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 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 Comparative Analysis of SNP Discovery and Genotyping in Fagus sylvatica L. and Quercus robur L. Using RADseq, GBS, and ddRAD Methods

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 222
Author(s):  
Bartosz Ulaszewski ◽  
Joanna Meger ◽  
Jaroslaw Burczyk
Keyword(s):  
Population Genomics ◽  
De Novo ◽  
Genetic Studies ◽  
Genomic Libraries ◽  
Reduced Representation ◽  
Large Numbers ◽  
Broadleaved Tree Species ◽  
Fagus Sylvatica L ◽  
Reference Genomes ◽  
Future Population

Next-generation sequencing of reduced representation genomic libraries (RRL) is capable of providing large numbers of genetic markers for population genetic studies at relatively low costs. However, one major concern of these types of markers is the precision of genotyping, which is related to the common problem of missing data, which appears to be particularly important in association and genomic selection studies. We evaluated three RRL approaches (GBS, RADseq, ddRAD) and different SNP identification methods (de novo or based on a reference genome) to find the best solutions for future population genomics studies in two economically and ecologically important broadleaved tree species, namely F. sylvatica and Q. robur. We found that the use of ddRAD method coupled with SNP calling based on reference genomes provided the largest numbers of markers (28 k and 36 k for beech and oak, respectively), given standard filtering criteria. Using technical replicates of samples, we demonstrated that more than 80% of SNP loci should be considered as reliable markers in GBS and ddRAD, but not in RADseq data. According to the reference genomes’ annotations, more than 30% of the identified ddRAD loci appeared to be related to genes. Our findings provide a solid support for using ddRAD-based SNPs for future population genomics studies in beech and oak.

scholarly journals Stirred but not shaken: population and recruitment genetics of the scallop (Pecten fumatus) in Bass Strait, Australia

2016 ◽  
Vol 73 (9) ◽  
pp. 2333-2341 ◽  
Author(s):  
Jennifer R. Ovenden ◽  
Bree J. Tillett ◽  
Michael Macbeth ◽  
Damien Broderick ◽  
Fiona Filardo ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Population Genetic Structure ◽  
Population Genetic ◽  
Allelic Variation ◽  
Open Water ◽  
Marine Species ◽  
Population Connectivity ◽  
Valuable Insight ◽  
Fine Scale ◽  
Scale Population

Abstract We report population genetic structure and fine-scale recruitment processes for the scallop beds (Pecten fumatus) in Bass Strait and the eastern coastline of Tasmania in southern Australia. Conventional population pairwise FST analyses are compared with novel discriminant analysis of principal components (DAPC) to assess population genetic structure using allelic variation in 11 microsatellite loci. Fine-scale population connectivity was compared with oceanic features of the sampled area. Disjunct scallop beds were genetically distinct, but there was little population genetic structure between beds connected by tides and oceanic currents. To identify recruitment patterns among and within beds, pedigree analyses determined the distribution of parent–offspring and sibling relationships in the sampled populations. Beds in northeastern Bass Strait were genetically distinct to adjacent beds (FST 0.003–0.005) and may not contribute to wider recruitment based on biophysical models of larval movement. Unfortunately, pedigree analyses lacked power to further dissect fine-scale recruitment processes including self-recruitment. Our results support the management of disjunct populations as separate stocks and the protection of source populations among open water beds. The application of DAPC and parentage analyses in the current study provided valuable insight into their potential power to determine population connectivity in marine species with larval dispersal.

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 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 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.

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