bioRxivGeneticsPreprint
Validation of human telomere length trans-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as novel human telomere length regulation genes
Rebecca KeeneriD, Surya B. ChhetriiD, Carla J. ConnellyiD, Margaret A. TaubiD, Matthew P. ConomosiD, Joshua S. WeinstockiD, Bohan NiiD, Benjamin J. StroberiD, Stella W. AslibekyaniD, Paul Livermore AueriD, Lucas Barwick, Lewis C. BeckeriD, John BlangeroiD, Eugene R. BleeckeriD, Jennifer A. BrodyiD, Brian E. CadeiD, Juan Carlos CeledoniD, Yi‐Cheng ChangiD, L. Adrienne CupplesiD, Brian CusteriD, Barry I. FreedmaniD, Mark T. GladwiniD, Susan R. HeckbertiD, Lifang HouiD, Marguerite R. IrviniD, , Alexis J. BattleiD
- Citations
- 1
Abstract
Telomere length genome-wide association studies (GWAS) have become well-powered to detect novel genes in telomere length regulation. However, no prior work has validated these putative novel genes to confirm the contribution of GWAS loci to telomere length regulation. We conducted a trans-ancestry meta-analysis of 211,369 individuals. Through enrichment analyses of chromatin state and cell-type heritability we identified blood and immune cells as the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6, a component of an E3 ubiquitin ligase complex, and POP5, a component of the Ribonuclease P/MRP complex, and demonstrating that both lengthened telomeres as predicted by our statistical analyses. CRISPR/Cas9 deletion of the predicted causal regions of these association peaks in K562 immortalized blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5, respectively. Together our results demonstrate the utility of telomere length GWAS in the identification of novel telomere length regulation mechanisms and highlight the importance of the proteasome-ubiquitin pathway in telomere length regulation.
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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