126 research outputs found
The date of interbreeding between Neandertals and modern humans
Comparisons of DNA sequences between Neandertals and present-day humans have
shown that Neandertals share more genetic variants with non-Africans than with
Africans. This could be due to interbreeding between Neandertals and modern
humans when the two groups met subsequent to the emergence of modern humans
outside Africa. However, it could also be due to population structure that
antedates the origin of Neandertal ancestors in Africa. We measure the extent
of linkage disequilibrium (LD) in the genomes of present-day Europeans and find
that the last gene flow from Neandertals (or their relatives) into Europeans
likely occurred 37,000-86,000 years before the present (BP), and most likely
47,000-65,000 years ago. This supports the recent interbreeding hypothesis, and
suggests that interbreeding may have occurred when modern humans carrying Upper
Paleolithic technologies encountered Neandertals as they expanded out of
Africa
Instabilities and waves in thin films of living fluids
We formulate the thin-film hydrodynamics of a suspension of polar self-driven
particles and show that it is prone to several instabilities through the
interplay of activity, polarity and the existence of a free surface. Our
approach extends, to self-propelling systems, the work of Ben Amar and Cummings
[Phys Fluids 13 (2001) 1160] on thin-film nematics. Based on our estimates the
instabilities should be seen in bacterial suspensions and the lamellipodium,
and are potentially relevant to the morphology of biofilms. We suggest several
experimental tests of our theory.Comment: 4 pages, pdflatex, accepted for publication in Phys Rev Let
dotears: Scalable, consistent DAG estimation using observational and interventional data
Learning causal directed acyclic graphs (DAGs) from data is complicated by a
lack of identifiability and the combinatorial space of solutions. Recent work
has improved tractability of score-based structure learning of DAGs in
observational data, but is sensitive to the structure of the exogenous error
variances. On the other hand, learning exogenous variance structure from
observational data requires prior knowledge of structure. Motivated by new
biological technologies that link highly parallel gene interventions to a
high-dimensional observation, we present [doo-tairs], a
scalable structure learning framework which leverages observational and
interventional data to infer a single causal structure through continuous
optimization. exploits predictable structural consequences
of interventions to directly estimate the exogenous error structure, bypassing
the circular estimation problem. We extend previous work to show, both
empirically and analytically, that the inferences of previous methods are
driven by exogenous variance structure, but is robust to
exogenous variance structure. Across varied simulations of large random DAGs,
outperforms state-of-the-art methods in structure
estimation. Finally, we show that is a provably consistent
estimator of the true DAG under mild assumptions
An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts
Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale
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Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture.
SNP-heritability is a fundamental quantity in the study of complex traits. Recent studies have shown that existing methods to estimate genome-wide SNP-heritability can yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and linkage disequilibrium (LD)-dependent genetic architectures, it remains unclear which estimates reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of genetic architecture, without specifying a heritability model or partitioning SNPs by allele frequency and/or LD. We show analytically and through extensive simulations starting from real genotypes (UK Biobank, N = 337 K) that, unlike existing methods, our closed-form estimator is robust across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach
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Cell-type-specific resolution epigenetics without the need for cell sorting or single-cell biology.
High costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types
Evidence of widespread selection on standing variation in Europe at height-associated SNPs.
Strong signatures of positive selection at newly arising genetic variants are well documented in humans(1-8), but this form of selection may not be widespread in recent human evolution(9). Because many human traits are highly polygenic and partly determined by common, ancient genetic variation, an alternative model for rapid genetic adaptation has been proposed: weak selection acting on many pre-existing (standing) genetic variants, or polygenic adaptation(10-12). By studying height, a classic polygenic trait, we demonstrate the first human signature of widespread selection on standing variation. We show that frequencies of alleles associated with increased height, both at known loci and genome wide, are systematically elevated in Northern Europeans compared with Southern Europeans (P < 4.3 × 10(-4)). This pattern mirrors intra-European height differences and is not confounded by ancestry or other ascertainment biases. The systematic frequency differences are consistent with the presence of widespread weak selection (selection coefficients ∼10(-3)-10(-5) per allele) rather than genetic drift alone (P < 10(-15))
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