CCR5-∆32 is deleterious in the homozygous state in humans.

We use the genotyping and death register information of 409,693 individuals of British ancestry to investigate fitness effects of the CCR5-∆32 mutation. We estimate a 21% increase in the all-cause mortality rate in individuals who are homozygous for the ∆32 allele. A deleterious effect of the ∆32/∆32 mutation is also independently supported by a significant deviation from the Hardy-Weinberg equilibrium (HWE) due to a deficiency of ∆32/∆32 individuals at the time of recruitment.

In the fall of 2018, a scientist from the Southern University of Science and Technology in Shenzhen, Jiankui He, announced the birth of two CRISPR edited human babies[1]. While no presentation of the experiment has appeared in the scientific literature, online information[2] describes an introduction of mutations in the CCR5 gene aimed at mimicking the effect of the CCR5-Δ32 mutation, which provides protection against HIV in Europeans[3]. Although the mutations were not identical to CCR5-Δ32[2], and the consequences of these mutations are unknown, the stated purpose was nonetheless HIV prevention. The CRISPR experiment raises a number of obvious ethical issues. In addition, it is not clear if the Δ32 mutation is beneficial. A mutation can be advantageous or disadvantageous depending on environmental conditions[4] and developmental stages[5]. In fact, even though Δ32 provides protection against HIV, and possibly other pathogens such as smallpox[6] and flavivirus[7], and facilitates recovery after stroke[8], it also appears to reduce protection against certain other infectious diseases such as influenza[9].

Direct fitness effects of individual segregating mutations are expected to be small, and are therefore very hard to measure directly. However, due to the recent availability of large databases with genomic data, direct studies of fitness effects of individual mutations have now become feasible[10]. We might expect that the Δ32 mutation is deleterious in the homozygous state based on previous reports, in smaller data sets, showing that individuals with the Δ32/Δ32 genotype have increased mortality when infected by influenza[9] and are four times more likely to develop certain infectious diseases[11]. We here investigate this hypothesis using the genotyping and death register information of 409,693 individuals of British ancestry in the UK Biobank[12]. Δ32 has a frequency of 0.1159 in the British population and the UK Biobank contains approx. 5500 homozygous individuals, providing an opportunity to compare the longevity of these individuals to that of Δ32/+ and +/+ individuals.

We calculate the survival rate (1 - death rate) per year for each of the three Δ32 genotypes from age 41 till age 78 (see Materials and methods), which is the entire range allowed by the data available (Fig. 1a). Due to the small sample size at age 77 and 78, we primarily report the survival probability before age 76 (see Materials and methods). The death rate at age 70–74 years in the UK Biobank volunteers is 46–56% lower than that in the general UK population of the same age[13], likely due to an ascertainment bias known as the “healthy-volunteer effect”[14]. Nonetheless, the relative death rates among different genotypes can still be compared to provide information about the fitness effects of specific mutations. The uncorrected survival probabilities to age 76 of individuals enrolled in the study is 0.8351 for Δ32/Δ32, 0.8654 for Δ32/+, and 0.8638 for +/+ (Fig. 1a), which implies that Δ32/Δ32 has an approx. 21% higher aggregated death rate before age 76 compared to the other genotypes. The average age of enrollment is 56.5 so this largely reflects differences in mortality in individuals above age 56.5. We can partially correct for the death registration delay and biased ascertainment, provided the general population’s death rate per year. After correction, the Δ32/Δ32 individuals are about 20% less likely to reach age 76 (see Materials and Methods). To test the significance of the nominally lower survival rate of Δ32/Δ32, we first perform a log-rank test comparing the death rate of Δ32/Δ32 individuals to that of the other two genotypes (Z-score = 2.37, one-tailed P = 0.0089). We also bootstrap the sample 1000 times and find that Δ32/Δ32 individuals have significantly higher death rate than the other two genotypes, while Δ32/+ and +/+ individuals have similar death rate (Supplementary Table 1). The increase in mortality of Δ32/Δ32 individuals is the highest at age 74, where it is 26.4% higher than the mortality of +/+ individuals (95% bootstrap confidence interval [3.0%,49.5%]). Similarly, a Cox-model[15] for left truncated and right censored data also suggests that Δ32/Δ32 individuals have an average of 21.4% elevated death rate across all ages (95% confidence interval 3.4% and 42.6%, one-tailed P = 0.0089). The 5th principal component is associated with Irish ancestry[12] and is also associated with a difference in mortality (two-sided P = 2.5×10) in the Cox-model. However, when correcting for this effect using PCA loadings as covariates, the increase in mortality of Δ32 is maintained (see Supplemental information). We note that despite the nominally large detected effect on survivorship, the P-value is only moderately small, due to the low frequency of Δ32/Δ32 individuals and the generally low mortality in the cohort. The accuracy of the estimates will likely improve in future years as the mortality rate of the cohort increases.

Figure 1.

Δ32 is deleterious at homozygous state. a, Survival probabilities of Δ32 genotypes. The observed survival probabilities of the three genotypes (+/+, Δ32/+ and Δ32/Δ32) are shown in red, blue, and black, respectively. The x-axis shows the age and the y-axis shows the survival probability. The one-tail P-values from the log-rank test till age 76 is shown on the panel. The number of samples whose genotype at Δ32 and age information are both available is 395704. b, The histogram of inbreeding coefficients, F, from 5932 SNPs whose allele frequencies closely resemble that of Δ32. The black arrow points to the observed F of Δ32 (F = −0.19), calculated for the Δ32/Δ32 individuals. The sample size used in estimating F for each of the 5932 SNPs varies from 7896 to 409607 with a mean of 405428, and the sample size for Δ32 is 395714.

Selection against homozygous individuals will lead to deviations from Hardy-Weinberg Equilibrium (HWE), which can be measured by the inbreeding coefficient (F). Deviations from HWE at the time of enrollment, which is the time at which samples are obtained for genotyping, provides an assessment of differential fitness of Δ32 genotypes that is independent from the previous analyses using death registry information obtained after enrollment. We test for deviations from HWE consistent with a deleterious effect of Δ32 in homozygous individuals by calculating the allele-specific inbreeding coefficient FΔ32. However, there might be deviations from HWE in the data for multiple other reasons, including inbreeding and population structure. Therefore, we compare FΔ32 (see Materials and Methods) with the locus specific value of F for other variants in the data with minor allele frequencies similar (plus/minus 0.0025) to that of Δ32. Only 20/5932 variants have a smaller F than FΔ32 (Fig. 1b; empirical one-tailed P = 0.0034). In addition, the deviation from HWE for each age group also correlates with the deviation predicted by the survival probability (Spearman’s ρ = 0.67, P = 1.4 × 10; see Supplementary information and Extended Data Figure 1). These two independent analyses are largely consistent with each other and both indicate a substantial increase in mortality associated with the Δ32/Δ32 genotype.

Extended Data Figure 1.

The deviation from HWE with age.

a, The observed deviation using age at recruitment estimated. Each dot represents one age group. The grey error bars show the 95% confidence intervals estimated from bootstrap the genotypes of individuals recruited at each age 1000 times. The sample size used for each error bar ranges from 15191 to 100117 with a mean of 65479. b, The predicted deviation from HWE using the corrected survival probability. A total of 395704 samples are used. The observed and predicted values are coefficient ρ = 0.67, P = 1.4 × 10−4).

Our results show that being homozygous for the Δ32 mutation is associated with reduced life expectancy in a modern cohort, despite the protective effect of the mutation against HIV[3]. This finding echoes the previous reports that the Δ32 reduces resistance against influenza[9] and other infectious diseases[11]. We did not observe any difference in mortality between Δ32/+ and +/+ individuals (Supplementary Table 1), despite the fact that Δ32/+ also provides protection against HIV[3]. It could reflect the “healthy volunteer effect” in the UK Biobank cohort[13] if individuals affected by HIV, or suffering from mortality due to HIV infection, are less likely to be recruited. In that case, our estimates of death rates reflect individuals that have reduced exposure to HIV, and the conclusion regarding increased mortality of Δ32/Δ32 is then with reference to such individuals. If so, it would also imply that in the presence of HIV, Δ32 is overdominant, i.e. that individuals heterozygous for the mutation have the highest fitness. In the absence of HIV or other infectious agents for which the mutation provides protection, the mutation will be under negative directional selection. But because only about 0.16% of the current British population is infected by HIV[16], the benefit from this protection is likely too small to have a detectable influence on survival probability in our study.

It is unclear exactly which factors are most important for the fitness effects of the Δ32 mutation. There are many phenotypic associations significant at 5% significance level after correction for multiple testing in the UK Biobank (see Supplementary information for the phenotypes), and the mutation is likely highly pleiotropic. Out of the 5932 SNPs with matching allele frequencies, only 76 have more phenotypic associations than Δ32 in terms of the UK Biobank phenotypes (empirical one-tail P = 0.0128, see Supplementary information).

It is perhaps not unexpected that homozygosity for a deletion in a functional gene is associated with reduced fitness. It underscores the notion that introduction of new or derived mutations in humans using CRISPR technology, or other methods for genetic engineering, comes with considerable risk even if the mutations provide a perceived advantage. In this case, the cost of resistance to HIV may be increased susceptibility to other, and perhaps more common, diseases.

Materials and Methods

The study population

This study uses the UK Biobank data under application number 33672 and basket ids 10997 and 2000429. It is regulated under ethical regulations of UC Berkeley and the data is accessed under the Material Transfer Agreement between the UK Biobank and UC Berkeley.

In the UK Biobank, 409,693 volunteers have self-reported British ancestry confirmed by principle component analysis[12], which constitutes roughly 0.62% of the entire British population. Our main analysis are performed on the British ancestry volunteers, unless otherwise stated. There are 75,970 volunteers in the UK Biobank labeled as non-British ancestry, which are used to investigate the effect of Δ32 in other populations than the British. The UK Biobank volunteers were recruited during 2006–2010 and 2.9% of the volunteers (13,831) have a recorded age at death (all cause).

Marker selection and validation

SNP rs62625034 (coordinate 3:46414975 in GRCh37) is a directly genotyped SNP which is used to identify Δ32 (rs333) based on the following validations: First, the Affymetrix probe used for this SNP is ‘CCATACAGTCAGTATCAATTCTGGAAGAATTTCCA[G/T]ACATTAAAGATAGTCATCTTGGGGCTGGTCCTGCC’ based on annotation files ‘Axiom_UKBiLEVE.na34.annot.csv’ and ‘Axiom_UKB_WCSG.na34.annot.csv’. The targeted region of this probe fully includes the 32 bp deletion in rs333, given rs333 (Δ32) has coordinate 3: 46414947–46414978 in GRCh37. Second, rs62625034 is not called as a SNP in the 1000 Genome database, and a recent study on variants in CCR5 gene[17] also confirmed that it could only be detected in one of the Denisovian samples. However, the detected allele frequency by the probe of rs62625034 in the UK Biobank is 0.1159 among the British ancestry genomes, which does not resemble the frequency of rs62625034, but closely resembles the frequency of rs333 (0.1237) in the European and the British population (CEU and GBR) in the 1000 Genomes data. Third, SNP rs113010081, a directly genotyped SNP in the UK Biobank data, is in strong linkage disequilibrium (LD) with rs333 in the 1000 Genomes data, with a r2 of 0.93 combining CEU and GBR in 1000 Genomes data (https://ldlink.nci.nih.gov/?var1=rs333&var2=rs113010081&pop=CEU%2BGBR&tab=ldpair). We calculate the Pearson correlation between rs113010081 and the probe of rs62625034 using the UK Biboank British ancestry genotypes, and obtain r2 = 0.94, which again resembles the correct LD between rs113010081 and rs333. In addition, there is no other SNP that is in as strong LD with rs113010081 in the targeted region of this probe (https://ldlink.nci.nih.gov/?var=rs113010081&pop=CEU%2BGBR&r2_d=r2&tab=ldproxy). Lastly, we also estimate the survival probability for rs113010081, and the results are similar to that obtained for rs62625034 (not shown).

Estimation of survival probability

The UK Biobank death records are updated quarterly with the NHS Information Centre for participants from England and Wales and by NHS Central Register, Scotland for participants from Scotland. However, the death records are not made available immediately to researchers. The latest date of death among all registered deaths in the downloaded data is 2016–02–16, and we use this date to approximate the time of last death entry, and assume that after that date we have no mortality/viability information of the volunteers. We use five entries from the UK Biobank data, the age at recruitment, the date of recruitment, the year of birth, month of birth, and the age at death, to calculate the number of individuals (N) who are ascertained from age i to age i + 1, and the occurrence of death observed from these N individuals during the interval of age i to age i + 1 is O. Using this information, we calculate the ascertained age for each individual. We ignore the partially ascertained age to avoid biases from censoring. For example, an individual recruited at age 45.2, and reaching age 52.3 on 2016–02–16, who does not have a reported death in our data, is treated as being observed from age 46 to age 52, thus this volunteer contributes to N46, N47, N48, N49, N50, N51. As another example, a person who is recruited at age 65.7, and could have reached age 72.6 by 2016–02–16, but has a reported death at age 69.7 will contribute to N66, N67, N68, N69, and this volunteer will also contribute to O69. This volunteer does not contribute to N70, because death has already occurred before age 70. The death rate per year is then calculated as h = O/N, and the probability of surviving to age i + 1 is S = . The UK Biobank data allows estimation of death rates from h41 to h77, but because N77 is smaller than 800, we have to assume that h76 = h77 and combined these two ages in our estimation. We estimate h separately for the three different Δ32 genotypes. We mainly report the survival probability before age 76, where there is sufficient data to obtain accurate estimates, but the estimated survival probabilities to age 77 and 78 are also shown in Fig. 1.

Because the exact birth dates of the volunteers are considered sensitive, we do not have access to them. The age at recruitment in the UK Biobank is rounded down to nearest integer age, and we approximate the exact age using the date of recruitment, the year of birth, and month of birth, assuming everyone is born on the 15th of their birth month. In rare cases, when the date of recruitment is very close to a person’s birthday, the approximated age could be smaller than the age at recruitment provided by the UK Biobank and in these rare cases we instead round up the estimated age. After applying this rounding scheme, if there are no errors in the data, under no scenario should the estimated age be smaller than the integer age at recruitment. However, there are 17 individuals whose estimated age is smaller than the age at recruitment, and we exclude these individuals in the death rate calculation. Among them, 15 are British ancestry.

Although the UK Biobank routinely imports death records from the national databases, the “healthy volunteer effect”[13] can still lead to a substantial underestimation of the death rate per year h compared to the general population. The delay of the death records may be affected by many factors including time of recruitment, age of death, cause of death, and various socio-economic factors[18]. However, if we assume that these biases are independent of the Δ32 genotype, we can then estimate the death rate correction factor C for each age i and estimate the death rate per year and the survival probability for the three different Δ32 genotypes in the general population. To do this, we download the national life tables in the UK (“nltuk1517reg.xls”) from the Office of National Statistics (https://www.ons.gov.uk) which contain the death rate per year for the entire British population each year from 1980 to 2017, estimated for males and females separately. We average the death rate per year from 2006 to 2016 to represent the death rate H of the general population. We then use h/H to estimate C. We then calculate a corrected death rate for each Δ32 genotype. For example, the corrected death rate for +/+ is h/C. We use the corrected death rates to estimate the corrected survival probability (S). The inferred survival probability after correction (S) to age 76 are 0.7565, 0.7589 and 0.7111 for genotypes +/+, Δ32/+, and Δ32/Δ32, respectively. With this crude correction, the probability of death before age 76 in the general population is (1 - S)/(1 - S) - 1, about 20% higher for Δ32/Δ32 individuals compared to heterozygous individuals. We note that while the calculations of death rates could be done more accurately, for example by using exact birthday (which we did not have access to), the significant difference in death rates between genotypes is unlikely to be explained by this effect. However, our survival analyses may underestimate the beneficial effects of Δ32 in some age groups due to ascertainment biases caused by the “healthy volunteer effect”[13].

Estimation of F

FΔ32 is estimated from the equation PΔ32 = (1 + FΔ32)PΔ32PΔ32, where PΔ32 and PΔ32 are the observed frequencies of Δ32 and Δ32/Δ32, respectively. When FΔ32 is significantly smaller than 0, it implies that the observed fraction of Δ32/Δ32 individuals is lower than expected under HWE, consistent with increased mortality of Δ32/Δ32 individuals. The F of other SNPs are similarly estimated.

Statistical analysis

One-tail P-values from log-rank test are used in Fig. 1a and Supplementary Table 1. In Fig. 1b, empirical one-tail P-values are used from the F of 5932 SNPs. 95% confidence intervals from bootstrap are shown as error bars in Extended Data Figure 1a, and are used in Supplementary Table 1. Spearman’s correlation is used in Extended Data Figure 1. In addition, the details of the statistical tests are given at places they are mentioned.

Life sciences reporting summary

Further information on experimental design is available in the Nature Research Reporting Summary linked to this article.

Data, code, and research notebook availability

The genotype and death registry information are available with the permission of the UK Biobank. Analytical results and scripts are accessible through (https://github.com/AprilWei001/CCR5-delta32). In addition, a detailed experimental notebook covering the entire development of this project is available at depository (https://xinzhuaprilwei.weebly.com/download/ccr5-delta32).