The document describes the construction and application of a high-density SNP linkage map in apple using a multi-parental population. A 20K SNP array was used to genotype over 1,600 individuals from 21 families. SNPs were organized into focal points suitable for building stable multi-allelic haplotypes. A new mapping approach called the focal point strategy was used, which analyzes haplotypes across families prior to map construction. This resulted in a very reliable genetic map for apple with correct linkage group and marker assignments.
Presentation of Pedimap software for breeding during FruitBreedomics workshop at Wageningen - Rhenen, The Netherlands on June12, 2014
Author: Eric Van de Weg, WUR
Presentation of Pedimap software for breeding during FruitBreedomics workshop at Wageningen - Rhenen, The Netherlands on June12, 2014
Author: Eric Van de Weg, WUR
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
The ASGCT Annual Meeting was packed with exciting progress in the field advan...
05 di pierro
1. YOUR LOGO
Construction and Application
of a Multi-parental High Density
SNP Linkage Map of Apple
Erica A. Di Pierro, Luca Gianfranceschi, Johannes W. Kruisselbrink, Marco C.A.M. Bink, Roeland E. Voorrips,
Mario Di Guardo, Herma Koehorst van Putten, Sara Longhi, Luca Bianco, Michela Troggio, Diego Micheletti,
Riccardo Velasco, Larisa Gustavsson, Stefano Tartarini, Giulia Pagliarani, Hélène Muranty, François Laurens,
Eric van de Weg
4. YOUR LOGO
➧Reliable and consistent assessment of
correlation trait ↔ molecular marker
• Discovery & Characterization
• Validation
– Reproducibility on multiple families, pedigrees
NEEDS FOR SUCCESSFUL MAB STRATEGIES
5. YOUR LOGO
➧Reliable and consistent assessment of
correlation trait ↔ molecular marker
• Discovery & Characterization
• Validation
– Reproducibility on multiple families, pedigrees
NEEDS FOR SUCCESSFUL MAB STRATEGIES
➧Power and accurateness of QTL mapping and PBA approaches
6. YOUR LOGO
➧Reliable and consistent assessment of
correlation trait ↔ molecular marker
• Discovery & Characterization
• Validation
– Reproducibility on multiple families, pedigrees
NEEDS FOR SUCCESSFUL MAB STRATEGIES
➧Power and accurateness of QTL mapping and PBA analysis
• Correct linkage group (LG) assignment
• Correct markers succession within LG
7. YOUR LOGO
➧Approaches reducing size data sets
• High-density SNP arrays
• Computer memory limitations, increase computation time
• Genotyping by SNP haplotypes
– Use as multi-allelic markers, more informative than single di-allelic SNPs
NEEDS FOR SUCCESSFUL MAB STRATEGIES
8. YOUR LOGO
➧Approaches reducing size data sets
• High-density SNP arrays
• Computer memory limitations, increase computation time
• Genotyping by SNP haplotypes
– Use as multi-allelic markers, more informative than single di-allelic SNPs
NEEDS FOR SUCCESSFUL MAB STRATEGIES
11. YOUR LOGO
1. HIGH DENSITY SNPs ARRAY:
Illumina Infinium 20 K SNPs array Luca Bianco et al. 2014
FUNDAMENTAL TOOLS
12. YOUR LOGO
1. HIGH DENSITY SNPs ARRAY:
Illumina Infinium 20 K SNPs array Luca Bianco et al. 2014
2. RELIABLE CALLS OF INFORMATIVE SNP
ASSIsT software Mario Di Guardo et al.
FUNDAMENTAL TOOLS
13. YOUR LOGO
1. HIGH DENSITY SNPs ARRAY:
Illumina Infinium 20 K SNPs array Luca Bianco et al. 2014
2. RELIABLE CALLS OF INFORMATIVE SNP
ASSIsT software Mario Di Guardo et al.
3. RELIABLE SINGLE FAMILY MAPS FOR 21 FAMILIES
(ca. 1600 individuals)
FUNDAMENTAL TOOLS
14. YOUR LOGO
1. SNPs ORGANIZED in Focal Points (FPs)
• Exploiting FPs design introduced in Chagné et al. 2012
and Bianco et al. 2014
STRATEGY STEPS
15. YOUR LOGO
1. SNPs ORGANIZED in Focal Points (FPs)
• Exploiting FPs design introduced in Chagné et al. 2012
and Bianco et al. 2014
2. INNOVATIVE MAPPING APPROACH
• FPs strategy
• Backcross strategy in outcrossing species and data
integration across families prior to map construction
STRATEGY STEPS
16. YOUR LOGO
1. SNPs ORGANIZED in Focal Points (FPs)
• Exploiting FPs design introduced in Chagné et al. 2012
and Bianco et al. 2014
2. INNOVATIVE MAPPING APPROACH
• FPs strategy
• Backcross strategy in outcrossing species and data
integration across families prior to map construction
3. SOFTWARE DEVELOPMENT FOR DATA INTEGRATION AND
CONVERSION (FP-mapper by J. Kruisselbrink)
STRATEGY STEPS
17. YOUR LOGO
➧Focal Points design
Regions of max 10kb having up to 10 SNPs
➝ Suitable for building stable multi-allelic SNP-haplotypes
➝ Distribution across genome
1. SNPs ORGANIZED IN FPs
18. YOUR LOGO
➧Focal Points design
Regions of max 10kb having up to 10 SNPs
➝ Suitable for building stable multi-allelic SNP-haplotypes
➝ Distribution across genome
1. SNPs ORGANIZED IN FPs
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
19. YOUR LOGO
➧Focal Points design
Regions of max 10kb having up to 10 SNPs
➝ Suitable for building stable multi-allelic SNP-haplotypes
➝ Distribution across genome
1. SNPs ORGANIZED IN FPs
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
20. YOUR LOGO
➧Focal Points design
Regions of max 10kb having up to 10 SNPs
➝ Suitable for building stable multi-allelic SNP-haplotypes
➝ Distribution across genome
1. SNPs ORGANIZED IN FPs
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
21. YOUR LOGO
➧Focal Points design
Regions of max 10kb having up to 10 SNPs
➝ Suitable for building stable multi-allelic SNP-haplotypes
➝ Distribution across genome
1. SNPs ORGANIZED IN FPs
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
22. YOUR LOGO
2. NEW MAPPING APPROACH: FPs STRATEGY
➧FPs strategy
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
FP-mapper by J. Kruisselbrink
23. YOUR LOGO
2. NEW MAPPING APPROACH: FPs STRATEGY
➧FPs strategy
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
FP-mapper by J. Kruisselbrink
• Stable SNP haplotypes
24. YOUR LOGO
2. NEW MAPPING APPROACH: FPs STRATEGY
➧FPs strategy
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
Female meiosis ab aa ab ab aa ab ab ab aa aa aa aa ab aa aa aa ab ab aa aa ab aa aa aa
Male meiosis aa aa ab ab ab ab ab aa aa ab ab ab ab ab ab aa aa aa ab ab ab aa ab ab
FP-mapper by J. Kruisselbrink
• Stable SNP haplotypes
• Haplotype data integration
bi-parental genotypes split into single parent datasets
25. YOUR LOGO
2. NEW MAPPING APPROACH: FPs STRATEGY
➧FPs strategy
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
Female meiosis ab aa ab ab aa ab ab ab aa aa aa aa ab aa aa aa ab ab aa aa ab aa aa aa
Male meiosis aa aa ab ab ab ab ab aa aa ab ab ab ab ab ab aa aa aa ab ab ab aa ab ab
FP-mapper by J. Kruisselbrink
• Stable SNP haplotypes
• Haplotype data integration
bi-parental genotypes split into single parent datasets
• Missing values reduction
26. YOUR LOGO
2. NEW MAPPING APPROACH: FPs STRATEGY
➧FPs strategy
SNP name FP segr phase segr phase segr phase
SNP_028768 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa <aaxab> {-0} -- aa ab ab -- aa ab ab
SNP_028769 FP_60 <abxaa> {1-} ab aa ab ab aa ab ab ab -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- --
SNP_028770 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <aaxab> {-0} aa ab ab ab ab ab ab aa -- -- -- -- -- -- -- --
SNP_028771 FP_60 <abxab> {00} ab -- bb -- ab bb ab ab <abxaa> {0-} aa -- -- aa ab aa -- aa <aaxab> {-0} aa aa ab ab ab aa ab ab
SNP_028772 FP_60 <abxab> {00} ab aa bb bb ab bb ab ab <abxaa> {0-} aa aa aa aa ab aa aa aa <abxab> {10} ab ab ab ab bb aa ab ab
SNP_028773 FP_60 <aaxab> {-0} aa aa ab -- ab ab ab aa -- -- -- -- -- -- -- -- <abxab> {10} -- -- ab ab bb aa ab ab
Pop 1 Pop 2 Pop 3
Female meiosis ab aa ab ab aa ab ab ab aa aa aa aa ab aa aa aa ab ab aa aa ab aa aa aa
Male meiosis aa aa ab ab ab ab ab aa aa ab ab ab ab ab ab aa aa aa ab ab ab aa ab ab
FP-mapper by J. Kruisselbrink
• Stable SNP haplotypes
• Haplotype data integration
bi-parental genotypes split into single parent datasets
• Missing values reduction
27. YOUR LOGO
2.NEW MAPPING APPROACH: BACKCROSS STRATEGY
➧Backcross strategy and
data integration across families (FP-mapper by J. Kruisselbrink)
Female meiosis ab aa ab ab aa ab ab ab aa aa aa aa ab aa aa aa ab ab aa aa ab aa aa aa
Male meiosis aa aa ab ab ab ab ab aa aa ab ab ab ab ab ab aa aa aa ab ab ab aa ab ab
Segr Pop 1 Pop 2 Pop 3
28. YOUR LOGO
2.NEW MAPPING APPROACH: BACKCROSS STRATEGY
➧Backcross strategy and
data integration across families (FP-mapper by J. Kruisselbrink)
FP_60 unique marker
Female meiosis ab aa ab ab aa ab ab ab aa aa aa aa ab aa aa aa ab ab aa aa ab aa aa aa
Male meiosis aa aa ab ab ab ab ab aa aa ab ab ab ab ab ab aa aa aa ab ab ab aa ab ab
Segr Pop 1 Pop 2 Pop 3
Merging single parent datasets in a single backcross-type population
➝ Twice the individual of the original population ➝ 3200 meiosis
29. YOUR LOGO
2.NEW MAPPING APPROACH: BACKCROSS STRATEGY
➧Backcross strategy and
data integration across families (FP-mapper by J. Kruisselbrink)
FP_60 unique marker
Female meiosis ab aa ab ab aa ab ab ab aa aa aa aa ab aa aa aa ab ab aa aa ab aa aa aa
Male meiosis aa aa ab ab ab ab ab aa aa ab ab ab ab ab ab aa aa aa ab ab ab aa ab ab
Segr Pop 1 Pop 2 Pop 3
Merging single parent datasets in a single backcross-type population
➝ Twice the individual of the original population ➝ 3200 meiosis
32. YOUR LOGO
Families
Individuals:
Focal Points (FPs):
Total SNPs:
Average SNPs/FP:
Average mv%/FP:
Average distance: cM
max d. (LG6) : cM
min d. (many LGs): cM
Total Map Length: cM
21
~1600
~3000
~15000
~5
~40
0.40
3.30
0.00
~1267
HIGH DENSITY FPs GENETIC MAP
Join Map (V4.1)
34. YOUR LOGO
LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 LG12 LG13 LG14 LG15 LG16 LG17
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
HIGH DENSITY FPs GENETIC MAP Join Map (V4.1)
63 cM
112 cM
35. YOUR LOGO
LG 3
Join Map (V4.1)
Test for alternative positions
(1000 iterations)
0%
50%
100%
o ROBUSTNESS OF MARKERS POSITIONS
QUALITY OF THE MAP
THE PROBABILITY OF
EACH MARKER (FP) TO
BE ASSIGNED TO ITS
POSITION IN THE MAP
36. YOUR LOGO
Join Map (V4.1)
Estimated Plausible Positions
0%
50%
100%
THE PROBABILITY OF
EACH MARKER (FP) TO
BE ASSIGNED TO ITS
POSITION IN THE MAP
0%
50%
100%
LG 3
QUALITY OF THE MAP
o ROBUSTNESS OF MARKERS POSITIONS
37. YOUR LOGO
Join Map (V4.1)
Estimated Plausible Positions
0%
50%
100%
THE PROBABILITY OF
EACH MARKER (FP) TO
BE ASSIGNED TO ITS
POSITION IN THE MAP
LG 3
0%
50%
100%
LG3_FP_1140
o ROBUSTNESS OF MARKERS POSITIONS
QUALITY OF THE MAP
38. YOUR LOGO
0%
50%
100%
THE PROBABILITY OF
EACH MARKER (FP) TO
BE ASSIGNED TO ITS
POSITION IN THE MAP
Join Map (V4.1)
Test for alternative positions
(1000 iterations)
LG 3
QUALITY OF THE MAP
o ROBUSTNESS OF MARKERS POSITIONS
44. YOUR LOGO
Joint genotypes of multiple
successive SNP
• single multi-allelic marker
• converted into haploblocks
• allow exploring entire allelic
variation
B
A
B
A
B
B
A
B
A
B
B
A
B
A
B
B
B
B
A
B
Haplotype24
B
A
B
A
A
A
A
B
A
A
Haplotype12
Haplotype13
LG1
HaploBlock 2
3 haplotype variants
10 SNP markers
HAPLOTYPING
Special approach elaborated by Voorrips et al. see POSTER SESSION
45. YOUR LOGO
➧ Flow of haplotype alleles along pedigrees
– LG1 apple, region flanking the Vf gene for scab
resistance
– Pedigree 1 commercial cv:
• Galarina Vf-resistant
HAPLOTYPING
Special approach elaborated by Voorrips et al. see POSTER SESSION
57. ACKNOWLEDGEMENTS
Eric Van de Weg
Johannes Kruisselbrink
Herma Koehorst
Sara Longhi
Stefano Tartarini
Giulia Pagliarani
Luca
Gianfranceschi
Mario Di Guardo
Diego Micheletti
Luca Bianco
Michela Troggio
Hélène Muranty
Larisa
Gustavsson
59. YOUR LOGO
Roeland E. Voorrips
Marco C.A.M. Bink
Johannes W. Kruisselbrink
Herma J.J. Koehorst - van Putten
W. Eric Van de Weg
POSTER SESSION
60. YOUR LOGO
ADVANTAGES
➧Advantages of the FPs strategies
• Reduced number of missing values
• Increased robustness of marker scores
• Complete exploitation of genetic information
• Fully informative markers
61. YOUR LOGO
LG1 LG2 LG3 LG4 LG5 LG6 LG7 LG8 LG9 LG10 LG11 LG12 LG13 LG14 LG15 LG16 LG17
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
110
CORE DATASET in Black
• at least 800 meiosis present
• 25% of present data
in Violet
• at least 300 meiosis present
• 10% of present data
THE FINAL INTEGRATED GENETIC MAP
62. YOUR LOGO
➧Advantages of the backcross strategy
NEW MAPPING APPROACH
Standard outcrossers
integrated map
• Integration of the 2 parental maps
• Integration across families
Integration of DATASETS prior to map
construction
Novel approach
VS
63. YOUR LOGO 63
HAPLOBLOCKS
HB 1
HB 2
HB 3
HB 4
HB 5
HB 6
HB 7
HB 8
Galarina Florina Gala • Tightly linked sets of SNPs
• Recombination occurs only
between haploblock and
NOT WITHIN
multi-allelic markers
based on correctly
assigned haplotypes
Voorrips et al. POSTER SESSION
LG1
64. YOUR LOGO
➧Approaches reducing size data sets
• High-density SNP arrays
• Computer memory limitations, increase computation time
• Genotyping by SNP haplotypes
– Use as multi-allelic markers, more informative than single di-allelic SNPs
NEEDS FOR SUCCESSFUL MAB STRATEGIES
Complexity of Apple Genome
➾ Apple Physical Map V2 with uncertainties
65. YOUR LOGO
NEXT STEP
➧PUBLICATION of the Integrated Genetic Map
➧ORIGINAL DATA will become publicly AVAILABLE on the
FB-database
➝ Allow USERS to FURTHER IMPROVE REGION of INTEREST by
further DATA SCRUTINIZING
adding data (e.g. additional families)
➝ MOST RELIABLE marker-loci ORDER for the HIGHEST
POSSIBLE NUMBER of MARKERS