Predicting skeletal stature using ancient DNA

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. Materials and methods: 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 polygenic scores, using summary statistics from four different genome wide association studies (GWAS) for height, and three 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 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.