Applying SNP-Derived Molecular Coancestry Estimates to Captive Breeding Programs

Summary Restriction site‐Associated DNA sequencing (RAD‐seq) has become a widely adopted method for genotyping populations of model and non‐model organisms. Generating a reliable set of loci for downstream analysis requires appropriate use of bioinformatics software, such as the program stacks. Using three empirical RAD‐seq datasets, we demonstrate a method for optimising a de novo assembly of loci using stacks. By iterating values of the program's main parameters and plotting resultant core metrics for visualisation, researchers can gain a much better understanding of their dataset and select an optimal set of parameters; we present the 80% rule as a generally effective method to select the core parameters for stacks. Visualisation of the metrics plotted for the three RAD‐seq datasets shows that they differ in the optimal parameters that should be used to maximise the amount of available biological information. We also demonstrate that building loci de novo and then integrating alignment positions is more effective than aligning raw reads directly to a reference genome. Our methods will help the community in honing the analytical skills necessary to accurately assemble a RAD‐seq dataset.

show abstract

“…However, published datasets with coverage below 109 are not uncommon (e.g. Xu et al 2014;Boehm et al 2015;Ivy et al 2016;Razkin et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Lost in parameter space: a road map for stacks

2017

View full text Add to dashboard Cite

show abstract

“…These advances could be especially important in refining captive breeding programmes for conservation when they already consist of highly related individuals (Attard, Brauer, et al., ; Attard, Möller, et al., ). Pedigree‐based ideas of relatedness in conservation breeding could also be replaced with measures of genome similarity (Ivy et al., ), such as used in agricultural breeding (Speed & Balding, ). This is because the allele frequencies of the original wild population are often poorly known, and so it is difficult to accurately estimate pedigree‐based relatedness (Ivy et al.…”

Section: Discussionmentioning

confidence: 99%

“…This is because the allele frequencies of the original wild population are often poorly known, and so it is difficult to accurately estimate pedigree‐based relatedness (Ivy et al. (), but see Svengren, Prettejohn, Bunge, Fundi, and Björklund ()).…”

Section: Discussionmentioning

confidence: 99%

“…For model organisms and organisms of agricultural importance, there are now commercial SNP panels that contain thousands to hundreds of thousands of SNPs used for marker–trait association studies and genomic selection for breeding (Goddard & Hayes, , ; Pe'er et al., ). In nonmodel species, time and expense have been placed into developing SNP panels of typically several tens to hundreds of loci to infer relationships (Kaiser et al., ; Labuschagne, Nupen, Kotzé, Grobler, & Dalton, ; Liu, Palti, Gao, & Rexroad Iii, ; Weinman, Solomon, & Rubenstein, ; Wright et al., ), or effort has been placed into assessing commercially available SNP panels for cross‐amplification in these species (Haynes & Latch, ; Ivy, Putnam, Navarro, Gurr, & Ryder, ). SNP panels have been shown to be as powerful as (Glaubitz et al., ; Kaiser et al., ; Weinman et al., ) or more powerful (Labuschagne et al., ; Santure et al., ) than microsatellite data sets.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Genotyping‐by‐sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias

Attard

Beheregaray

Möller

2018

Molecular Ecology Resources

View full text Add to dashboard Cite

There has been remarkably little attention to using the high resolution provided by genotyping-by-sequencing (i.e., RADseq and similar methods) for assessing relatedness in wildlife populations. A major hurdle is the genotyping error, especially allelic dropout, often found in this type of data that could lead to downward-biased, yet precise, estimates of relatedness. Here, we assess the applicability of genotyping-by-sequencing for relatedness inferences given its relatively high genotyping error rate. Individuals of known relatedness were simulated under genotyping error, allelic dropout and missing data scenarios based on an empirical ddRAD data set, and their true relatedness was compared to that estimated by seven relatedness estimators. We found that an estimator chosen through such analyses can circumvent the influence of genotyping error, with the estimator of Ritland (Genetics Research, 67, 175) shown to be unaffected by allelic dropout and to be the most accurate when there is genotyping error. We also found that the choice of estimator should not rely solely on the strength of correlation between estimated and true relatedness as a strong correlation does not necessarily mean estimates are close to true relatedness. We also demonstrated how even a large SNP data set with genotyping error (allelic dropout or otherwise) or missing data still performs better than a perfectly genotyped microsatellite data set of tens of markers. The simulation-based approach used here can be easily implemented by others on their own genotyping-by-sequencing data sets to confirm the most appropriate and powerful estimator for their data.

show abstract

“…A subset of these loci can be used for the development of cost-effective genotyping tools suitable for the assessment of diverse aspects of interest in conservation and management (e.g., taxonomic status, hybrids, sex, carriers of genetic diseases, population structure, individual assignment and population of origin, among others). These tools can be incorporated in conservation plans of threatened species (Norman, Street, & Spong, 2013;Muñoz et al, 2015;Ivy, Putnam, Navarro, Gurr, & Ryder, 2016;Stetz et al, 2016;Fussi et al, 2016;Vandergast, 2017;Grossen, Biebach, Angelone-Alasaad, Keller, & Croll, 2017) and in management plans of commercially valuable species (Martinsohn & Ogden, 2009;Habicht et al, 2012;Bekkevold et al, 2015;Bradbury et al, 2015;Aykanat, Lindqvist, Pritchard, & Primmer, 2016;Sinclair-Waters, 2017). The scarcity of genomic resources for most species under conservation concern coupled with the still high cost of high-throughput sequencing and the elevated demand for computing resources, however, have limited the implementation of WGR in conservation biology.…”

Section: Organismmentioning

confidence: 99%

Whole‐genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations

2017

View full text Add to dashboard Cite

Whole-genome resequencing (WGR) is a powerful method for addressing fundamental evolutionary biology questions that have not been fully resolved using traditional methods. WGR includes four approaches: the sequencing of individuals to a high depth of coverage with either unresolved or resolved haplotypes, the sequencing of population genomes to a high depth by mixing equimolar amounts of unlabelled-individual DNA (Pool-seq) and the sequencing of multiple individuals from a population to a low depth (lcWGR). These techniques require the availability of a reference genome. This, along with the still high cost of shotgun sequencing and the large demand for computing resources and storage, has limited their implementation in nonmodel species with scarce genomic resources and in fields such as conservation biology. Our goal here is to describe the various WGR methods, their pros and cons and potential applications in conservation biology. WGR offers an unprecedented marker density and surveys a wide diversity of genetic variations not limited to single nucleotide polymorphisms (e.g., structural variants and mutations in regulatory elements), increasing their power for the detection of signatures of selection and local adaptation as well as for the identification of the genetic basis of phenotypic traits and diseases. Currently, though, no single WGR approach fulfils all requirements of conservation genetics, and each method has its own limitations and sources of potential bias. We discuss proposed ways to minimize such biases. We envision a not distant future where the analysis of whole genomes becomes a routine task in many nonmodel species and fields including conservation biology.

show abstract

Applying SNP-Derived Molecular Coancestry Estimates to Captive Breeding Programs

Cited by 38 publications

References 67 publications

Lost in parameter space: a road map for stacks

Lost in parameter space: a road map for stacks

Genotyping‐by‐sequencing for estimating relatedness in nonmodel organisms: Avoiding the trap of precise bias

Whole‐genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations

Contact Info

Product

Resources

About