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A high-throughput amplicon-based method for estimating outcrossing rates.

ABSTRACT: Background:The outcrossing rate is a key determinant of the population-genetic structure of species and their long-term evolutionary trajectories. However, determining the outcrossing rate using current methods based on PCR-genotyping individual offspring of focal plants for multiple polymorphic markers is laborious and time-consuming. Results:We have developed an amplicon-based, high-throughput enabled method for estimating the outcrossing rate and have applied this to an example of scented versus non-scented Capsella (Shepherd's Purse) genotypes. Our results show that the method is able to robustly capture differences in outcrossing rates. They also highlight potential biases in the estimates resulting from differential haplotype sharing of the focal plants with the pollen-donor population at individual amplicons. Conclusions:This novel method for estimating outcrossing rates will allow determining this key population-genetic parameter with high-throughput across many genotypes in a population, enabling studies into the genetic determinants of successful pollinator attraction and outcrossing.

SUBMITTER: Jantzen F

PROVIDER: S-EPMC6525360 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A high-throughput amplicon-based method for estimating outcrossing rates.

Jantzen Friederike F Wozniak Natalia N Kappel Christian C Sicard Adrien A Lenhard Michael M

Plant methods 20190518

<h4>Background</h4>The outcrossing rate is a key determinant of the population-genetic structure of species and their long-term evolutionary trajectories. However, determining the outcrossing rate using current methods based on PCR-genotyping individual offspring of focal plants for multiple polymorphic markers is laborious and time-consuming.<h4>Results</h4>We have developed an amplicon-based, high-throughput enabled method for estimating the outcrossing rate and have applied this to an example ...[more]

PMID: 31131016

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Project description:BackgroundHost genetic backgrounds affect gene functions. The genetic backgrounds of genetically engineered organisms must be identified to confirm their genetic backgrounds identity with those of recipients. Marker-assisted backcrossing (MAB), transgenesis and clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) editing are three commonly used genetic engineering techniques. However, methods for genetic background screening between genetically engineered organisms and corresponding recipients suffer from low efficiency, low accuracy or high cost.ResultsHere, we improved our previously reported AmpSeq-SSR method, an amplicon sequencing-based simple sequence repeat (SSR) genotyping method, by selecting SSR loci with high polymorphism among varieties. Ultimately, a set of 396 SSRs was generated and applied to evaluate the genetic backgrounds identity between rice lines developed through MAB, transgenesis, and CRISPR/Cas9 editing and the respective recipient rice. We discovered that the percentage of different SSRs between the MAB-developed rice line and its recipient was as high as 23.5%. In contrast, only 0.8% of SSRs were different between the CRISPR/Cas9-system-mediated rice line and its recipient, while no SSRs showed different genotypes between the transgenic rice line and its recipient. Furthermore, most differential SSRs induced by MAB technology were located in non-coding regions (62.9%), followed by untranslated regions (21.0%) and coding regions (16.1%). Trinucleotide repeats were the most prevalent type of altered SSR. Most importantly, all altered SSRs located in coding regions were trinucleotide repeats.ConclusionsThis method is not only useful for the background evaluation of genetic resources but also expands our understanding of the unintended effects of different genetic engineering techniques. While the work we present focused on rice, this method can be readily extended to other organisms.

Project description:Advances in sequencing technologies and bioinformatics have made the analysis of microbial communities almost routine. Nonetheless, the need remains to improve on the techniques used for gathering such data, including increasing throughput while lowering cost and benchmarking the techniques so that potential sources of bias can be better characterized.We present a triple-index amplicon sequencing strategy to sequence large numbers of samples at significantly lower c ost and in a shorter timeframe compared to existing methods. The design employs a two-stage PCR protocol, incorpo rating three barcodes to each sample, with the possibility to add a fourth-index. It also includes heterogeneity spacers to overcome low complexity issues faced when sequencing amplicons on Illumina platforms.The library preparation method was extensively benchmarked through analysis of a mock community in order to assess biases introduced by sample indexing, number of PCR cycles, and template concentration. We further evaluated the method through re-sequencing of a standardized environmental sample. Finally, we evaluated our protocol on a set of fecal samples from a small cohort of healthy adults, demonstrating good performance in a realistic experimental setting. Between-sample variation was mainly related to batch effects, such as DNA extraction, while sample indexing was also a significant source of bias. PCR cycle number strongly influenced chimera formation and affected relative abundance estimates of species with high GC content. Libraries were sequenced using the Illumina HiSeq and MiSeq platforms to demonstrate that this protocol is highly scalable to sequence thousands of samples at a very low cost.Here, we provide the most comprehensive study of performance and bias inherent to a 16S rRNA gene amplicon sequencing method to date. Triple-indexing greatly reduces the number of long custom DNA oligos required for library preparation, while the inclusion of variable length heterogeneity spacers minimizes the need for PhiX spike-in. This design results in a significant cost reduction of highly multiplexed amplicon sequencing. The biases we characterize highlight the need for highly standardized protocols. Reassuringly, we find that the biological signal is a far stronger structuring factor than the various sources of bias.

Dataset Information

A high-throughput amplicon-based method for estimating outcrossing rates.

Publications

A high-throughput amplicon-based method for estimating outcrossing rates.

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