Dataset Information

Validation of Genotyping-By-Sequencing Analysis in Populations of Tetraploid Alfalfa by 454 Sequencing.

ABSTRACT: Genotyping-by-sequencing (GBS) is a relatively low-cost high throughput genotyping technology based on next generation sequencing and is applicable to orphan species with no reference genome. A combination of genome complexity reduction and multiplexing with DNA barcoding provides a simple and affordable way to resolve allelic variation between plant samples or populations. GBS was performed on ApeKI libraries using DNA from 48 genotypes each of two heterogeneous populations of tetraploid alfalfa (Medicago sativa spp. sativa): the synthetic cultivar Apica (ATF0) and a derived population (ATF5) obtained after five cycles of recurrent selection for superior tolerance to freezing (TF). Nearly 400 million reads were obtained from two lanes of an Illumina HiSeq 2000 sequencer and analyzed with the Universal Network-Enabled Analysis Kit (UNEAK) pipeline designed for species with no reference genome. Following the application of whole dataset-level filters, 11,694 single nucleotide polymorphism (SNP) loci were obtained. About 60% had a significant match on the Medicago truncatula syntenic genome. The accuracy of allelic ratios and genotype calls based on GBS data was directly assessed using 454 sequencing on a subset of SNP loci scored in eight plant samples. Sequencing depth in this study was not sufficient for accurate tetraploid allelic dosage, but reliable genotype calls based on diploid allelic dosage were obtained when using additional quality filtering. Principal Component Analysis of SNP loci in plant samples revealed that a small proportion (<5%) of the genetic variability assessed by GBS is able to differentiate ATF0 and ATF5. Our results confirm that analysis of GBS data using UNEAK is a reliable approach for genome-wide discovery of SNP loci in outcrossed polyploids.

SUBMITTER: Rocher S

PROVIDER: S-EPMC4482585 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Validation of Genotyping-By-Sequencing Analysis in Populations of Tetraploid Alfalfa by 454 Sequencing.

Rocher Solen S Jean Martine M Castonguay Yves Y Belzile François F

PloS one 20150626 6

Genotyping-by-sequencing (GBS) is a relatively low-cost high throughput genotyping technology based on next generation sequencing and is applicable to orphan species with no reference genome. A combination of genome complexity reduction and multiplexing with DNA barcoding provides a simple and affordable way to resolve allelic variation between plant samples or populations. GBS was performed on ApeKI libraries using DNA from 48 genotypes each of two heterogeneous populations of tetraploid alfalf ...[more]

PMID: 26115486

Similar Datasets

Project description:Perennial ryegrass is an outbreeding forage species and is one of the most widely used forage grasses in temperate regions. The aim of this study was to investigate the possibility of implementing genomic prediction in tetraploid perennial ryegrass, to study the effects of different sequencing depth when using genotyping-by-sequencing (GBS), and to determine optimal number of single-nucleotide polymorphism (SNP) markers and sequencing depth for GBS data when applied in tetraploids. A total of 1,515 F2 tetraploid ryegrass families were included in the study and phenotypes and genotypes were scored on family-pools. The traits considered were dry matter yield (DM), rust resistance (RUST), and heading date (HD). The genomic information was obtained in the form of allele frequencies of pooled family samples using GBS. Different SNP filtering strategies were designed. The strategies included filtering out SNPs having low average depth (FILTLOW), having high average depth (FILTHIGH), and having both low average and high average depth (FILTBOTH). In addition, SNPs were kept randomly with different data sizes (RAN). The accuracy of genomic prediction was evaluated by using a "leave single F2 family out" cross validation scheme, and the predictive ability and bias were assessed by correlating phenotypes corrected for fixed effects with predicted additive breeding values. Among all the filtering scenarios, the highest estimates for genomic heritability of family means were 0.45, 0.74, and 0.73 for DM, HD and RUST, respectively. The predictive ability generally increased as the number of SNPs included in the analysis increased. The highest predictive ability for DM was 0.34 (137,191 SNPs having average depth higher than 10), for HD was 0.77 (185,297 SNPs having average depth lower than 60), and for RUST was 0.55 (188,832 SNPs having average depth higher than 1). Genomic prediction can help to optimize the breeding of tetraploid ryegrass. GBS data including about 80-100 K SNPs are needed for accurate prediction of additive breeding values in tetraploid ryegrass. Using only SNPs with sequencing depth between 10 and 20 gave highest predictive ability, and showed the potential to obtain accurate prediction from medium-low coverage GBS in tetraploids.

Project description:Key messageGenomic prediction models for starch content and chipping quality show promising results, suggesting that genomic selection is a feasible breeding strategy in tetraploid potato. Genomic selection uses genome-wide molecular markers to predict performance of individuals and allows selections in the absence of direct phenotyping. It is regarded as a useful tool to accelerate genetic gain in breeding programs, and is becoming increasingly viable for crops as genotyping costs continue to fall. In this study, we have generated genomic prediction models for starch content and chipping quality in tetraploid potato to facilitate varietal development. Chipping quality was evaluated as the colour of a potato chip after frying following cold induced sweetening. We used genotyping-by-sequencing to genotype 762 offspring, derived from a population generated from biparental crosses of 18 tetraploid parents. Additionally, 74 breeding clones were genotyped, representing a test panel for model validation. We generated genomic prediction models from 171,859 single-nucleotide polymorphisms to calculate genomic estimated breeding values. Cross-validated prediction correlations of 0.56 and 0.73 were obtained within the training population for starch content and chipping quality, respectively, while correlations were lower when predicting performance in the test panel, at 0.30-0.31 and 0.42-0.43, respectively. Predictions in the test panel were slightly improved when including representatives from the test panel in the training population but worsened when preceded by marker selection. Our results suggest that genomic prediction is feasible, however, the extremely high allelic diversity of tetraploid potato necessitates large training populations to efficiently capture the genetic diversity of elite potato germplasm and enable accurate prediction across the entire spectrum of elite potatoes. Nonetheless, our results demonstrate that GS is a promising breeding strategy for tetraploid potato.

Project description:BACKGROUND: High-throughput sequencing technologies offer new perspectives for biomedical, agronomical and evolutionary research. Promising progresses now concern the application of these technologies to large-scale studies of genetic variation. Such studies require the genotyping of high numbers of samples. This is theoretically possible using 454 pyrosequencing, which generates billions of base pairs of sequence data. However several challenges arise: first in the attribution of each read produced to its original sample, and second, in bioinformatic analyses to distinguish true from artifactual sequence variation. This pilot study proposes a new application for the 454 GS FLX platform, allowing the individual genotyping of thousands of samples in one run. A probabilistic model has been developed to demonstrate the reliability of this method. RESULTS: DNA amplicons from 1,710 rodent samples were individually barcoded using a combination of tags located in forward and reverse primers. Amplicons consisted in 222 bp fragments corresponding to DRB exon 2, a highly polymorphic gene in mammals. A total of 221,789 reads were obtained, of which 153,349 were finally assigned to original samples. Rules based on a probabilistic model and a four-step procedure, were developed to validate sequences and provide a confidence level for each genotype. The method gave promising results, with the genotyping of DRB exon 2 sequences for 1,407 samples from 24 different rodent species and the sequencing of 392 variants in one half of a 454 run. Using replicates, we estimated that the reproducibility of genotyping reached 95%. CONCLUSIONS: This new approach is a promising alternative to classical methods involving electrophoresis-based techniques for variant separation and cloning-sequencing for sequence determination. The 454 system is less costly and time consuming and may enhance the reliability of genotypes obtained when high numbers of samples are studied. It opens up new perspectives for the study of evolutionary and functional genetics of highly polymorphic genes like major histocompatibility complex genes in vertebrates or loci regulating self-compatibility in plants. Important applications in biomedical research will include the detection of individual variation in disease susceptibility. Similarly, agronomy will benefit from this approach, through the study of genes implicated in productivity or disease susceptibility traits.

Dataset Information

Validation of Genotyping-By-Sequencing Analysis in Populations of Tetraploid Alfalfa by 454 Sequencing.

Publications

Validation of Genotyping-By-Sequencing Analysis in Populations of Tetraploid Alfalfa by 454 Sequencing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets