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Additionally, it is not clear that phenotypic inference from single variants for which a function is known on the modern human genetic background provides sufficient evidence for extrapolating effects in Neanderthals, especially given the challenges with predicting complex phenotypes in present-day humans on the basis of genomic data.32
In addition to the introgressed haplotypes contributing to skin and hair traits, we also found two archaic haplotypes that contribute significantly to differences in sleep patterns (Table 1). One of the introgressed SNPs modifies the coding sequence of ASB1 (MIM: 605758; rs3191996, p.Ser37Lys; Material and Methods). Archaic alleles near ASB1 (tag aSNP: rs75804782; Figure 2 and Table 1) and EXOC6 (MIM: 609672; tag aSNP rs71550011; Table 1) are associated with a preference for being an “evening person” and an increased tendency for daytime napping and narcolepsy, respectively. Humans show wide variation in diurnal preferences and can be divided into “chronotypes,” which have been shown to have a genetic component.33 Two previous studies of chronotypes identified strongly associated SNPs in the ASB1 region.34, 35 Of the 540 SNPs with significant genome-wide associations in Hu et al.34 (p < 1.0 × 10−8), ten overlapped the region identified near ASB1, and four of these were labeled as introgressed archaic variants. Lane et al. identified two ASB1-adjacent SNPs that showed significant associations with chronotype.35 Neither of these are of archaic origin, but they are in high LD with aSNPs on the associated haplotype (maximum r2 = 0.73, based on Europeans in 1000 Genomes phase 3), suggesting that these are not independent signals. Given the association scores calculated by Hu et al.,34 the association is stronger for the set of aSNPs (p values ranging from 3.4 × 10−6 to 2.6 × 10−9; rs75804782 has the second-most-significant association at p = 4.4 × 10−9) than for the non-archaic SNPs reported by Lane et al.35 (rs3769118, p = 1. 9 × 10−6; rs11895698, p = 3.2 × 10−6), suggesting that the association is likely to be driven by the introgressed archaic haplotype. Because the natural length of day-night cycles differs according to latitude and influences circadian rhythms, we tested for a correlation between the Neanderthal allele frequency at ASB1 and latitude in worldwide non-African populations.36 We found a significant correlation between the frequency of the Neanderthal allele near ASB1 (rs75804782) and latitude (Spearman’s rho = 0.21, p = 0.03). The fact that populations further from the equator have higher frequencies of the Neanderthal allele at ASB1 than populations nearer the equator (Figure 2B) is consistent with the influence of daylight exposure on circadian rhythm,37 although the functional link between these genes and chronotype traits is unclear.
Figure 2
Archaic Haplotype Associated with Chronotype
(A) The Neanderthal allele frequency in percentage (x axis) and the number of individuals in the UK Biobank cohort for the four reported chronotype phenotypes (y axis; from top to bottom: definitely an evening person, more an evening than a morning person, more a morning than an evening person, definitely a morning person) for the archaic tag SNP with the strongest association with chronotype (position chr2: 239,316,043 [rs75804782] near ASB1).
(B) Worldwide frequency of the archaic allele (C, blue) and the modern human allele (T, orange) in the Simons Genome Diversity Panel populations.
(C) The association p values (y axis; in the form of −log10(p)) with chronotype for all archaic and non-archaic SNPs (squares) genotyped by the UK Biobank study in the region of the inferred archaic haplotype at chr2: 239,316,043–239,470,654. The tag SNP at chr2: 239,316,043 (rs75804782) is shown in red, other aSNPs are shown in orange, and non-archaic SNPs are shown in black. The genome-wide significance cutoff of p = 1.0 × 10−8 and the extent of the inferred archaic haplotype are illustrated with dashed horizontal and vertical gray lines, respectively. At the top, we show all aSNPs that are within the inferred archaic haplotype and are present in any 1000 Genomes individual. The directly genotyped SNPs from the UK Biobank are illustrated as red (the archaic tag SNP) and orange bars. One archaic allele that leads to a missense mutation in ASB1 is marked as a green bar.
(D) The cumulative density distribution of p values (zoom in for p < 0.01, x axis log scale) for associations between archaic alleles and chronotype (red line) and the 95% confidence interval region for 1,000 cumulative density distributions of associations between non-archaic alleles matched to the Neanderthal allele frequency and chronotype (gray shading).
Given the large number of associations with skin and hair traits, it is tempting to speculate that Neanderthals might have had an outsized contribution to these phenotypes. However, the number of significant associations that can be identified for a trait is dependent on how polygenic the traits are and how they are measured. Power to measure the contribution of an allele depends also on the minor allele frequency. In the case of archaic alleles, which are generally less frequent (∼1%–5%), this is of particular relevance. We therefore tested whether the impact of archaic alleles on particular traits is more or less than that of non-archaic alleles by comparing the contributions of archaic alleles with the contributions of 1,000 similarly sized sets of frequency-matched non-archaic tag SNPs. Phenotypes with an enrichment of low association p values for archaic alleles could indicate a larger-than-expected contribution of introgressed archaic DNA to these phenotypes, whereas an enrichment of low p values for non-archaic alleles suggests a lower contribution from archaic alleles to the phenotype. We note that our frequency matching of archaic and non-archaic alleles does not account for multiple other factors that might differ between these two sets of variants. For example, the longer haplotypes associated with archaic introgression mean that archaic variants might be more likely to occur together. However, it is unclear whether the higher number of archaic alleles on archaic haplotypes would increase or decrease the chance of being significantly associated with phenotypes in modern humans. We believe that further matching of, for example, haplotype length or number of SNPs of a haplotype introduces new potential biases and does not solve this problem. For each phenotype, we selected the lower tail of the p value distributions (p < 1.0 × 10−4) for archaic and non-archaic SNPs and then tested whether the archaic p value distribution was significantly different from 1,000 non-archaic distributions (Material and Methods). For the majority of phenotypes (130/136), we found no difference between the relative contribution of archaic alleles and that of non-archaic alleles, indicating that for most phenotypes measured here, Neanderthal alleles contribute phenotypic variation proportionally to non-archaic SNPs at similar frequencies (Table S3). We detected six phenotypes where there was a significant difference between the p values distributions for archaic alleles and those for non-archaic alleles (FDR < 0.05). Neanderthal alleles contributed more variation in four behavioral phenotypes influencing sleep, mood, and smoking behaviors, suggesting that Neanderthal alleles contribute more to these traits than expected from their frequency in modern humans. Conversely, for two associations (ease of skin tanning and pork intake), non-archaic alleles showed lower association p values (Table S3), indicating that introgressed Neanderthal alleles contribute less than frequency-matched non-archaic alleles to these traits.
In addition to the introgressed haplotypes contributing to skin and hair traits, we also found two archaic haplotypes that contribute significantly to differences in sleep patterns (Table 1). One of the introgressed SNPs modifies the coding sequence of ASB1 (MIM: 605758; rs3191996, p.Ser37Lys; Material and Methods). Archaic alleles near ASB1 (tag aSNP: rs75804782; Figure 2 and Table 1) and EXOC6 (MIM: 609672; tag aSNP rs71550011; Table 1) are associated with a preference for being an “evening person” and an increased tendency for daytime napping and narcolepsy, respectively. Humans show wide variation in diurnal preferences and can be divided into “chronotypes,” which have been shown to have a genetic component.33 Two previous studies of chronotypes identified strongly associated SNPs in the ASB1 region.34, 35 Of the 540 SNPs with significant genome-wide associations in Hu et al.34 (p < 1.0 × 10−8), ten overlapped the region identified near ASB1, and four of these were labeled as introgressed archaic variants. Lane et al. identified two ASB1-adjacent SNPs that showed significant associations with chronotype.35 Neither of these are of archaic origin, but they are in high LD with aSNPs on the associated haplotype (maximum r2 = 0.73, based on Europeans in 1000 Genomes phase 3), suggesting that these are not independent signals. Given the association scores calculated by Hu et al.,34 the association is stronger for the set of aSNPs (p values ranging from 3.4 × 10−6 to 2.6 × 10−9; rs75804782 has the second-most-significant association at p = 4.4 × 10−9) than for the non-archaic SNPs reported by Lane et al.35 (rs3769118, p = 1. 9 × 10−6; rs11895698, p = 3.2 × 10−6), suggesting that the association is likely to be driven by the introgressed archaic haplotype. Because the natural length of day-night cycles differs according to latitude and influences circadian rhythms, we tested for a correlation between the Neanderthal allele frequency at ASB1 and latitude in worldwide non-African populations.36 We found a significant correlation between the frequency of the Neanderthal allele near ASB1 (rs75804782) and latitude (Spearman’s rho = 0.21, p = 0.03). The fact that populations further from the equator have higher frequencies of the Neanderthal allele at ASB1 than populations nearer the equator (Figure 2B) is consistent with the influence of daylight exposure on circadian rhythm,37 although the functional link between these genes and chronotype traits is unclear.
Figure 2
Archaic Haplotype Associated with Chronotype
(A) The Neanderthal allele frequency in percentage (x axis) and the number of individuals in the UK Biobank cohort for the four reported chronotype phenotypes (y axis; from top to bottom: definitely an evening person, more an evening than a morning person, more a morning than an evening person, definitely a morning person) for the archaic tag SNP with the strongest association with chronotype (position chr2: 239,316,043 [rs75804782] near ASB1).
(B) Worldwide frequency of the archaic allele (C, blue) and the modern human allele (T, orange) in the Simons Genome Diversity Panel populations.
(C) The association p values (y axis; in the form of −log10(p)) with chronotype for all archaic and non-archaic SNPs (squares) genotyped by the UK Biobank study in the region of the inferred archaic haplotype at chr2: 239,316,043–239,470,654. The tag SNP at chr2: 239,316,043 (rs75804782) is shown in red, other aSNPs are shown in orange, and non-archaic SNPs are shown in black. The genome-wide significance cutoff of p = 1.0 × 10−8 and the extent of the inferred archaic haplotype are illustrated with dashed horizontal and vertical gray lines, respectively. At the top, we show all aSNPs that are within the inferred archaic haplotype and are present in any 1000 Genomes individual. The directly genotyped SNPs from the UK Biobank are illustrated as red (the archaic tag SNP) and orange bars. One archaic allele that leads to a missense mutation in ASB1 is marked as a green bar.
(D) The cumulative density distribution of p values (zoom in for p < 0.01, x axis log scale) for associations between archaic alleles and chronotype (red line) and the 95% confidence interval region for 1,000 cumulative density distributions of associations between non-archaic alleles matched to the Neanderthal allele frequency and chronotype (gray shading).
Given the large number of associations with skin and hair traits, it is tempting to speculate that Neanderthals might have had an outsized contribution to these phenotypes. However, the number of significant associations that can be identified for a trait is dependent on how polygenic the traits are and how they are measured. Power to measure the contribution of an allele depends also on the minor allele frequency. In the case of archaic alleles, which are generally less frequent (∼1%–5%), this is of particular relevance. We therefore tested whether the impact of archaic alleles on particular traits is more or less than that of non-archaic alleles by comparing the contributions of archaic alleles with the contributions of 1,000 similarly sized sets of frequency-matched non-archaic tag SNPs. Phenotypes with an enrichment of low association p values for archaic alleles could indicate a larger-than-expected contribution of introgressed archaic DNA to these phenotypes, whereas an enrichment of low p values for non-archaic alleles suggests a lower contribution from archaic alleles to the phenotype. We note that our frequency matching of archaic and non-archaic alleles does not account for multiple other factors that might differ between these two sets of variants. For example, the longer haplotypes associated with archaic introgression mean that archaic variants might be more likely to occur together. However, it is unclear whether the higher number of archaic alleles on archaic haplotypes would increase or decrease the chance of being significantly associated with phenotypes in modern humans. We believe that further matching of, for example, haplotype length or number of SNPs of a haplotype introduces new potential biases and does not solve this problem. For each phenotype, we selected the lower tail of the p value distributions (p < 1.0 × 10−4) for archaic and non-archaic SNPs and then tested whether the archaic p value distribution was significantly different from 1,000 non-archaic distributions (Material and Methods). For the majority of phenotypes (130/136), we found no difference between the relative contribution of archaic alleles and that of non-archaic alleles, indicating that for most phenotypes measured here, Neanderthal alleles contribute phenotypic variation proportionally to non-archaic SNPs at similar frequencies (Table S3). We detected six phenotypes where there was a significant difference between the p values distributions for archaic alleles and those for non-archaic alleles (FDR < 0.05). Neanderthal alleles contributed more variation in four behavioral phenotypes influencing sleep, mood, and smoking behaviors, suggesting that Neanderthal alleles contribute more to these traits than expected from their frequency in modern humans. Conversely, for two associations (ease of skin tanning and pork intake), non-archaic alleles showed lower association p values (Table S3), indicating that introgressed Neanderthal alleles contribute less than frequency-matched non-archaic alleles to these traits.