An oceanic subduction origin for Archaean granitoids revealed by silicon isotopes

Co-auteur étrangerInternational audienceModern oceanic crust is constantly produced at oceanic ridges and recycled back into the mantle at subduction zones via plate tectonics. An outstanding question in geology is whether the Earth started in a non-plate tectonic regime, and if it did, when the transition to the modern regime occurred. This is a complicated question to address because Archaean rocks lack mod-ern equivalents to anchor interpretations. Here, we present a silicon isotopic study of 4.0–2.8-Gyr-old tonalite–trondhjemite–granodiorites, as well as Palaeozoic granites and modern adakites. We show that Archaean granitoids have heavier silicon isotopic compositions than granites and adakites, regardless of melting pressure. This is best explained if Archaean granitoids were formed by melting of subducted basaltic crust enriched in sedimentary silica through interaction with seawater. Before the appearance of silica-forming organisms 0.5–0.6 billion years ago, the oceans were close to silicon saturation, which led to extensive precipitation of cherts on the seafloor. This is in contrast to modern oceans, where silica biomineralization maintains dissolved silicon at low concentration. The unique heavy silicon isotope signature of cherts has been transferred to Archaean granitoids during an oceanic subduction process, which was probably responsible for the formation of felsic rocks on Archaean emerged lands

Earth's oldest stable crust in the Pilbara Craton formed by cyclic gravitational overturns

During the early Archaean, the Earth was too hot to sustain rigid lithospheric plates subject to Wilson Cycle-style plate tectonics. Yet by that time, up to 50% of the present-day continental crust was generated. Preserved continental fragments from the early Archaean have distinct granite-dome/greenstone-keel crust that is interpreted to be the result of a gravitationally unstable stratification of felsic proto-crust overlain by denser mafic volcanic rocks, subject to reorganization by Rayleigh–Taylor flow. Here we provide age constraints on the duration of gravitational overturn in the East Pilbara Terrane. Our U–Pb ages indicate the emplacement of ~3,600–3,460-million-year-old granitoid rocks, and their uplift during an overturn event ceasing about 3,413 million years ago. Exhumation and erosion of this felsic proto-crust accompanied crustal reorganization. Petrology and thermodynamic modelling suggest that the early felsic magmas were derived from the base of thick (~43 km) basaltic proto-crust. Combining our data with regional geochronological studies unveils characteristic growth cycles on the order of 100 million years. We propose that maturation of the early crust over three of these cycles was required before a stable, differentiated continent emerged with sufficient rigidity for plate-like behaviour

A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies

One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data

A Method to Exploit the Structure of Genetic Ancestry Space to Enhance Case-Control Studies

One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data.One goal of human genetics is to understand the genetic basis of disease, a challenge for diseases of complex inheritance because risk alleles are few relative to the vast set of benign variants. Risk variants are often sought by association studies in which allele frequencies in case subjects are contrasted with those from population-based samples used as control subjects. In an ideal world we would know population-level allele frequencies, releasing researchers to focus on case subjects. We argue this ideal is possible, at least theoretically, and we outline a path to achieving it in reality. If such a resource were to exist, it would yield ample savings and would facilitate the effective use of data repositories by removing administrative and technical barriers. We call this concept the Universal Control Repository Network (UNICORN), a means to perform association analyses without necessitating direct access to individual-level control data. Our approach to UNICORN uses existing genetic resources and various statistical tools to analyze these data, including hierarchical clustering with spectral analysis of ancestry; and empirical Bayesian analysis along with Gaussian spatial processes to estimate ancestry-specific allele frequencies. We demonstrate our approach using tens of thousands of control subjects from studies of Crohn disease, showing how it controls false positives, provides power similar to that achieved when all control data are directly accessible, and enhances power when control data are limiting or even imperfectly matched ancestrally. These results highlight how UNICORN can enable reliable, powerful, and convenient genetic association analyses without access to the individual-level data