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Data from: An integrative skeletal and paleogenomic analysis of stature variation suggests relatively reduced health for early European farmers

Citation

Marciniak, Stephanie et al. (2022), Data from: An integrative skeletal and paleogenomic analysis of stature variation suggests relatively reduced health for early European farmers, Dryad, Dataset, https://doi.org/10.5061/dryad.b5mkkwhfp

Abstract

Human culture, biology, and health were shaped dramatically by the onset of agriculture ~12,000 years before present (BP). This shift is hypothesized to have resulted in increased individual fitness and population growth as evidenced by archaeological and population genomic data alongside a decline in physiological health as inferred from skeletal remains. Here, we consider osteological and ancient DNA data from the same prehistoric individuals to study human stature variation as a proxy for health across a transition to agriculture. Specifically, we compared ‘predicted’ genetic contributions to height from paleogenomic data and ‘achieved’ adult osteological height estimated from long bone measurements for 167 individuals across Europe spanning the Upper Paleolithic to Iron Age (~38,000-2,400 BP). We found that individuals from the Neolithic were shorter than expected (given their individual polygenic height scores) by an average of -3.82 cm relative to individuals from the Upper Paleolithic and Mesolithic (P=0.040) and -2.21 cm shorter relative to post-Neolithic individuals (P=0.068, with osteological vs. expected stature steadily increasing across the Copper (+1.95 cm relative to the Neolithic), Bronze (+2.70 cm), and Iron (+3.27 cm) Ages. These results were attenuated when we additionally accounted for genome-wide genetic ancestry variation, for example with Neolithic individuals -2.82 cm shorter than expected on average relative to pre-Neolithic individuals (P=0.120). We also incorporated observations of paleopathological indicators of non-specific stress that can persist from childhood to adulthood in skeletal remains into our model. Overall, our work highlights the potential of integrating disparate datasets to explore proxies of health in prehistory.

Usage Notes

1. ReadMe file

Description of the methods used to generate the imputed data sets. In brief, the 1000 Genomes Phase 3 genetic variants reference panel was used for genotyping and imputation, as provided by BEAGLE (ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/). UnifiedGenotyper was used to obtain genotypes and likelihood scores. Due to the potential for post-mortem damage impacting C>T and G>A allele changes, the per chromosome VCF files of the called genotypes were filtered for potential post-mortem damage. Genotype likelihoods were then estimated using the per-chromosome VCF files, followed by imputation of missing SNPs based on the genotype probability score using the 1000 Genomes phase 3 haplotypes (http://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes_phase3_v5a/) and GRCh37 genomic maps (http://bochet.gcc.biostat.washington.edu/beagle/genetic_maps/). Genotyping calling and imputation was repeated for data not subject to filtering for potential deamination signals.

README_Marciniak_et_al_2022.txt

2. VCF and index files containing the imputed genotypes for 167 ancient individuals with and without filtering for deamination.

VCF (*.vcf.gz) and index (*.tbi) files of imputed genotypes for 167 ancient individuals (previously published and publicly available), with and without filtering for signals of deamination (prior to imputation).

a) ALL_MERGED_CHRS_DEAM_n167.preGLfilter.vcf.gz, ALL_MERGED_CHRS_DEAM_n167.preGLfilter.vcf.gz.tbi; b) ALL_MERGED_CHRS_NODEAM_n167.preGLfilter.vcf.gz, ALL_MERGED_CHRS_NODEAM_n167.preGLfilter.vcf.gz.tbi

3. VCF and index files containing the imputed genotypes for 167 ancient individuals filtered for a minimum genotype probability of 0.99.

VCF (*.vcf.gz) and index (*.tbi) files of imputed genotypes for 167 ancient individuals (previously published and publicly available), with and without filtering for signals of deamination (prior to imputation) as well as filtered for a minimum genotype probably of 0.99.

a) ALL_MERGED_CHRS_DEAM_n167.GL99.vcf.gz, ALL_MERGED_CHRS_DEAM_n167.GL99.vcf.gz.tbi; b) ALL_MERGED_CHRS_NODEAM_n167.GL99.vcf.gz, ALL_MERGED_CHRS_NODEAM_n167.GL99.vcf.gz.tbi