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Horse domestication revolutionized warfare and accelerated travel, trade, and the geographic expansion of languages. Here, we present the largest DNA time series for a non-human organism to date, including genome-scale data from 149 ancient animals and 129 ancient genomes (>= 1-fold coverage), 87 of which are new. This extensive dataset allows us to assess the modem legacy of past equestrian civilisations. We find that two extinct horse lineages existed during early domestication, one at the far western (Iberia) and the other at the far eastern range (Siberia) of Eurasia. None of these contributed significantly to modern diversity. We show that the influence of Persian-related horse lineages increased following the Islamic conquests in Europe and Asia. Multiple alleles associated with elite-racing, including at the MSTN "speed gene," only rose in popularity within the last millennium. Finally, the development of modem breeding impacted genetic diversity more dramatically than the previous millennia of human management.
The genomic changes underlying both early and late stages of horse domestication remain largely unknown. We examined the genomes of 14 early domestic horses from the Bronze and Iron Ages, dating to between similar to 4.1 and 2.3 thousand years before present. We find early domestication selection patterns supporting the neural crest hypothesis, which provides a unified developmental origin for common domestic traits. Within the past 2.3 thousand years, horses lost genetic diversity and archaic DNA tracts introgressed from a now-extinct lineage. They accumulated deleterious mutations later than expected under the cost-of-domestication hypothesis, probably because of breeding from limited numbers of stallions. We also reveal that Iron Age Scythian steppe nomads implemented breeding strategies involving no detectable inbreeding and selection for coat-color variation and robust forelimbs.
This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.