Predicting physiological aging rates from a range of quantitative traits using machine learning

Eric D. Sun; Yong Qian; Richard Oppong; Thomas J. Butler; Jesse Zhao; Brian H. Chen; Toshiko Tanaka; Jian Kang; Carlo Sidore; Francesco Cucca; Stefania Bandinelli; Gon&#xE7;alo R. Abecasis; Myriam Gorospe; Luigi Ferrucci; David Schlessinger; Ilya Goldberg; Jun Ding

doi:doi:10.18632/aging.203660

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Research Paper Volume 13, Issue 20 pp 23471—23516

Predicting physiological aging rates from a range of quantitative traits using machine learning

Figure 4. Significant genetic loci obtained from genome-wide association study. (A) CFI/GAR1 was significantly associated with the physiological aging rate (PAR). CFI is a complement factor and has been linked to age-related macular degeneration and other age-related disorders. GAR1 is an accessory protein for the active telomerase complex and is an eQTL target of the top CFI/GAR1 SNPs. (B) LINC00202 was significantly associated with the PAR and corresponded to a long non-coding RNA that has been indirectly linked to age-related disease. Plots were made using LocusZoom (Pruim et al., 2010).