Keywords
Molecular breeding, KASP, Genotyping workflow, Marker-assisted selection, Quality Control, National Agricultural Research Systems (NARS), Developing countries
-
Views
-
Downloads
How to cite this article
Offornedo Q, Menkir A, Babalola D and Gedil M. Developing and deploying an efficient genotyping workflow for accelerating maize improvement in developing countries [version 3; peer review: 3 approved, 1 approved with reservations]. Gates Open Res 2022, 6:3 (https://doi.org/10.12688/gatesopenres.13338.3)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
Export Citation
Sciwheel
EndNote
Ref. Manager
Bibtex
ProCite
Sente
▬
✚
Method Article
Revised
Developing and deploying an efficient genotyping workflow for accelerating maize improvement in developing countries
[version 3; peer review: 3 approved, 1 approved with reservations]
PUBLISHED 03 Aug 2022
Abstract
Corresponding author:
Melaku Gedil
Competing interests:
No competing interests were disclosed.
Grant information:
This work was supported by the Bill & Melinda Gates Foundation and USAID [OPP1134248] under the project titled "Stress Tolerant Maize for Africa". This work was also supported by the Accelerating Genetic Gains in Maize and Wheat (AGG) Project and CGIAR Research Program on Maize (MAIZE).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Copyright:
© 2022 Offornedo Q et al.
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite: Offornedo Q, Menkir A, Babalola D and Gedil M. Developing and deploying an efficient genotyping workflow for accelerating maize improvement in developing countries [version 3; peer review: 3 approved, 1 approved with reservations]. Gates Open Res 2022, 6:3 (https://doi.org/10.12688/gatesopenres.13338.3)
First published: 17 Jan 2022, 6:3 (https://doi.org/10.12688/gatesopenres.13338.1)
Latest published: 03 Aug 2022, 6:3 (https://doi.org/10.12688/gatesopenres.13338.3)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Revised Amendments from Version 2
Version 3 has been updated based on the reviewers' comments on the previous version.
We have included additional context to the Introduction section (P3) to articulate the use cases better and provided a genotyping cost comparison of the procedure as indicated by Reviewer 1.
As pointed out by Reviewer 2, we have rephrased the indicated sentences in the Introduction and Methods section. The misplaced reference has been replaced and listed in the reference table. We have included additional context to suitably articulate the study objective towards the end of the Introduction section, which is "This study aims to develop a genotyping workflow optimized for cost-effective and fast turn-around time that can be deployed by less sophisticated and reasonably equipped laboratories in developing countries, to accelerate maize improvement research." We have furnished Table 1 with details of the exact number of genotypes and samples used for the experiments. We have also provided a new figure (Figure 5) and table (Table 5) to aptly articulate the KASP genotyping analysis for the hybrid verification experiment.
See the authors' detailed response to the review by Bhoja R. Basnet
See the authors' detailed response to the review by John Damien Platten
Introduction
Agriculture is the mainstay of millions of low-income households in Sub-Saharan Africa (SSA). However, productivity is way below the yield potential of significant crops due to several interacting factors contributing to the yield reduction. The paucity of nutritionally improved resilient crop varieties is a crucial constraint. This constraint can be mitigated by the rapid development of cultivars adapted to specific agroecology zones1. The current yield gain trend in major food crops has shown that relying on conventional breeding alone is insufficient to meet the food needs of an estimated nine billion people in 20252. There is a need to accelerate genetic gain by deploying new breeding strategies3,4. This need has led to the scientific community's massive investment in developing genomic resources and support systems, to provide valuable tools to accelerate breeding processes5.
Various bottlenecks have hindered the substantial impact of molecular breeding for crop improvement, particularly in developing countries6,7. The major limiting factors are a lack of infrastructure and capacity for genomics resources and poor information flow, resulting in reduced access to operational and decision support tools8. Private companies in developed countries usually own the proprietary rights to many emerging genomics resources and systems, making it difficult for public research sectors, non-profit research institutes, and small laboratories in developing countries to have direct access. These challenges are being curbed by various international initiatives such as the Excellence in Breeding (EiB) platform, which coordinates its activities with the Genomic and Open-source Breeding Informatics Initiative (GOBii), and High Through-Put Genotyping (HTPG). In addition, the Integrated Breeding Platform (IBP)-hosted Generation Challenge Program (GCP) and the Breeding Management System (BMS)9 target the development and adoption of molecular breeding in developing countries. These and other consultative group-hosted initiatives and platforms galvanise worldwide partners drawn from public, private, and governmental institutions towards the common goal of increasing agricultural productivity through efficient tools, technologies, and data management systems6.
Despite the availability of many low-cost genotyping platforms and resources, it is not easy to meet the genotyping needs of many users who work on different crops, different locations, and often fewer samples due to cost implications7,8. The current available genotyping platforms have a minimum sample size requirement. For instance, the EiB facilitated genotyping at Intertek offers reduced cost if the user orders genotyping of 1536 samples; fewer samples are acceptable, but the price increases. Intertek's standard cost for routine KASP genotyping is $2.6 per sample per 10 SNPs, excluding shipping costs, compared to our in-house genotyping at $2.95. Even though large volume sizes can be consolidated and shipped for genotyping, there are times when breeders and partners may want to fingerprint a few dozen lines for identity or parentage analysis for quick decision making. In such cases, sending less than the minimum number of samples is not only more priced per datapoint but entails shipping cost and a turn-around time of 2–3 weeks. Using other markers, such as SSR, is more expensive and cumbersome. The use of genotyping systems such as KASP in-house alleviates all these issues. Also, the issue of inefficient courier services in this part of the world, which often results in reduced or damaged perishable specimens, can be circumvented if a reasonably affordable system is available locally. More so, we re-purposed standard laboratory instruments for the genotyping workflow. For instance, the qPCR machine, which is mostly used for expression analysis, was adapted to KASP genotyping with the installation of appropriate software for SNP calling. Likewise, the Fluostar plate reader was used for plate-level DNA quantification in lieu of single sample analysis by Spectrophotometer.
For these reasons it is imperative to devise a sustainable strategy for routine, cost-effective, and easily accessible genotyping services to complement these international outsourcing initiatives by providing in-house or local (regional) genotyping platforms, where possible, to accelerate the genotyping workflow. One such regional initiative in Africa is the Integrated Genotyping Support Services (IGSS) genotyping facility at Biosciences eastern and central Africa/International Livestock Research Institute (BeCA/ILRI), Kenya. This strategy will allow breeders to outsource to a regional genotyping service provider or set up a core facility in-house.
One factor that influences breeders' choice of genotyping platform is the level of throughput. Other factors considered are the data turn-around time, ease of data analysis (available informatics), reproducibility, flexibility, and cost per datapoint or cost per sample10,11. For high and ultra-high throughput markers, breeders outsource to array- and sequenced-based genotyping service providers. These platforms are suitable for discovery applications and approaches requiring hundreds to thousands of samples to be genotyped with tens to thousands of markers, such as genome-wide association studies (GWAS), gene mapping, and large-scale genomic selection10,12 They are also suitable for genotyping a few samples with many markers (multiplexing), such as genetic diversity analysis or background selection. While multiplex platforms provide higher throughput with lower reagent consumption, it limits scientists to using a multiplexed set of several thousand single nucleotide polymorphisms (SNPs) per assay13. They are also demanding in informatics resources and presently produce datasets with a significant percentage of missing data13. The high cost per sample and the initial assay development time of highly multiplexed platforms can be problematic for crop improvement applications, usually requiring low- to medium-density markers11. For these low- to mid-density genotyping approaches, a uniplex SNP genotyping platform is appropriate14.
Uniplex genotyping assays are low-throughput genotyping systems that are ideally flexible regarding assay design, ease of running, and cost-effectiveness15. These systems provide plant breeders with the flexibility to mix and match different SNPs for a given sample set. They allow breeders to use a smaller subset of informative SNPs such as functional SNPs and trait-specific haplotypes, thereby eliminating the generation of unintended datapoints when using fixed-array SNPs. Even though a range of uniplex SNP genotyping assays exists, the most competitive uniplex systems that have been successfully applied in crop improvement research are TaqMan16–19, competitive allele-specific PCR (KASP)11,20, Amplifuor21, and rhAmP22 assays. These uniplex genotyping systems vary in reaction chemistry, detection method, and reaction format. Uniplex systems can either be outsourced or installed in-house.
In this study, we utilised the KASP assay, as it is one of the most used assays among plant breeders and biologists15,19. KASP is an endpoint PCR-based SNP genotyping method from KBiosciences, now LGC Biosearch Technologies, UK. KASP uses fluorescently-labelled allele-specific primers for the bi-allelic discrimination of SNPs and insertion-deletion mutations (INDELs)23. KASP was developed to reduce cost, mainly from probe design, and improve genotyping efficiency, becoming a preferred alternative to TaqMan11,24. The KASP genotyping system has been successfully applied in crops such as maize11,15,25, wheat10,20,26, rice27, soybean28, peanut29, amongst others. KASP has developed into a global benchmark technology for genotyping crop plants11,23,29–31 following the validation of KASP markers across crops of global importance (such as maize - 1250 markers, wheat - 1864 markers, and rice - 2015 markers) by the Generation Challenge Program of the Integrated Breeding Platform9. The International Maize and Wheat Improvement Center (CIMMYT) has successfully utilised the 1,250 maize KASP markers for various genetic applications, including quantitative trait loci (QTL) mapping, marker-assisted recurrent selection (MARS), allele mining, and QC analysis11. The Maize Improvement Program of the International Institute of Tropical Agriculture (IITA) has generated over 2,000 datapoints using KASP in-house for different genotype analyses, including QC and MAS.
However, some bottlenecks in the genotyping workflow slow down the genotyping process, delaying crop improvement: (1) method of sample collection and processing, (2) level of DNA extraction and quantitation, and (3) DNA-based genotyping8. Gedil and Menkir (2019) provided a thorough review of the Maize Improvement Program's (MIP) molecular marker-based crop improvement activities. However, reports of research accelerating the entire genotyping process by minimizing these bottlenecks and providing a cost-effective genotyping workflow suitable for small-scale breeders and laboratories in developing countries are lacking. This study aims to develop a genotyping workflow optimized for cost-effective and fast turn-around time which can be deployed by less sophisticated and reasonably equipped laboratories in developing countries, to accelerate maize improvement research.
Methods
Plant materials
The overall genotyping workflow was applied in some experiments representative of the genotyping activities common in small to medium breeding programs. Table 1 below describes the plant materials used in each experiment. The genetic identity experiment was performed using four well-adapted maize varieties originating from IITA but regenerated at four locations. For the hybrid verification experiment, 60 maize F1 progenies originating from five bi-parental crosses were used. Lines KS23-3, KS23-5, and KS23-6 are resistant to maize lethal necrosis (MLN) disease, while IITATZI1653 and IITATZI1667 are IITA-adopted elite maize lines with high PVA content. Another 330 F1 plants originating from four sets of bi-parental crosses involving Striga-susceptible (TZdEEI 102, TZdEEI 99, TZdEEI 4, and TZdEEI 13) and Striga-resistant (TZEEI 29, and TZEEI 79) parents were also screened to identify true hybrids. A total of 70 PVA-QPM enriched maize inbred lines were genotyped to select lines harbouring the favourable allele for the crtRB1 gene associated with PVA content in maize. In the fourth breeding cycle of the maize enrichment project using marker-assisted backcrossing to introgress resistance to aflatoxin accumulation in elite tropical maize lines, we genotyped a total of 159 BC1S2 maize lines. We applied a 15% selection intensity to identify lines harbouring the favourable alleles of the QTLs associated with resistance to aflatoxin accumulation. These plants were grown in maize fields at IITA Ibadan, Nigeria.
Table 1. Plant materials used for the experimentation of the optimized genotyping workflow.
Sample collection and preparation, and DNA extraction and quantitation
A total of 16 to 20 leaf discs were collected from young leaves of each tagged plant, directly into Corning 96-well Polypropylene 1.2 ml cluster tubes with strip caps (Merck, Germany) using Haris Uni-core 4.0 mm puncher and cutting mat (Merck, Germany). Two 4.0 mm stainless steel grinding balls (SPEX SamplePrep) were placed in each tube. Plant tissues were preserved on ice for transport from the field to the laboratory. They were stored in a -80°C freezer before lyophilising for 48 hours using FreeZone Freeze Dryer (Labconco) following the manufacturer's manual. Lyophilised leaf tissues were ground into powder by shaking at 1,500 strokes per minute for 1.5 min using an automated high-throughput tissue homogeniser, Geno/Grinder 2010 (SPEX SamplePrep).
Genomic DNA was extracted from ground leaf tissues using a cetyltrimethylammonium bromide (CTAB)-based DNA extraction method as described by Diversity Array Technology (DArT)32 with minor modifications (Table 2). Dry leaf tissues were used instead of fresh ones; we included a 30-minute incubation period during the alcohol precipitation step; the DNA pellet was resuspended in a nuclease-free water and RNaseA solution. The DNA quality and quantity were determined by spectrophotometry using the FLUOstar Omega Microplate Reader (BMG LABTECH) following the manufacturer's manual.
Table 2. DArT DNA extraction protocol with minor modification.
| The chemicals and reagents used were as outlined in the Diversity Array Technology (DArT) Plant DNA extraction protocol (Accessed on June 2, 2020). Extraction procedure: 1. Aliquot freshly prepared, well-mixed "fresh buffer solution" and preheat in a 65°C water bath. 2. Grind sample leaf discs in 1.2 ml cluster tubes using a Geno/Grinder 2010 (Spex Sample Prep) to a fine powder 3. Add 500 μl buffer solution to dissolve the powder completely 4. Incubate at 65°C for 1 hr, with gentle shaking 5. Cool down for 5 min and add 500 μl of chloroform: isoamyl alcohol (24:1) mixture 6. Mix well by gentle inversion for 30 min, and spin for 20 min, at 10,000 x g, at room temperature 7. Transfer about 400 μl of the water phase to a fresh 1.2 ml tube, add the same volume of ice-cold isopropanol and invert the tube approximately ten times, nucleic acids should become visible 8. Incubate for 30 min at -20 °C, and spin for 30 min, at 10,000 x g, at room temperature 9. Discard supernatant, and wash pellet with 400 μl 70 % EtOH 10. Discard EtOH, dry pellet and dissolve in 100 µl of nuclease-free water-RNAseA solution in a 90:10 ratio. |
KASP genotyping and data analysis
The isolated genomic DNA was diluted to a working concentration of 30 ng/µl and used as template DNA for the KASP genotyping reaction. A total of 28 KASP SNPs were used to determine the selected maize varieties' genetic identity, while 10 KASP SNPs were used to verify true hybrids among the F1 maize lines. The SNPs (Table 3) were taken from a maize QC SNP panel9 recommended by CIMMYT7,33 and chosen for their high polymorphic information content (PIC) and uniform maize genome coverage. Trait-specific KASP markers (Table 4) were used to screen BC1S2 lines carrying the favourable allele for resistance to aflatoxin accumulation and identify inbred lines with high PVA content. The KASP reaction was performed in 96- and 384-well plates. For the 96-well plate, a total reaction volume of 10 µl consisting of 5 µl template DNA and 5 µl of the prepared genotyping mix (2×KASP master mix and primer mix) was used. In contrast, for the 384-well plate, a total reaction volume of 5 µl consisting of 2.5 µl template DNA and 2.5 µl of the prepared genotyping mix was used. All reaction was performed following the KASP manual (accessed on June 24, 2020). The KASP assay and master mix were purchased from LGC Biosearch Technologies (LGC Group). The amplification reaction was run in-house (Bioscience Centre of IITA Ibadan, Nigeria) using the LightCycler 480 II PCR System (Roche Life Sciences, Germany) and GeneAmp PCR System 9700 (Applied Biosystems, USA). The description of the parameters for the LC480 II qPCR machine is outlined in the LC480 operator’s manual. To perform the KASP genotyping experiment on the LC480 II machine, we used the Endpoint Genotyping Analysis module within the LightCycler software, adjusting the parameters as outlined in the KASP genotyping protocol provided by LGC Biosearch Technologies. The Endpoint genotyping analysis module is based on the use of dual hydrolysis probes, which are designed for wild-type and mutant target DNA and are labelled with different dyes (FAM and HEX). However, when using a non-qPCR machine (such as the GeneAmp PCR System 9700) for amplification, a third colour probe (ROX) normalizes the fluorescence measurement. The LightCycler software within the LC480 II machine determines the sample genotypes automatically by measuring the intensity distribution of the two probes after a PCR amplification step. The relative dye intensities are then visualized in a scatter (cluster) plot that discriminates them as wild-type, heterozygous mutant, or homozygous mutant samples. The LightCycler software automatically groups similar samples and assigns genotypes based on the intensity distribution of the two dyes. The KASP amplification conditions included one cycle of KASP unique Taq activation at 94°C for 15 min, followed by 36 cycles of denaturation at 94°C for 20 s, and annealing and elongation at 60°C (dropping 0.6°C per cycle) for 1 min. Endpoint detection of the fluorescence signal was acquired for 1 min at 30°C when using the LightCycler 480 II real time-PCR System or read using the FLUOstar Omega Microplate reader (BMG Labtech, SA) when using the GeneAmp PCR System 9700. For fluorescence detection, the filter combination for the Excitation and Emission wavelength of both dyes was set at 465 – 533 (FAM) and 523 – 568 (HEX), respectively, when using LC480 II, and 485 - 520 (FAM), 544 - 590 (HEX) and 584 - 620 (ROX) when using FLUOstar Omega Microplate reader. The genotype calls were exported from the LightCycler software as fluorescent intensities of each sample in ".txt" file format and imported for analysis in the KlusterCaller analysis software (LGC Biosearch Technologies). The KlusterCaller software adjusted the cluster plot axes to enable the proper calling of genotypes. The genotype calls were grouped as homozygous for allele X (allele reported by FAM, X-axis), homozygous for allele Y (allele reported by HEX, Y-axis), heterozygous (alleles reported by FAM and HEX, between X- and Y-axis), or uncallable. The result from the KlusterCaller was exported in two file formats (".csv" and ".txt"). The ".csv" file was imported into the SNPviewer2 version 4.0.0 software (LGC Biosearch Technologies), where the cluster plot image was viewed and downloaded for publication. The genotype calls in the ".txt" file were used to calculate the genetic distance using the PowerMaker 3.25 statistical software34.
Table 3. List of KASP single nucleotide polymorphisms (SNPs) used in the QC experiments.
LEGEND: QC = Quality control; GID = Genetic Identity; HV = Hybrid verification; GCP/IBP = Generation Challenge Program/Integrated Breeding Platform. Source: Integrated Breeding Platform (Accessed June 26, 2020).
Table 4. List of trait-specific KASP single nucleotide polymorphisms SNPs used in the MAS experiment.
Source data
The list of KASP SNPs for genotyping maize was obtained freely from the Integrated Breeding Platform website.
The trait-specific KASP SNPs (Supplementary Table 1, Underlying data) and QC KASP SNPs (Supplementary Table 2, Underlying data) were purchased as KBDs (KASP-by-Design) from LGC Biosearch Technologies, UK, for use in our laboratory.
Results
Optimising in-house genotyping workflow
Our laboratory's routine sampling procedure spans seven days, from plant sampling and preparation to DNA extraction and quantitation. We present an expedited workflow (Figure 1) that ensures a good sample tracking system. Firstly, barcoding software, barcode readers, barcode labels, and barcode printers were introduced to facilitate sample tracking and data management. Waterproof/tear-proof tags and labels designed using BarTender barcoding software (Seagull Scientific) were printed using ZT230 Printer (Zebra, USA) and attached to plants before sample collection. Plate maps created in the BarTender software were linked to the sample location on the field and in the lab storage facility. Next, young plant leaf tissues were collected by punching leaf discs directly into the 96-well 1.2 mL polypropylene cluster tubes in wet-ice cooler bags, which reduced the sampling time and the time required for freeze-drying.
Figure 1. Diagram showing improvement to minimize bottlenecks in the genotyping workflow.
The sample DNA was extracted using the DArT DNA extraction protocol, slightly modified to maximise reagent and increase throughput, by using a reduced volume of reagents optimised to extract maize DNA from a smaller amount of leaf tissue (16–20 leaf discs, 4.0 mm). We also used freeze-dried leaf tissue, which allowed grinding using an automated high-throughput tissue homogeniser, Geno/Grinder 2010, with a 384-samples grinding capacity (4 × 96-sample plates) in two minutes.
The UV absorbance protocol for the FLUOstar Omega microplate reader (BMG LABTECH) was used to measure the concentration and purity of the DNA samples. By using this method, the 637 DNA samples were quantified in less than 10 minutes. The DNA purity (A260/A280 ratio) ranged from 1.7 to 2.0, with an average concentration of 985 ng/µl.
Following the optimized workflow, the total time from sampling and processing to DNA extraction and quantitation of the 637 leaf samples was reduced from seven to five days.
In order to optimise and use the KASP system in-house, KASP assays and allele-calling software (KlusterCaller) were purchased from LGC, UK. The amplification parameters on the compatible PCR (GeneAmp 9700) and real-time PCR machines (Roche LightCycler 480 II) were optimised. Microtiter 96- and 384-well plates compatible with the different machines were acquired from Roche, Germany. We also optimised the FLUOstar Omega microplate reader for fluorescence measurement of amplified products following the manufacturer's manual. Then, we ran a KASP trial kit provided freely by LGC Biosearch to test for functionality with the different amplification equipment.
Application of the optimised genotyping workflow
Following the KASP set-up, we genotyped plant samples for QC and MAS in-house, with low-density markers. The QC genotyping ensured on-time identification of errors and mislabeling in inbred lines and false hybrids in F1 maize breeding populations. Using the in-house KASP genotyping platform significantly reduced genotyping cost and time compared to outsourcing.
Genetic identity. Using a subset of 28 maize QC KASP SNPs, we were able to identify the genetic origin of a set of twenty well-adapted maize varieties originating from IITA, which were regenerated at four other locations. Genetic identification was performed using the original maize varieties' molecular marker profile and the genetic distance approach. Seed sources having <5% genetic distance were considered the same. The genetic distance among the four original maize lines, and between lines from IITA and each of the four seed sources, was calculated using PowerMaker 3.25 statistical software. The genetic distance among the four designation lines from IITA ranged from 0.0563 to 0.1239, indicating that the lines were different. The genetic distance among the different seed sources of the same line designation was: 0.0105-0.0314 (SAMMAZ15), 0.0105–0.0418 (SAMMAZ16), 0.0105–0.0837 (SAMMAZ27), and 0.000–0.0563 (SAMMAZ39). The SNPviewer, a tool that enables viewing genotyping data as a cluster plot, was used to view and generate an image of the genotyping result. The SNPviewer image showed that designated lines from three out of the four seed sources grouped with lines from IITA (Figure 2). The dendrogram image (Figure 3) also showed a grouping of different seed sources of the same line designation except for SAMMAZ39-1, SAMMAZ16-3, and SAMMAZ27-4. This clustering pattern indicates that all seeds from the same line had a common origin. SAMMAZ27-4 appeared to be genetically distant from SAMMAZ27-IITA by 0.0837. However, it grouped with SAMMAZ15 (Figure 3: blue circle), suggesting a possible mislabeling or mix-up of seeds during harvesting and storage. SAMMAZ16-2 and SAMMAZ39-1 grouped on a different tree limb (Figure 3: red circle), indicating possible pollen contamination or seed mix-up during handling.
Figure 2. SNPviewer screenshot showing clustering of IITA's maize lines with same lines from three out of four seed sources.
(a) Sammaz15-2, -3, and -4 grouped with IITA’s Sammaz15 (blue dots) using SNP PZA02746_2; (b) Sammaz39-2, -3, and -4 grouped with IITA’s Sammaz39 (blue dots) using SNP PZB01658_1. For each SNP marker, blue dots represent homozygous genotypes, green dots represent heterozygote genotypes, and the black dots represent no-template controls (NTC) as indicated on the left side of each image.
Figure 3. Neighbor-joining tree for four maize varieties taken from five seed sources based on genetic distance, performed with 1,000 bootstrap.
Bootstrap values are indicated on the tree branches. The suffixes "-1", "-2", "-3", "-4", and "-IITA", after line name indicate seed source 1, 2, 3, 4, and IITA.
Hybrid verification. In another QC experiment using our workflow, we screened two groups of F1 plants for hybrid verification, including their parental inbred lines, with 10 KASP SNP markers. The parental inbred lines were screened with an initial 50 KASP SNP taken from a defined panel of maize QC KASP markers to identify polymorphic markers. Only 10 KASP markers polymorphic between the parental lines were used to screen the F1 plants to verify their parentage. The KASP genotyping assay was useful in distinguishing between the parental genotypes and identifying the true hybrid lines. Cluster analysis of Group1 F1s (Figure 4) grouped the genotypes into three clusters. The heterozygous F1 progenies were in the middle of the plot, and the homozygous parental inbred lines diverged from each other (along the X- and Y-axis of the plot) for all markers. The genotyping result (Table 5) and the clustering pattern indicate that the F1 progenies were true hybrids. Similar clustering was observed among F1s in Group 2 except in Set 3b, where 38 F1s grouped with parental genotypes. The homozygous F1s could be due to contamination from foreign pollens during the crossing in the field or seed mix-up during storage or planting.
Figure 4. SNPviewer screenshot showing the result of hybrids verification in two sets of F1 Plants.
(a) Genotyping 12 F1 lines produced from a cross between KS23-5 and IITATZI1653, using SNP PZA03135_1. (b) Genotyping of 12 F1 lines produced from a cross between KS23-5 and IITATZI1667, using SNP PZA02779_1. For each SNP marker, blue dots represent homozygous parental genotype reported by FAM, red dots represent homozygous parental genotype reported by HEX, green dots represent heterozygous hybrid genotypes, and the black dots represent no-template controls (NTC). Legend: FAM = Carboxyfluorescein; HEX = Hexachloro-fluorescein.
Table 5. KASP genotyping result for the hybrid verification experiment.
Nonetheless, the KASP genotyping assay suffers some genotyping errors, especially during the automatic calling of genotypes. For instance, one F1 line (SCH-4) developed from the bi-parental cross, KS23-6 and IITATZI1653, appeared to cluster with the parent 2 (IITATZI1653) when genotyped with marker PZB01658_1 (Figure 5). The datapoint representing IITATZI1653 (Figure 5, information in the yellow square) was plotted higher up, away from the X-axis, which brought it closer to the datapoint representing SCH-4 plotted slightly away from the other F1s in the middle. Because genotype calls are generated based on the relative position of datapoints on the plot, SCH-4 was automatically called as the nearby parental genotype, A:A, which was an error seeing that line SCH-4 was heterozygous (true hybrid) for the rest of the markers. The upward positioning of line IITATZI1653 away from the X-axis could be possibly due to trace contamination of line IITATZI1653 sample DNA with line KS23-6 sample DNA during sample preparation. A monomorphic marker is seen in the genotyping of F1 lines developed from the bi-parental crosses KS23-3 x IITATZI1653 using marker PHM5502_31.
Figure 5. SNPviewer screenshot showing the result of hybrid verification of F1 Plants.
Genotyping 12 F1 lines produced from a cross between KS23-6 and IITATZI1653, using SNP PZB01658_1.The blue dots represent homozygous parent 2 (IITATZI1653) genotype reported by FAM, red dots represent homozygous parent 1 (KS23-6) genotype reported by HEX, green dots represent heterozygous (F1s) genotypes, and the black dots represent no-template controls (NTC). Legend: SCH-4 = Single cross hybrid (F1) sample 4; FAM = Carboxyfluorescein; HEX = Hexachloro-fluorescein.
Marker-assisted backcrossing. We performed multiple field selections annually by applying our workflow in MAS projects, which accelerated the maize breeding process. For instance, in the MABC project, a set of trait-specific KASP SNPs was used to select 24 BC1S2 maize lines potentially introgressed with resistance to aflatoxin accumulation after four selection cycles in less than two years. Potentially introgressed lines are undergoing field evaluation under artificial infestation for resistance to aflatoxin accumulation. The result of the MAS of high PVA lines, on the other hand, identified nine out of 70 inbred maize lines harbouring favourable alleles of the crtRB1 gene, which is associated with high PVA content in maize.
Discussion
There are different methods of plant tissue sampling, including collecting samples in silica gel35, NaCl/CTAB36, alcohol37, blotter paper, gel pack, dry ice, and liquid nitrogen38. These methods provide reasonably good quality and quantity of DNA for molecular marker genotyping. However, deciding which method to use is based on the number of samples and distance from the field to the laboratory38. We routinely use wet ice in Styrofoam boxes and cooler bags. It is cost-effective and suitable for close-proximity sample collection, and leaf samples are preserved by freeze-drying39 before DNA extraction. We collected fresh leaf tissues directly into 96-well extraction tubes rather than the traditional jute or tea bags, which means our procedure provides high throughput sampling. This sampling process also ensured that sample DNA was not degraded by prolonged exposure of leaf tissues to moisture as it occurs in post-freeze drying cutting of leaf tissues stored in jute and tea bags.
Our protocol aimed to extract high-quality DNA suitable for KASP genotyping from a smaller amount of leaf tissues. The reduced sample volume lowered the cost of reagents and the time for DNA extraction. The automated grinding in 96-well plates increased throughput and minimised the time required for manual grinding. Thus, this method would benefit MAS breeding programs that often screen thousands of plant samples each season40. A similar high-throughput result was achieved by Anderson et al. (2018)41. They optimised the DNA extraction method by Whitlock et al. (2008)42, used a 96-well plate for extraction and achieved a consistent yield across the plate with a low failure rate.
Three steps of the original DArT DNA extraction method were slightly modified to achieve our aim. The first modification was made in the sample grinding step, where we used dried leaf tissues instead of fresh ones;—using dried samples enabled high-throughput grinding using a Geno/Grinder, reducing the time used in manual grinding with liquid nitrogen. The second modification was at the alcohol precipitation step: the sample tubes were incubated at -20°C for 30 minutes after adding the ice-cold isopropanol, instead of only mixing by inversion. This incubation is necessary for slow and complete DNA precipitation. The third modification was reconstituting the DNA pellet: we dissolved the DNA in a solution of nuclease-free water and RNaseA instead of using a Tris-EDTA (TE) buffer to prevent the chelating effect of EDTA on Mg2+ during PCR43,44. The success of the KASP genotyping experiment is dependent on the quality and quantity of genomic DNA. Usually, a final minimum DNA concentration of 5 ng/µl is required for maize, to generate clear and consistent allele calls using the KASP assay45. Our slightly modified DNA extraction method provided good quality DNA, suitable for KASP genotyping. Jain et al. (2013) extracted suitable quality DNA from honey that was amplifiable by PCR, using an optimised DArT DNA extraction protocol.
Some commonly used DNA quality and quantity analysis methods include agarose-gel electrophoresis, fluorescence, and Ultraviolet (UV) absorbance-based measurement38. Fluorescence-based measurement using DNA-binding dyes such as PicoGreen is fast, sensitive, and dsDNA-specific; however, it comes with the DNA-binding reagent's added cost46,47. Agarose gel electrophoresis is laborious and carries the risk of exposure to hazardous chemicals like ethidium bromide47. The UV absorbance measurement is the most common DNA quantitation method. It is based on DNA absorbing UV light at a specific wavelength; DNA concentration is calculated by measuring the absorbance at 260nm and using the relationship A260 of 1.0 equals 50 µg/ml pure dsDNA46. DNA purity is estimated based on two UV absorbance ratios: A260/A280 ≥1.7 and A230/A260 ≥ 1.5 for pure DNA46. Our workflow optimized the nucleic acid quantitation method to a high throughput using a microplate reader and 96- and 384-well plates. The FLUOstar microplate reader uses ultrafast UV/Vis spectrometers for absorbance measurements, measuring 96 samples (96-well plate) to 384 samples (384-well plate) simultaneously within one second per well. It combines speed and the acquisition of complete absorbance spectra (220 to 1000 nm), making it ideal for nucleic acid quantification48.
Although outsourcing KASP offers a lower cost per data point, this lower genotyping cost is usually driven by a high volume of samples, impracticable for most MAS projects genotyping smaller sample volumes with select markers49. Our in-house genotyping system provides reduced cost, mainly from logistics, and faster data turn-around times, ultimately accelerating the genotyping workflow.
A few studies serve as the benchmark for QC analysis in maize using the KASP genotyping system. Semagn et al. (2012) suggested using a subset of 50 to 100 KASP markers for routine QC; Chen et al. (2016) used a smaller subset of markers (10 markers) to assess mislabeling of entries across a panel of CIMMYT Maize Lines (CMLs) achieving up to 99% detection probability. The latter also proposed using a rapid QC approach, with a smaller subset of markers, to ensure effective QC, lower genotyping costs, and shorten data turn-around time during seed production. Using a subset of markers, we were able to identify seed mix-up and labelling errors. For instance, the grouping of SAMMAZ27-4 with SAMMAZ15 (Figure 3: blue circle) suggests a possible mislabeling or mix-up of seeds during harvesting and storage. Also, the grouping of SAMMAZ16-2 and SAMMAZ39-1 (Figure 3: red circle) indicates possible pollen contamination or seed mix-up during handling. Similar errors due to seed mix-up and contamination were reported in Semagn et al. (2012), where 50 KASP SNPs were used to determine genetic identity among two to four seed sources of the same inbred line. Ertiro et al. (2015) also reported a high discrepancy in genetic purity and identity by the origin of seed sources irrespective of the genotyping platform used. They concluded that using a small subset of pre-selected high-quality markers was sufficient for performing QC analysis using low-marker density genotyping platforms like KASP. This study showed that the rapid QC method using 28 KASP SNPs efficiently distinguished the four maize varieties taken from five seed sources.
Hybrid verification is often performed during seed production or population breeding to confirm that a particular hybrid is derived from the intended parental lines (free from contamination by foreign pollens). Reducing the data turn-around time is essential to ensure that an accurate hybrid is selected to be carried forward in breeding programs or dissemination to farmers in seed production33. A reduced turn-around time also saves the cost of inputs applied to undesired genotypes since they can be discarded as soon as they have been identified upon genotyping. Our expedited workflow was able to achieve this. The possibility of contamination by self-pollination or foreign pollen exists; as such, hybrid verification is necessary to enable a seed producer to check whether accurate crosses are made for the production of the hybrid; this increases the confidence of the end-users on the quality and integrity of seeds produced33. Our results showed that 10 KASP markers were sufficient in distinguishing between maize parental inbred lines and identified true hybrid lines, residual contaminations, and possible sampling errors. A small subset of KASP markers has also been used to verify hybrids in other plant species. Patterson et al. (2017)50 achieved a highly accurate picture of Myriophyllum species distribution dynamics in North American lakes by genotyping 39 individuals from both parental watermilfoil and their hybrids, using a subset of three KASP markers. Osei et al. (2020)51 used 38 KASP markers to screen tomato genotypes to identify true F1 hybrids for the possible development of inbreds with long shelf life through marker-assisted backcrossing (MABC).
Following our optimised workflow, we were able to identify high-PVA maize lines harbouring the favourable allele of the crtRB1 gene, which could serve as donor lines for the maize PVA breeding program. The KASP-based selection of aflatoxin-resistant maize lines promises to fast-track the development of tropical lines resistant to aflatoxin, which will contribute to genetic gain in maize production. Similar success was achieved by the Biotechnology Center of the University of California, Davis, USA, where KASP SNPs associated with Phytophthoria capsici resistance were used to identify and selectively breed pepper strains52. So far, we have generated over 2,000 data points using our in-house genotyping workflow. Applying our optimised workflow to the QC and MAS experiments outlined above reduced the volume of reagents and consumables used, shortened the data turn-around, and ultimately accelerated the crop improvement process.
Conclusions
This study describes for the first time an improvement of an entire conventional DNA-based genotyping workflow, including the benchmark KASP genotyping platform in-house in our facility to fast-track molecular marker-based selection for crop improvement. We acknowledge the initial capital investment to procure some of these instruments. However, it is not always necessary to equip each lab or breeding program. The use of shared facilities locally and regionally, and the re-purposing of existing equipment such as the PCR machine and the spectrophotometer, help overcome the high cost of essential instruments. The improved genotyping workflow promises to accelerate the marker-assisted selection process and push crop improvement activities to attain the yield potential over a shorter time period. The result of this work can be readily adopted by national institutions, public and small plant breeding laboratories in developing countries to accelerate molecular marker-based genotyping for crop improvement activities, including QC and MAS. The results will also be helpful to accelerate the QC activities of seed producers and facilitate cultivar identification and adoption-tracking studies.
Data availability
Underlying data
Figshare: SNP data for "Developing and deploying an efficient genotyping workflow for accelerating maize improvement in developing countries.", https://doi.org/10.6084/m9.figshare.1715791453.
This project contains the following underlying data:
- Supplementary Table 1. List of trait-specific KASP SNPs used in the MAS experiment with sequence information
- Supplementary Table 2. List of KASP SNPs used in the QC experiments with sequence information
Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
Download
Export To
metrics
| Views | Downloads | |
|---|---|---|
| Gates Open Research | - | - |
|
PubMed Central
Data from PMC are received and updated monthly.
|
- | - |
Citations
CITE
how to cite this article
Offornedo Q, Menkir A, Babalola D and Gedil M. Developing and deploying an efficient genotyping workflow for accelerating maize improvement in developing countries [version 3; peer review: 3 approved, 1 approved with reservations]. Gates Open Res 2022, 6:3 (https://doi.org/10.12688/gatesopenres.13338.3)
NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.
track
receive updates on this article
Track an article to receive email alerts on any updates to this article.
Comments on this article Comments (2)
Version 3
VERSION 3
PUBLISHED 03 Aug 2022
Revised
Version 1
VERSION 1
PUBLISHED 17 Jan 2022
Discussion is closed on this version, please comment on the latest version above.
-
Author Response 15 Mar 2022Queen Offornedo, Bioscience Center and Maize Improvement Program, International Institute of Tropical Agriculture (IITA) Headquarters, Ibadan, 200001, Nigeria15 Mar 2022Author ResponseThe authors thank all who have viewed and downloaded our article.
We would appreciate it if you could take a minute to tell us what you think of the article ... Continue reading -
Discussion is closed on this version, please comment on the latest version above.
Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations -
A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
Competing Interests Policy
Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
Examples of 'Non-Financial Competing Interests'
- Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper.
- You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors.
- You are a close professional associate of any of the authors (e.g. scientific mentor, recent student).
- You work at the same institute as any of the authors.
- You hope/expect to benefit (e.g. favour or employment) as a result of your submission.
- You are an Editor for the journal in which the article is published.
Examples of 'Financial Competing Interests'
- You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements.
- You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors.
- You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on.
Stay Updated
Sign up for content alerts and receive a weekly or monthly email with all newly published articles
Register with Gates Open Research
Already registered? Sign in
Comments on this article Comments (2)
We would appreciate it if you could take a minute to tell us what you think of the article ... Continue reading The authors thank all who have viewed and downloaded our article.
We would appreciate it if you could take a minute to tell us what you think of the article in the "Add a comment" section.
Thank you.
We would appreciate it if you could take a minute to tell us what you think of the article in the "Add a comment" section.
Thank you.