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- 2019
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Journal article (peer-reviewed)
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| 3. |
- Wang, Li-San, et al.
(author)
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Rarity of the Alzheimer Disease-Protective APP A673T Variant in the United States.
- 2015
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In: JAMA neurology. - : American Medical Association (AMA). - 2168-6157 .- 2168-6149. ; 72:2
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Journal article (peer-reviewed)abstract
- Recently, a rare variant in the amyloid precursor protein gene (APP) was described in a population from Iceland. This variant, in which alanine is replaced by threonine at position 673 (A673T), appears to protect against late-onset Alzheimer disease (AD). We evaluated the frequency of this variant in AD cases and cognitively normal controls to determine whether this variant will significantly contribute to risk assessment in individuals in the United States.
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| 4. |
- Bouckaert, R., et al.
(author)
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BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis
- 2019
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In: Plos Computational Biology. - : Public Library of Science (PLoS). - 1553-7358. ; 15:4
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Journal article (peer-reviewed)abstract
- Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release. Author summary Bayesian phylogenetic inference methods have undergone considerable development in recent years, and joint modelling of rich evolutionary data, including genomes, phenotypes and fossil occurrences is increasingly common. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing scientific software is increasingly crucial to advancement in many fields of biology. The challenges range from practical software development and engineering, distributed team coordination, conceptual development and statistical modelling, to validation and testing. BEAST 2 is one such computational software platform for phylogenetics, population genetics and phylodynamics, and was first announced over 4 years ago. Here we describe the full range of new tools and models available on the BEAST 2.5 platform, which expand joint evolutionary inference in many new directions, especially for joint inference over multiple data types, non-tree models and complex phylodynamics.
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- Shard, A. G., et al.
(author)
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Measuring Compositions in Organic Depth Profiling: Results from a VAMAS Interlaboratory Study
- 2015
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In: Journal of Physical Chemistry B. - : American Chemical Society (ACS). - 1520-6106 .- 1520-5207. ; 119:33, s. 10784-10797
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Journal article (peer-reviewed)abstract
- We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) interlaboratory study on the measurement of composition in organic depth profiling. Layered samples with known binary compositions of Irganox 1010 and either Irganox 1098 or Fmoc-pentafluoro-L-phenylalanine in each layer were manufactured in a single batch and distributed to more than 20 participating laboratories. The samples were analyzed using argon cluster ion sputtering and either X-ray photoelectron spectroscopy (XPS) or time-of-flight secondary ion mass spectrometry (ToF-SIMS) to generate depth profiles. Participants were asked to estimate the volume fractions in two of the layers and were provided with the compositions of all other layers. Participants using XPS provided volume fractions within 0.03 of the nominal values. Participants using ToF-SIMS either made no attempt, or used various methods that gave results ranging in error from 0.02 to over 0.10 in volume fraction, the latter representing a 50% relative error for a nominal volume fraction of 0.2. Error was predominantly caused by inadequacy in the ability to compensate for primary ion intensity variations and the matrix effect in SIMS. Matrix effects in these materials appear to be more pronounced as the number of atoms in both the primary analytical ion and the secondary ion increase. Using the participants' data we show that organic SIMS matrix effects can be measured and are remarkably consistent between instruments. We provide recommendations for identifying and compensating for matrix effects. Finally, we demonstrate, using a simple normalization method, that virtually all ToF-SIMS participants could have obtained estimates of volume fraction that were at least as accurate and consistent as XPS.
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- Morris, John A, et al.
(author)
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An atlas of genetic influences on osteoporosis in humans and mice.
- 2019
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In: Nature genetics. - : Springer Science and Business Media LLC. - 1546-1718 .- 1061-4036. ; 51, s. 258-266
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Journal article (peer-reviewed)abstract
- Osteoporosis is a common aging-related disease diagnosed primarily using bone mineral density (BMD). We assessed genetic determinants of BMD as estimated by heel quantitative ultrasound in 426,824 individuals, identifying 518 genome-wide significant loci (301 novel), explaining 20% of its variance. We identified 13 bone fracture loci, all associated with estimated BMD (eBMD), in ~1.2 million individuals. We then identified target genes enriched for genes known to influence bone density and strength (maximum odds ratio (OR) = 58, P = 1 × 10-75) from cell-specific features, including chromatin conformation and accessible chromatin sites. We next performed rapid-throughput skeletal phenotyping of 126 knockout mice with disruptions in predicted target genes and found an increased abnormal skeletal phenotype frequency compared to 526 unselected lines (P < 0.0001). In-depth analysis of one gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This genetic atlas provides evidence linking associated SNPs to causal genes, offers new insight into osteoporosis pathophysiology, and highlights opportunities for drug development.
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