Detecting Selection in Multiple Populations by Modeling Ancestral Admixture Components.

One of the most powerful and commonly used approaches for detecting local adaptation in the genome is the identification of extreme allele frequency differences between populations. In this article, we present a new maximum likelihood method for finding regions under positive selection. It is based on a Gaussian approximation to allele frequency changes and it incorporates admixture between populations. The method can analyze multiple populations simultaneously and retains power to detect selection signatures specific to ancestry components that are not representative of any extant populations. Using simulated data, we compare our method to related approaches, and show that it is orders of magnitude faster than the state-of-the-art, while retaining similar or higher power for most simulation scenarios. We also apply it to human genomic data and identify loci with extreme genetic differentiation between major geographic groups. Many of the genes identified are previously known selected loci relating to hair pigmentation and morphology, skin, and eye pigmentation. We also identify new candidate regions, including various selected loci in the Native American component of admixed Mexican-Americans. These involve diverse biological functions, such as immunity, fat distribution, food intake, vision, and hair development.

Ancestral Population Genomics with Jocx, a Coalescent Hidden Markov Model.

Coalescence theory lets us probe the past demographics of present-day genetic samples and much information about the past can be gleaned from variation in rates of coalescence event as we trace genetic lineages back in time. Fewer and fewer lineages will remain, however, so there is a limit to how far back we can explore. Without recombination, we would not be able to explore ancient speciation events because of this-any meaningful species concept would require that individuals of one species are closer related than they are to individuals of another species, once speciation is complete. Recombination, however, opens a window to the deeper past. By scanning along a genomic alignment, we get a sequential variant of the coalescence process as it looked at the time of the speciation. This pattern of coalescence times is fixed at speciation time and does not erode with time; although accumulated mutations and genomic rearrangements will eventually hide the signal, it enables us to glance at events in the past that would not be observable without recombination. So-called coalescence hidden Markov models allow us to exploit this, and in this chapter, we present the tool Jocx that uses a framework of these models to infer demographic parameters in ancient speciation events.

Natural Selection on Genes Related to Cardiovascular Health in High-Altitude Adapted Andeans.

The increase in red blood cell mass (polycythemia) due to the reduced oxygen availability (hypoxia) of residence at high altitude or other conditions is generally thought to be beneficial in terms of increasing tissue oxygen supply. However, the extreme polycythemia and accompanying increased mortality due to heart failure in chronic mountain sickness most likely reduces fitness. Tibetan highlanders have adapted to high altitude, possibly in part via the selection of genetic variants associated with reduced polycythemic response to hypoxia. In contrast, high-altitude-adapted Quechua- and Aymara-speaking inhabitants of the Andean Altiplano are not protected from high-altitude polycythemia in the same way, yet they exhibit other adaptive features for which the genetic underpinnings remain obscure. Here, we used whole-genome sequencing to scan high-altitude Andeans for signals of selection. The genes showing the strongest evidence of selection-including BRINP3, NOS2, and TBX5-are associated with cardiovascular development and function but are not in the response-to-hypoxia pathway. Using association mapping, we demonstrated that the haplotypes under selection are associated with phenotypic variations related to cardiovascular health. We hypothesize that selection in response to hypoxia in Andeans could have vascular effects and could serve to mitigate the deleterious effects of polycythemia rather than reduce polycythemia itself.

Fast admixture analysis and population tree estimation for SNP and NGS data.

MOTIVATION: Structure methods are highly used population genetic methods for classifying individuals in a sample fractionally into discrete ancestry components. CONTRIBUTION: We introduce a new optimization algorithm for the classical STRUCTURE model in a maximum likelihood framework. Using analyses of real data we show that the new method finds solutions with higher likelihoods than the state-of-the-art method in the same computational time. The optimization algorithm is also applicable to models based on genotype likelihoods, that can account for the uncertainty in genotype-calling associated with Next Generation Sequencing (NGS) data. We also present a new method for estimating population trees from ancestry components using a Gaussian approximation. Using coalescence simulations of diverging populations, we explore the adequacy of the STRUCTURE-style models and the Gaussian assumption for identifying ancestry components correctly and for inferring the correct tree. In most cases, ancestry components are inferred correctly, although sample sizes and times since admixture can influence the results. We show that the popular Gaussian approximation tends to perform poorly under extreme divergence scenarios e.g. with very long branch lengths, but the topologies of the population trees are accurately inferred in all scenarios explored. The new methods are implemented together with appropriate visualization tools in the software package Ohana. AVAILABILITY AND IMPLEMENTATION: Ohana is publicly available at https://github.com/jade-cheng/ohana . In addition to source code and installation instructions, we also provide example work-flows in the project wiki site. CONTACT: jade.cheng@birc.au.dk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Extreme genomic erosion after recurrent demographic bottlenecks in the highly endangered Iberian lynx.

BACKGROUND: Genomic studies of endangered species provide insights into their evolution and demographic history, reveal patterns of genomic erosion that might limit their viability, and offer tools for their effective conservation. The Iberian lynx (Lynx pardinus) is the most endangered felid and a unique example of a species on the brink of extinction. RESULTS: We generate the first annotated draft of the Iberian lynx genome and carry out genome-based analyses of lynx demography, evolution, and population genetics. We identify a series of severe population bottlenecks in the history of the Iberian lynx that predate its known demographic decline during the 20th century and have greatly impacted its genome evolution. We observe drastically reduced rates of weak-to-strong substitutions associated with GC-biased gene conversion and increased rates of fixation of transposable elements. We also find multiple signatures of genetic erosion in the two remnant Iberian lynx populations, including a high frequency of potentially deleterious variants and substitutions, as well as the lowest genome-wide genetic diversity reported so far in any species. CONCLUSIONS: The genomic features observed in the Iberian lynx genome may hamper short- and long-term viability through reduced fitness and adaptive potential. The knowledge and resources developed in this study will boost the research on felid evolution and conservation genomics and will benefit the ongoing conservation and management of this emblematic species.

Ancestral population genomics using coalescence hidden Markov models and heuristic optimisation algorithms.

With full genome data from several closely related species now readily available, we have the ultimate data for demographic inference. Exploiting these full genomes, however, requires models that can explicitly model recombination along alignments of full chromosomal length. Over the last decade a class of models, based on the sequential Markov coalescence model combined with hidden Markov models, has been developed and used to make inference in simple demographic scenarios. To move forward to more complex demographic modelling we need better and more automated ways of specifying these models and efficient optimisation algorithms for inferring the parameters in complex and often high-dimensional models. In this paper we present a framework for building such coalescence hidden Markov models for pairwise alignments and present results for using heuristic optimisation algorithms for parameter estimation. We show that we can build more complex demographic models than our previous frameworks and that we obtain more accurate parameter estimates using heuristic optimisation algorithms than when using our previous gradient based approaches. Our new framework provides a flexible way of constructing coalescence hidden Markov models almost automatically. While estimating parameters in more complex models is still challenging we show that using heuristic optimisation algorithms we still get a fairly good accuracy.