Biology subjects

Gravel, S.

Publications and source records attributed to Gravel, S..

5 recordsLinked to original sources

bioRxiv2018

Revealing multi-scale population structure in large cohorts

Genetic structure in large cohorts results from technical, sampling and demographic variation. Visualisation is therefore a first step in most genomic analyses. However, existing data exploration methods struggle with unbalanced sampling and the many scales of population structure. We investigate an approach to dimension reduction of genomic data that combines principal components analysis (PCA) with uniform manifold approximation and projection (UMAP) to succinctly illustrate population structure in large cohorts and capture their relationships on local and global scales. Using data from large-scale genomic datasets, we demonstrate that PCA-UMAP effectively clusters closely related individuals while placing them in a global continuum of genetic variation. This approach reveals previously overlooked subpopulations within the American Hispanic population and fine-scale relationships between geography, genotypes, and phenotypes in the UK population. This opens new lines of investigation for demographic research and statistical genetics. Given its small computational cost, PCA-UMAP also provides a general-purpose approach to exploratory analysis in population-scale datasets. Author summaryBecause of geographic isolation, individuals tend to be more genetically related to people living nearby than to people living far. This is an example of population structure, a situation where a large population contains subgroups that share more than the average amount of DNA. This structure can tell us about human history, and it can also have a large effect on medical studies. We use a newly developed method (UMAP) to visualize population structure from three genomic datasets. Using genotype data alone, we reveal numerous subgroups related to ancestry and correlated with traits such as white blood cell count, height, and FEV1, a measure used to detect airway obstruction. We demonstrate that UMAP reveals previously unobserved patterns and fine-scale structure. We show that visualizations work especially well in large datasets containing populations with diverse backgrounds, which are rapidly becoming more common, and that unlike other visualization methods, we can preserve intuitive connections between populations that reflect their shared ancestries. The combination of these results and the effectiveness of the strategy on large and diverse datasets make this an important approach for exploratory analysis for geneticists studying ancestral events and phenotype distributions.

genomics↗

bioRxiv2018

Perspective: Genomic inference using diffusion models and the allele frequency spectrum

Evolutionary, biological, and demographic processes combine to shape the variation observed in populations. Understanding how these processes are expected to influence variation allows us to infer past demographic events and the nature of selection in human populations. Forward models such as the diffusion approximation provide a powerful tool for analyzing the distribution of allele frequencies in contemporary populations due to their computational tractability and model flexibility. Here, we discuss recent computational developments and their application to reconstructing human demographic history and patterns of selection at new mutations. We also reexamine how some classical assumptions that are still commonly used in inference studies fare when applied to modern data. We use whole-genome sequence data for 797 French Canadian individuals to examine the neutrality of synonymous sites. We find that selection can lead to strong biases in the inferred demography, mutation rate, and distributions of fitness effects. We use these distributions of fitness effects together with demographic and phenotype-fitness models to predict the relationship between effect size and allele frequency, and contrast those predictions to commonly used models in statistical genetics. Thus the simple evolutionary models investigated by Kimura and Ohta still provide important insight into modern genetic research.

genetics↗

bioRxiv2017

On the decidability of population size histories from finite allele frequency spectra

Understanding the historical events that shaped current genomic diversity has applications in historical, biological, and medical research. However, the amount of historical information that can be inferred from genetic data is finite, which leads to an identifiability problem. For example, different historical processes can lead to identical distribution of allele frequencies. This identifiability issue casts a shadow of uncertainty over the results of any study which uses the frequency spectrum to infer past demography. It has been argued that imposing mild reasonableness constraints on demographic histories can enable unique reconstruction, at least in an idealized setting where the length of the genome is nearly infinite. Here, we discuss this problem for finite sample size and genome length. Using the diffusion approximation, we obtain bounds on likelihood differences between similar demographic histories, and use them to construct pairs of very different reasonable histories that produce almost-identical frequency distributions. The finite-genome problem therefore remains poorly determined even among reasonable histories. Where fits to few-parameter models produce narrow parameter confidence intervals, large uncertainties lurk hidden by model assumption.

genetics↗

bioRxiv2017

Genetic Diversity in Circulating Tumor Cell Clusters

Genetic diversity plays a central role in tumor progression, metastasis, and resistance to treatment. Experiments are shedding light on this diversity at ever finer scales, but interpretation is challenging. Using recent progress in numerical models, we simulate macroscopic tumors to investigate the interplay between growth dynamics, microscopic composition, and circulating tumor cell cluster diversity. We find that modest differences in growth parameters can profoundly change microscopic diversity. Simple outwards expansion leads to spatially segregated clones and low diversity, as expected. However, a modest cell turnover can result in an increased number of divisions and mixing among clones resulting in increased microscopic diversity in the tumor core. Using simulations to estimate power to detect such spatial trends, we find that multiregion sequencing data from contemporary studies is marginally powered to detect the predicted effects. Slightly larger samples, improved detection of rare variants, or sequencing of smaller biopsies or circulating tumor cell clusters would allow one to distinguish between leading models of tumor evolution. The genetic composition of circulating tumor cell clusters, which can be obtained from non-invasive blood draws, is therefore informative about tumor evolution and its metastatic potential.\n\nHighlightsO_LINumerical and theoretical models show interaction of front expansion, mutation, and clonal mixing in shaping tumor heterogeneity.\nC_LIO_LICell turnover increases intratumor heterogeneity.\nC_LIO_LISimulated circulating tumor cell clusters and microbiopsies exhibit substantial diversity with strong spatial trends.\nC_LIO_LISimulations suggest attainable sampling schemes able to distinguish between prevalent tumor growth models.\nC_LI

cancer biology↗

bioRxiv2017

Inferring the Joint Demographic History of Multiple Populations: Beyond the Diffusion Approximation

Understanding variation in allele frequencies across populations is a central goal of population genetics. Classical models for the distribution of allele frequencies, using forward simulation, coalescent theory, or the diffusion approximation, have been applied extensively for demographic inference, medical study design, and evolutionary studies. Here we propose a tractable model of ordinary differential equations for the evolution of allele frequencies that is closely related to the diffusion approximation but avoids many of its limitations and approximations. We show that the approach is typically faster, more numerically stable, and more easily generalizable than the state-of-the-art software implementation of the diffusion approximation. We present a number of applications to human sequence data, including demographic inference with a five-population joint frequency spectrum and a discussion of the transferability of demographic histories across populations.

evolutionary biology↗