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The microscopic mechanisms underpinning the spontaneous surface passivation of metals from ubiquitous water have remained largely elusive. Here, using in situ environmental electron microscopy to atomically monitor the reaction dynamics between aluminum surfaces and water vapor, we provide direct experimental evidence that the surface passivation results in a bilayer oxide film consisting of a crystalline-like Al(OH)₃top layer and an inner layer of amorphous Al₂O₃. The Al(OH)₃layer maintains a constant thickness of ~5.0 Å, while the inner Al₂O₃layer grows at the Al₂O₃/Al interface to a limiting thickness. On the basis of experimental data and atomistic modeling, we show the tunability of the dissociation pathways of H₂O molecules with the Al, Al₂O₃, and Al(OH)₃surface terminations. The fundamental insights may have practical significance for the design of materials and reactions for two seemingly disparate but fundamentally related disciplines of surface passivation and catalytic H₂production from water. 

Abstract Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or ‘fill-in’ missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets. 

Abstract Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes. 

Some correlations between human traits can be explained by cross-trait assortative mating and not purely genetics. 

Abstract Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6, and GM11545 with bone mineral density, and Psmb9 with weight. However, replication at a nominal threshold of 0.05 between the two component studies was low, with less than one-third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner’s Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Leveraging these observations, we integrated information about replication rates, study-specific heterogeneity, and Winner’s Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study. 

Abstract Despite the ubiquitous presence of passivation on most metal surfaces, the microscopic‐level picture of how surface passivation occurs has been hitherto unclear. Using the canonical example of the surface passivation of aluminum, here in situ atomistic transmission electron microscopy observations and computational modeling are employed to disentangle entangled microscopic processes and identify the atomic processes leading to the surface passivation. Based on atomic‐scale observations of the layer‐by‐layer expansion of the metal lattice and its subsequent transformation into the amorphous oxide, it is shown that the surface passivation occurs via a two‐stage oxidation process, in which the first stage is dominated by intralayer atomic shuffling whereas the second stage is governed by interlayer atomic disordering upon the progressive oxygen uptake. The first stage can be bypassed by increasing surface defects to promote the interlayer atomic migration that results in direct amorphization of multiple atomic layers of the metal lattice. The identified two‐stage reaction mechanism and the effect of surface defects in promoting interlayer atomic shuffling can find broader applicability in utilizing surface defects to tune the mass transport and passivation kinetics, as well as the composition, structure, and transport properties of the passivation films.