Biology subjects

Chikhi, L.

Publications and source records attributed to Chikhi, L..

3 recordsLinked to original sources

bioRxiv2018

The IICR and the Non-Stationary Structured Coalescent: demographic inference with arbitrary changes in population structure

In the last years, a wide range of methods allowing to reconstruct past population size changes from genome-wide data have been developed. At the same time, there has been an increasing recognition that population structure can generate genetic data similar to those produced under models of population size change. Recently, Mazet et al. (2016) showed that, for any model of population structure, it is always possible to find a panmictic model with a particular function of population size changes, having exactly the same distribution of T2 (the coalescence time for a sample of size two) to that of the structured model. They called this function IICR (Inverse Instantaneous Coalescence Rate) and showed that it does not necessarily correspond to population size changes under non panmictic models. Besides, most of the methods used to analyse data under models of population structure tend to arbitrarily fix that structure and to minimise or neglect population size changes. Here we extend the seminal work of Herbots (1994) on the structured coalescent and propose a new framework, the Non-Stationary Structured Coalescent (NSSC) that incorporates demographic events (changes in gene flow and/or deme sizes) to models of nearly any complexity. We show how to compute the IICR under a wide family of stationary and non-stationary models. As an example we address the question of human and Neanderthal evolution and discuss how the NSSC framework allows to interpret genomic data under this new perspective.\n\nAuthor summaryGenomic data are becoming available for a rapidly increasing number of species, and contain information about their recent evolutionary history. If we wish to understand how they expanded, contracted or admixed as a consequence of recent and ancient environmental changes, we need to develop general inferential methods. Currently, demographic inference is either done assuming that a species is a single panmictic population or using arbitrary structured models. We use the concept of IICR (Inverse of the Instantaneous Coalescence Rate) together with Markov chains theory to develop a general inferential framework which we call the Non-Stationary Structured Coalescent and apply it to explain human and Neanderthal genomic data in a single structured model.

evolutionary biology↗

bioRxiv2017

Mouse lemurs and degraded habitat

Madagascar is known for its unique biodiversity including its endemic primates, the lemurs. This biodiversity is threatened by deforestation, forest degradation and anthropogenic disturbances. Several mouse lemurs (genus Microcebus) have been shown to cope with habitat disturbances and degradation. However, there are 24 recognized mouse lemur species living in very different habitats, and it is not clear whether all these species respond similarly to forest degradation. Here, we review the literature on mouse lemur use of degraded habitat. We further question whether mouse lemurs show variation in degraded habitat use, with respect to forest type, conservation status and distribution range. We show that data on degraded forest (DF) use is available for 14 species and geographically aggregated in a few locations. However, data are scarce for most species, and lacking for almost half of the currently recognized species. Our results however confirm that most mouse lemur species are able to cope with, but do not necessarily respond positively to habitat degradation. We found no variation in degraded habitat use, with respect to forest type, conservation status and distribution range. However, we identified food resources availability, understory structure, predation, and tree hole availability to be the most frequently invoked factors potentially influencing DF use. The relative frequency of these four factors vary among forest types suggesting that differences may exist but still require research efforts for ecological and environmental differences among regions to be fully understood.\n\nRESEARCH HIGHLIGHTSO_LILittle differences in the use of degraded forest (DF) between forest types, distribution ranges or conservation status.\nC_LIO_LIVarying factors potentially affecting DF use, such as food resources, forest structure, tree hole availability and predation.\nC_LI

ecology↗

bioRxiv2017

The genetic legacy of Zoroastrianism in Iran and India: Insights into population structure, gene flow and selection.

Zoroastrianism is one of the oldest extant religions in the world, originating in Persia (present-day Iran) during the second millennium BCE. Historical records indicate that migrants from Persia brought Zoroastrianism to India, but there is debate over the timing of these migrations. Here we present novel genome-wide autosomal, Y-chromosome and mitochondrial data from Iranian and Indian Zoroastrians and neighbouring modern-day Indian and Iranian populations to conduct the first genome-wide genetic analysis in these groups. Using powerful haplotype-based techniques, we show that Zoroastrians in Iran and India show increased genetic homogeneity relative to other sampled groups in their respective countries, consistent with their current practices of endogamy. Despite this, we show that Indian Zoroastrians (Parsis) intermixed with local groups sometime after their arrival in India, dating this mixture to 690-1390 CE and providing strong evidence that the migrating group was largely comprised of Zoroastrian males. By exploiting the rich information in DNA from ancient human remains, we also highlight admixture in the ancestors of Iranian Zoroastrians dated to 570 BCE-746 CE, older than admixture seen in any other sampled Iranian group, consistent with a long-standing isolation of Zoroastrians from outside groups. Finally, we report genomic regions showing signatures of positive selection in present-day Zoroastrians that might correlate to the prevalence of particular diseases amongst these communities.

genomics↗