<scp>RAD</scp>seq provides unprecedented insights into molecular ecology and evolutionary genetics: comment on Breaking <scp>RAD</scp> by Lowry <i>et al</i>. (2016)

McKinney, Garrett J.; Larson, Wesley A.; Seeb, Lisa W.; Seeb, James E.

doi:10.1111/1755-0998.12649

Cited by 126 publications

(125 citation statements)

References 53 publications

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“…A recent study testing the impact of data processing on population genetic inferences using RAD‐seq data observed large differences between reference‐based and de novo approaches in population genetic summary statistics, particularly those based on the site frequency spectrum (Shafer et al., 2016). In addition, the recent debate over the effectiveness of RAD‐seq for discovering loci under selection (Catchen et al., 2017; Lowry et al., 2016; McKinney, Larson, Seeb, & Seeb, 2017) has highlighted the importance of testing the extent of linkage disequilibrium (LD) over the genome, whenever possible, in order to assess the power of genome scans to detect selected loci (e.g., Kardos, Taylor, Ellegren, Luikart, & Allendorf, 2016). …”

Section: Genotyping Error and Improving Data Qualitymentioning

confidence: 99%

“…Focusing on the choice of sequencing method, a particular point of discussion at the ConGen 2017 workshop was the recent set of papers addressing the limitations of RADseq to illuminate the genetic basis of adaptation (Catchen et al., 2017; Lowry et al., 2016; McKinney et al., 2017). The primary criticism raised by Lowry et al.…”

Section: Genotyping Error and Improving Data Qualitymentioning

confidence: 99%

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Recent advances in conservation and population genomics data analysis

Hendricks

Anderson

Antão

et al. 2018

Evolutionary Applications

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New computational methods and next‐generation sequencing (NGS) approaches have enabled the use of thousands or hundreds of thousands of genetic markers to address previously intractable questions. The methods and massive marker sets present both new data analysis challenges and opportunities to visualize, understand, and apply population and conservation genomic data in novel ways. The large scale and complexity of NGS data also increases the expertise and effort required to thoroughly and thoughtfully analyze and interpret data. To aid in this endeavor, a recent workshop entitled “Population Genomic Data Analysis,” also known as “ConGen 2017,” was held at the University of Montana. The ConGen workshop brought 15 instructors together with knowledge in a wide range of topics including NGS data filtering, genome assembly, genomic monitoring of effective population size, migration modeling, detecting adaptive genomic variation, genomewide association analysis, inbreeding depression, and landscape genomics. Here, we summarize the major themes of the workshop and the important take‐home points that were offered to students throughout. We emphasize increasing participation by women in population and conservation genomics as a vital step for the advancement of science. Some important themes that emerged during the workshop included the need for data visualization and its importance in finding problematic data, the effects of data filtering choices on downstream population genomic analyses, the increasing availability of whole‐genome sequencing, and the new challenges it presents. Our goal here is to help motivate and educate a worldwide audience to improve population genomic data analysis and interpretation, and thereby advance the contribution of genomics to molecular ecology, evolutionary biology, and especially to the conservation of biodiversity.

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Section: Genotyping Error and Improving Data Qualitymentioning

confidence: 99%

Section: Genotyping Error and Improving Data Qualitymentioning

confidence: 99%

Recent advances in conservation and population genomics data analysis

Hendricks

Anderson

Antão

et al. 2018

Evolutionary Applications

View full text Add to dashboard Cite

show abstract

“…The quality of DNA, the success of library preparation, and the sequencing strategy – all contributing to differential allelic sampling – can separate the pathbreaking RADseq studies from the rest. Often, the differences between these studies generated substantial discussion in the community (Catchen et al, ; Lowry et al, ; McKinney, Larson, Seeb, & Seeb, ) and a lot of speculation as to the inherent limitations of reduced representation sequencing.…”

Section: Introductionmentioning

confidence: 99%

Stacks 2: Analytical methods for paired‐end sequencing improve RADseq‐based population genomics

2019

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For half a century population genetics studies have put type II restriction endonucleases to work. Now, coupled with massively‐parallel, short‐read sequencing, the family of RAD protocols that wields these enzymes has generated vast genetic knowledge from the natural world. Here, we describe the first software natively capable of using paired‐end sequencing to derive short contigs from de novo RAD data. Stacks version 2 employs a de Bruijn graph assembler to build and connect contigs from forward and reverse reads for each de novo RAD locus, which it then uses as a reference for read alignments. The new architecture allows all the individuals in a metapopulation to be considered at the same time as each RAD locus is processed. This enables a Bayesian genotype caller to provide precise SNPs, and a robust algorithm to phase those SNPs into long haplotypes, generating RAD loci that are 400–800 bp in length. To prove its recall and precision, we tested the software with simulated data and compared reference‐aligned and de novo analyses of three empirical data sets. Our study shows that the latest version of Stacks is highly accurate and outperforms other software in assembling and genotyping paired‐end de novo data sets.

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“…Restriction‐site associated DNA sequencing (RADSeq) offers rapid and large‐scale marker generation at relatively low cost and without requiring a reference genome. As such, it has become a popular and widespread sequencing method in molecular ecological research, where it serves as a basis for variant discovery, genotyping, identifying loci under selection (Catchen et al, ; Lowry et al, ; McKinney, Larson, Seeb, & Seeb, ), establishing phylogenetic relationships (Cruaud et al, ; Díaz‐Arce, Arrizabalaga, Murua, Irigoien, & Rodríguez‐Ezpeleta, ; Eaton, Spriggs, Park, & Donoghue, ; Nagano et al, ; Razkin et al, ), and demographic inference (Nunziata, Lance, Scott, Lemmon, & Weisrock, ; Pujolar, Dalén, Hansen, & Madsen, ; Shafer, Gattepaille, Stewart, & Wolf, ).…”

Section: Introductionmentioning

confidence: 99%

Genotype‐free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data

Warmuth

Ellegren

2019

Molecular Ecology Resources

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Restriction‐site associated DNA sequencing (RADSeq) facilitates rapid generation of thousands of genetic markers at relatively low cost; however, several sources of error specific to RADSeq methods often lead to biased estimates of allele frequencies and thereby to erroneous population genetic inference. Estimating the distribution of sample allele frequencies without calling genotypes was shown to improve population inference from whole genome sequencing data, but the ability of this approach to account for RADSeq‐specific biases remains unexplored. Here we assess in how far genotype‐free methods of allele frequency estimation affect demographic inference from empirical RADSeq data. Using the well‐studied pied flycatcher (Ficedula hypoleuca) as a study system, we compare allele frequency estimation and demographic inference from whole genome sequencing data with that from RADSeq data matched for samples using both genotype‐based and genotype free methods. The demographic history of pied flycatchers as inferred from RADSeq data was highly congruent with that inferred from whole genome resequencing (WGS) data when allele frequencies were estimated directly from the read data. In contrast, when allele frequencies were derived from called genotypes, RADSeq‐based estimates of most model parameters fell outside the 95% confidence interval of estimates derived from WGS data. Notably, more stringent filtering of the genotype calls tended to increase the discrepancy between parameter estimates from WGS and RADSeq data, respectively. The results from this study demonstrate the ability of genotype‐free methods to improve allele frequency spectrum‐ (AFS‐) based demographic inference from empirical RADSeq data and highlight the need to account for uncertainty in NGS data regardless of sequencing method.

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RADseq provides unprecedented insights into molecular ecology and evolutionary genetics: comment on Breaking RAD by Lowry et al. (2016)

Cited by 126 publications

References 53 publications

Recent advances in conservation and population genomics data analysis

Recent advances in conservation and population genomics data analysis

Stacks 2: Analytical methods for paired‐end sequencing improve RADseq‐based population genomics

Genotype‐free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data

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