“Predicting Skeletal Stature Using Ancient DNA”, Samantha L. Cox, Hannah M. Moots, Jay T. Stock, Andrej Shbat, Bárbara D. Bitarello, Nicole Nicklisch, Kurt W. Alt, Wolfgang Haak, Eva Rosenstock, Christopher B. Ruff, Iain Mathieson2021-11-02 (, )⁠:

Objectives: Ancient DNA provides an opportunity to separate the genetic and environmental bases of complex traits by allowing direct estimation of genetic values in ancient individuals. Here, we test whether genetic scores for height in ancient individuals are predictive of their actual height, as inferred from skeletal remains. We estimate the contributions of genetic and environmental variables to observed phenotypic variation as a first step towards quantifying individual sources of morphological variation.

Method & Materials: We collected stature estimates and femur lengths from West Eurasian skeletal remains with published genome-wide ancient DNA data (n = 182, dating from 33,000–850 BP). We also recorded genetic sex, genetic ancestry, date and paleoclimate data for each individual, and δ13C and δ15N stable isotope values where available (n = 69). We tested different methods of calculating popolygenic scoresusing summary statistics from 4 different genome wide association studies (GWAS) for height, and 3 methods for imputing missing genotypes.

Results: A polygenic score for height predicts 6.3% of the variance in femur length in our data (n = 132, SD = 0.0069%, p = 0.001), controlling for sex, ancestry, and date. This is consistent with the predictive power of height PRS in present-day populations and the low coverage of ancient samples. Comparatively, sex explains about 17% of the variance in femur length in our sample. Environmental effects also likely play a role in variation, independent of genetics, though with considerable uncertainty (longitude: R2 = 0.033, SD = 0.008, p = 0.011). Genotype imputation did not improve polygenic prediction, and results varied based on the GWAS summary statistics we used.

Discussion: Polygenic scores explain a small but statistically-significant proportion of the variance in height in ancient individuals, though not enough to make useful predictions of individual phenotypes. However, environmental variables also contribute to phenotypic outcomes and understanding their interaction with direct genetic predictions will provide a framework with which to model how plasticity and genetic changes ultimately combine to drive adaptation and evolution.

Figure 2: (a) Plot of the linear relationship between polygenic score (PRS) and femur length. Higher PRS values are associated with longer femur lengths in the data. Colors indicate sex, the lines are the regression lines for males and females separately, and the gray shadows are the 95% confidence intervals. For our main results, we assume the slope of this regression is identical between sexes. The R<sup>2</sup> of PRS is 0.063 and of sex is 0.17. (b) Plot of the fitted quadratic relationship between date and femur length (R2 = 0.072). Colors indicate sex, the solid gray line is the quadratic fit line for the pooled-sex group, the gray shadow is the 95% confidence interval, and the vertical dashed line indicates the change in x-axis plotting scale.
Figure 2: (a) Plot of the linear relationship between polygenic score (PRS) and femur length. Higher PRS values are associated with longer femur lengths in the data. Colors indicate sex, the lines are the regression lines for males and females separately, and the gray shadows are the 95% confidence intervals. For our main results, we assume the slope of this regression is identical between sexes. The R2 of PRS is 0.063 and of sex is 0.17. (b) Plot of the fitted quadratic relationship between date and femur length (R2 = 0.072). Colors indicate sex, the solid gray line is the quadratic fit line for the pooled-sex group, the gray shadow is the 95% confidence interval, and the vertical dashed line indicates the change in x-axis plotting scale.