Why marker assisted gene pyramiding?
For traits that are simply inherited, but that are difficult or expensive to measure phenotypically, and/or that do not have a consistent phenotypic expression under specific selection conditions, marker-based selection is more effective than phenotypic selection.
Traits which are traditionally regarded as quantitative and not targeted by gene pyramiding program can be improved using gene pyramiding if major genes affecting the traits are identified.
Genes with very similar phenotypic effects, which are impossible or difficult to combine in single genotype using phenotypic selection, can be pyramided through marker assisted selection.
Markers provides a more effective option to control linkage drag and make the use of genes contained in unadapted resources easier.
Pyramiding is possible through conventional breeding but is extremely difficult or impossible at early generations..
DNA markers may facilitate selection because DNA marker assays are non destructive and markers for multiple specific genes/QTLs can be tested using a single DNA sample without phenotyping.
CONCLUSION:
• Molecular marker offer great scope for improving the efficiency of conventional plant breeding.
• Gene pyramiding may not be the most suitable strategy when many QTL with small effects control the trait and other methods such as marker-assisted recurrent selection should be considered.
• With MAS based gene pyramiding, it is now possible for breeder to conduct many rounds of selections in a year.
• Gene pyramiding with marker technology can integrate into existing plant breeding program all over the world to allow researchers to access, transfer and combine genes at a rate and with precision not previously possible.
• This will help breeders get around problems related to larger breeding populations, replications in diverse environments, and speed up the development of advance lines.
For further queries please contact at isag2010@gmail.com
Marker Assisted Selection in Crop BreedingPawan Chauhan
Marker Assisted Selection is a value addition to conventional methods of Crop Breeding. It has been gaining importance in plant breeding with new generation of plant breeders and to get accurate and fast desired result from plant breeding.
Molecular Breeding in Plants is an introduction to the fundamental techniques...UNIVERSITI MALAYSIA SABAH
This slide describe the process of molecular breeding in plants which involves the application of molecular markers for Marker Assisted Selection and Marker Assisted Breeding.
Marker Assisted Selection in Crop BreedingPawan Chauhan
Marker Assisted Selection is a value addition to conventional methods of Crop Breeding. It has been gaining importance in plant breeding with new generation of plant breeders and to get accurate and fast desired result from plant breeding.
Molecular Breeding in Plants is an introduction to the fundamental techniques...UNIVERSITI MALAYSIA SABAH
This slide describe the process of molecular breeding in plants which involves the application of molecular markers for Marker Assisted Selection and Marker Assisted Breeding.
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Within the last twenty years, molecular biology has revolutionized conventional breeding techniques in all areas. Biochemical and Molecular techniques have shortened the duration of breeding programs from years to months, weeks, or eliminated the need for them all together. The use of molecular markers in conventional breeding techniques has also improved the accuracy of crosses and allowed breeders to produce strains with combined traits that were impossible before the advent of DNA technology
I would like to share this presentation file.
Some basics information regarding to molecular plant breeding, hope this help the beginner who start working in this field.
Thanks for many original source of information (mainly from slideshare.net, IRRI, CIMMYT and any paper received from professor and some over the internet)
Multiple inbred founder lines are inter-mated for several generations prior to creating inbred lines, resulting in a diverse population whose genomes are fine scale mosaics of contributions from all founders.
QTL is a gene or the chromosomal region that affects a quantitative trait, which should be polymorphic (have allelic variation) to have an effect in a population, must be linked to a polymorphic marker allele to be detected. The QTL mapping consists of 4 steps, like the development of mapping population, generation of polymorphic marker data set among the parents, construction of linkage map, and finally the QTL analysis
All the above steps are described in these slides very briefly along with two case studies.
A concise and well fabricated presentation the current techniques used for plant genome editing including CRISPER/cas9 system, TALENS, TELES, ZINC FINGER NUCLEASES(ZFN), HEJ (homologous endjoing) and many other high throughout techniques along references.
Yellow rust seminar by Priyanka (Phd Scholar Genetics and Plant Breeding CSK ...Priyanka Guleria
This seminar explains about the yellow rust disease of wheat: Its genetics and prevention methods as well as molecular techniques to combat yellow rust
Association mapping, also known as "linkage disequilibrium mapping", is a method of mapping quantitative trait loci (QTLs) that takes advantage of linkage disequilibrium to link phenotypes to genotypes.Varioius strategey involved in association mapping is discussed in this presentation
Within the last twenty years, molecular biology has revolutionized conventional breeding techniques in all areas. Biochemical and Molecular techniques have shortened the duration of breeding programs from years to months, weeks, or eliminated the need for them all together. The use of molecular markers in conventional breeding techniques has also improved the accuracy of crosses and allowed breeders to produce strains with combined traits that were impossible before the advent of DNA technology
I would like to share this presentation file.
Some basics information regarding to molecular plant breeding, hope this help the beginner who start working in this field.
Thanks for many original source of information (mainly from slideshare.net, IRRI, CIMMYT and any paper received from professor and some over the internet)
Multiple inbred founder lines are inter-mated for several generations prior to creating inbred lines, resulting in a diverse population whose genomes are fine scale mosaics of contributions from all founders.
QTL is a gene or the chromosomal region that affects a quantitative trait, which should be polymorphic (have allelic variation) to have an effect in a population, must be linked to a polymorphic marker allele to be detected. The QTL mapping consists of 4 steps, like the development of mapping population, generation of polymorphic marker data set among the parents, construction of linkage map, and finally the QTL analysis
All the above steps are described in these slides very briefly along with two case studies.
A concise and well fabricated presentation the current techniques used for plant genome editing including CRISPER/cas9 system, TALENS, TELES, ZINC FINGER NUCLEASES(ZFN), HEJ (homologous endjoing) and many other high throughout techniques along references.
Yellow rust seminar by Priyanka (Phd Scholar Genetics and Plant Breeding CSK ...Priyanka Guleria
This seminar explains about the yellow rust disease of wheat: Its genetics and prevention methods as well as molecular techniques to combat yellow rust
Priorities of breeding approaches in bt cottons.dr. yanal alkuddsiDr. Yanal A. Alkuddsi
In few years of Bt era – over Six hundred of Bt cotton hybrids are released – Just Handful of them are popular
Ultimately it’s the genetic potentiality for productivity that determines success of a Bt genotype
Breeding efforts of improving genetic potentiality of Bt cottons assumes greater importance
Possible New Species of Araecerus (Coleoptera: Anthribidae) associated with M...Agriculture Journal IJOEAR
— Araecerus is genus of beetles of the Anthribidae family which are important economic pests of various crops including coffee (Rubiaceae), with A.fasciculatus (Degeer) being the common pest (weevil) of coffee beans. This paper presents a study in which five undescribed species of genus Araecerus were reared predominantly from the seeds of M.pachyclados (Rubiceae), a native tree of Papua New Guinea (PNG). Fruits of M. pachyclados were regularly sampled and insects attacking them were reared, preserved and identified. Fruits were hand collected, photographed, weighed and reared. Insects emerging from the fruits were captured and preserved in 99% ethanol. All the specimens were identified into morphospecies at the laboratory. The five new species discovered were designated as A. sp.1, A. sp.2, A. sp.3, A. sp.4 and A.sp.5. This was accorded based on differences in body length; scutellum color, size, hair-scales and visibility; length of first and second segments of fore tarsus; apical and subapical teeth-size (mandible and maxillary palpi); declivity of dorsal abdomen; basal-anterior eye markings; lateral eye markings; absence of eye markings; and shape of pygidium. We discovered A. sp.1 has yellowish gold marking inside the base of the eye, A. sp.2 with pygidium almost vertically-flat at abdominal apex, A. sp.3 has eyes without yellowish gold marking and generally dark in color, A. sp.4 with distinct yellowish gold interior-lateral marking in its eye, and A. sp.5 with pygidium pointed at abdominal apex.
Characterization of finger millet blast pathogen (Pyricularia grisea) and Its...ILRI
Presented by Getachew Gashaw, Tesfaye Alemu and Kassahun Tesfaye at the First Bio-Innovate Regional Scientific Conference, Addis Ababa, Ethiopia, 25-27 February 2013
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
2. Xa4
Gm2, Pi-7(t)
Marker Assisted Gene Pyramiding for
Disease Resistance in Rice
Pi-5(t) Xa3 Pi-2(t) Xa5 Bph2
Xa7 Xa21 Pi-4(t) Pi(t) Xa13 Gm4t
Name of speaker: - Thakare Indrapratap S. Course No: - MBB 692
Degree : - Ph.D(Agri.) Reg No:- 04-1247-2010
Major Guide : - Dr. Patel D. B. Date : - 06/04/2013
Minor Guide : - Dr. Fougat R. S. Time : - 16.00 hrs 2
4. INTRODUCTION
Rice is the world’s most important food crop and a staple food for
more than half of the world’s population. More than 90% of the
world’s rice is produced and consumed in Asia, where 60% of the
people live.
In the last six decades, rice production has steadily kept in pace with
the population growth rate, mainly due to the gains from the
technologies of green revolution era such as semi-dwarf, fertilizer
responsive high yielding varieties and other associated managerial
technologies.
Rice is the 1st choice of Biotechnologists
Rice is a model crop for genetic and breeding research
Small genome size :45 x 10 6 bp. Gene bank with 1,00,000
Highly dense molecular map accessions
Several wild species
YAC and BAC libraries
Transformation protocols &
T-DNA insertion and deletion
4 mutants Huge database
4
5. AREA, PRODUCTION AND PRODUCTIVITY OF RICE
Table no.1 Area, production and productivity
Area Productivity
Regions Production (MT)
(Mha) (kg/ha)
World
India 44 2207 100
Gujarat 0.68 1903 1.62
5
6. LIST OF VARIOUS DISEASES IN RICE
Table 2 :Estimated yield loss range in yield
Bacterial Diseases
Estimated annual rice
1. Bacterial Blight [Xanthomonas oryzae pv. oryzae (Ishiyama) Swing et alloss %
.]
2. Bacterial Leaf Streak [Xanthomonas oryzae pv. oryzicola (Fang et al.) Swing et al.]
Diseases
Fungal Diseases
1. Rice Blast [Magnaporthe grisea (Cooke) Sacc.]
2. Sheath Blight [Rhizoctonia solani Kuhn]
Blast 40 – 75
Spot [Bipolaris oryzae
3. BrownBacterial leaf blight (Breda de Haan) Shoemaker] - 60
4. Leaf Scald [Microdochium oryzae (Hashioka &Yokogi) Samuels & I.C. Hallett]
5. Narrow Brown Spot [Cercospora janseana (Racib.) O. Const.]
20
6. Stem Rot [Sclerotium oryzae Cattaneo]
7. Sheath Rot [Sarocladium oryzae (Sawada) W. Gams & D. Hawksworth]
Brown spot 12- 43
8. Bakanae [Fusarium fujikuroi Nirenberg]
9. False SmutSheath blight virens (Cooke) Takahashi]
[Ustilaginoidea
Virus Diseases
7 – 40
1. Tungro [Rice tungro bacillifor virus and spherical virus]
2. Grassy Stunt [Ricesmut stunt virus]
False grassy
3. Ragged Stunt [Rice ragged stunt virus]
10 - 44
Nematode Diseases
1. Root Knot [Meloidogyne graminicola Golden & Birchfield]
2. White Tip [Aphelenchoides besseyi Christie]
Sheath rot 3 – 20
Rice knowledge portal, 6
7. Bacterial leaf Blight (Xanthomonas oryzae pv. oryzae)
Disease is characterized by linear yellow to
straw coloured stripes with wavy margin,
generally on both edges of the leaf, rarely on
one edge.
Stripes usually starts from tip and extend
downwards.
Drying, twisting of the leaf tip and rapid
extension of marginal blight lengthwise and
crosswise to cover large areas of leaf.
Blighting may extend to the leaf sheaths and
culms, killing the tiller or the whole clump.
The blight phase of disease usually appears 4-6
weeks after transplanting.
7
8. Table 3: Bacterial blight resistance genes in rice
Gene Cultivar Isolate/race References
Xa-1 and Xa-2 Kogyoku Japanese race I and II Sakaguchi (1967)
Ezuka et al., (1975), Ogawa et al.,
Xa-3 Wase Aikoku, Chukogu-45 Japanese race II and III
(1986)
Petpisit et al., (1977), Sidhu et al.,
Xa-4 IR20, IR22, IR1529-680-3 Philippine race I
(1978)
Petpisit et al., (1977), Sidhu et al.,
xa-5 IR1545-248, BJ-1,IR291-7, DV85 Japanese races (1978), Singh et al., (1983), Blair and
McCouch (1997)
Malaget sunsong, IR994-102,
Xa-6 IR1698-241, Zenith
Philippine race I Sidhu et al., (1978)
Xa-7 DV85, DV87 Philippine race I Sidhu et al., (1978,1979)
xa-8 P1231129 Philippine isolates Sidhu et al., (1978,1979)
Xa-9 Sateng Philippine isolates Singh et al., (1983)
Xa-10 Cas209 Philippine and Japanese isolates Yoshimura et al., (1983)
Ogawa and Yamamoto (1986),
xa-11 IR8, RP9-3 Japanese isolates
Ogawa et al., (1991)
Xa-12 Kogyoku and Java14 Japanese and Indonesian isolates Ogawa et al., (1978a,b)
xa-13 Long grain Philippine isolates Zhang et al., (1996b)
8 Xa-14 TN(1) Japanese isolates Taura et al., (1989)
8
Continue….
9. Gene Cultivar Isolate/race References
xa-15 M41 Japanese isolates Noda (1989)
Xa-16 Tetep and IR24 Japanese isolates Noda (1989)
Xa-17 Asominori Japanese isolates Ogawa et al., (1989)
Xa-18 Toyonishiki Burmese isolates Ogawa and Yamamoto (1986)
Xa-19 XM5 Japanese isolates Taura et al., (1991)
Xa-20 XM6 Japanese isolates Taura et al., (1992)
Xa-21 O. longistaminata Philippine and Japanese isolates Khush et al., (1990)
xa-22 Zhachanglong Chinese isolates Lin et al., (1996)
Xa-23 O. nivara Indian isolates Kumar (1999)
DV85, DV86,
Xa-24 Aus295
Philippine race 6 Lee et al., (2001)
Philippine, Chinese and Japanese
Xa25 HX3
isolates
Gao et al., (2001)
Xa26 Minghui 63 Chinese isolate Yang et al., (2003)
Philippine, Chinese and Japanese
Xa27 O. minuta
isolates
Gu et al., (2004)
Xa28 Lota sail Philippine 2 and 5 Lee et al., (2003)
Xa29 O. officinalis Not fully characterized Tan et al., (2004)
Xa30 O. rufipogan Philippine isolate Jaiswal et al., (2004)
9
Xa31 ZCL Chinese isolates Wang et al., (2008) 9
11. Large lesions usually develop a greyish
center, with a brown margin on older
lesions.
Under conducive conditions, lesions on
the leaves of susceptible lines expand
rapidly and tend to coalesce, leading to
complete drying of infected leaves.
Resistant plants may develop minute
brown specks, indicative of a
hypersensitive reaction.
Besides attacking the leaves, the fungus
may also attack the stem at the nodes,
causing neck rot, or at the panicle,
causing panicle blast.
11
12. Management
• Practicing field sanitation such as removing weed hosts,
rice straws, ratoons, and volunteer seedlings is
important to avoid infection caused by this disease.
• Proper application of fertilizer, especially nitrogen, and
proper plant spacing are recommended for the
management of bacterial leaf blight.
The use of resistant varieties is the most effective and
the most common management practices.
12
13. CONVENTIONAL TO MOLECULAR TECHNIQUES:
Through conventional breeding, Selection for crop improvement is carried out
on phenotypic character, which is the result of genotypic and environmental
effects.
Some traits like disease resistance are governed by two or more (poly)genes,
or may appear to be quantitatively expressed due to low heritability.
The difficulties of phenotype based selection can be overcome by direct
selection for genotype using DNA markers that co segregate with the genes of
interest (disease resistant genes etc.)
The development of DNA (or molecular) markers has irreversibly changed the
disciplines of genetics and plant breeding.
To date, many potential genes (including many single genes and QTL’s) that
confer resistance to potential plant pathogens have been mapped in
economical crops.
13
14. WHAT IS A MARKER?
All living organisms are made up of cells that are programmed by genetic material called
DNA. This molecule is made up of a long chain of nitrogen-containing bases (there are four
different bases-adenine [A], cytosine [C], guanine [G] and thymine [T]).
A Molecular marker is a small region of DNA showing sequence polymorphism in different
individuals with in a species (or) among different species.
It is readily detected and whose inheritance can easily be monitored.
A wide range of molecular techniques are now available to detect the polymorphism at
DNA level.
14
15. Hybridization based e.g RFLP
Hybridization based e.g RFLP PCR based
Arbirtary primers e. g RAPD, ISSR, AFLP Specific primer
Specific sequence based e .g SCAR, CAPs,
Repeat based e.g SSR
SNPs
15
17. MARKER-ASSISTED SELECTION
According to Bertrand and Mackill (2008), “The marker aided
selection (MAS) assumes that the target gene is identified and
selected based on the closely linked markers”. A successful
MAS requires that a gene be mapped and closely linked to a
marker, otherwise which is very difficult to examine or evaluate by
conventional approaches
Why Marker Assisted Selection ?
Selection at seedling stage possible
Selection of traits with low heritability
Distinguishing homozygotes from heterozygotes
Pyramiding of Resistance Genes
Selection for recessive gene, etc.
17
19. Reliability: Marker should co-segregate or be closely linked with the desired trait.
Marker A
QTL
<5 cM
DNA quality and quantity: some marker technique require large amount and high quality
of DNA.
Technical procedure: The screening technique should have high reproducibility across
laboratories.
Cost: It should be economical to use and be user friendly.
Level of polymorphism: Marker must be polymorphic.
Marker
Marker
19
20. P1 x P2
Susceptible Resistant
F1
F2 large populations (e.g. 2000 plants)
20
21. Plants are equipped with a variety of mechanisms to defend themselves
against infection by fungi, viruses, bacteria, nematodes, insects, and even
other plants.
After the rediscovery of Mendel’s laws, plant breeders have used disease
resistance (R) genes to produce more resistant varieties.
Plant defenses are activated by the specific interaction between the product of
a disease (R) resistance gene in the plant and the product of a corresponding
avirulence (Avr) gene in the pathogen (Flor, 1971).
Properties of R-gene:
“R” genes enable plants to recognize specific races of a pathogen and mount
effective defence response including a rapid induction of localized necrosis at
the site of infection (the hypersensitive response), increasing expression of
defence-related genes, production of anti microbial compounds, lignin
formation and oxidative burst in many plant-pathogen interactions
21
22. What is gene pyramiding?
Gene pyramiding is defined as a method aimed at assembling multiple
desirable genes from multiple parents into a single genotype for
specific trait.
Objectives:-
1. Enhancing trait performance by combining
two or more complementary genes
2. Remedying deficits by introgression of genes
X Major Gene from other sources
a
Pi-4 Pi 3. Increasing the durability of disease and/or
4
(t) (t) disease resistance
Minor Gene 4. Broadening the genetic basis of released
Xa Pi-
21
Xa5
2(t) cultivars
Xa Xa
7 3
Source of gene ?
22
23. WHY MARKER ASSISTED PYRAMIDING?
For traits that are simply inherited, Markers provides a more effective
but that are difficult or expensive to option to control linkage drag and
measure phenotypically, and/or make the use of genes contained in
that do not have a consistent unadapted resources easier.
phenotypic expression under
specific selection conditions! Pyramiding is possible through
conventional breeding but is
Traits which are traditionally extremely difficult or impossible at
regarded as quantitative and not early generations..
targeted by gene pyramiding
program! DNA markers may facilitate
selection because DNA marker
Genes with very similar phenotypic assays are non destructive and
effects, which are impossible or markers for multiple specific
difficult to combine in single genes/QTLs can be tested using a
genotype using phenotypic single DNA sample without
selection! phenotyping.
23
24. GENERAL PRINCIPLES AND MARKER ASSISTED GENE PYRAMIDING
Basic
assumptions
Locations of a series of genes of interest (target genes) thus the linkage relationship
between them is known
Target genotype for these genes is defined prior to selection as the genotype with
favorable alleles at all loci of interest
The genotype of an individual can be identified by these genes or markers linked to
them
A collection of lines containing all the target genes should be available
Minimal population size for recovery of desirable
genotype
Number of genes is large and/or linkage relationships are complex, many
computations are required if a purely mathematical prediction method is used
Computational requirements will be further increased if markers are not
completely linked to the target genes (i. e. are not diagnostic)
Special computer software has been developed to compute the frequencies of all
possible genotypes in the segregating populations (Servin et al., 2002)
24
25. MAIN FACTORS AFFECTING GENE PYRAMIDING
1. Characteristics of the target
traits/genes
The genes to be pyramided are functionally well characterized and markers
used for selection equal to the gene itself (perfect marker), gene pyramiding
will be more successful.
One or two markers per gene can be used for tracing the presence/absence of
the target genes.
Bulk Segregant Analysis (BSA) is the preferred method for identification of
markers tightly linked to a major gene (Michelmore et al., 1991)
In BSA, plants from a segregating population are grouped according to
phenotypic expression of the trait into two bulks.
These bulks are screened with a large numbers of markers to identify the
markers that are genetically linked to trait locus
25
25
26. 2. Reproductive
characteristics
Propagation capability of a crop is determined by the number of seeds
produced by a single plant.
A fairly large F2 population can be obtained by collecting seed from many
F1 plants of the cross between two homozygous parents, from F3
generation seed can only be collected from a single plant.
Efficiency of hybridization may be an important constraint for some crop
species.
When wild relatives are used as donor of desirable genes, many more
reproduction related constraints may exist including cross incompatibility
between wild species and cultivated crop.
26
31. INTEGRATING GENE DISCOVERY, VALIDATION AND PYRAMIDING
Advanced back cross QTL
Tanksley and Nelson (1996)
To identify and introgress favourable alleles from unadapted donors into elite
background.
Generating an elite by donor hybrid
Backcrossing to the elite parent to produce BC1 population which is subjected to
marker/or phenotypic selection against undesirable donor alleles
Genotyping BC2 or BC3 population with polymorphic molecular markers
Evaluating the segregating BC2F2 or BC2F3 population for traits of interest and QTL
analysis
Selecting target genomic regions containing useful donor alleles for the production of
NILs in the genetic background
Evaluation of the agronomic traits of the NILs and elite controls in replicated
environments
Ye and Smith.,
2008 31
32. Introgression lines (ILs)
Eshed and Zamir (1994a, 1994b)
ILs are produced by systematic backcrossing and introgression of marker
defined exotic segments in the background of elite varieties.
Considered to be similar to a genomic library with a huge genome of insert.
ILs enable phenotypic analysis of specific QTL and offer a common genetic
background in which direct comparison of two line can be used to evaluate
phenotype conditioned by a single introgressed exotic segment
(Tanksley et al., 1996)
ILs are a valuable resources for the unravelling of gene function by
expression profiling or map based cloning (Eshed and Zamir 1995)
If necessary, undesirable genes should and can be eliminated by
chromosome recombination in the progeny between IL and recurrent
parent and screened by MAS.
Ye and Smith.,
2008 32
34. Introgression of Xa4, Xa7 and Xa21 for resistance to bacterial blight in
1 thermo-sensitive genetic male sterile rice (Oryza sativa L.) for the
development of two-line hybrids
TGMS 1 x AR32-19-3-3 R=0-5 cm lesion
(No Xa gene) (Xa21) length
MR=5.1-10 cm
MS=10.1-15 cm
S=15.1 and above
F1 x 1R-BB4/7
(Xa4/Xa7)
Figure:- 4
(3-way cross) F1 Phenotypic
distribution of
1,364 F2 plants
F2 (1364 plants) from the cross of
TGMS 1/Ar32-19-
a) PXO99, 3/IRBB4+7.
Races of XOO b) PXO86,
pathogen
c) PXO61
Maligaya, Philippines Perez et 34
35. Table 5 : Mean lesion length of sterile F2
Table 4 :- Distribution of Xa gene/ gene 13 plants showing resistance
combination in 111 potential reaction to PXO61 , PXO86 &
TGMS F2 plants showing pollen PXO99 14 days after inoculation.
sterility under green house
condition and pollen fertility in
~25˚C indoor growth chambera
Xa gene/gene No of Mean lesion length(cm)
combinations plants
(F2) PX061 PX086 PX099
Xa4 alone 12 2.65 11.46 13.40
Xa7 alone 1 10.80 3.07 17.25
Xa4/Xa47 78 1.16 1.50 13.81
Xa4/Xa7/Xa21 20 1.27 1.65 4.72
Maligaya, Philippines Perez et
35
al.,2008
36. Table 6 :Fertile F2 plants showing highly resistant to
PXO61, PCO86 and PXO99
Xa Mean lesion Xa gene(s)
gene/gene length(cm) present
combinations
PX061 PX086 PX099
PR36944-96 0.73 1.00 1.50 Xa7 + Xa21
PR36944-131 0.55 0.45 2.37 Xa7 + Xa21
PR36944-158 0.57 0.57 2.17 Xa7 + Xa21
PR36944-169 0.45 0.53 1.55 Xa7 + Xa21
Fig 5 PCR detection of Xa7 and PR36944-175 0.45 0.44 0.8 Xa7 + Xa21(Aa)
Xa21 in representative F2 PR36944-176 0.53 0.53 2.77 Xa7 + Xa21
plants showing resistant PR36944-190 0.40 0.50 2.97 Xa7 + Xa21
reaction to three Xoo races. PR36944-452 1.25 0.80 2.00 Xa7 + Xa21
They found 11 lines with
PR36944-470 1.30 2.47 2.35 Xa21
presence of 294 bp alleles
carrying Xa7 gene alone. PR36944-1147 1.29 0.50 1.20 Xa7 + Xa21(Aa)
PR36944-1345 0.38 0.43 1.25 Xa7 + Xa21
Maligaya, Philippines Perez et
36
al.,2008
37. Marker assisted introgression of bacterial blight resistance in
2 Samba Mahsuri, an elite rice variety.
Table 7 : Microsatellite markers that are polymorphic
between SS1113 and Samba Mahsuri
Samba Mahsuri- medium
slender grain indica rice
variety
Very popular among farmer
and consumer
Highly susceptible to many
pest and diseases
Chemical control is not
effective
Hyderabad, India Sundaram et
37
al.,2008
38. Donor Line-SS1113 (Xa21,Xa13, Xa5) & Recipient line-Sambha Mashuri
Xa21- PTA248-0.2 cM
Xa13-RG136-~1.5 cM
Xa5-RG556-~0.1 cM
SS1113 (Xa21,Xa13, Xa5) X Sambha Mashuri
F1 Plants
Confirmed for heterozygosity using ‘R’ gene(s) linked markers
Back crossed with recurrent parent
11 plants heterozygous for three ‘R’ genes (Xa21,Xa13, Xa5)
Subjected to background selection using 50 SSR marker found to be
Polymorphic between the parental lines across the genome
Plants having maximum recurrent parent genome
were backcrossed to generate BC2F1 plants
BC4F1 stage
Selfing
BC4F2 lines
Screening for ‘R’ genes using linked molecular marker 38
39. Figure: - 6 Foreground selection at BC1 F1 generation using R gene
linked PCR based markers
Hyderabad, India Sundaram et
al.,2008 39
40. Table no 8: Number of R gene heterozygotes identified and estimation
of recurrent parent genome contribution.
Table no. 9: Number of line with multiple R gene combinations
Hyderabad, India Sundaram et
40
al.,2008
41. Fig 7: Evaluation of bacterial blight
resistance in gene pyramid lines.
Hyderabad, India Sundaram et 41
al.,2008
42. Table no 10: Grain yield of three-gene pyramid lines along with donor and
recipient lines as recorded in Advanced variety trial 1- NIL of All
India Coordinated Rice Improvement.
Hyderabad, India Sundaram et
al.,2008 42
43. Marker-assisted breeding of Xa4, Xa21 and Xa27 in the restorer lines of
3 hybrid rice for broad-spectrum and enhanced disease resistance to BLB
Introduced the Xa4, Xa21 and Xa27 genes into the restorer lines of Mianhui 725 or
9311 genetic backgrounds and pyramided the three R genes in the progeny derived
from the cross between the two lines.
NIL - Xa27 gene in the genetic background of 9311 [9311(Xa27)] and another line with
the Xa4 and Xa21 genes in the genetic background of Mianhui 725 (WH421) were
firstly developed through MAS.
A new restorer line carrying Xa4, Xa21 and Xa27, designated as XH2431, was selected
from the F8 progeny of the cross between 9311(Xa27) and WH421 through marker-
assisted breeding and pedigree selection.
XH2431 and II You 2431, the hybrids derived from cytoplasmic male-sterile line II-
32A and restorer line XH2431, conferred high resistance to all 23 Xoo strains collected
from 10 countries.
The development of XH2431, 9311(Xa27) and WH421 provides a set of restorer lines
with broad-spectrum and enhanced resistance to BB for hybrid rice.
Restorer lines CMS line Source of Res genes
Cultivar Res Genes
9311 IRBB27 Xa27
II-32A
Mianhui 725 IRBB21 Xa21
IR-64 Xa4
Singapore Luo et 43
44. Improvement of Restorer MH725 (Xa4 and Xa21)
(Xa4) IR64 X MH725 (Xa21) IRBB21 X MH725
BC4F1 plants X BC 4F1 plants
F1 plants (183 individuals)
Selection for Xa4 and Xa21 homozygous
plants were selected using markers RN224 and PTA248
F2 plants hommozygous for
(Xa4Xa4, Xa21Xa21)
WH421 44
45. WH421 X Improved R9311
(Xa4Xa4,Xa21Xa21) (Xa21Xa21)
F1
F2 (172)
First round of selection
Xa21 (PTA248)
54 plants homozygous for Xa27
Second round of selection Xa27 RFLP marker 5198
10 plants homozygous for Xa 21 & Xa27
Xa24-RM224
2 Plants (Xa4, Xa21, Xa27)
Intercrossed
Progeny was evaluated for agronomic traits
XA2431
45
46. Fig. 8 MAS of NIL of Xa27 in 9311 genetic Fig. 9 MAS of NIL of Xa4 and Xa21 in MH725
background. Genomic DNA of genetic background. PCR products
individual B6F2 plants, the Xa27 donor amplified from genomic DNA of F2
IRBB27 and the recurrent female individuls, the Xa4 donor IR64 and the
parent 9311 was digested with Xa21 donor IRBB21 with marker
restricted enzymes SpeI and SacI, RM224 for Xa4 (a) and marker pTA248
fractionated on a 0.8 % agarose gel and for Xa21 (b) were fractionated on 3.5 %
hybridized with the 32P-labeled Xa27 (a) and 1.5 % (b) agarose gels,
probe 5198 in Southern blot analysis respectively.
Singapore Luo et
46
al.,2012
47. Fig. 10 This figure shows selection of F2 plants crossing Xa4 and Xa21 in homozygous
condition
Singapore Luo et 47
al.,2012
48. Fine mapping and DNA marker-assisted pyramiding of the three major
4 genes for blast resistance in rice
Table no.11 : -Plant material , restriction enzymes and RFLP markers used in Southern analysis
Blast Chrom Isoline Donor Populat Number of RFLP Polymorp
resistan osome Parents ion Size restriction markers hic
ce gene enzyme tested markers
test
Pi1 11 C101LA Lac23 160 30 10 5
C
Piz5 6 C101A A5173 120 12 9 5
51
Pita 12 C101PK Pai-kan- 80 30 14 6
T tao
The polymorphic markers were subsequently identified and used to probe the
Segregating populations to identify any additional closely linked markers.
Manila, Philippines Hittalmani et
al.,2000 48
49. Table no12:- Distances of the DNA markers from the blast resistance genes on different
chromosomes
Gene Chromosome Marker Restriction Distance
enzyme
Pi1 11 Npb181 DraI 3.5 cM
RZ576 DraI 7.9 cM (14 cM)a
Piz5 6 RZ64 EcoRI 2.1 cM (2.8 cM) a
RZ612 EcoRI 7.2 cM
RG456 XbaI (5.4 cM) a
RG64-SAP HaeIII (2.8 cM) a
Pita 12 RG869 HEcoRV 5.4 cM (15.3 cM) a
RZ397 EcoRV 3.3 cM (18.1 cM) a
RG241 ScaI 5.2 cM
Manila, Philippines Hittalmani et
49
al.,2000
50. Fig.11 Schematic diagram showing marker-assisted selection for
pyramiding the three major genes for blast resistance
Manila, Philippines Hittalmani et
al.,2000 50
51. Table no 12: List of isogenic lines and the segregating populations
used in pyramiding gene
LINES/ LINE/ Resist Popl
VARIETIES CROSS genes size
CO39 Recurrent None
parent
C101LAC Isogenic line Pi1
C101A51 Isogenic line Piz5
C101PKT Isogenic line Pita A
F2 population
BL12 Pi1/Piz5 Pi1+Piz5 150
BL14 Pi1/Pita Pi1+Pita 250
BL24 Piz5/Pita Piz5+Pita 150
B
BL124 Piz5/Pi1/Pita Piz5+Pi1+Pit 180
a
Fig12 : PCR banding pattern of RG64 marker linked to Piz-5 blast resistant gene segregating in the F2
population of the cross C101PKT (isoline for Pita).
Manila, Philippines Hittalmani et
51
al.,2000
52. Table no 13: Evaluation of susceptibility of isolines and the gene
pyramids to select blast isolates
Lines IK81-25 C9232-5 C9240-2 C9240-5 V86010 P06-06
C101LAC S S MR R R R
C101A51 R R S S R R
C101PKT R R MR R S S
BL12 R R S R R R
BL14 R R S R R R
BL24 R R S R R R
BL124 R R S R R R
CO39 S S S S S S
Manila, Philippines Hittalmani et
al.,2000 52
53. Figure13: Identification of the Piz-5 resistant
gene in the F2 generation
segregating
Figure 14 : Two and the three gene pyramids
for the three genes.
as identified by RZ536 for Pi1
(A), RZ397 for Pita (B) and the
RG64 PCR marker for the Piz-5
gene (C) M= Molecular weight
Manila, Philippines markerHittalmani et
used as the standard
53
al.,2000
54. 5 Marker aided pyramiding of rice for BLB and blast disease
Using marker-assisted selection in a backcross breeding
program, four bacterial blight resistant genes namely Xa4,
xa5, xa13, Xa21 have been introgressed into the hybrid rice
parental lines KMR3, PRR78, IR8025B, Pusa 6B and the
popular cultivar Mahsuri.
Genes Markers Types
Xa21 pTA248 STS
Xa5 RM122 SSR
Xa4 Npb181 STS
Xa13 RG136 CAPS
Hyderabad, India Shanti et al., (2010)
54
55. Flow chart 1:- for Pyramiding restorer genes
55
Hyderabad, India Shanti et al., (2010)
56. Flow chart 2:- Pyramiding restorer genes
56
Hyderabad, India Shanti et al., (2010)
57. Figure 15: Foreground selection using resistance gene linked PCR based
markers for the four BB genes at BC1F1
57
Hyderabad, India Shanti et al., (2010)
59. Breeding of R8012, a Rice Restorer Line Resistant to Blast and Bacterial
6 Blight Through Marker-Assisted Selection
Made 25 crosses between five blast and five BB resistant germplasm accessions.
Only one pair of parents, DH146 × TM487, showed polymorphism for all the
markers to identify one blast resistance gene Pi25 and three BB resistance genes,
Xa21, xa13 and xa5, thus it was used in the marker-assisted selection (MAS).
F2 individuals of DH146 × TM487 were genotyped using flanking (SSR) markers of
RM3330 and sequence tagged site (STS) marker SA7 for Pi25.
The resistant F2 plants with Pi25 were used for pyramiding BB resistance genes
Xa21, xa13 and xa5 identified by the markers pTA248 (STS), RM264 and RM153
(SSR), respectively in subsequent generations.
After selection for agronomic traits and restoration ability among 12 pyramided
lines, they acquired an elite restorer line, R8012 including all four target genes
(Pi25+Xa21+xa13+xa5).
Hybrid Zhong 9A/R8012 derived from the selected line showed stronger resistance
to blast and BB, and higher grain yield than the commercial checks.
China Zhan et al.,2012 59
60. Fig. 16. Reaction of pyramiding parents inoculated with M. grisea
isolate 05-20-1 for neck blast resistance.
Parent TM487 was susceptible while parent DH146 was
resistant to the isolate 05-20-1.
China Zhan et al.,2012
60
61. Table 15. The linkage markers of the bacterial blight and blast
resistance genes and their primer sequences.
Gene Character Chr. Marker Distance Sequence of the primer (5’-3’)
(cM)
Xa21 Dominant 11 pTA248 0.0 F: AGACGCGGAAGGGTTCCCGGA
R: AGACGCGGTTCGAAGATGAAA
Xa13 Recessive 8 RM264 2.6 F: GTTGCGTCCTACTGCTACTTC
R: GATCCGTGTCGATGATTAGC
Xa5 Recessive 5 RM153 5.6 F: GCCTCGAGCATCATCATCAG
R: ATCAACCTGCACTTGCCTGG
Pi25 Dominant 6 SA7 1.7 F: CGGGTGAGTAAAACTTATCTGG
R:TAGTGATTGAAACGGGTGCACT
Pi25 Dominant 6 RM3330 2.4 F: ATTATTCCCCTCTTCCGCTC
R: AAGAAACCCTCGGATTCCTG
China Zhan et al.,2012
61
62. Table 16. Reaction of the 12 selected pyramided F3 lines after
inoculation with bacterial blight and blast pathogens.
• ++, +– and – – represent homozygote, heterozygote and negative genotypes
of the flanking markers, respectively.
• LR, Leaf blast resistance; NR, Neck blast resistance; R, Resistance; S,
Susceptible; MR, Moderate resistance.
China Zhan et al.,2012 62
63. Fig. 17. Resistance reaction of pyramided lines inoculated
with
bacterial blight strains.
1, A leaf of a resistant plant TM487;
2, A leaf of a susceptible plant DH146;
3, A leaf of a susceptible plant L3;
4, A leaf of a moderately resistant plant L4.
China Zhan et al.,2012
63
64. ICAR-Molecular Breeding for Biotic Stress
Resistance in India (2005-2009)
Centre Cultivar Genes for resistance to
Bacterial Blight Blast Gall Midge
DRR, BPT 5204 xa13 + Xa21 Pi2 + Pi-kh Gm1+ Gm4
Hyderabad
CRRI, Tapaswani, xa13+ Xa21 Pi2 + Pi9 Gm1 + Gm4
Cuttack Lalat,
IR 64,
Swarna
IARI, Pusa Basmati 1, Xa13 + Xa21 Pi-kh + Piz-5 Not required
New Delhi Pusa6A/6B,
PRR 78
64
65. DBT-GCP/ACIP Molecular Breeding for Biotic
Stress Resistance in India (2009-2014)
Name of the Bacterial Blast Gall Brown Target Varieties
Centers Blight Midge Plant
hopper
Directorate of xa13 + Pi-kh GM1+ Bph13+ Sampada,
Rice Research Xa21 + Pi9 GM4 Bph18 Akshayadhan,
(DRR), DRR17B and
Hyderabad RPHR-1005
(hybrid rice
parental lines)
IARI, New xa13 + Piz- - Bph18+ Pusa1121 and
Delhi Xa21 5+ Bph20+ Pusa1401
Pi-kh Bph21
Punjab xa13 + Bph13+ PAU-201,
Agricultural Xa21+ Bph18 PAU3075-3-38
University, Xa30 PAU3105-45
Ludhiana
65
66. Cultivar development incorporating BLB R genes using
Marker–Aided Selection
Gene Pyramids
Xa4, xa5,xa13,Xa21, Xa7 Samba Mahsuri
IR64 Samba Mahsuri
(xa5, Xa7, Xa21) (Xa5, xa13 and Xa21)
(Xa5, xa13 and Xa21)
CRIFC
DRR
Indonesia
India
IR64, Hybrid rice lines Swarna, IR64
(Xa4, xa5, Xa7, Xa21) (Xa4, xa5, xa13, Xa21)
PhilRice
CRRI
Philippines
India
PR106 Pusa Basmati-1
Pusa Basmati-1
(Xa4, xa5, xa13, Xa21) (xa13, Xa21)
(xa13, Xa21)
PAU IARI
India India
BLB pyramided lines of India
1. IR24 3. Samba Mahsuri 5. PR-106 7. Tapaswini
2. IR64 4. Pusa Basmati-1 6. Lalat 8. Swarna 66
67. Improved Samba Mahsuri
first Variety by Marker Assisted gene Pyramiding
Samba Mahsuri
Samba Mahsuri
(Xa5, xa13 and Xa21)
(Xa5, xa13 and Xa21)
67
68. Conclusion:
Molecular marker offer great scope for improving the efficiency of
conventional plant breeding.
Gene pyramiding may not be the most suitable strategy when many
QTL with small effects control the trait and other methods such as
marker-assisted recurrent selection should be considered.
With MAS based gene pyramiding, it is now possible for breeder to
conduct many rounds of selections in a year.
Gene pyramiding with marker technology can integrate into
existing plant breeding program all over the world to allow
researchers to access, transfer and combine genes at a rate and with
precision not previously possible.
This will help breeders get around problems related to larger
breeding populations, replications in diverse environments, and
speed up the development of advance lines
68
69. Future Thrust
Need to have better scoring methods, larger population
sizes, multiple replications and environments, appropriate
quantitative genetic analysis, various genetic backgrounds
and independent verification through advanced
generations.
Development of software for QTL mapping and minimal
population requirement calculation,
Mapping of disease resistance gene in major crops,
Identify the new resources of desirable resistant genes.
Development of stable/durable resistance varieties in rice.
69