scholarly journals Genome-Wide Heterogeneity of Nucleotide Substitution Model Fit

2011 ◽  
Vol 3 ◽  
pp. 896-908 ◽  
Author(s):  
Leonardo Arbiza ◽  
Mateus Patricio ◽  
Hernán Dopazo ◽  
David Posada
Keyword(s):  
Nucleotide Substitution ◽  
Model Fit ◽  
Substitution Model ◽  
Genome Wide ◽  
Nucleotide Substitution Model

scholarly journals Assessing the Applicability of the GTR Nucleotide Substitution Model through Simulations

2006 ◽  
Vol 2 ◽  
pp. 117693430600200 ◽  
Author(s):  
Laurent Gatto ◽  
Daniele Catanzaro ◽  
Michel C. Milinkovitch
Keyword(s):  
Nucleotide Substitution ◽  
Substitution Model ◽  
Nucleotide Substitution Model

scholarly journals Update of the global distribution of human gammaherpesvirus 8 genotypes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amanda de Oliveira Lopes ◽  
Natália Spitz ◽  
Christian Robson de Souza Reis ◽  
Vanessa Salete de Paula
Keyword(s):  
Nucleotide Substitution ◽  
Genotypic Diversity ◽  
Global Distribution ◽  
American Continent ◽  
Coding Region ◽  
Substitution Model ◽  
Reading Frame ◽  
Genotype F ◽  
Nucleotide Substitution Model ◽  
Genotype E

AbstractHuman gammaherpesvirus 8 (HHV-8) consists of six major clades (A–F) based on the genetic sequence of the open reading frame (ORF)-K1. There are a few conflicting reports regarding the global distribution of the different HHV-8 genotypes. This study aimed to determine the global distribution of the different HHV-8 genotypes based on phylogenetic analysis of the ORF-K1 coding region using sequences published in the GenBank during 1997–2020 and construct a phylogenetic tree using the maximum likelihood algorithm with the GTR + I + G nucleotide substitution model. A total of 550 sequences from 38 countries/origins were analysed in this study. Genotypes A and C had similar global distributions and were prevalent in Africa and Europe. Genotype B was prevalent in Africa. Of the rare genotypes, genotype D was reported in East Asia and Oceania and genotype E in South America, while genotype F was prevalent in Africa. The highest genotypic diversity was reported in the American continent, with Brazil housing five HHV-8 genotypes (A, B, C, E, and F). In this study, we present update of the global distribution of HHV-8 genotypes, providing a basis for future epidemiological and evolutionary studies of HHV-8.

scholarly journals Non-B DNA: a major contributor to small- and large-scale variation in nucleotide substitution frequencies across the genome

2021 ◽  
Vol 49 (3) ◽  
pp. 1497-1516
Author(s):  
Wilfried M Guiblet ◽  
Marzia A Cremona ◽  
Robert S Harris ◽  
Di Chen ◽  
Kristin A Eckert ◽  
...  
Keyword(s):  
Large Scale ◽  
Nucleotide Substitution ◽  
Genetic Diseases ◽  
Nucleotide Polymorphisms ◽  
Dna Structures ◽  
Cellular Processes ◽  
Genome Wide ◽  
Dna Types ◽  
Flanking Regions

Abstract Approximately 13% of the human genome can fold into non-canonical (non-B) DNA structures (e.g. G-quadruplexes, Z-DNA, etc.), which have been implicated in vital cellular processes. Non-B DNA also hinders replication, increasing errors and facilitating mutagenesis, yet its contribution to genome-wide variation in mutation rates remains unexplored. Here, we conducted a comprehensive analysis of nucleotide substitution frequencies at non-B DNA loci within noncoding, non-repetitive genome regions, their ±2 kb flanking regions, and 1-Megabase windows, using human-orangutan divergence and human single-nucleotide polymorphisms. Functional data analysis at single-base resolution demonstrated that substitution frequencies are usually elevated at non-B DNA, with patterns specific to each non-B DNA type. Mirror, direct and inverted repeats have higher substitution frequencies in spacers than in repeat arms, whereas G-quadruplexes, particularly stable ones, have higher substitution frequencies in loops than in stems. Several non-B DNA types also affect substitution frequencies in their flanking regions. Finally, non-B DNA explains more variation than any other predictor in multiple regression models for diversity or divergence at 1-Megabase scale. Thus, non-B DNA substantially contributes to variation in substitution frequencies at small and large scales. Our results highlight the role of non-B DNA in germline mutagenesis with implications to evolution and genetic diseases.

Substitution Model Distance Analysis for SARS-CoV-2 in China, USA, and Europe (Preprint)

2020 ◽  
Author(s):  
Hsiuying Wang ◽  
Yi-Hau Chen
Keyword(s):  
Common Ancestor ◽  
Nucleotide Substitution ◽  
Virus Transmission ◽  
The Other ◽  
Substitution Model ◽  
Distance Analysis ◽  
Study Results ◽  
The World ◽  
Substitution Models ◽  
The Usa

BACKGROUND Coronavirus pandemic has been a wake-up call for the world. A dispute over the origin of SARS-CoV-2 has been raised. Study results showed that all SARS-CoV-2 sequences around the world sharing a common ancestor towards the end of 2019. Nevertheless, it is hard to reach conclusion regarding SARS-CoV-2 origin. OBJECTIVE In this study, we compare the divergence of SARS-CoV-2 sequences from the three areas, China, the USA, and Europe. METHODS We download SARS-CoV-2 sequences of China, USA, and Europe from the National Center for Biotechnology Information (NCBI). To investigate the diversity of these sequences from these three areas, we apply 17 different nucleotide substitution models to compare the diversity of these SARS-CoV-2 sequences. In the three groups of SARS-CoV-2 sequences, we calculate the pairwise nucleotide substitution distance of any two sequences in each group and then compare the distances in these three groups. RESULTS The analyzed results are consistent in most of the 17 substitution models. The outcomes from 14 substitution models show that China has the lowest diversity, followed by Europe and lastly by the USA. For the other 3 models, in one model, China has the lowest diversity, followed by the USA and lastly by Europe; in another model, USA has the lowest diversity, followed by China and lastly by Europe, and in the last one model, Europe has the lowest diversity, followed by China and lastly by the USA. CONCLUSIONS In this study, we compare the diversity of SARS-CoV-2 samples from China, Europe, and the USA. Different substitution models were applied to analyze the data. Our outcome shows that China has the smallest mean distance value, followed by Europe and lastly by the USA, which consists with the virus transmission time order that SARS-CoV-2 starts in China, then outbreaks in Europe and finally in the USA.

scholarly journals Natural selection influenced the genetic architecture of brain structure, behavioral and neuropsychiatric traits

2020 ◽  
Author(s):  
Frank R Wendt ◽  
Gita A Pathak ◽  
Cassie Overstreet ◽  
Daniel S Tylee ◽  
Joel Gelernter ◽  
...  
Keyword(s):  
Natural Selection ◽  
Complex Traits ◽  
Association Studies ◽  
Model Fit ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Risk Alleles ◽  
Genome Wide ◽  
Improved Model

AbstractNatural selection has shaped the phenotypic characteristics of human populations. Genome-wide association studies (GWAS) have elucidated contributions of thousands of common variants with small effects on an individual’s predisposition to complex traits (polygenicity), as well as wide-spread sharing of risk alleles across traits in the human phenome (pleiotropy). It remains unclear how the pervasive effects of natural selection influence polygenicity in brain-related traits. We investigate these effects by annotating the genome with measures of background (BGS) and positive selection, indications of Neanderthal introgression, measures of functional significance including loss-of-function (LoF) intolerant and genic regions, and genotype networks in 75 brain-related traits. Evidence of natural selection was determined using binary annotations of top 2%, 1%, and 0.5% of selection scores genome-wide. We detected enrichment (q<0.05) of SNP-heritability at loci with elevated BGS (7 phenotypes) and in genic (34 phenotypes) and LoF-intolerant regions (67 phenotypes). BGS (top 2%) significantly predicted effect size variance for trait-associated loci (σ2 parameter) in 75 brain-related traits (β=4.39×10−5, p=1.43×10−5, model r2=0.548). By including the number of DSM-5 diagnostic combinations per psychiatric disorder, we substantially improved model fit (σ2 ~ BTop2% × Genic × diagnostic combinations; model r2=0.661). We show that GWAS with larger variance in risk locus effect sizes are collectively predicted by the effects of loci under strong BGS and in regulatory regions of the genome. We further show that diagnostic complexity exacerbates this relationship and perhaps dampens the ability to detect psychiatric risk loci.

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