No abstract

The increasing number of non-model organism scaffold-level genome assemblies provides new information on biodiversity and how evolutionary processes have shaped it (Ellegren, 2014). An essential part of genome assembly and annotation is the identification of autosomes and sex chromosomes. Vertebrate species are generally diploid with the majority of their genome represented by a variable number of autosomes and two sex chromosomes (Graves, 2008).In mammals, the homogametic sex is the female (XX) and the heterogametic sex is the male (XY). This is opposite in birds, where males are homogametic (ZZ) and females heterogametic (ZW). Due to their inheritance, sex chromosomes differ from autosomes in several aspects of their population genetics and molecular evolution,

Model based methods for genetic clustering of individuals, such as those implemented in structure or ADMIXTURE, allow the user to infer individual ancestries and study population structure. The underlying model makes several assumptions about the demographic history that shaped the analysed genetic data. One assumption is that all individuals are a result of K homogeneous ancestral populations that are all well represented in the data, while another assumption is that no drift happened after the admixture event. The histories of many real world populations do not conform to that model, and in that case taking the inferred admixture proportions at face value might be misleading. We propose a method to evaluate the fit of admixture models based on estimating the correlation of the residual difference between the true genotypes and the genotypes predicted by the model. When the model assumptions are not violated, the residuals from a pair of individuals are not correlated. In the case of a bad fitting admixture model, individuals with similar demographic histories have a positive correlation of their residuals. Using simulated and real data, we show how the method is able to detect a bad fit of inferred admixture proportions due to using an insufficient number of clusters K or to demographic histories that deviate significantly from the admixture model assumptions, such as admixture from ghost populations, drift after admixture events and nondiscrete ancestral populations. We have implemented the method as an open source software that can be applied to both unphased genotypes and low depth sequencing data.

African wild pigs have a contentious evolutionary and biogeographic history. Until recently, desert warthog (Phacochoerus aethiopicus) and common warthog (P. africanus) were considered a single species. Molecular evidence surprisingly suggested they diverged at least 4.4 million years ago, and possibly outside of Africa. We sequenced the first whole-genomes of four desert warthogs and 35 common warthogs from throughout their range. We show that these two species diverged much later than previously estimated, 400,000–1,700,000 years ago depending on assumptions of gene flow. This brings it into agreement with the paleontological record. We found that the common warthog originated in western Africa and subsequently colonized eastern and southern Africa. During this range expansion, the common warthog interbred with the desert warthog, presumably in eastern Africa, underlining this region’s importance in African biogeography. We found that immune system–related genes may have adaptively introgressed into common warthogs, indicating that resistance to novel diseases was one of the most potent drivers of evolution as common warthogs expanded their range. Hence, we solve some of the key controversies surrounding warthog evolution and reveal a complex evolutionary history involving range expansion, introgression, and adaptation to new diseases.

Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.