Author

Gary R. Conrad

University of Kentucky

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genetic & Genomic Medicine· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Assay:GWAS summary statistics· 1 Approach:machine learning (classical)· 1 Research Focus:polygenic risk· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Adam FleisheriD· 2 shared Angela OliveriD· 2 shared Arthur W. TogaiD· 2 shared Beau M. AncesiD· 2 shared Betty Lind· 2 shared Brittany CerboneiD· 2 shared Bryan M. Spann· 2 shared Charles D. SmithiD· 2 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersGenetic & Genomic Medicine

Adam FleisherORCID

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2 papers

Recent·Most cited

Polygenic effects on the risk of Alzheimer's disease in the Japanese population

Masataka Kikuchi, Akinori Miyashita, Norikazu Hara, Kensaku Kasuga, Yuko Saito, Shigeo Murayama, Akiyoshi Kakita, Hiroyasu Akatsu, Kouichi Ozaki, Shumpei Niida, Ryozo Kuwano, Takeshi Iwatsubo, Akihiro Nakaya, Takeshi Ikeuchi, the Alzheimer’s Disease Neuroimaging Initiative, , Gary R. Conrad

This paper evaluates the polygenic risk score for Alzheimer's disease in Japanese individuals, showing its utility despite genetic differences from European populations.

Alzheimer s Research & Therapy · Feb 27, 2024Peer-reviewedGenetic & Genomic Medicine

A blood-based signature of cerebral spinal fluid Aβ1-42 status

Benjamin Goudey, Bowen John Fung, Christine Schieber, Noel Garry Faux, Michael Walter Weiner, Paul S. S. Aisen, Ronald Carl Petersen, Clifford R. Jack, William J. Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel A. Beckett, Robert C. Green, Andrew J. Saykin, John C. Morris, , Kyle B. Womack

This paper develops a blood-based machine learning model to predict cerebral spinal fluid Aβ1-42 levels, showing high accuracy in identifying Alzheimer's disease risk.

Scientific Reports · Mar 10, 2019Peer-reviewedNeuroscience