Steve Horvath

Steve Horvath
Known for developing the epigenetic clock (Horvath clock) and weighted correlation network analysis.

Steve Horvath is a UCLA professor known for developing the Horvath aging clock, which is a highly accurate molecular biomarker of aging, and for developing weighted correlation network analysis. The recipient of several research awards, including an Allen Distinguished Investigator award,[1] he has studied genomic biomarkers of aging, the aging process, and many age related diseases/conditions.

Steve Horvath, UCLA professor, presenting a talk June 2015

Background

Horvath was born in Frankfurt, Germany. He received his Ph.D. in mathematics at the University of North Carolina in 1995 and his Sc.D. in biostatistics at Harvard in 2000. In 2000 Horvath joined the faculty of the University of California, Los Angeles, where he is a Professor of human genetics at the David Geffen School of Medicine at UCLA and of biostatistics at the UCLA Fielding School of Public Health.

Work on the epigenetic clock

Horvath's development of the DNA methylation based age estimation method known as epigenetic clock was featured in Nature magazine.[2] In 2011 Horvath co-authored the first article that described an age estimation method based on DNA methylation levels from saliva.[3] In 2013 Horvath published a single author article on a multi-tissue age estimation method that applies to all nucleated cells, tissues, and organs.[4] This discovery, known as the Horvath clock, was unexpected because cell types differ in terms of the their DNA methylation patterns and age related DNA methylation changes tend to be tissue specific. In his article, he demonstrated that estimated age, also referred to as DNA methylation age, has the following properties: it is close to zero for embryonic and induced pluripotent stem cells, it correlates with cell passage number; it gives rise to a highly heritable measure of age acceleration; and it is applicable to chimpanzees.[4] Since the Horvath clock allows one to contrast the ages of different tissues from the same individuals, it can be used to identify tissues that show evidence of increased or decreased age.[5]

Horvath co-authored the first articles demonstrating that DNA methylation age predicts life-expectancy [6][7][8] and is positively associated with obesity,[9] HIV infection,[10] Alzheimer's disease,[11] cognitive decline,[12] Parkinson's disease,[13] Huntington's disease,[14] early menopause,[15] Werner syndrome.[16]

Genetics of aging

Horvath published the first article demonstrating that trisomy 21 (Down syndrome) is associated with strong epigenetic age acceleration effects in both blood and brain tissue.[17] Using genome-wide association studies, Horvath's team identified the first genetic markers (SNPs) that exhibit genome-wide significant associations with epigenetic aging rates.[18][19] In particular, the first genome-wide significant genetic loci associated with epigenetic aging rates in blood notably the telomerase reverse transcriptase gene (TERT) locus .[20] As part of this work, his team uncovered a paradoxical relationship: genetic variants associated with longer leukocyte telomere length in the TERT gene paradoxically confer higher epigenetic age acceleration in blood.[20]

Work in biodemography

Horvath proposed that slower epigenetic aging rates could explain the mortality advantage of women and the Hispanic mortality paradox.[21]

Lifestyle factors and nutrition

Horvath published the first large scale study of the effect of lifestyle factors on epigenetic aging rates.[22] These cross sectional of epigenetic aging rates in blood confirm the conventional wisdom regarding the benefits of education, eating a high plant diet with lean meats, moderate alcohol consumption, physical activity and the risks associated with metabolic syndrome.

Epigenetic clock theory of aging

Horvath and his collaborator Kenneth Raj [23] proposed an epigenetic clock theory of aging which views biological aging as an unintended consequence of both developmental programs and maintenance program, the molecular footprints of which give rise to DNA methylation age estimators. DNAm age is viewed as a proximal readout of a collection of innate ageing processes that conspire with other, independent root causes of aging, to the detriment of tissue function.

Weighted correlation network analysis

Horvath and members of his lab developed a widely used systems biological data mining technique known as weighted correlation network analysis.[24][25][26] He published a book on weighted network analysis and genomic applications.[27]

References

  1. ↑ "The Paul G. Allen Frontiers Group Names Five Allen Distinguished Investigators". Cision PR Newswire. June 15, 2017.
  2. ↑ Gibbs, WT (2014). "Biomarkers and ageing: The clock-watcher". Nature. 508: 168–170. doi:10.1038/508168a. PMID 24717494.
  3. ↑ Bocklandt, S; Lin, W; Sehl, ME; Sánchez, FJ; Sinsheimer, JS; Horvath, S; Vilain, E (2011). "Epigenetic Predictor of Age". PLoS ONE. 6 (6): e14821. doi:10.1371/journal.pone.0014821. PMC 3120753. PMID 21731603.
  4. 1 2 Horvath, S (2013). "DNA methylation age of human tissues and cell types". Genome Biology. 14: R115. doi:10.1186/gb-2013-14-10-r115. PMC 4015143. PMID 24138928.
  5. ↑ Horvath, S; Mah, V; Lu, AT; Woo, JS; Choi, OW; Jasinska, AJ; Riancho, JA; Tung, S; Coles, NS; Braun, J; Vinters, HV; Coles, LS (2015). "The cerebellum ages slowly according to the epigenetic clock" (PDF). Aging. 7 (5): 294–306. doi:10.18632/aging.100742. PMC 4468311. PMID 26000617.
  6. ↑ Marioni, R; Shah, S; McRae, A; Chen, B; Colicino, E; Harris, S; Gibson, J; Henders, A; Redmond, P; Cox, S; Pattie, A; Corley, J; Murphy, L; Martin, N; Montgomery, G; Feinberg, A; Fallin, M; Multhaup, M; Jaffe, A; Joehanes, R; Schwartz, J; Just, A; Lunetta, K; Murabito, JM; Starr, J; Horvath, S; Baccarelli, A; Levy, D; Visscher, P; Wray, N; Deary, I (2015). "DNA methylation age of blood predicts all-cause mortality in later life". Genome Biology. 16 (1): 25. doi:10.1186/s13059-015-0584-6. PMC 4350614. PMID 25633388.
  7. ↑ Horvath, S (2015). "Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring". Aging (Dec).
  8. ↑ Chen, B; Marioni, ME (2016). "DNA methylation-based measures of biological age: meta-analysis predicting time to death". Aging. 8 (9): 1844–1865. doi:10.18632/aging.101020. PMC 5076441. PMID 27690265.
  9. ↑ Horvath, S; Erhart, W; Brosch, M; Ammerpohl, O; von Schoenfels, W; Ahrens, M; Heits, N; Bell, JT; Tsai, PC; Spector, TD; Deloukas, P; Siebert, R; Sipos, B; Becker, T; Roecken, C; Schafmayer, C; Hampe, J (2014). "Obesity accelerates epigenetic aging of human liver". Proc Natl Acad Sci U S A. 111: 15538–43. doi:10.1073/pnas.1412759111. PMC 4217403. PMID 25313081.
  10. ↑ Horvath, S; Levine, AJ (2015). "HIV-1 infection accelerates age according to the epigenetic clock". J Infect Dis. 212: 1563–73. doi:10.1093/infdis/jiv277. PMC 4621253. PMID 25969563.
  11. ↑ Levine, M (2015). "Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer's disease related cognitive functioning". Aging. 7 (Dec): 1198–211. PMC 4712342. PMID 26684672.
  12. ↑ Marioni, R; Shah, S; McRae, A; Ritchie, S; Muniz-Terrera, GH; SE; Gibson, J; Redmond, P; SR, C; Pattie, A; Corley, J; Taylor, A; Murphy, L; Starr, J; Horvath, S; Visscher, P; Wray, N; Deary, I (2015). "The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936". International Journal of Epidemiology. 44: 1388–1396. doi:10.1093/ije/dyu277. PMC 4588858. PMID 25617346.
  13. ↑ Horvath, S (2015). "Increased epigenetic age and granulocyte counts in the blood of Parkinson's disease patients". Aging. 7: 1130–42. doi:10.18632/aging.100859. PMC 4712337. PMID 26655927.
  14. ↑ Horvath, S (2016). "Huntington's disease accelerates epigenetic aging of human brain and disrupts DNA methylation levels". Aging. 8 (7): 1485–512. doi:10.18632/aging.101005. PMC 4993344. PMID 27479945.
  15. ↑ Levine, M (2016). "Menopause accelerates biological aging". Proc Natl Acad Sci USA. 113: 201604558. doi:10.1073/pnas.1604558113. PMC 4995944. PMID 27457926.
  16. ↑ Maierhofer, A (2017). "Accelerated epigenetic aging in Werner syndrome". Aging. 9 (4): 1143–1152. doi:10.18632/aging.101217. PMC 5425119. PMID 28377537.
  17. ↑ Horvath, S; Garagnani, P; Bacalini, MG; Pirazzini, C; Salvioli, S; Gentilini, D; Di Blasio, AM; Giuliani, C; Tung, S; Vinters, HV; Franceschi, C (Feb 2015). "Accelerated epigenetic aging in Down syndrome". Aging Cell. 14: 491–5. doi:10.1111/acel.12325. PMC 4406678. PMID 25678027.
  18. ↑ Lu, A (2016). "Genetic variants near MLST8 and DHX57 affect the epigenetic age of the cerebellum". Nature Communications. 7: 10561. doi:10.1038/ncomms10561. PMC 4740877. PMID 26830004.
  19. ↑ Lu, A (2017). "Genetic architecture of epigenetic and neuronal ageing rates in human brain regions". Nature Communications. 8 (15353). doi:10.1038/ncomms15353. PMC 5454371. PMID 28516910.
  20. 1 2 Lu AT., & Xue L., & Chen BH., et al., Horvath S. (2018). [ https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/29374233/ GWAS of epigenetic aging rates in blood reveals a critical role for TERT]. Nat Commdoi:10.1038/s41467-017-02697-5
  21. ↑ Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, Ritz BR, Chen B, Lu AT, Rickabaugh TM, Jamieson BD, Sun D, Li S, Chen W, Quintana-Murci L, Fagny M, Kobor MS, Tsao PS, Reiner AP, Edlefsen KL, Absher D, Assimes TL (2016). "An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease". Genome Biol. 17 (1): 171. doi:10.1186/s13059-016-1030-0. PMC 4980791. PMID 27511193.
  22. ↑ Quach A., & Levine ME., & Tanaka T.,…., & Horvath S. (2018). [ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5361673/ Epigenetic clock analysis of diet, exercise, education, and lifestyle factors.]. Aging (Albany NY)doi: 10.18632/aging.101168
  23. ↑ Horvath, S., & Raj, K. (2018). DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nature Reviews Genetics, doi:10.1038/s41576-018-0004-3
  24. ↑ Zhang B, Horvath S (2005) A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17 PMID 16646834
  25. ↑ Horvath, S; Zhang, B; Carlson, M; Lu, KV; Zhu, S; Felciano, RM; Laurance, MF; Zhao, W; Shu, Q; Lee, Y; Scheck, AC; Liau, LM; Wu, H; Geschwind, DH; Febbo, PG; Kornblum, HI; Cloughesy, TF; Nelson, SF; Mischel, PS (2006). "Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target". PNAS. 103 (46): 17402–17407. doi:10.1073/pnas.0608396103. PMC 1635024.
  26. ↑ Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 PMID 19114008 PMC 2631488 BMC Bioinformatics
  27. ↑ Horvath S (2011). Weighted Network Analysis: Applications in Genomics and Systems Biology. Springer Book. 1st Edition., 2011, XXII, 414 p Hardcover ISBN 978-1-4419-8818-8 website
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