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. 2016 Mar 2;25(11):2324–2330. doi: 10.1093/hmg/ddw070

Shorter telomere length in Europeans than in Africans due to polygenetic adaptation

Matthew EB Hansen 1,2,†, Steven C Hunt 4,5,†, Rivka C Stone 6, Kent Horvath 6, Utz Herbig 6, Alessia Ranciaro 1, Jibril Hirbo 1, William Beggs 1, Alexander P Reiner 7, James G Wilson 8, Masayuki Kimura 6, Immaculata De Vivo 9, Maxine M Chen 9, Jeremy D Kark 10, Daniel Levy 11, Thomas Nyambo 12, Sarah A Tishkoff 1,3,‡, Abraham Aviv 6,‡,*
PMCID: PMC5081046  PMID: 26936823

Abstract

Leukocyte telomere length (LTL), which reflects telomere length in other somatic tissues, is a complex genetic trait. Eleven SNPs have been shown in genome-wide association studies to be associated with LTL at a genome-wide level of significance within cohorts of European ancestry. It has been observed that LTL is longer in African Americans than in Europeans. The underlying reason for this difference is unknown. Here we show that LTL is significantly longer in sub-Saharan Africans than in both Europeans and African Americans. Based on the 11 LTL-associated alleles and genetic data in phase 3 of the 1000 Genomes Project, we show that the shifts in allele frequency within Europe and between Europe and Africa do not fit the pattern expected by neutral genetic drift. Our findings suggest that differences in LTL within Europeans and between Europeans and Africans is influenced by polygenic adaptation and that differences in LTL between Europeans and Africans might explain, in part, ethnic differences in risks for human diseases that have been linked to LTL.

Introduction

Natural selection for short telomeres and repressed telomerase activity has been invoked to explain increased cancer resistance in long-living, large mammals (1,2). Contemporary humans are the longest-living terrestrial mammals and their telomeres are comparatively short (1). Since short telomeres diminish the maximum number of cell divisions, there might be a trade-off for this form of cancer resistance, expressed in replicative aging of cells and increased propensity to degenerative diseases.

In humans, telomere length (TL), a highly heritable complex genetic trait (3–5), is longer in African Americans than in individuals of recent European ancestry (Europeans) (6–8). To better understand the genetic and evolutionary forces influencing this ethnic difference in LTL, which is similar across somatic cells (9), we first measured LTL in sub-Saharan Africans, Europeans and African Americans. Secondly, using data from phase 3 of the 1000 genomes project (www.1000genomes.org) (10), we compared between Europeans and Africans the frequencies of 11 diallelic single nucleotide polymorphisms (SNPs), shown previously to be associated with LTL at genome-wide level of significance in genome-wide association studies (GWAS) (11–14). These 11 SNPs account for ∼2.0% of the LTL variation within the respective discovery cohorts, assuming additive variance contributions. Thirdly, we determined whether the allele frequency differences between Europeans and Africans are consistent with neutral genetic drift or with polygenetic adaptation (15).

Results

We measured LTL in Eastern African populations (N = 100; Burunge, Hadzabe, Maasai and Sandawe from Tanzania; 25 samples/population), and age- and sex-matched samples of Europeans (N = 90) and African Americans (N = 97) (Supplementary Material, Table S1).

LTL was inversely correlated with age, showing similar age-dependent attrition in the three ethnicities (Fig. 1). In an ethnic- and sex-adjusted general linear model, LTL shortening was 25.9 bp/year (P < 0.0001). LTL, adjusted for ethnicity and age, was longer in women than in men (women = 7.56 ± 0.05 kb; men = 7.40 ± 0.07 kb; P = 0.08).

Figure 1.

Figure 1.

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Age-dependent leukocyte telomere length attrition by ethnicity. Africans = 27.7 bp/year; African Americans = 25.6 bp/year; Europeans = 27.3 bp/year.

Using general linear models adjusted for age and sex, the mean LTL in Africans was significantly longer by 0.6 kb than in Europeans (P < 0.0001) and by 0.4 kb than in African Americans (P < 0.0001) (Fig. 2; Supplementary Material, Table S2). There were no statistically significant differences in LTL between the African populations, but statistical power was low. There was no interaction between age and ethnicity (P = 0.90) or age and sex (P = 0.99).

Figure 2.

Figure 2.

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Age- and sex-adjusted leukocyte telomere length by ethnicity. LTL, leukocyte telomere length; AfrAms, African Americans. *P-value 2.3 × 10−5 for LTL compared with Africans; **P-value 1.2 × 10−9 for LTL compared with Africans.

Next, we examined the differences in frequency for each of the 11 previously identified LTL-associated SNPs (11–14) in four European and five African populations from the 1000 Genomes Project (Supplementary Material, Table S3). We observed that three of the SNPs (rs4452212, rs10936599, rs3027234), which are highly polymorphic in Europeans, were nearly fixed in Africans. Additionally, for all but one locus, allele frequencies associated with a longer LTL are at lower frequency in non-Africans, eight of which are derived mutations (Fig. 3; Supplementary Material, Table S3).

Figure 3.

Figure 3.

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Frequency shifts in long LTL associated alleles in sub-Saharan Africans compared to Europeans. Dark grey bars indicate SNPs associated with melanoma risk.

We used the Berg and Coop Qx framework (15) to determine if differences in allele frequencies at the 11 LTL associated SNPs between populations show patterns expected based on neutral evolution or polygenic adaptation. Each population was assigned a ‘genetic value’ V that is the average over 11 LTL-associated SNPs of the effect size of each SNP times the population frequency (smaller V is expected to correspond to shorter TL). We next compared the pattern of genetic variation at the LTL-associated SNPs in relation to the pattern observed for frequency and gene distance matched loci to infer the co-variances expected due to neutral drift alone.

First, we tested the total variance in genetic values across the nine populations, Qx, for an excess of variance compared with the neutral drift expectation. Qx is an indicator of whether non-neutral processes are operating across the test population set. Compared with a null distribution, corresponding to frequency shifts expected under a model of neutral genetic drift, extremely large Qx values can indicate differential selection pressures acting on the trait between populations while extremely low Qx values can indicate stabilizing selection. The observed value of Qx = 23.7 is at the extreme high end of the expected neutral distribution for the nine test populations, with an empirical one-tailed P-value 3.5 × 10−4 (Supplementary Material, Fig. S1). When calculating Qx just within the European or African population sets, we find that there is a significant excess of variance within Europe but no significant excess within Africans (Supplementary Material, Table S5), consistent with selection acting differentially on LTL within Europeans.

Secondly, we tested for extreme genetic values relative to expectation under neutral drift for the European populations as a group when conditioned on the African genetic values, and vice versa. These are measured in terms of a normalized Z score (Supplementary Material, Table S4). We find significant differences between the European and African populations using this test, Z(Eur | Afr) = −3.05 (empirical two-tailed P = 0.0017) and Z(Afr | Eur) = 2.79 (empirical two-tailed P = 0.0040), indicating that the European populations have significantly shorter LTL genetic values than expected when conditioned on the African values, and that Africans have significantly longer LTL genetic values than expected when conditioned on European values.

Thirdly, we tested for extreme genetic values relative to neutral drift for each population when conditioned on the remaining eight populations, similarly measured in terms of a Z score (Supplementary Material, Table S4). Two populations stand out by this test. The British population (GBR) had a significantly lower genetic value than expected by drift, Z(GBR) = −3.16 (empirical two-tailed P = 3.8 × 10−4), while the Toscani population in Italy (TSI) had a larger genetic value than expected, Z(TSI) = 2.39 (empirical two-tailed P = 0.00044). None of the five African populations had significant Z scores by this test, which is consistent with the non-significant value of Qx within Africa (Supplementary Material, Table S5). It is unclear whether the lack of excess variance is due to a lack of selection pressure on LTL within Africa, and/or a lack of linkage disequilibrium (LD) in Africans between the 11 LTL-associated SNPs and the true causative loci, and/or novel genetic associations with LTL within Africa.

Discussion

Our findings are consistent with polygenic adaptation leading to shorter LTL in Europeans compared with sub-Saharan Africans. One possible source of selection for relatively short LTL in Europeans could be to attenuate the risk of melanoma in individuals with light skin pigmentation. Melanoma is a lethal skin cancer that is augmented by ultraviolet radiation (UVR) (16). Although a consensus exists on the role of dark skin in protecting against UVR-evoked melanoma, the hypothesis that melanoma may be an evolutionary selective force in light-skinned humans was questioned (17), but has been recently revived, given that this cancer might strike individuals throughout their reproductive years (18).

Melanoma is broadly divided into familial melanoma and sporadic melanoma. Familial melanoma occurs in <10% of melanoma cases, because of highly penetrant but rare germ line mutations (19). However, sporadic melanoma is also 55% heritable (20) and might result from the effect of common alleles, each engendering low risk for the disease (21). Jointly, these alleles increase melanoma risk and a subset of these alleles may play a role in regulating TL.

Previous research has shown that in Europeans LTL-associated genes are over-represented among genes engaged in established melanoma pathways (14). The following findings further support the potential role of polygenic adaption of TL in attenuating melanoma risk in Europeans: seven of the 11 LTL-associated SNPs we have used to test for polygenic adaptation were found to be associated with increased melanoma risk among Europeans (22), where the alleles jointly associated with a longer LTL were also associated with increased melanoma risk (see Fig. 3). Among these 11 LTL-associated SNPs is rs2535913, which lies within DCAF4—a gene that encodes a protein that plays a role in the protective response to UVR (14), thus linking telomere biology with the UVR response and potentially melanoma risk. Moreover, LTL is longer in individuals with increased number and size of melanocytic nevi (23), which augment melanoma risk (24), and in those with melanoma (25–27). Longer telomeres might increase susceptibility to melanoma due to the accumulation of de novo mutations in replicating cells of the developing nevus. Two findings support this idea. First, nevi comprise senescent cells due to telomere dysfunction (28). Secondly, melanoma develops through different trajectories that are marked by the progressive accumulation of mutations in a nevus that ultimately cause the transformation of a benign lesion to malignant melanoma (29). This model predicts that involution of a nevus before malignant transformation would relate to its replicative potential (29), which is intrinsically dependent on TL. Thus, nevi in individuals with a longer TL are less likely to experience involution and more likely to undergo malignant transformation.

Selective pressure to shorten TL, as expressed in LTL, due to melanoma risk in Europeans might also have diminished their risks against other types of cancers. Although earlier studies provided mixed findings related to the LTL-cancer association, more recent studies showed that major cancers other than melanoma, including adenocarcinoma of the lung (30), cancers of the pancreas (31) and breast (32) and aggressive cancer of the prostate (33) are associated with longer LTL. Notably, the same seven alleles that were found to be jointly associated with increased melanoma risk (22) were recently found to be associated with increased lung cancer risk (34).

African Americans, whose LTL is longer than in Europeans (6–8), display a higher incidence of cancers of the lung (35), pancreas (36), triple-negative breast cancer (37) and prostate cancer (38) than Europeans. Members of these two ethnicities differ in numerous traits and environmental settings, which might influence their cancer susceptibility. That said, the different LTLs across ethnic groups raise the possibility that TL might partially explain ethnic differences in a number of cancers.

As short telomeres might be associated with degenerative disease, the anti-cancer effect of short telomeres may have a biological trade-off. For example, atherosclerosis, which is associated with shorter LTL in Europeans (39), is a prevalent age-dependent degenerative disease. While African Americans display higher susceptibility to a number of cancers associated with longer LTL, they show less susceptibility than Europeans to atherosclerosis, as expressed in coronary artery calcification (40) and coronary artery disease (41).

Notably, in Europeans the same seven LTL-associated alleles that are associated with increased risk for melanoma (22) are also associated with increased risk for coronary artery disease (12). However, the joint impact of the seven SNPs exerts opposing effects, such that the combination of alleles associated with a shorter LTL and a low risk for melanoma, entails a high risk for coronary artery disease. In this sense, the cost for modern Europeans of increased resistance to melanoma (and perhaps other cancers) might be increased susceptibility to atherosclerosis.

Finally, LTL of African Americans is closer in value to that of the Europeans than the Africans (Fig. 2). This may seem surprising at face value given that the genetic ancestral make-up of most African Americans is roughly 80% African and 20% European. However, the African ancestry of most African Americans originates from western Africa, while the Africans in this study are from Tanzania, in eastern Africa. Given the genetic and phenotypic diversity within Africa (42), there may be non-negligible differences in TLs between the Tanzanians used in this study and the (largely western) African ancestry of the African American cohort. Such putative differences may arise from random genetic drift or as a response to a currently unknown differential selection pressure on TL across equatorial Africa and/or in African Americans. Another possibility is a shared environmental component between African Americans and the subjects with European ancestry that influences LTL and that differs from the environment of the Tanzanian subjects. Further LTL measurements across a broader sample of Africans could help resolve this issue.

In conclusion, we have found that LTL is shorter in Europeans than in sub-Saharan Africans and African Americans. Based on LTL-associated variants, we find evidence of non-neutral polygenic frequency shifts across a set of European and African populations, lending support to the hypothesis that polygenic adaptation may have contributed to LTL shortening in Europeans compared with Africans. We hypothesize that selection pressure to attenuate melanoma risk has shortened LTL in Europeans which might also explain, in part, ethnic differences in the incidence of diseases linked to relatively long or short LTL, as expressed, for instance, in a cancer–atherosclerosis trade-off.

Materials and Methods

Subjects

DNA samples donated by Europeans and African Americans originated from repositories (43,44) in the USA and from Africans in Tanzania. For the Tanzanian samples, 6–9 ml of blood was collected and DNA was extracted in the lab with a Gentra Purgene DNA extraction kit (Qiagen Inc., Valencia, CA, USA). Written informed consent was obtained from all subjects and the study was approved by the Institutional Review Boards of the participating institutions in the NHLBI Family Heart Study, University of Mississippi Medical Center for the Jackson Heart Study, the University of Maryland at College Park and the University of Pennsylvania. In addition, research/ethics approval and permits were obtained from COSTECH (Tanzania Commission for Science and Technology) and National Institute of Medical Research (NIMR) in Dar es Salaam, Tanzania.

LTL measurements

TL in DNA derived from leukocyte samples was measured using Southern blots of the terminal restriction fragment length, as previously described (45). All DNA samples were subjected to DNA integrity tests. Six European samples were unavailable for age/sex matching with samples from Africans. In addition, seven samples did not pass the DNA integrity test (four European and three African American samples). Thus, LTL measurements were performed in 100 samples from Africans, 97 African Americans and 90 Europeans.

Statistical analysis

There were no significant outliers (>3 SD from the next closest point) for the LTL results for any of the three groups. A general linear model, with LTL as the dependent variable, was used to test for main effects and interactions, followed by nested analysis of age within race and sex groups to examine the homogeneity of the age versus LTL slopes.

SNP selection

Eleven diallelic SNPs were chosen for statistical analysis of polygenic adaptation due to their significant association with LTL in GWAS. The discovery cohorts used in all GWAS were based on individuals of self-identified European ancestry; individuals that self-identified as African American or as having recent African ancestry were not included in these studies. To date, these 11 SNPs have the strongest known associations with LTL in individuals of European ancestry, and have passed stringent tests for population substructure and replication tests at genome-wide significance. We do not view these SNPs as inclusive of all loci that impact LTL but, rather, as a representative subset whose associations meet the current best practices of significance testing. See Supplementary Material for further details.

Assessment of polygenic adaptation

We applied the methods developed in Berg and Coop (15) to the 11 LTL associated loci over a set of four European and five African populations (Supplementary Material, Table S3) from the phase 3 release of the 1000 Genomes Project (www.1000genomes.org; release number 20130502, accessed in December 2014) (10). For this analysis, a subset of 1 034 730 autosomal loci were used, composed of the loci in the Illumina Human 1M-duo array and the 11 LTL associated loci. The Berg and Coop test for polygenic adaption provides a flexible framework to test for signals of selection on a phenotype over a set of populations ‘… while accounting for the hierarchical structure among populations induced by shared history and genetic drift’ (15). We note, in particular, that the Berg and Coop framework is robust with respect to gene-flow and admixture among the test populations, so long as neutral SNP frequencies approximately obey a multivariate normal distribution due to drift.

The analysis by Berg and Coop (15) is based on assigning each population a mean ‘genetic value’ for a given trait, defined as the mean population effect size averaged over a set of trait associated loci. Given a set of L trait associated loci and their respective linear regression coefficients β found in some GWAS discovery population, the genetic value of a given test population is defined as the sum of the β values weighted by the population frequency of the trait increasing allele (for which β ≥ 0). The method assumes each locus i is biallelic with alleles A1 and A2, whereby definition the A2 allele is the trait decreasing allele and A1 is defined to be the positive effect allele (βi(A1)≡βi≥0). The genetic score is defined as

Vp=2∑i=1Lβiνip,

where p indexes the population, i indexes the trait associated loci, 2βi denotes the average change in trait value when an A2 allele is replaced by an A1 allele and νip denotes the frequency of allele A1 in population p. Note that this model is not intended to predict phenotypes but to examine how the additive variance changes across populations.

It is likely that the 11 LTL associated SNPs in Europeans tag causal variants rather than being directly causal themselves. Consequently, the 11 SNPs may exhibit a pattern of differential LD with the causal variants when comparing European to African populations. As argued by Berg and Coop (1 5), a pattern of differential LD with causal variants is not thought to increase the false positive rate in the polygenic adaptation tests employed here (Qx, Z scores).

As described by Berg and Coop (15), the observed Qx = 23.7 can be written as the sum of two terms: an LD-component that is due to frequency covariance between SNPs and an FST-component that is due to frequency variance across populations. The LD-component has an expected value of zero under neutral drift, as the direction of frequency changes would be uncorrelated among neutral variants (assuming no LD between the variants). We observe an FST-component value of 10.6, and an LD-component value of 13.1. Evidently the LD-component is substantially contributing to the large value of Qx.

The vector of V values over the test populations is transformed to a frame of reference where the covariances due to neutral drift have been removed. This transformation relies on sets of loci matched to the LTL-associated loci in a reference population, which are used as background loci whose frequency shifts are assumed to be due only to drift between populations. In our implementation, the loci are matched on two criteria: (i) the minor allele frequency in the reference population must be in the same frequency bin, with a bin size of 0.02 and (ii) the distance to the nearest gene must be in the same log-distance bin b, where b ≡ floor[log10(1 + d)/log10(11)] and d is the distance to the nearest gene. The gene annotations were taken from the UCSC annotation database (UCSC Gene track from the Table Browser at genome.ucsc.edu for human reference build hg19). Other computational parameters used are as follows: (i) number of replicate cycles for covariance estimation: 60; (ii) number of SNPs chosen per cycle for covariance estimation: 10 000; (iii) number of replicate cycles for estimating the empirical P-values for the null phenotype genetic value: 60; (iv) number of SNPs chosen per cycle for null phenotype estimation: 2000. For a reference population, we used the CEU population from the 1000 Genomes Project, which is a cohort of individuals from Utah with western and northern European ancestry. This population was chosen as the reference because it is assumed to reflect the ancestry of the individuals used in the referenced LTL GWAS.

All calculations were performed with R 3.1.2 (www.r-project.org) and perl 5.18 (www.perl.org) using scripts provided by Jeremy Berg at the following Github code repository: github.com/jjberg2/PolygenicAdaptationCode. All P-values were derived empirically through the resampling scheme implemented in the aforementioned code and refer to two-tailed P-value calculations. Additional data manipulations, file formatting and graphing were performed with Plink 1.9 (46,47), python 2.7 (www.python.org) and the matplotlib library (48) (matplotlib.org) run from the IPython (49) interactive computing environment (ipython.org).

Supplementary Material

Supplementary Material is available at HMG online.

Funding

M.E.B.H. is supported by National Institutes of Health training grant T32ES019851 and the Center of Excellence for Environmental Toxicology (CEET) at the University of Pennsylvania. This research is supported by National Institutes of Health grants 5DP1ES022577, 1R01DK104339, and 1R01GM113657 to S.A.T., and National Institutes of Health grants R01GM113657, 8DP1ES022577, R01-AG020132, R01-HL116446, R01CA136533, R01CA184572, R01AG18734. The Jackson Heart Study is supported by contracts (HHSN268201300046C, HHSN268201300047C, HHSN268201300048C, HHSN268201300049C, HHSN268201300050C) from the National Heart Lung Blood Institute and the National Institute on Minority Health and Health Disparities.

Supplementary Material

Supplementary Data
supp_25_11_2324__index.html (848B, html)

Acknowledgements

We thank Graham Coop for critical review of the manuscript and helpful suggestions.

Conflict of Interest statement. None declared.

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