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. 2023 Mar 23;22(5):e13819. doi: 10.1111/acel.13819

Current allele distribution of the human longevity gene APOE in Europe can mainly be explained by ancient admixture

Daniel Kolbe 1, Nicolas A da Silva 1, Janina Dose 1, Guillermo G Torres 1, Amke Caliebe 2, Ben Krause‐Kyora 1, Almut Nebel 1,✉
PMCID: PMC10186601  PMID: 36951219

Abstract

Variation in apolipoprotein E (APOE) has been shown to have the strongest genetic effect on human longevity. The aim of this study was to unravel the evolutionary history of the three major APOE alleles in Europe by analysing ancient samples up to 12,000 years old. We detected significant allele frequency shifts between populations and over time. Our analyses indicated that selection led to large frequency differences between the earliest European populations (i.e., hunter‐gatherers vs. first farmers), possibly due to changes in diet/lifestyle. In contrast, the allele distributions in populations from ~4000 BCE onward can mainly be explained by admixture, suggesting that it also played an important role in shaping current APOE variation. In any case, the resulting allele frequencies strongly influence the predisposition for longevity today, likely as a consequence of past adaptations and demographic processes.

Keywords: age‐related disease, aging, Alzheimer's disease, ancient DNA, ancient population genomics, evolution, longevity

We investigated the evolutionary history of the three major APOE alleles in Europe by analysing ancient DNA samples up to 12,000 years old. Our analyses indicated that selection led to large frequency differences between the earliest European populations (i.e., hunter‐gatherers vs. early farmers), possibly due to changes in diet/lifestyle. However, the allele distributions in populations from ~4000 BCE onward can mainly be explained by admixture, suggesting that it also played an important role in shaping current APOE variation.

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1. INTRODUCTION

Apolipoprotein E (APOE) was the first locus shown to be involved in human longevity and is still the genetic factor with the strongest effect on the phenotype (Deelen et al., 2019; Nebel et al., 2011). Two coding polymorphisms (rs429358‐T/C and rs7412‐C/T) define the three haplotypes ε2, ε3, and ε4 which are commonly referred to as alleles. These alleles have a large influence on the development of age‐related neurodegenerative and cardiovascular disorders. For both diseases, ε4 is deleterious compared with ε3 (Mahley, 2016). It is responsible for most of the genetic susceptibility to sporadic late‐onset Alzheimer's disease (LOAD) (Cruts & van Broeckhoven, 1998). The detrimental effect of ε4 is reflected in a low odds ratio of becoming long‐lived (OR = 0.60 and OR = 0.52 for 90th and 99th percentile survivors, respectively) (Deelen et al., 2019). In contrast, ε2 is associated with a reduced risk of LOAD and is deemed a pro‐longevity allele (OR = 1.28 and OR = 1.47 for 90th and 99th percentile survivors, respectively) (Deelen et al., 2019). In addition, ε2 has recently been reported to increase lifespan irrespective of AD (Shinohara et al., 2020).

Although ε4 has been described as the ancestral allele (Fullerton et al., 2000), ε3 is dominant in most parts of the world. Globally, an unequal allele distribution has been observed, especially for ε2 and ε4 (Singh et al., 2006). Even in Europe, frequency ranges are wide (roughly 0.74–0.88 for ε3, 0.06–0.22 for ε4 and 0.04–0.12 for ε2), and there is a decreasing north‐to‐south cline for ε4 (Lucotte et al., 1997). The differences in allele frequencies, both at the European and global level, have so far mainly been attributed to natural selection (Eisenberg et al., 2010; Finch, 2010; Huebbe et al., 2015, 2011; Singh et al., 2006). However, previous analyses on the evolutionary history of APOE considered only modern‐day sequences or genotypes (Eisenberg et al., 2010; Finch, 2010; Fullerton et al., 2000). Therefore, conclusions about the place and time of origin of the three alleles, their later dispersal or the possible influence of demographic processes had to remain vague.

Thanks to the recent advances in the field of ancient DNA (aDNA) analysis, a more detailed picture of the APOE history can now be drawn by incorporating allele calls from archaeologically well‐dated and well‐defined human remains. In this study, we assembled 358 diploid APOE genotypes from 3521 publicly available aDNA data sets spanning more than 12,000 years. Our aim was to investigate the past events which led to the current APOE allele distribution in Europe. For this purpose, we took into consideration that the present‐day European gene pool was basically derived from three prehistorical populations, the western hunter‐gatherers (WHG), the Anatolian Neolithic (AN) farmers and western steppe herders (Haak et al., 2015; Mathieson et al., 2015). By analysing various ancient, medieval and modern groups of sufficient sample sizes, we obtained reliable frequency estimates and established spatiotemporal allele trajectories (Marciniak & Perry, 2017). We concluded that past admixture events played a greater role in shaping the European APOE variation than previously assumed.

2. MATERIALS AND METHODS

2.1. Processing ancient DNA data and calling APOE variants

Genomic data of Eurasian samples were acquired from a large compendium of published sources (as cited by the Allen Ancient DNA Resource (AADR), https://reich.hms.harvard.edu/allen‐ancient‐dna‐resource‐aadr‐downloadable‐genotypes‐present‐day‐and‐ancient‐dna‐data) through the European Nucleotide Archive (https://www.ebi.ac.uk/ena/browser/home). Additionally, 216 shotgun‐sequenced genomes were acquired from https://reich.hms.harvard.edu/ancient‐genome‐diversity‐project as well as whole‐genome sequencing data of 42 individuals from the Late Neolithic site Niedertiefenbach (Immel et al., 2021).

Data was preferentially obtained in BAM format and in cases where such a format was unavailable, the raw fastq files were used instead. The methodology used to process the samples in fastq format was previously described by Immel et al. (2021). To minimize downstream genotype errors, the terminal positions of reads with >0.05 deamination rates were trimmed off with bamUtil v1.0.15.

BAM files from samples that passed the quality control step were piled up using SAMtools (Li et al., 2009) v1.15, filtering by base and mapping quality >20. The APOE polymorphic sites (rs429358‐T/C and rs7412‐C/T) were called using bcftools (Li, 2011) v1.15, removing genotypes with a depth of coverage <3. Samples missing a diploid genotype at both polymorphic sites were removed and not considered for further analysis. We completely avoided the use of pseudo‐haploid calls, unlike a previous study (Abondio et al., 2019). This was necessary to obtain more reliable allele frequencies and to be able to distinguish between homozygous and heterozygous samples.

2.2. Population analysis

As described in a previous study (Immel et al., 2021), a principal component analysis was conducted on the merged genotyping data of ancient samples and selected modern reference populations from West Eurasia. The grouping of the samples into different ancient subpopulations (see Figure 1b) was based on this projection as well as archaeological data (taken from AADR annotation and Immel et al., 2021).

FIGURE 1.

FIGURE 1

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Population genetics of all samples used to create APOE allele frequencies. (a) Time‐points of samples. (b) Principal component analysis of ancient individuals (358 samples—colored) and 1000 Genomes populations (CEU, GBR, IBS, and TSI—gray) projected onto modern West Eurasian variation. (c, d) Supervised admixture‐analysis of ancient samples using WHG and EF (c) and LF and steppe populations (d) as parental components. BA/IA_N, Bronze Age/Iron Age North Europe; BA/IA_S, Bronze Age/Iron Age South Europe; EF, European early farmers; LF, European late farmers; Steppe, Steppe herders originating from Yamnaya region; Viking, Vikings from North Europe, Middle Ages; CEU, GBR: Northern modern European 1000 Genomes populations; TSI, IBS: Southern modern European 1000 Genomes populations; WHG, western hunter‐gatherers.

The proportions of parental populations in admixed more recent populations were calculated using supervised ADMIXTURE (Alexander et al., 2009) v1.3.0 analyses (100 bootstraps) on a pruned version of the previously mentioned genotyping data set. Only samples with genotype calls for at least one of the two APOE variants were included. Information on the parental samples used in the admixture analyses can be found in Tables S1a,1b.

2.3. Allele frequencies

The frequency for ε3 was calculated from the counts of the rs429358‐T – rs7412‐C haplotype within a target population. To maximize sample size, frequencies for ε4 and ε2 were calculated from the counts of the allele‐defining variant only, namely rs429358‐C for ε4 or rs7412‐T for ε2 (Table S2). APOE allele frequencies were also calculated for the 1000 Genomes (The 1000 Genomes Project Consortium et al., 2015) populations CEU, GBR, IBS, and TSI as reference (Table S3). Significant differences between population frequencies were calculated using Fisher's exact test (Virtanen et al., 2020) (Table S4). The p‐values of all pair‐wise comparisons (n = 105) were corrected for multiple testing using a false discovery rate of 0.05 (Benjamini & Hochberg, 1995).

2.4. Testing for selection

A two‐sided binomial test was used to estimate whether allele frequencies in offspring populations could be explained by the admixture components of their parental populations. For an offspring population and two parental populations A and B, the expected allele frequency of the offspring population, fexp, was calculated as linear proportions of the admixture components, kA and kB, and allele frequencies, fA and fB, of parental populations:

fexp=kA×fA+kB×fB

Then, a binomial test (Virtanen et al., 2020) was applied to test for a significant deviation of the observed frequency in the offspring population from fexp (Tables S5a,b).

To scan for signatures of selection, Tajima's D values for chromosome 19 were calculated from sequencing data of both ancient and modern populations. Only samples with a minimum chromosome 19 coverage of 70% (mean chr19 coverage was ~90%) and a mean depth of coverage of >5 in a 100‐kb window around APOE were used. Variants on chromosome 19 were initially filtered in the same way as the APOE variants (described above), additionally only keeping sites with QUAL >50. For each population, we calculated Tajima's D values using vcftools (Danecek et al., 2011) v0.1.16 in 15‐kb windows across chromosome 19 and compared the mean D value of the TOMM40‐APOE‐APOC1 locus (three 15‐kb windows) to the distribution of all calculated D values. Using these same samples and variants, FST values were calculated with vcftools v0.1.16 for all chromosome 19 sites and between all populations (Table S6).

2.5. Software

Python 3.9.4 (Van Rossum & Drake, 2009) and Snakemake (Mölder et al., 2021) were used for the bioinformatics workflow. SciPy (Virtanen et al., 2020) was applied for statistical tests. Plotly 5.8 (Plotly Technologies Inc., 2015) was used to generate graphics/figures.

3. RESULTS

We scanned the two APOE allelic sites (rs429358‐T/C and rs7412‐C/T) in 3521 published aDNA samples from the past 12,000 years. Of these 3521 samples, only 10.2% (358) passed all filtering steps (see Methods). The samples were divided into various groups based on their archaeological context, such as their geographical origin and chronology (Figure 1a) as well as their genetic ancestry components (Figure 1b). Supervised admixture analysis (Figure 1c,d) was performed to trace genomic changes that had been caused by the two major demographic events in Europe during the period studied.

The first demographic event occurred around ~4000 BCE and saw the emergence of the late farmers (LF). This population formed through the admixture of local WHG with the early farmers (EF), who had migrated to Europe from Anatolia (Haak et al., 2015; Immel et al., 2021; Mathieson et al., 2015; Olalde et al., 2014) (Figure 1c). The second significant admixture event happened around 2700–2500 BCE when a large group of western steppe herders associated with the Yamnaya culture moved to Europe and mixed with the LF (Figure 1d). This last episode resulted in an unequal admixture distribution across Europe, with a higher steppe component in the north and a higher farmer component in the south, both of which can still be seen in populations today (Haak et al., 2015).

We noted interesting trends in the ancient APOE frequencies. As in modern Europeans, ε3 was also the most common allele in all ancient groups. However, the distribution of the three APOE alleles differed across ancient and modern populations. This was most apparent in the earliest European groups, the WHG and the EF, who showed substantial differences in observed APOE allele frequencies (Figure 2) at p = 4.07×10−4 for ε4 (Fisher's exact test, FDR‐adjusted, Figure 2a) and p = 0.011 for ε3 (Fisher's exact test, FDR‐adjusted, Figure 2b). The difference in ε2 frequency, however, remained non‐significant (p > 0.05, Fisher's exact test, FDR‐adjusted, Figure 2c). The WHG had a significantly higher ε4 frequency compared with the EF and all descendant European populations, while they completely missed the ε2 allele. On the other hand, the EF had the highest ε3 and lowest ε4 frequency of all examined populations, but an ε2 frequency that was more comparable with present‐day Europeans. The admixture‐informed expected frequencies were calculated from the observed allele frequencies of parental populations in conjunction with the mixture proportions (Figure 1c,d). The APOE frequencies of the LF were consistent with expected frequencies, and no significant difference between observed and admixture‐informed expected frequencies was found (binomial test, p > 0.05). Given the LF's high EF and low WHG ancestry (0.82 vs. 0.18), it is not unexpected that LF frequencies only significantly differentiated from those in WHG (Fisher's exact test; FDR‐adjusted, ε4: p = 4.07×10−4, ε3: p = 0.0038, ε2: p > 0.05), but not from those in EF.

FIGURE 2.

FIGURE 2

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Observed (solid white) and expected (shaded) APOE allele frequencies of ancient populations. (a, b) ε4 frequencies. (c, d) ε3 frequencies. (e, f) ε2 frequencies. Expected frequencies were calculated as a product of parental allele frequencies and parental admixture components in two demographic steps (WHG and EF form LF; LF and steppe form modern European gene pool (BA/IA_S, BA/IA_N, Viking, IBS + TSI, CEU + GBR). A binomial test was used to assess significant deviation between observed and expected frequencies (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001). Error bars signify 90% confidence interval (Clopper–Pearson beta distribution) of observed allele frequencies. For population abbreviations, see Figure 1.

Following the expansion of steppe herders into Europe, APOE frequencies of European populations became more uniformly distributed. Although we no longer observed significant differences between any of the post‐steppe populations, we noticed interesting tendencies. Northern European groups (Bronze Age/Iron Age North, Vikings, CEU + GBR) had an ε4 frequency comparable with that of the steppe population, which is consistent with their greater steppe component. The same observation can be made for southern European populations (Bronze Age/Iron Age South and IBS + TSI), which have a stronger LF component and, as a result, a lower ε4 frequency. The only ε4 frequency that significantly deviated from the admixture‐informed expected frequency was found in CEU + GBR, which was higher than expected (binomial test, p = 0.019). We noticed a slightly different trend for ε2 frequencies.

While the northern Bronze/Iron Age (BA/IA_N) group had a frequency comparable with the steppe, the southern Bronze/Iron Age ε2 frequency was higher than both LF and the steppe but did not significantly deviate from the admixture‐informed expected frequency, likely due to low sample sizes. Interestingly, the frequencies of ε2 increased in all European common era populations and significantly differentiated from their admixture‐informed expected frequencies (binomial test; Viking: p = 0.031, IBS + TSI: p = 0.031, CEU + GBR: p = 3.1×10−4). This deviation was especially noticeable in northern populations, where ε2 frequencies were higher than in any ancestral population. However, in this case, the power to determine accurate frequency estimates for individual populations was limited due to the low overall frequency of the ε2 allele.

Subsequently, we assessed whether selection may have acted on APOE alleles. Using a selected set of high‐coverage samples from each population, we calculated cross‐population FST values for all sites on chromosome 19 as well as Tajima's D in 15‐kb windows across chromosome 19 (on which APOE is located) (Figure 3). We found that the FST for the two APOE allelic sites was highest between WHG and other populations (Figure 3a,b,c). For rs429358 (ε4), this value exceeded the upper 95th percentile of all single‐site FST values on chromosome 19 for the population pairs WHG/EF, WHG/TSI, and WHG/IBS and the 97.5th percentile for WHG/LF (Figure 3c,d,e), suggesting that the ε3/ε4 alleles were likely under selection in the WHG and/or EF. We did not detect any signs of selection for rs7412 (ε2) that could have explained the increase in ε2 seen in northern European populations following the steppe expansion. This was most likely due to sample size constraints combined with the low overall frequency of ε2. Although the mean Tajima's D value of the TOMM40‐APOE‐APOC1 locus did not fall below the 5th or above the 95th percentile thresholds (of all measured Tajima's D values across chromosome 19), the TOMM40‐APOE‐APOC1 Tajima's D value for EF, LF, and offspring Bronze Age populations was consistently lower than the respective median Tajima's D values across chromosome 19 (Figure 3e). This finding may hint at previous selective sweeps that affected APOE in the early farming populations of Europe, possibly explaining the higher ε3 frequency that we observed in EF compared with WHG.

FIGURE 3.

FIGURE 3

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Selection scans of APOE region/alleles in ancient populations. (a, b). FST values for rs429358 (ε4) and rs7412 (ε2). *: upper 95th percentile of all calculated FST values on chromosome 19; **: upper 97.5th percentile of all calculated FST values on chromosome 19. (c) Mean FST values for all chromosome 19 sites that passed all filtering steps. Since the ε3 allele is determined by both variants it has not been included as a standalone analysis. (d). Tajima's D of TOMM40‐APOE‐APOC1 region across all ancient populations as well as European 1000 Genomes populations. (e) Tajima's D in 15‐kb windows of TOMM40‐APOE‐APOC1 (labelled APOE region—red line) projected on box plots of chromosome 19 percentiles. The middle of the box represents the median, the edges of the box lower and upper quartile, and the whisker ends the 5th and 95th percentiles of all chromosome 19‐values (in 15‐kb windows). For population abbreviations, see Figure 1.

4. DISCUSSION

In this study, we compiled the to‐date largest set of diploid calls for the two most important APOE polymorphisms from published Eurasian ancient DNA samples. We found that the ε4 frequency was significantly higher in WHG than in the first farming populations or modern Europeans. Although the ε4 allele is considered detrimental to late‐life health, it has also beneficial effects on early‐life fitness today (i.e., antagonistic pleiotropy) (Finch, 2010; Huebbe et al., 2011; van Exel et al., 2017). In addition, it has been shown to be the ancestral allele (Fullerton et al., 2000) and likely benefited our ancestors. Hence, ε4 probably represented an adaptation to the hunter‐gatherer lifestyle, the only form of subsistence in all human ancestors until the introduction of agriculture. Modern indigenous populations with a long history as hunter‐gatherers show similarly high frequencies of ε4 as the WHG (Singh et al., 2006) and seem to be exempt from its detrimental effects (Vasunilashorn et al., 2011). The generally heightened inflammatory response associated with ε4 (Dose et al., 2016; Gale et al., 2014) may have protected the WHG from certain viral and bacterial pathogens, as it has been shown, for example, for malaria and hepatitis C in modern populations (Dose et al., 2016; Fujioka et al., 2014; Wozniak et al., 2002). Additionally, recent research has revealed that ε4 is associated with improved cognitive development and increased fertility in a high‐pathogen environment (Oriá et al., 2010; van Exel et al., 2017). The ε4 allele may also have helped to maintain a stable vitamin D metabolism in the dark‐skinned WHG during low sunlight conditions of (especially northern) Europe (Huebbe et al., 2011; Olalde et al., 2014). Although we do not know if ε4 was already associated with age‐dependent detrimental effects in the WHG, their high levels of aerobic exercise (compared with the average modern European) could have relaxed these constraints (Raichlen & Alexander, 2014). Furthermore, ε3/ε4, which is the most common genotype amongst WHG presented in this study, has been significantly associated with increased VO2max (a measure for cardiovascular fitness) after exercise relative to the other APOE genotypes (Huebbe et al., 2015). This may have been beneficial to the WHG who routinely went for long periods of endurance running or walking.

In contrast to the WHG, the EF had the lowest ε4 frequency out of all ancient populations and modern European references. Our FST‐analysis showed that the deviation in frequency of the ε4‐SNP (rs429358) between WHG and EF was more extreme than over 95% of all other tested sites on chromosome 19, suggesting selection acted on the APOE alleles. The results from our Tajima's D analysis hint at possible selective sweeps in the APOE region of EF/LF, which may explain their low ε4 and high ε3 frequency, respectively. The transition from hunter‐gathering to farming was one of the most drastic shifts in subsistence in human history. Food became monotonous and supply susceptible to bad harvests, while physical activity shifted from aerobic exercise (endurance running) to more strenuous labour, which negatively affected overall health. There was also an increase of non‐infectious inflammagens from various sources, such as indoor smoke caused by domestic fire, the intake of saturated fats, milk and gluten, and the cooking of food in general (Finch, 2012). This may have resulted in a mismatch between a highly pro‐inflammatory environment (Finch, 2012) and the pro‐inflammatory ε4 allele (Gale et al., 2014), possibly further increasing the risk for inflammatory disorders such as cardiovascular disease (CVD). While there are signs that CVD was already present in the late Neolithic/Copper Age (Gostner et al., 2011), we cannot say if the ε4 allele was also associated with CVD in past populations. Furthermore, recent aDNA studies have indicated that infectious agents may have played a lesser role in the Neolithic than previously assumed (Fuchs et al., 2019), relativizing any protective effects of ε4 against pathogens in this period.

In our data, the observed ε4 frequency of the LF can be explained by demographic processes, namely the admixture between its parental populations, the WHG and EF. The same can be said for offspring populations of the steppe and LF. Many studies have already described the north‐to‐south cline of ε4 in modern Europe and attributed it largely to selection (Eisenberg et al., 2010). We have shown here that the higher ε4 frequency observed in both ancient and modern northern European populations (Bronze Age/Iron Age North, Viking, CEU + GBR) is instead likely the result of a higher steppe ancestral component. It is intriguing that the ε4 frequencies have largely remained stable for ~4500 years following the last major demographic event in Europe. If we assume that the farming subsistence was the main selective force against the ε4 allele, we would have expected to see a decrease in its frequency over the past millennia. It is possible that the increase in population density and a concurrently higher infectious burden in later times (Bonczarowska et al., 2022) could have favoured the maintenance of the ε4 allele.

Compared with any other ancient or modern European population, the EF had the highest ε3 frequency. We hypothesize that ε3‐ vs. ε4‐carriers in early Neolithic populations were more resistant to famines (i.e., those caused by a bad harvest). This is in line with findings that show that the ε3 allele is associated with the potential to more efficiently use dietary energy and deposit fat in adipose tissue (Huebbe et al., 2015).

Interestingly, we did not detect ε2 in either the WHG or a smaller population of eastern hunter‐gatherers (EHG; N = 13, Table S2), suggesting that ε2 may have been completely lost or reduced to negligible frequencies in the hunter‐gatherer populations of Mesolithic Europe. Astonishingly, the same observation can be made for many modern indigenous hunter‐gatherers from South and Central America (Marin et al., 1997; Vasunilashorn et al., 2011) or for Australian aborigines (Kamboh et al., 1991). Although it is possible that this decrease in allele frequency (or rather allele loss) happened as a result of genetic drift, it is unlikely that both WHG and EHG as well as modern hunter‐gatherers were independently affected in the same manner. Rather, it seems that the evolutionarily older ε3 and ε4 alleles (Fullerton et al., 2000) were more advantageous for WHG.

Based on the results from this study, it seems that the ε2 allele was (re)introduced to the European gene pool after the arrival of the EF (and later the steppe populations). Recent research has shown an association of the murine gut microbiome with APOE status. Mice expressing the human APOE2 protein had increased relative abundance of the order Clostridiales and family Ruminococcaceae (Tran et al., 2019). This may have been advantageous in adapting to the Neolithic diet. For instance, repeated heating and cooling of starch‐rich foods increase the content of resistant starches, and it has been shown that especially Ruminococcaceae may help with their digestion (Umu et al., 2015). It is plausible that the EF handled food in this manner and that their diet had a higher overall starch content compared with WHG (Ollivier et al., 2016).

Following the two major demographic events, the observed ε2 frequency did not significantly differentiate from the admixture‐informed expected frequency in populations before the common era. However, the observed ε2 frequency was significantly higher than its expected frequency in all common era populations (i.e., Viking, IBS + TSI, CEU + GBR). This was especially noticeable in northern Europeans, where ε2 frequencies were higher than in any ancestral population, suggesting that admixture alone was not responsible for this increase. However, the overall increase was rather modest, which may explain why we did not detect any signatures of selection.

We conclude that the APOE allele frequencies of modern Europeans, which strongly influence the likelihood of becoming long‐lived today, are most probably the consequence of the past demographic processes and adaptations outlined in this study. The different APOE allele frequencies of the WHG and EF likely resulted from adaptations to diet, physical activity and inflammatory load (Finch, 2012). We have also shown that the north‐to‐south cline of ε4 in modern Europe was the result of a higher steppe admixture rather than of selection/environmental differences. The persistence of the ε4 allele since the Bronze Age may be attributed to a rise in infectious diseases. It remains uncertain whether the increase in ε2 frequency in modern populations was the result of selection and, if so, which selective pressures may have been involved. Furthermore, it is unclear if and to what extent the APOE alleles already influenced longevity in past populations. Future association studies using ancient samples would be needed, which in turn would require accurate anthropological documentation of age at death. Longevity is a post‐reproductive trait and therefore unlikely to have been the main driver of selection, unless we assume that a grandmother/grandfather effect played a positive role in past populations.

5. LIMITATIONS

Unfortunately, the overall quality of aDNA data is much lower than of modern genomic data. While we made sure to use only higher quality data for our analyses, the number of samples that passed this quality control was limited, which in turn restricted statistical power.

AUTHOR CONTRIBUTIONS

Idea/concept: AN and BKK. Data curation/processing: DK and NdS. Data analysis: DK. Interpretation: DK, AN, and BKK. Writing: DK. Advice: AC, GT, and JD.

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interests.

Supporting information

Data S1:

Click here for additional data file. (133KB, xlsx)

ACKNOWLEDGMENTS

We acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Research Training Group for Translational Evolutionary Research (RTG 2501—project number 400993799). GT was funded by the DFG Excellence Cluster ROOTS (EXC 2150—project number 390870439) and NdS by the DFG Collaborative Research Centre 1266 Scales of Transformation—Human‐Environmental Interaction in Prehistoric and Archaic Societies (CRC 1266—project number 290391021). Open Access funding enabled and organized by Projekt DEAL.

Kolbe, D. , da Silva, N. A. , Dose, J. , Torres, G. G. , Caliebe, A. , Krause‐Kyora, B. , & Nebel, A. (2023). Current allele distribution of the human longevity gene APOE in Europe can mainly be explained by ancient admixture. Aging Cell, 22, e13819. 10.1111/acel.13819

DATA AVAILABILITY STATEMENT

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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