Montréal, Quebec, Canada:MIT Press, 2015. 1279-1287
[56]	Wang H, Nie F P, Huang H, Yan J W, Kim S, Nho K, Risacher S L, Saykin A J, Shen L. From phenotype to genotype:an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs. Bioinformatics, 2012, 28(18):i619-i625 doi: 10.1093/bioinformatics/bts411
[57]	Hao X K, Yan J W, Yao X H, Risacher S L, Saykin A J, Zhang D Q, Shen L. Diagnosis-guided method for identifying multi-modality neuroimaging biomarkers associated with genetic risk factors in Alzheimer's disease. In:Proceedings of the Pacific Symposium on Biocomputing. Kohala Coast, Hawaii, USA:Stanford, 2016. 108-119
[58]	Wang H, Nie F P, Huang H, Kim S, Nho K, Risacher S L, Saykin A J, Shen L, The Alzheimer's Disease Neuroimaging Initiative. Identifying quantitative trait loci via group-sparse multitask regression and feature selection:an imaging genetics study of the ADNI cohort. Bioinformatics, 2012, 28(2):229-237 https://academic.oup.com/bioinformatics/article/28/2/229/199331/Identifying-quantitative-trait-loci-via-group
[59]	Vounou M, Nichols T E, Montana G, The Alzheimer's Disease Neuroimaging Initiative. Discovering genetic associations with high-dimensional neuroimaging phenotypes:a sparse reduced-rank regression approach. NeuroImage, 2010, 53(3):1147-1159 doi: 10.1016/j.neuroimage.2010.07.002
[60]	Vounou M, Janousova E, Wolz R, Stein J L, Thompson P M, Rueckert D, Montana G, The Alzheimer's Disease Neuroimaging Initiative. Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease. NeuroImage, 2012, 60(1):700-716 doi: 10.1016/j.neuroimage.2011.12.029
[61]	Wang H, Nie F P, Huang H, Risacher S L, Saykin A J, Shen L, The Alzheimer's Disease Neuroimaging Initiative. Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics, 2012, 28(1):i127-i136 http://paperity.org/p/41820021/identifying-quantitative-trait-loci-via-group-sparse-multitask-regression-and-feature
[62]	Liu J Y, Pearlson G, Windemuth A, Ruano G, Perrone-Bizzozero N I, Calhoun V. Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA. Human Brain Mapping, 2009, 30(1):241-255 doi: 10.1002/hbm.v30:1
[63]	Meda S A, Narayanan B, Liu J Y, Perrone-Bizzozero N I, Stevens M C, Calhoun V D, Glahn V D, Shen L, Risacher S L, Saykin A J, Pearlson G D. A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer's disease in the ADNI cohort. NeuroImage, 2012, 60(3):1608-1621 doi: 10.1016/j.neuroimage.2011.12.076
[64]	Hotelling H. The most predictable criterion. Journal of Educational Psychology, 1935, 26(2):139-142 doi: 10.1037/h0058165
[65]	Correa N M, Li Y O, Adali T, Calhoun V D. Canonical correlation analysis for feature-based fusion of biomedical imaging modalities and its application to detection of associative networks in schizophrenia. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(6):998-1007 doi: 10.1109/JSTSP.2008.2008265
[66]	Wold S, Martens H, Wold H. The multivariate calibration problem in chemistry solved by the PLS method. Matrix Pencils. Berlin, Heidelberg:Springer, 1983:286-293
[67]	Krishnan A, Williams L J, McIntosh A R, Abdi H. Partial Least Squares (PLS) methods for neuroimaging:a tutorial and review. NeuroImage, 2011, 56(2):455-475 doi: 10.1016/j.neuroimage.2010.07.034
[68]	Witten D M, Tibshirani R, Hastie T. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics, 2009, 10(3):515-534 doi: 10.1093/biostatistics/kxp008
[69]	Lê Cao K A, Martin P G P, Robert-Granié C, Besse P. Sparse canonical methods for biological data integration:application to a cross-platform study. BMC Bioinformatics, 2009, 10(1):Article No. 34 doi: 10.1186/1471-2105-10-34
[70]	Chi E C, Allen G I, Zhou H, Kohannim O, Lange K, Thompson P M. Imaging genetics via sparse canonical correlation analysis. In:Proceedings of the 10th International Symposium on Biomedical Imaging (ISBI). San Francisco, CA, USA:IEEE, 2013. 740-743
[71]	Le Floch É, Guillemot V, Frouin V, Pinel P, Lalanne C, Trinchera L, Tenenhaus A, Moreno A, Zilbovicius M, Bourgeron T, Dehaene S, Thirion B, Poline J B, Duchesnay é. Significant correlation between a set of genetic polymorphisms and a functional brain network revealed by feature selection and sparse Partial Least Squares. NeuroImage, 2012, 63(1):11-24 doi: 10.1016/j.neuroimage.2012.06.061
[72]	Lê Cao K A, Rossouw D, Robert-Granié C, Philippe B. A sparse PLS for variable selection when integrating omics data. Statistical Applications in Genetics and Molecular Biology, 2008, 7(1):1-32 https://hal.archives-ouvertes.fr/docs/00/32/37/97/PDF/modifs.pdf
[73]	Yan J W, Du L, Kim S, Risacher S L, Huang H, Moore J H, Saykin A J, Shen L, The Alzheimer's Disease Neuroimaging Initiative. Transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm. Bioinformatics, 2014, 30(17):i564-i571 doi: 10.1093/bioinformatics/btu465
[74]	Du L, Huang H, Yan J W, Kim S, Risacher S L, Inlow M, Moore J H, Saykin A J, Shen L, The Alzheimer's Disease Neuroimaging Initiative. Structured sparse canonical correlation analysis for brain imaging genetics:an improved Graphnet method. Bioinformatics, 2016, 32(10):1544-1551 doi: 10.1093/bioinformatics/btw033
[75]	Lin D D, Calhoun V D, Wang Y P. Correspondence between fMRI and SNP data by group sparse canonical correlation analysis. Medical Image Analysis, 2014, 18(6):891-902 doi: 10.1016/j.media.2013.10.010
[76]	Fang J, Lin D D, Schulz S C, Xu Z B, Calhoun V D, Wang Y P. Joint sparse canonical correlation analysis for detecting differential imaging genetics modules. Bioinformatics, 2016, 32(22):3480-3488 http://www.tulane.edu/~wyp/resource/papers/Bioinformatics-2016-Fang-bioinformatics-btw485.pdf
[77]	Yao X H, Yan J W, Kim S, Nho K, Risacher S L, Inlow M, Moore J H, Saykin A J, Shen L, The Alzheimer's Disease Neuroimaging Initiative. Two-dimensional enrichment analysis for mining high-level imaging genetic associations. Brain Informatics and Health. Cham:Springer, 2015. 115-124
[78]	Huys Q J M, Maia T V, Frank M J. Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 2016, 19(3):404-413 doi: 10.1038/nn.4238
[79]	Birnbaum R, Weinberger D R. Functional neuroimaging and schizophrenia:a view towards effective connectivity modeling and polygenic risk. Dialogues in Clinical Neuroscience, 2013, 15(3):279-289 https://www.researchgate.net/publication/258205045_Functional_neuroimaging_and_schizophrenia_A_view_towards_effective_connectivity_modeling_and_polygenic_risk
[80]	Hibar D P, Stein J L, Jahanshad N, Kohannim O, Toga A W, McMahon K L, de Zubicaray G I, Montgomery G W, Martin N G, Wright M J, Weiner M W, Thompson P M. Exhaustive search of the SNP-SNP interactome identifies epistatic effects on brain volume in two cohorts. In:Proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention. Nagoya, Japan:Springer, 2013. 600-607
[81]	Wang Y, Goh W, Wong L, Montana G, The Alzheimer's Disease Neuroimaging Initiative. Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes. BMC Bioinformatics, 2013, 14(S16):Article No.S6 https://www.researchgate.net/publication/258169761_Random_forests_on_Hadoop_for_genome-wide_association_studies_of_multivariate_neuroimaging_phenotypes
[82]	Batmanghelich N K, Dalca A V, Sabuncu M R, Golland P. Joint modeling of imaging and genetics. In:Proceedings of the 23rd International Conference on Information Processing in Medical Imaging. Asilomar, CA, USA:Springer, 2013. 766-777
[83]	Hao X K, Yao X H, Yan J W, Risacher S L, Saykin A J, Zhang D Q, Shen L. Identifying multimodal intermediate phenotypes between genetic risk factors and disease status in Alzheimer's disease. Neuroinformatics, 2016, 14(4):439-452 doi: 10.1007/s12021-016-9307-8
[84]	Cao H B, Duan J B, Lin D D, Calhoun V, Wang Y P. Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method. BMC Medical Genomics, 2013, 6(S3):Article No.S2 http://www.tulane.edu/~wyp/resource/papers/H%20Cao%201-s2.0-S1053811914000421-main.pdf
[85]	Gross S M, Tibshirani R. Collaborative regression. Biostatistics, 2015, 16(2):326-338 doi: 10.1093/biostatistics/kxu047