Background: It is common belief that all cellular life forms on earth have a common origin. This view is supported by the universality of the genetic code and the universal conservation of multiple genes, particularly those that encode key components of the translation system. A remarkable recent study claims to provide a formal, homology independent test of the Universal Common Ancestry hypothesis by comparing the ability of a common-ancestry model and a multiple-ancestry model to predict sequences of universally conserved proteins.
Results: We devised a computational experiment on a concatenated alignment of universally conserved proteins which shows that the purported demonstration of the universal common ancestry is a trivial consequence of significant sequence similarity between the analyzed proteins. The nature and origin of this similarity are irrelevant for the prediction of "common ancestry" of by the model-comparison approach. Thus, homology (common origin) of the compared proteins remains an inference from sequence similarity rather than an independent property demonstrated by the likelihood analysis.
Conclusion: A formal demonstration of the Universal Common Ancestry hypothesis has not been achieved and is unlikely to be feasible in principle. Nevertheless, the evidence in support of this hypothesis provided by comparative genomics is overwhelming.
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http://dx.doi.org/10.1186/1745-6150-5-64 | DOI Listing |
J Clin Med
January 2025
Department of Neurology, Endeavor Health, Evanston, IL 60201, USA.
: Migraine is a common neurological disorder with highly variable characteristics. While genome-wide association studies have identified genetic risk factors that implicate underlying pathways, the influence of genetic susceptibility on disease characteristics or treatment response is incompletely understood. We examined the relationships between a previously developed standardized integrative migraine polygenic genetic risk score (PRS) and migraine characteristics in a real-world, treated patient cohort.
View Article and Find Full Text PDFJCO Precis Oncol
January 2025
Karmanos Cancer Institute and Department of Oncology, Wayne State University School of Medicine, Detroit, MI.
Purpose: Although lung cancer is one of the most common malignancies, the underlying genetics regarding susceptibility remain poorly understood. We characterized the spectrum of pathogenic/likely pathogenic (P/LP) germline variants within DNA damage response (DDR) genes among lung cancer cases and controls in non-Hispanic Whites (NHWs) and African Americans (AAs).
Materials And Methods: Rare, germline variants in 67 DDR genes with evidence of pathogenicity were identified using the ClinVar database.
PLoS One
January 2025
Department of Entomology and Plant Pathology, NC State University, Raleigh, North Carolina, United States of America.
We examined the evolutionary history of Phytophthora infestans and its close relatives in the 1c clade. We used whole genome sequence data from 69 isolates of Phytophthora species in the 1c clade and conducted a range of genomic analyses including nucleotide diversity evaluation, maximum likelihood trees, network assessment, time to most recent common ancestor and migration analysis. We consistently identified distinct and later divergence of the two Mexican Phytophthora species, P.
View Article and Find Full Text PDFGenetics
January 2025
Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Leipzig 04317, Germany.
Long, identical haplotypes shared between pairs of individuals, known as identity-by-descent (IBD) segments, result from recently shared co-ancestry. Various methods have been developed to utilize IBD sharing for demographic inference in contemporary DNA data. Recent methodological advances have extended the screening for IBD segments to ancient DNA (aDNA) data, making demographic inference based on IBD also possible for aDNA.
View Article and Find Full Text PDFGenome Biol Evol
January 2025
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA 15219.
Homology is a key concept underpinning the comparison of sequences across organisms. Sequence-level homology is based on a statistical framework optimized over decades of work. Recently, computational protein structure prediction has enabled large-scale homology inference beyond the limits of accurate sequence alignment.
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