Hasil untuk "Genetics"

B. Sun, Joshua Chiou, M. Traylor et al.

The Pharma Proteomics Project generates the largest open-access plasma proteomics dataset to date, offering insights into trans protein quantitative trait loci across multiple biological domains, and highlighting genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks. The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand–receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public–private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics^ 1 .

K. Tamura, G. Stecher, Sudhir Kumar

Abstract The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for building timetrees of species, pathogens, and gene families using rapid relaxed-clock methods. Methods for estimating divergence times and confidence intervals are implemented to use probability densities for calibration constraints for node-dating and sequence sampling dates for tip-dating analyses. They are supported by new options for tagging sequences with spatiotemporal sampling information, an expanded interactive Node Calibrations Editor, and an extended Tree Explorer to display timetrees. Also added is a Bayesian method for estimating neutral evolutionary probabilities of alleles in a species using multispecies sequence alignments and a machine learning method to test for the autocorrelation of evolutionary rates in phylogenies. The computer memory requirements for the maximum likelihood analysis are reduced significantly through reprogramming, and the graphical user interface has been made more responsive and interactive for very big data sets. These enhancements will improve the user experience, quality of results, and the pace of biological discovery. Natively compiled graphical user interface and command-line versions of MEGA11 are available for Microsoft Windows, Linux, and macOS from www.megasoftware.net.

C. Neftel, J. Laffy, M. Filbin et al.

Diverse genetic, epigenetic, and developmental programs drive glioblastoma, an incurable and poorly understood tumor, but their precise characterization remains challenging. Here, we use an integrative approach spanning single-cell RNA-sequencing of 28 tumors, bulk genetic and expression analysis of 401 specimens from the The Cancer Genome Atlas (TCGA), functional approaches, and single-cell lineage tracing to derive a unified model of cellular states and genetic diversity in glioblastoma. We find that malignant cells in glioblastoma exist in four main cellular states that recapitulate distinct neural cell types, are influenced by the tumor microenvironment, and exhibit plasticity. The relative frequency of cells in each state varies between glioblastoma samples and is influenced by copy number amplifications of the CDK4, EGFR, and PDGFRA loci and by mutations in the NF1 locus, which each favor a defined state. Our work provides a blueprint for glioblastoma, integrating the malignant cell programs, their plasticity, and their modulation by genetic drivers.

Sudhir Kumar, G. Stecher, Michael Li et al.

Sudhir Kumar, G. Stecher, K. Tamura

K. Tamura, G. Stecher, D. Peterson et al.

G. Acquaah

So far in this course we have dealt entirely either with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus or with the evolution of molecular sequences. Even last week when we were dealing with population genomic data, data from hundreds or thousands of loci, we were treating the variation at each locus separately and combining results across loci. I have some old notes on gametic disequilibrium and how allele frequencies change at two loci simultaneously, but they’re in the “Old notes, no longer updated” section of the book version of these notes (https://figshare.com/articles/journal_contribution/Lecture_notes_ in_population_genetics/100687), and we didn’t discuss them. In every example we’ve considered so far we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

K. Tamura, D. Peterson, N. Peterson et al.

K. Tamura, J. Dudley, M. Nei et al.

Jeffrey H. Miller

M. Nei

Sudhir Kumar, K. Tamura, M. Nei

N. Munakata

Sudhir Kumar, K. Tamura, I. B. Jakobsen et al.

F. Sherman, G. Fink, J. Hicks

Cedric A. B. Smith, D. Falconer

D. Hartl, A. Clark

J. Crow, M. Kimura

W. Ewens

C. Fletcher, K. Unni, F. Mertens