**4. Sequencing based estimates for population genetic studies of telomere length**

TelSeq has been used in several large WGS datasets, including the TOPMed study, which is one of the largest population-based studies with available WGS information. Telomere length was estimated using TelSeq in 109,122 TOPMed participants of diverse ancestry, including European, African, Asian, and Hispanic/Latino [33]. A strong correlation was observed between TelSeq estimates and TRF and flow-FISH in a subset of samples (r = 0.68 and 0.80, respectively). The results of GWAS on TelSeqestimated telomere length were compared to those of GWAS on qPCR estimates and showed great consistency in effect estimates of the identified genetic predictors: Pearson's r = 0.92 for 37 overlapping variants with 23,096 Chinese Singaporeans and r = 0.86 for 43 overlapping variants with 78,592 Europeans. A genetic correlation of 0.81 between TelSeq and qPCR-estimated telomere length suggests that these methods share a high degree of genetic determinants.

In another study, TelSeq was applied to WES data from the UK Biobank, a population cohort of more than 500,000 UK adult residents [36]. Telomere length was estimated using TelSeq in 49,738 participants and compared to qPCR estimates *Current Technologies for Measuring or Predicting Telomere Length from Genomic Datasets DOI: http://dx.doi.org/10.5772/intechopen.113048*

from 472,594 participants within the UK Biobank and WGS estimates from 63,302 TOPMed participants using TelSeq. The WES-based telomere length (mean of 0.83 kb) was found to be shorter than the qPCR measures within the same population and even shorter than the WGS estimates in TOPMed (mean of 3.27 kb). Additionally, when estimating the effect sizes of SNPs in predicting telomere length, WES data showed the lowest correlation with SNPs and a deflation in effect size estimates. Since WES restricts sequencing to targeted regions of the genome, estimating telomere length from WES data may require further correction and adjustment.

## **5. Conclusions**

In conclusion, genetic variants play an important role in determining leukocyte telomere length, enabling the estimation of an individual's telomere length through genotyping data. Furthermore, next-generation sequencing data offers an alternative to qPCR for measuring telomere length in large-scale population studies. The ability to estimate absolute telomere length in base pairs also facilitates the comparison of results across different studies. The use of sequencing-based telomere length estimates holds great promise for genetic studies, particularly given the likely overlap between sources of genetic variant and sequencing data.

### **Acknowledgements**

This work was supported by National Cancer Institute grant U01ES029520.

### **Conflict of interest**

The authors declare no conflict of interest.

### **Author details**

Ting Zhai and Zachary D. Nagel\* John B. Little Center for Radiation Sciences, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, USA

\*Address all correspondence to: znagel@hsph.harvard.edu

© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
