Biology subjects

Fernando Racimo

Publications and source records attributed to Fernando Racimo.

6 recordsLinked to original sources

bioRxiv2016

Signatures of archaic adaptive introgression in present-day human populations

Comparisons of DNA from archaic and modern humans show that these groups interbred, and in some cases received an evolutionary advantage from doing so. This process - adaptive introgression - may lead to a faster rate of adaptation than is predicted from models with mutation and selection alone. Within the last couple of years, a series of studies have identified regions of the genome that are likely examples of adaptive introgression. In many cases, once a region was ascertained as being introgressed, commonly used statistics based on both haplotype as well as allele frequency information were employed to test for positive selection. Introgression by itself, however, changes both the haplotype structure and the distribution of allele frequencies, thus confounding traditional tests for detecting positive selection. Therefore, patterns generated by introgression alone may lead to false inferences of positive selection. Here we explore models involving both introgression and positive selection to investigate the behavior of various statistics under adaptive introgression. In particular, we find that the number and allelic frequencies of sites that are uniquely shared between archaic humans and specific present-day populations are particularly useful for detecting adaptive introgression. We then examine the 1000 Genomes dataset to characterize the landscape of uniquely shared archaic alleles in human populations. Finally, we identify regions that were likely subject to adaptive introgression and discuss some of the most promising candidate genes located in these regions.

Evolutionary Biology↗

bioRxiv2015

Archaic adaptive introgression in TBX15/WARS2

A recent study conducted the first genome-wide scan for selection in Inuit from Greenland using SNP chip data. Here, we report that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population. The region contains two genes, WARS2 and TBX15, and has previously been associated with adipose tissue differentiation and body-fat distribution in humans. We show that the adaptively introgressed allele has been under selection in a much larger geographic region than just Greenland. Furthermore, it is associated with changes in expression of WARS2 and TBX15 in multiple tissues including the adrenal gland and subcutaneous adipose tissue, and with regional DNA methylation changes in TBX15.

Evolutionary Biology↗

bioRxiv2015

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

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%.

Preprint↗

bioRxiv2015

Testing for ancient selection using cross-population allele frequency differentiation

1A powerful way to detect selection in a population is by modeling local allele frequency changes in a particular region of the genome under scenarios of selection and neutrality, and finding which model is most compatible with the data. Chen et al. [2010] developed a composite likelihood method called XPCLR that uses an outgroup population to detect departures from neutrality which could be compatible with hard or soft sweeps, at linked sites near a beneficial allele. However, this method is most sensitive to recent selection and may miss selective events that happened a long time ago. To overcome this, we developed an extension of XP-CLR that jointly models the behavior of a selected allele in a three-population tree. Our method - called 3P-CLR - outperforms XP-CLR when testing for selection that occurred before two populations split from each other, and can distinguish between those events and events that occurred specifically in each of the populations after the split. We applied our new test to population genomic data from the 1000 Genomes Project, to search for selective sweeps that occurred before the split of Yoruba and Eurasians, but after their split from Neanderthals, and that could have led to the spread of modern-human-specific phenotypes. We also searched for sweep events that occurred in East Asians, Europeans and the ancestors of both populations, after their split from Yoruba. In both cases, we are able to confirm a number of regions identified by previous methods, and find several new candidates for selection in recent and ancient times. For some of these, we also find suggestive functional mutations that may have driven the selective events.

Evolutionary Biology↗

bioRxiv2014

Long-term balancing selection in LAD1 maintains a missense trans-species polymorphism in humans, chimpanzees and bonobos

Balancing selection maintains advantageous genetic and phenotypic diversity in populations. When selection acts for long evolutionary periods selected polymorphisms may survive species splits and segregate in present-day populations of different species. Here, we investigated the role of long-term balancing selection in the evolution of protein-coding sequences in the Pan-Homo clade. We sequenced the exome of 20 humans, 20 chimpanzees and 20 bonobos and detected eight coding trans-species polymorphisms (trSNPs) that are shared among the three species and have segregated for approximately 14 million years of independent evolution. While the majority of these trSNPs were found in three genes of the MHC cluster, we also uncovered one coding trSNP (rs12088790) in the gene LAD1. All these trSNPs show clustering of sequences by allele rather than by species and also exhibit other signatures of long-term balancing selection, such as segregating at intermediate frequency and lying in a locus with high genetic diversity. Here we focus on the trSNP in LAD1, a gene that encodes for Ladinin-1, a collagenous anchoring filament protein of basement membrane that is responsible for maintaining cohesion at the dermal-epidermal junction; the gene is also an autoantigen responsible for linear IgA disease. This trSNP results in a missense change (Leucine257Proline) and, besides altering the protein sequence, is associated with changes in gene expression of LAD1.

Evolutionary Biology↗

bioRxiv2014

Approximation to the distribution of fitness effects across functional categories in human segregating polymorphisms

Quantifying the proportion of polymorphic mutations that are deleterious or neutral is of fundamental importance to our understanding of evolution, disease genetics and the maintenance of variation genome-wide. Here, we develop an approximation to the distribution of fitness effects (DFE) of segregating single-nucleotide mutations in humans. Unlike previous methods, we do not assume that synonymous mutations are neutral or not strongly selected, and we do not rely on fitting the DFE of all new nonsynonymous mutations to a single probability distribution, which is poorly motivated on a biological level. We rely on a previously developed method that utilizes a variety of published annotations (including conservation scores, protein deleteriousness estimates and regulatory data) to score all mutations in the human genome based on how likely they are to be affected by negative selection, controlling for mutation rate. We map this score to a scale of fitness coefficients via maximum likelihood using diffusion theory and a Poisson random field model on SNP data. Our method serves to approximate the deleterious DFE of mutations that are segregating, regardless of their genomic consequence. We can then compare the proportion of mutations that are negatively selected or neutral across various categories, including different types of regulatory sites. We observe that the distribution of intergenic polymorphisms is highly peaked at neutrality, while the distribution of nonsynonymous polymorphisms is bimodal, with a neutral peak and a second peak at s {approx} -10-4. Other types of polymorphisms have shapes that fall roughly in between these two. We find that transcriptional start sites, strong CTCF-enriched elements and enhancers are the regulatory categories with the largest proportion of deleterious polymorphisms.\n\nAuthor SummaryThe relative frequencies of polymorphic mutations that are deleterious, nearly neutral and neutral is traditionally called the distribution of fitness effects (DFE). Obtaining an accurate approximation to this distribution in humans can help us understand the nature of disease and the mechanisms by which variation is maintained in the genome. Previous methods to approximate this distribution have relied on fitting the DFE of new mutations to a single probability distribution, like a normal or an exponential distribution. Generally, these methods also assume that a particular category of mutations, like synonymous changes, can be assumed to be neutral or nearly neutral. Here, we provide a novel method designed to reflect the strength of negative selection operating on any segregating site in the human genome. We use a maximum likelihood mapping approach to fit these scores to a scale of neutral and negative fitness coefficients. Finally, we compare the shape of the DFEs we obtain from this mapping for different types of functional categories. We observe the distribution of polymorphisms has a strong peak at neutrality, as well as a second peak of deleterious effects when restricting to nonsynonymous polymorphisms.

Genetics↗