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. 2021 Oct;53(10):1425-1433.
doi: 10.1038/s41588-021-00944-6. Epub 2021 Oct 5.

Polygenic basis and biomedical consequences of telomere length variation

Veryan Codd  1   2 , Qingning Wang #  3   4 , Elias Allara #  5   6 , Crispin Musicha #  3   4 , Stephen Kaptoge #  5   6   7 , Svetlana Stoma  3 , Tao Jiang  5 , Stephen E Hamby  3   4 , Peter S Braund  3 , Vasiliki Bountziouka  3   4 , Charley A Budgeon  3   4   8 , Matthew Denniff  3 , Chloe Swinfield  3 , Manolo Papakonstantinou  3 , Shilpi Sheth  3 , Dominika E Nanus  3 , Sophie C Warner  3 , Minxian Wang  9   10 , Amit V Khera  9   10   11   12 , James Eales  13 , Willem H Ouwehand  7   14   15   16 , John R Thompson  17 , Emanuele Di Angelantonio  5   6   7   18 , Angela M Wood  5   6   7   18   19 , Adam S Butterworth  5   6   7   18 , John N Danesh  5   6   7   18   20 , Christopher P Nelson  3   4 , Nilesh J Samani  21   22
Affiliations

Polygenic basis and biomedical consequences of telomere length variation

Veryan Codd et al. Nat Genet. 2021 Oct.

Abstract

Telomeres, the end fragments of chromosomes, play key roles in cellular proliferation and senescence. Here we characterize the genetic architecture of naturally occurring variation in leukocyte telomere length (LTL) and identify causal links between LTL and biomedical phenotypes in 472,174 well-characterized UK Biobank participants. We identified 197 independent sentinel variants associated with LTL at 138 genomic loci (108 new). Genetically determined differences in LTL were associated with multiple biological traits, ranging from height to bone marrow function, as well as several diseases spanning neoplastic, vascular and inflammatory pathologies. Finally, we estimated that, at the age of 40 years, people with an LTL >1 s.d. shorter than the population mean had a 2.5-year-lower life expectancy compared with the group with ≥1 s.d. longer LDL. Overall, we furnish new insights into the genetic regulation of LTL, reveal wide-ranging influences of LTL on physiological traits, diseases and longevity, and provide a powerful resource available to the global research community.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Conditionally independent genome-wide significant hits.
a, Manhattan plot curtailed at P < 1 × 10−50. We highlight the regions containing our 197 sentinel variants that are genome-wide significant (P < 8.31 × 10−9; horizontal dashed reference line) in the exact joint conditional model (Supplementary Table 1). We defined the region as known (blue) if a previous variant within 1 Mb of our sentinel has been reported at either genome-wide significance or at an FDR threshold of <5%. Regions were considered new (red) if a variant within 1 Mb of our sentinel that reached genome-wide significance was not previously identified. Non-significant variants are shown in either light or dark gray on alternate chomosomes. b, The estimated effect sizes (beta) against the P value from the GWAS analysis. c, Estimated effect sizes for the minor allele (beta) against the MAF from all participants in the GWAS.
Fig. 2
Fig. 2. Identification of eQTL signals at genome-wide significant loci.
Circular representation of colocalized eQTLs across 48 tissues in GTEx. Strong colocalization is shown as a colored tile, with the color determined by the degree of tissue specificity of colocalization: ubiquitous, ≥33 tissues; tissue-group specific, 17–32 tissues; multiple tissues, 2–16 tissues; and single tissue, one tissue. Tissues are represented numerically with full details in Supplementary Table 5. Genes are labeled using HGNC gene symbols. Tissues are ordered by GTEx tissue groupings, and genes are ordered by hierarchical clustering of the data, which groups genes with a similar colocalization pattern.
Fig. 3
Fig. 3. Genes with known regulatory roles in telomere maintenance located within GWAS loci.
Key components of telomere regulatory complexes found within genome-wide significant loci. Proteins encoded within GWAS loci are depicted in green, and those not found within GWAS loci are depicted in blue. We found the majority of components of core telomere binding complexes alongside many proteins involved in the formation and activity of telomerase. Note that not all components of the RNA exosome are shown.
Fig. 4
Fig. 4. Biomedical traits associated with genetically determined LTL.
Biomedical trait MR associations from the inverse-variance-weighted analysis (Supplementary Table 10) are shown with a solid square and expressed in beta per s.d. longer genetically determined LTL. Observational associations are shown with an empty circle and expressed in beta per s.d. longer usual LTL from linear regression models.
Fig. 5
Fig. 5. Diseases associated with genetically determined LTL.
Disease MR associations from the inverse-variance-weighted analysis (Supplementary Table 12) are shown with a solid square and expressed as odds ratio (OR) per s.d. longer genetically determined LTL. Observational associations are shown with an empty circle and expressed as the hazard ratio (HR) per s.d. longer usual LTL from Cox proportional hazards models. n refers to the number of cases for each condition.
Fig. 6
Fig. 6. Years of life lost using UK 2015 mortality rates.
a,b, The number of years of life lost were estimated by applying HRs for cause-specific mortality calculated from UKB data (specific to age-at-risk and stratified by sex) to population mortality rates for the United Kingdom during 2015 (by sex and 5-year age groups). Data are presented for four standardized LTL groups: group 1, >1 s.d. below the mean; group 2, ≤1 s.d. below the mean; group 3, <1 s.d. above or equal to the mean; and group 4, ≥1 s.d. above the mean) from 40 to 95 years of age. Group 4 was used as the reference group. Data are shown for males and females separately. This was performed for all-cause (a) and disease-specific (b) mortality. The UKB data included 458,309 participants and 28,345 deaths (comprising 5,984 vascular deaths, 14,916 cancer deaths, 7,244 non-vascular, non-cancer deaths and 201 deaths of unknown causes).
Extended Data Fig. 1
Extended Data Fig. 1. Manhattan plot unrestricted by P value.
We highlight our 197 sentinel variant regions that are genome-wide significant (P < 8.31 × 10−9, horizontal dashed reference line) in the exact joint conditional model (Supplementary Table 1). We define the region as known (blue) if a previous variant within 1 Mb of our sentinel has previously been reported at genome-wide significance. We consider our other regions novel (red) as the first evidence of a variant within 1 Mb of our sentinel that reaches genome-wide significance.
Extended Data Fig. 2
Extended Data Fig. 2. Biomedical traits associated with usual LTL only.
Mendelian randomization (MR) associations are shown with a solid square and expressed as beta per standard deviation (s.d.) longer genetically determined leukocyte telomere length (LTL) for the inverse-variance weighted (IVW) analysis. Observational associations are shown with an empty circle and expressed in beta per s.d. longer usual LTL from linear regression models. CI, confidence interval. Full data for each trait can be found in Supplementary Table 10.
Extended Data Fig. 3
Extended Data Fig. 3. Diseases associated with usual LTL only.
Mendelian randomization (MR) associations are shown with a solid square and expressed in odds ratio (OR) per standard deviation (s.d.) longer genetically determined leukocyte telomere length (LTL). Observational associations are shown with an empty circle and expressed in hazard ratio (HR) per s.d. longer usual LTL from Cox proportional hazards models. CI, confidence interval. Full data for each disease can be found in Supplementary Table 12.
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