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Variation of network connections, as subject\u2010specific feature variables, has been found to predict disease clinical outcome. In this work, we develop a two\u2010stage method to estimate biomarker networks that account for heterogeneity among subjects and evaluate network's association with disease clinical outcome. In the first stage, we propose a conditional Gaussian graphical model with mean and precision matrix depending on covariates to obtain covariate\u2010dependent networks with connection strengths varying across subjects while assuming homogeneous network structure. In the second stage, we evaluate clinical utility of network measures (connection strengths) estimated from the first stage. The second\u2010stage analysis provides the relative predictive power of between\u2010region network measures on clinical impairment in the context of regional biomarkers and existing disease risk factors. We assess the performance of proposed method by extensive simulation studies and application to a Huntington's disease (HD) study to investigate the effect of HD causal gene on the rate of change in motor symptom through affecting brain subcortical and cortical gray matter atrophy connections. We show that cortical network connections and subcortical volumes, but not subcortical connections are identified to be predictive of clinical motor function deterioration. We validate these findings in an independent HD study. Lastly, highly similar patterns seen in the gray matter connections and a previous white matter connectivity study suggest a shared biological mechanism for HD and support the hypothesis that white matter loss is a direct result of neuronal loss as opposed to the loss of myelin or\u00a0dysmyelination.<\/jats:p>","DOI":"10.1111\/biom.13201","type":"journal-article","created":{"date-parts":[[2019,12,18]],"date-time":"2019-12-18T13:06:34Z","timestamp":1576674394000},"page":"995-1006","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Identifying disease\u2010associated biomarker network features through conditional graphical model"],"prefix":"10.1093","volume":"76","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5132-3887","authenticated-orcid":false,"given":"Shanghong","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Biostatistics Mailman School of Public Health Columbia University  New 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Chapel Hill North Carolina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1510-3315","authenticated-orcid":false,"given":"Yuanjia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Mailman School of Public Health Columbia University  New York"},{"name":"Department of Psychiatry Columbia University Medical Center New 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