scholarly journals Peer Review #2 of "An empirical examination of sample size effects on population demographic estimates in birds using single nucleotide polymorphism (SNP) data (v0.2)"

2020 ◽  
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Sample Size ◽  
Peer Review ◽  
Size Effects ◽  
Nucleotide Polymorphism ◽  
Empirical Examination ◽  
Single Nucleotide ◽  
Snp Data ◽  
Population Demographic

scholarly journals An empirical examination of sample size effects on population demographic estimates in birds using single nucleotide polymorphism (SNP) data

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9939
Author(s):  
Jessica F. McLaughlin ◽  
Kevin Winker
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Sample Size ◽  
Population Size ◽  
Effective Population Size ◽  
Population Level ◽  
Sample Sizes ◽  
Nucleotide Polymorphism ◽  
Effective Population ◽  
Single Nucleotide ◽  
Population Demographic

Sample size is a critical aspect of study design in population genomics research, yet few empirical studies have examined the impacts of small sample sizes. We used datasets from eight diverging bird lineages to make pairwise comparisons at different levels of taxonomic divergence (populations, subspecies, and species). Our data are from loci linked to ultraconserved elements and our analyses used one single nucleotide polymorphism per locus. All individuals were genotyped at all loci, effectively doubling sample size for coalescent analyses. We estimated population demographic parameters (effective population size, migration rate, and time since divergence) in a coalescent framework using Diffusion Approximation for Demographic Inference, an allele frequency spectrum method. Using divergence-with-gene-flow models optimized with full datasets, we subsampled at sequentially smaller sample sizes from full datasets of 6–8 diploid individuals per population (with both alleles called) down to 1:1, and then we compared estimates and their changes in accuracy. Accuracy was strongly affected by sample size, with considerable differences among estimated parameters and among lineages. Effective population size parameters (ν) tended to be underestimated at low sample sizes (fewer than three diploid individuals per population, or 6:6 haplotypes in coalescent terms). Migration (m) was fairly consistently estimated until <2 individuals per population, and no consistent trend of over-or underestimation was found in either time since divergence (T) or theta (Θ = 4Nrefμ). Lineages that were taxonomically recognized above the population level (subspecies and species pairs; that is, deeper divergences) tended to have lower variation in scaled root mean square error of parameter estimation at smaller sample sizes than population-level divergences, and many parameters were estimated accurately down to three diploid individuals per population. Shallower divergence levels (i.e., populations) often required at least five individuals per population for reliable demographic inferences using this approach. Although divergence levels might be unknown at the outset of study design, our results provide a framework for planning appropriate sampling and for interpreting results if smaller sample sizes must be used.

NIASGBsnp: integration of single nucleotide polymorphism data of rice (Oryzasativa L.) genetic resources

2013 ◽  
Vol 11 (3) ◽  
pp. 221-224
Author(s):  
Masaru Takeya ◽  
Fukuhiro Yamasaki ◽  
Sachiko Hattori ◽  
Kaworu Ebana
Keyword(s):  
Single Nucleotide Polymorphism ◽  
Plant Pathology ◽  
Single Nucleotide Polymorphisms ◽  
Genetic Resources ◽  
Snp Markers ◽  
Nucleotide Polymorphisms ◽  
Nucleotide Polymorphism ◽  
Single Nucleotide ◽  
Snp Data ◽  
Polymorphism Data

The NIASGBsnp system manages data on single nucleotide polymorphisms (SNPs) of rice (Oryzasativa L.) genetic resources in the National Institute of Agrobiological Science (NIAS) Genebank. NIASGBsnp currently holds data on 768 SNP markers for 301 rice accessions and plans to add the SNP data of active rice accessions in the NIAS Genebank. It can show differences between accessions by graphical genotyping. Passport, characteristics and evaluation data of accessions can be retrieved to allow phenotype to be associated with genotype. NIASGBsnp will support various research purposes such as genomic selection and plant pathology research.

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