This webpage was created as part of an undergraduate assignment at Davidson College

The phenotypic legacy of admixture between modern humans and Neandertals

Background:

Simonti et al. published a paper in Science examining the impact of Neandertal (Homo neanderthalensis) DNA found in the modern human (Homo sapiens) genome on human traits (Simonti et al., 2016). This DNA makes up 1.5-4% of the modern Eurasian genome and is present because as modern human groups moved out of Africa and into Europe, they interbred with other ancient hominin populations, including Neantertals. At the time, many of the traits that entered the human gene pool/phenome were probably advantageous as humans adapted to their new climate, but over time these genes have lost their advantage. However, they still are present in the genome, yet no one has provided an in-depth analysis of the impact of these introgressed alleles on modern human phenotypes. Simonti et al. sought to fill this void, and present conclusions to three hypotheses in the aforementioned paper:

1) Is DNA inherited from Neandertals associated with modern human disease?

2) Can current phenotypes be related to specific Neandertal haplotypes?

3) Are electronic health records useful for this type of analysis?

Relevant terms:

• AMH: anatomically modern human (Homo sapiens)
• EHR: electronic health record, which provides data for phenotypic analysis
• eMERGE: Electronic Medical Records and Genomics Network, which provides EHRs as well as patient genetic data
• SNP: single-nucleotide polymorphism. A mutation in which one DNA base does not match the reference strand. This may or may not effect the phenotype.
• Phenome: set of all phenotypes expressed by an organism
• Haplotype: group of alleles inherited as a unit (usually close together on a chromosome)
• Introgression: incorporation of a locus/allele/haplotype from one species into the gene pool of a second, divergent species
  

Figure 1 is very busy. It depicts both methods and some sample conclusions of the EHR analysis of genotypic and phenotypic data. It is necessary first to break it down one panel at a time. However, each panel contains information to help the reader understand other panels. There are two critical things to note overall: the method of labeling is consistent throughout panels A, B, and D; and each panel represents only one or a few examples of the kind of analysis that was done for the 1689 considered phenotypes.

Panel A:

Panel A shows how the phenotypes derived from EHR's were compared to the genotype of each patient. These data are specific to cohort E1; cohort E2 followed the same procedure. The first column identifies each patient with a P followed by two subscript numbers. The first is a site number, which indicates the site at which the data were collected (for example, the Mayo Clinic). The second identifies patients within the site. So patient P_1,1 is patient 1 at site 1.
The second column ("Genotypes") shows three DNA nucleotides that were part of the patient's genotype. Nucleotides that are shaded in red match one of the Neandertal SNPs.
In the most right column, the EHR was used to identify whether or not each patient expressed any of the studied phenotypes, identified here as 1, 2...all the way up to M (the last phenotype). A check means the patient expresses the phenotype; an X means the patient did not.
Because phenotype 2 has been hypothesized to be associated with Neandertal variants at the third position shown, a box is drawn around the associated SNP and phenotype. These boxes will be relevant later when we consider the larger figure.

Panel B:

Panel B shows the genotypic and phenotypic similarity of patients. The left side shows the overall genetic similarity of pairs of patients, comparing genotype at all of the Neandertal SNP.s The darker the coloration, the more similar the two individuals (which is why the intersection of P_1,1 and P_1,1 is darkest). On the right, phenotypes are compared. If both patients express (or don't express) the specific trait, the intersection is dark, while if only one patient expresses it, it is white. This information was used to determine which phenotypes are impacted by the Neandertal SNP's (see Panel C).

Panel C:

Genome-wide complex trait analysis (GCTA) was used to determine which phenotypes in AMHs seem to be impacted by the presence of Neandertal variants in the corresponding parts of the genome. GCTA is used to estimate the proportion of phenotypic variance explained by genome-wide SNPs. Panel C identifies a few of the phenotypes that were found to be explained to a nominally significant extent by Neandertal SNPs.

Panel D:

Panel D is a forest plot which displays an example of the results of a phenome-wide association study (PheWAS), a meta-analysis used to test individual Neandertal alleles for trait association. The top shows the first iteration, which used the E1 cohort ("Discovery"). The number of cases (individuals exhibiting the trait) and controls are listed. The P-value indicates that the correlation between having the SNP (rs3917862) and the phenotype (hypercoagulable state) is significant. To the far right, a chart shows the odds of having the phenotype with the SNP versus without it. After displaying these data for each individual site in the E1 cohort, the meta-analysis data are shown (numbers for the overall association across sites). The problem with this particular plot is that it requires a lot of deduction. We are never directly told what odds are being calculated, or what the p-value is for.
Below the Discovery PheWAS are the data for the Replication PheWAS. The same analysis was conducted using individuals from the E2 cohort. The odds ratio was found to be similar, but more importantly the P-value was still much smaller than 0.05, indicating that significant association between the two is reproducible. The association depicted here is just one of four Neandertal SNP-AMH phenotype associations that was found to be significant in the E1 analysis and reproducible in the E2 analysis.

Panel E:

Panel E is a box plot that shows the expression of SELP when the human allele for rs3917862 is present (Human/Human) compared to when the Neandertal allele is present (Neandertal/Human). The y-axis is the relative level of expression compared to the average expression when the human allele is present. SELP codes for a cell adhesion protein that attracts leukocytes to injuries during inflammation, and plays a key role in blood coagulation. The take-away of this figure is simply that there is a significant increase in SELP expression if the Neandertal allele is present, which is consistent with the association of the Neandertal haplotype with hypercoagulation.

Putting it all together: Figure 1

Now that we understand a little more about each panel, we can look at Figure 1 cohesively. First, genotype and phenotype were matched by aligning genome sequencing data with phenotypic data from EHRs. Boxes indicate SNPs associated with phenotype 2. The arrows connecting panels A and D show how the blue shaded Site 1 corresponds to samples from Marshfield, while the green shaded Site 2 corresponds to samples from Mayo. Panel B demonstrates how similarity of individual genotypes and phenotypes shown in panel A was determined using pair-wise comparisons. From these data, panel C lists some phenotypes associated with Neandertal SNPs, determined via GCTA. The forest plot in panel D demonstrates the association between a Neandertal SNP and an AMH phenotype, in part using data collected in panel A. Finally, panel E is another visual representation of the increased phenotype occurrence from D when the corresponding SNP is present. Overall, this figure displays some methods used throughout the paper and demonstrates the association between Neandertal alleles and AMH phenotypes, as well as relationships between specific SNP-phenotype pairs.

Table 1:

The GCTA analysis in figure 1 identified 8 traits for which Neandertal alleles explained a reproducible, nominally significant proportion of variance. They are here listed according to P value in the replication (E2) cohort, with actinic keratosis having the smallest P value (and thus the greatest confidence).
Three of these 8 were reproducible when a stricter model that also accounted for non-Neandertal SNPs elsewhere in the genome were considered. To further understand this figure and the methods behind it, please refer to the following methods-results flow chart:

Figure by Prudencio, 2016.

Table 2:

Table 2 displays the four Neandertal SNP-phenotype associations that remained reproducibly significant after a Bonferroni correction. From left to right, it includes a description of each phenotype, the chromosomal position of the SNP, the name of the SNP, the associated gene(s), the ratio of phenotype occurrence with Neandertal SNP to occurrence without the SNP and P value in the discovery (E1) cohort, and the ratio and P value in the replication (E2) cohort. The first association, hypercoagulable state and rs3917862, was seen above in Figure 1. To be included, P must be <0.05. The take-away is that specific SNPs can be identified as associated with specific phenotypes.

Figure 2:

This figure shows the types of phenotypes most frequently associated with Neandertal SNPs. Zero on the y-axis indicates that the number of Neandertal SNPs associated with phenotypes in the group is equal to the number expected, based on association with non-Neandertal SNPs. The y-axis displays a positive value if more phenotypes were associated than expected (enrichment), and a negative value if fewer were associated (depletion). Along the x-axis, we see the category of phenotype and in parentheses, the number of Neandertal SNPs associated with that group. Asterisks indicate significant enrichment in neurologic phenotype association (11 associations) and significant depletion in digestive phenotype association (zero associations).