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We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer\u2019s Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this paper, we propose a new additive model to learn the non-linear associations between SNPs and brain endophenotypes in Alzheimer\u2019s disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>An executable is available at https:\/\/github.com\/littleq1991\/additive_FNNRW.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/bty557","type":"journal-article","created":{"date-parts":[[2018,7,7]],"date-time":"2018-07-07T05:42:12Z","timestamp":1530942132000},"page":"i866-i874","source":"Crossref","is-referenced-by-count":12,"title":["Quantitative trait loci identification for brain endophenotypes via new additive model with random networks"],"prefix":"10.1093","volume":"34","author":[{"given":"Xiaoqian","family":"Wang","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Yan","sequence":"additional","affiliation":[{"name":"Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwangsik","family":"Nho","sequence":"additional","affiliation":[{"name":"Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shannon L","family":"Risacher","sequence":"additional","affiliation":[{"name":"Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew J","family":"Saykin","sequence":"additional","affiliation":[{"name":"Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the ADNI","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2018,9,8]]},"reference":[{"key":"2023061402422129800_bty557-B1","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","article-title":"Voxel-based morphometry\u2014the methods","volume":"11","author":"Ashburner","year":"2000","journal-title":"Neuroimage"},{"key":"2023061402422129800_bty557-B2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1186\/gm34","article-title":"Genetics of alzheimer\u2019s disease: recent advances","volume":"1","author":"Avramopoulos","year":"2009","journal-title":"Genome Med."},{"key":"2023061402422129800_bty557-B3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/ng1934","article-title":"Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database","volume":"39","author":"Bertram","year":"2007","journal-title":"Nat. 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