scholarly journals The first set of universal nuclear protein-coding loci markers for avian phylogenetic and population genetic studies

2018 ◽  
Author(s):  
Yang Liu ◽  
Simin Liu ◽  
Chia-Fen Yeh ◽  
Nan Zhang ◽  
Guoling Chen ◽  
...  
Keyword(s):  
Population Genetic ◽  
Nuclear Protein ◽  
Bird Species ◽  
Resolving Power ◽  
Z Chromosome ◽  
Species Trees ◽  
Success Rates ◽  
Genetic Studies ◽  
Protein Coding ◽  
Population Demographic

AbstractMultiple nuclear markers provide genetic polymorphism data for molecular systematics and population genetic studies. They are especially required for the coalescent-based analyses that can be used to accurately estimate species trees and infer population demographic histories. However, in avian evolutionary studies, these powerful coalescent-based methods are hindered by the lack of a sufficient number of markers. In this study, we designed PCR primers to amplify 136 nuclear protein-coding loci (NPCLs) by scanning the published Red Junglefowl (Gallus gallus) and Zebra Finch (Taeniopygia guttata) genomes. To test their utility, we amplified these loci in 41 bird species representing 23 Aves orders. The sixty-three best-performing NPCLs, based on high PCR success rates, were selected which had various mutation rates and were evenly distributed across 17 avian autosomal chromosomes and the Z chromosome. To test phylogenetic resolving power of these markers, we conducted a Neoavian phylogenies analysis using 63 concatenated NPCL markers derived from 48 whole genomes of birds. The resulting phylogenetic topology, to a large extent, is congruence with results resolved by previous whole genome data. To test the level of intraspecific polymorphism in these makers, we examined the genetic diversity in four populations of the Kentish Plover (Charadrius alexandrinus) at 17 of NPCL markers chosen at random. Our results showed that these NPCL markers exhibited a level of polymorphism comparable with mitochondrial loci. Therefore, this set of pan-avian nuclear protein-coding loci has great potential to facilitate studies in avian phylogenetics and population genetics.

scholarly journals The first set of universal nuclear protein-coding loci markers for avian phylogenetic and population genetic studies

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Yang Liu ◽  
Simin Liu ◽  
Chia-Fen Yeh ◽  
Nan Zhang ◽  
Guoling Chen ◽  
...  
Keyword(s):  
Population Genetic ◽  
Nuclear Protein ◽  
Genetic Studies ◽  
Protein Coding

scholarly journals Population Genomics of the Foothill Yellow-Legged Frog (Rana boylii) and RADseq Parameter Choice for Large-Genome Organisms

2017 ◽  
Author(s):  
Evan McCartney-Melstad ◽  
Müge Gidiş ◽  
H. Bradley Shaffer
Keyword(s):  
Sierra Nevada ◽  
Population Genetic ◽  
Population Genomics ◽  
Allelic Variation ◽  
Sequence Similarity ◽  
Resolving Power ◽  
Coast Range ◽  
Genetic Studies ◽  
Large Genome ◽  
Rana Boylii

AbstractGenomic data are useful for attaining high resolution in population genetic studies and have become increasingly available for answering questions in biological conservation. We analyzed RADseq data for the protected foothill yellow-legged frog (Rana boylii) throughout its native range in California and Oregon, including many of the same localities included in an earlier study based on mitochondrial DNA. We recovered five primary clades that correspond to geographic regions within California and Oregon, with better resolution and more spatially consistent patterns than the previous study, confirming the increased resolving power of genomic approaches compared to single-locus analyses. Bayesian clustering, PCA and population differentiation with admixture analyses all indicated that approximately half the range of R. boylii consists of a single, relatively uniform population, while regions in the Sierra Nevada and Central Coast Range of California are deeply differentiated genetically. Additionally, a major methodological challenge for large genome organisms, including many amphibians, is deciding on sequence similarity clustering thresholds for population genetic analyses using RADseq data, and we develop a novel set of metrics that allow researchers to set a sequence similarity threshold that maximizes the separation of paralogous regions while minimizing the oversplitting of naturally occurring allelic variation within loci.

scholarly journals MIC16 gene represents a potential novel genetic marker for population genetic studies of Toxoplasma gondii

2016 ◽  
Vol 16 (1) ◽  
Author(s):  
Wen-Ge Liu ◽  
Xiao-Pei Xu ◽  
Jia Chen ◽  
Qian-Ming Xu ◽  
Si-Long Luo ◽  
...  
Keyword(s):  
Toxoplasma Gondii ◽  
Genetic Marker ◽  
Population Genetic ◽  
Genetic Studies

scholarly journals Population genetic studies on indigenous conifers in Korea

2006 ◽  
Vol 2 (2) ◽  
pp. 137-148
Author(s):  
S. W. Lee ◽  
Y. P. Hong ◽  
H. Y. Kwon ◽  
Z. S. Kim
Keyword(s):  
Population Genetic ◽  
Genetic Studies

scholarly journals Why most Principal Component Analyses (PCA) in population genetic studies are wrong

2021 ◽  
Author(s):  
Eran Elhaik
Keyword(s):  
Population Genetics ◽  
Population Genetic ◽  
Genetic Model ◽  
Principal Component ◽  
Population Data ◽  
Test Cases ◽  
Validity Of Results ◽  
Genetic Studies ◽  
Minimal Loss ◽  
Admixture Population

Principal Component Analysis (PCA) is a multivariate analysis that allows reduction of the complexity of datasets while preserving data's covariance and visualizing the information on colorful scatterplots, ideally with only a minimal loss of information. PCA applications are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics), implemented in well-cited packages like EIGENSOFT and PLINK. PCA outcomes are used to shape study design, identify and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, whereabouts, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We employed an intuitive color-based model alongside human population data for eleven common test cases. We demonstrate that PCA results are artifacts of the data and that they can be easily manipulated to generate desired outcomes. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns on the validity of results reported in the literature of population genetics and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations. An alternative mixed-admixture population genetic model is discussed.

scholarly journals SINE jumping contributes to large-scale polymorphisms in the pig genomes

2021 ◽  
Author(s):  
Cai Chen ◽  
Enrico D'Alessandro ◽  
Eduard Murani ◽  
Yao Zheng ◽  
Domenico Giosa ◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Population Genetic ◽  
Large Scale ◽  
Structural Variations ◽  
Population Genetic Analysis ◽  
Protein Coding ◽  
Pig Breeds ◽  
A Genome ◽  
Trait Locus ◽  
And Cluster Analysis

Abstract Background: Molecular markers based on retrotransposon insertion polymorphisms (RIPs) have been developed and are widely used in plants and animals. Short interspersed nuclear elements (SINEs) exert wide impacts on gene activity and even on phenotypes. However, SINE RIP profiles in livestock remain largely unknown, and not be revealed in pigs. Results: Our data revealed that SINEA1 displayed the most polymorphic insertions (22.5% intragenic and 26.5% intergenic), followed by SINEA2 (10.5% intragenic and 9% intergenic) and SINEA3 (12.5% intragenic and 5.0% intergenic). We developed a genome-wide SINE RIP mining protocol and obtained a large number of SINE RIPs (36,284), with over 80% accuracy and an even distribution in chromosomes (14.5/Mb), and 74.34% of SINE RIPs generated by SINEA1 element. Over 65% of pig SINE RIPs overlap with genes, with significant enrichment in the first and second introns of protein-coding and long non-coding RNA genes. Nearly half of the RIPs are common in these pig breeds. Sixteen SINE RIPs were applied for population genetic analysis in 23 pig breeds, the phylogeny tree and cluster analysis were generally consistent with the geographical distributions of native pig breeds in China. Conclusions: Our analysis revealed that SINEA1–3 elements, particularly SINEA1, are high polymorphic across different pig breeds, and generate large-scale structural variations in the pig genomes. And over 35, 000 SINE RIP markers were obtained. These data indicate that young SINE elements play important roles in creating new genetic variations and shaping the evolution of pig genome, and also provide strong evidences to support the great potential of SINE RIPs as genetic markers, which can be used for population genetic analysis and quantitative trait locus (QTL) mapping in pig.

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