Horvath's development of the DNA methylation based age estimation method known as epigenetic clock was featured in Nature magazine.[3]
In 2011, Horvath co-authored the first article that described an age estimation method based on DNA methylation levels from saliva.[5] In 2013 Horvath published a single author article on a multi-tissue age estimation method that applies to all nucleated cells, tissues, and organs.[6][3] This discovery, known as the Horvath clock, was unexpected because cell types differ in terms of their DNA methylation patterns and age related DNA methylation changes tend to be tissue specific.[3] 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.[6]
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.[7]
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.[19]
Using genome-wide association studies, Horvath's team identified the first genetic markers (SNPs) that exhibit genome-wide significant associations with epigenetic aging rates[20][21] – in particular, the first genome-wide significant genetic loci associated with epigenetic aging rates in blood notably the telomerase reverse transcriptase gene (TERT) locus.[22]
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.[22]
Work in biodemography
Horvath proposed that slower epigenetic aging rates could explain the mortality advantage of women and the Hispanic mortality paradox.[23]
Lifestyle factors and nutrition
Horvath published the first large scale study of the effect of lifestyle factors on epigenetic aging rates.[24]
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 Raj proposed an epigenetic clock theory of aging[25] 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.[25]
^ abLu, Ake T.; Xue, Luting; Salfati, Elias L.; Chen, Brian H.; Ferrucci, Luigi; Levy, Daniel; Joehanes, Roby; Murabito, Joanne M.; Kiel, Douglas P.; Tsai, Pei-Chien; Yet, Idil; Bell, Jordana T.; Mangino, Massimo; Tanaka, Toshiko; McRae, Allan F.; Marioni, Riccardo E.; Visscher, Peter M.; Wray, Naomi R.; Deary, Ian J.; Levine, Morgan E.; Quach, Austin; Assimes, Themistocles; Tsao, Philip S.; Absher, Devin; Stewart, James D.; Li, Yun; Reiner, Alex P.; Hou, Lifang; Baccarelli, Andrea A.; Whitsel, Eric A.; Aviv, Abraham; Cardona, Alexia; Day, Felix R.; Wareham, Nicholas J.; Perry, John R. B.; Ong, Ken K.; Raj, Kenneth; Lunetta, Kathryn L.; Horvath, Steve (26 January 2018). "GWAS of epigenetic aging rates in blood reveals a critical role for TERT". Nature Communications. 9 (1): 387. Bibcode:2018NatCo...9..387L. doi:10.1038/s41467-017-02697-5. PMC5786029. PMID29374233.
^Horvath, Steve; Gurven, Michael; Levine, Morgan E.; Trumble, Benjamin C.; Kaplan, Hillard; Allayee, Hooman; Ritz, Beate R.; Chen, Brian; Lu, Ake T.; Rickabaugh, Tammy M.; Jamieson, Beth D.; Sun, Dianjianyi; Li, Shengxu; Chen, Wei; Quintana-Murci, Lluis; Fagny, Maud; Kobor, Michael S.; Tsao, Philip S.; Reiner, Alexander P.; Edlefsen, Kerstin L.; Absher, Devin; Assimes, Themistocles L. (11 August 2016). "An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease". Genome Biology. 17 (1): 171. doi:10.1186/s13059-016-1030-0. PMC4980791. PMID27511193.
^Quach, Austin; Levine, Morgan E.; Tanaka, Toshiko; Lu, Ake T.; Chen, Brian H.; Ferrucci, Luigi; Ritz, Beate; Bandinelli, Stefania; Neuhouser, Marian L.; Beasley, Jeannette M.; Snetselaar, Linda; Wallace, Robert B.; Tsao, Philip S.; Absher, Devin; Assimes, Themistocles L.; Stewart, James D.; Li, Yun; Hou, Lifang; Baccarelli, Andrea A.; Whitsel, Eric A.; Horvath, Steve (14 February 2017). "Epigenetic clock analysis of diet, exercise, education, and lifestyle factors". Aging. 9 (2): 419–446. doi:10.18632/aging.101168. PMC5361673. PMID28198702.
^ abHorvath, Steve; Raj, Kenneth (11 April 2018). "DNA methylation-based biomarkers and the epigenetic clock theory of ageing". Nature Reviews Genetics. 19 (6): 371–384. doi:10.1038/s41576-018-0004-3. PMID29643443. S2CID4709691.
^Horvath S (2011). Weighted Network Analysis: Applications in Genomics and Systems Biology. Springer Book. 1st Edition., 2011, XXII, 414 p Hardcover ISBN978-1-4419-8818-8website