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Biology subjects

Loya, H.

Publications and source records attributed to Loya, H..

3 recordsLinked to original sources

Genome-wide genealogies reveal deep admixtures forming modern humans

Over the past decade, genomic modelling has revealed a rich tapestry of admixtures shaping present-day human populations. These have largely focused on the past few thousand years, when ancestral populations are either well characterised by present-day genomic diversity or directly observed through ancient DNA. Genomic modelling and fossil evidence have so far only provided a fragmented picture of the coexistence and mixing of human groups in the deeper past. Here, we propose a new method, GhostBuster, that leverages inferred genome-wide genealogies to detect admixture events of unsampled ghost populations, while simultaneously inferring accurate local ancestry. Local ancestry enables us to identify ancestry-specific genomic signatures that independently corroborate the events. We identify at least three waves of "back-to-Africa" migrations starting [~]14,000 years ago. Applying GhostBuster to deeper timescales reveals that modern humans were shaped by repeated episodes of mixture. Around 50,000 years ago, we identify a human lineage that expanded to form present-day non-Africans, while also expanding within Africa, mixing with the other local African group in varying proportions. These ancient groups help explain polygenic score portability differences within Africa, and exhibit differences in population size and recombination landscapes. Extending our analysis further back to between 300,000 and 1 million years ago reveals two deeply diverged ancestral lineages. These lineages evolved profoundly different recombination landscapes, with different PRDM9 alleles (PRDM9-A and C) and recombination hotspots. We demonstrate that both Neanderthals and ancestral modern humans are formed through a mixture of these two lineages, with no evidence of gene flow from the PRDM9-A-carrying group into Denisovans.

evolutionary biology↗

Fast variance component analysis using large-scale ancestral recombination graphs

Recent algorithmic advancements have enabled the inference of genome-wide ancestral recombination graphs (ARGs) from genomic data in large cohorts. These inferred ARGs provide a detailed representation of genealogical relatedness along the genome and have been shown to complement genotype imputation in complex trait analyses by capturing the effects of unobserved genomic variants. An inferred ARG can be used to construct a genetic relatedness matrix, which can be leveraged within a linear mixed model for the analysis of complex traits. However, these analyses are computationally infeasible for large datasets. We introduce a computationally efficient approach, called ARG-RHE, to estimate narrow-sense heritability and perform region-based association testing using an ARG. ARG-RHE leverages a method for computing genotype-matrix products from genealogical data in sublinear time, along with scalable randomized algorithms. This enables fast estimation of variance components and their statistical significance, supports parallel analysis of multiple quantitative traits, and facilitates other linear mixed-model analyses. We conduct extensive simulations to verify the computational efficiency, statistical power, and robustness of this approach. We then apply it to detect associations between 21,159 genes and 52 blood-related traits, using an ARG inferred from genotype data of 337,464 individuals from the UK Biobank. In these analyses, combining ARG-based and imputation-based testing yields 8% more gene-trait associations than using imputation alone, suggesting that inferred genome-wide genealogies may effectively complement genotype imputation in the analysis of complex traits.

genetics↗

Stochastic activation and bistability in a Rab GTPase regulatory network.

Rab GTPases are the central regulators of intracellular traffic. Their function relies on a conformational change triggered by nucleotide exchange and hydrolysis. While this switch is well understood for an individual protein, how Rab GTPases collectively transition between states to generate a biochemical signal in space and time is unclear. Here, we combine in vitro reconstitution experiments with theoretical modeling to study a minimal Rab5 activation network. We find that positive feedback in this network gives rise to bistable switching of Rab5 activation and provide evidence that controlling the inactive population of Rab5 on the membrane can shape the network response. Together, our findings reveal new insights into the non-equilibrium properties and general principles of biochemical signaling networks underlying the spatiotemporal organization of the cell.

systems biology↗