# Joint estimation of contamination, error and demography for nuclear DNA from ancient humans

## TL;DR

This paper introduces a new method to estimate contamination, sequencing errors, and demographic history in ancient human DNA samples.

## Contribution

The novel contribution is an MCMC algorithm that jointly estimates contamination, error rates, and demographic parameters from ancient nuclear genomes.

## Key findings

- The method accurately estimates contamination rates even at low sequencing coverage.
- It successfully infers demographic parameters like drift times and admixture rates.
- The algorithm correctly identifies the most likely contaminating population.

## Abstract

When sequencing an ancient DNA sample from a hominin fossil, DNA from present-day humans involved in excavation and extraction will be sequenced along with the endogenous material. This type of contamination is problematic for downstream analyses as it will introduce a bias towards the population of the contaminating individual(s). Quantifying the extent of contamination is a crucial step as it allows researchers to account for possible biases that may arise in downstream genetic analyses. Here, we present an MCMC algorithm to co-estimate the contamination rate, sequencing error rate and demographic parameters – including drift times and admixture rates – for an ancient nuclear genome obtained from human remains, when the putative contaminating DNA comes from present-day humans. We assume we have a large panel representing the putative contaminant population (e.g. European, East Asian or African). The method is implemented in a C++ program called ’Demographic Inference with Contamination and Error’ (DICE). We applied it to simulations and genome data from ancient Neanderthals and modern humans. With reasonable levels of genome sequence coverage (> 3X), we find we can recover accurate estimates of all these parameters, even when the contamination rate is as high as 50%.