BIF 302 Genomic Mapping (1+2)

Practical Manual & Record

Course Teacher

Dr.N.SENTHIL

AssociateProfessor

Dr.Manikanda Boopathi

Assistant professor

Course Associate

Ms.E.Vijaya Gowri

Ms.B.Abirami

Department of Plant Molecular Biology and Biotechnology

Centre for Plant Molecular Biology,

Tamil Nadu Agricultural University,

Coimbatore 641003

2009

Practical Record

Certificate

This is a bonafide record of Mr/Ms_________________________ I.D.NO____________ in the course of BIF 302 Genomic Mapping during the ________ semester of 2009

External Examiner Course Teacher

Tamil Nadu Agricultural University

Department of Plant Molecular Biology and Biotechnology

Centre for Plant Molecular Biology

Tamil Nadu Agricultural University

Coimbatore - 641003

Ex. No: 1

Date:

Observation of morphological Markers in different plant Species

Observation of morphological variations in Rice

Morphological characters

Characteristics States

Coleoptile: colour colourless, green ,purple

Basal leaf: sheath colour green ,light purple, purple lines , purple

Leaf: intensity of green colour light ,medium, dark

Leaf: anthocyanin colouration absent,present

Leaf : distribution of anthocyanin colouration

on tips only ,on margins only in blotches only uniform

Leaf sheath: anthocyanin colouration absent ,present

Leaf sheath : intensity of anthocyanin colouration

very weak ,weak ,medium strong ,very strong

Leaf: pubescence of blade surface absent ,weak ,medium ,strong ,very strong

Leaf : auricles absent ,present

Leaf: anthocyanin colouration of auricles

Colourless,light purple,purple

Leaf: collar absent ,present  

Leaf: anthocyanin colouration of collar

absent ,present

Leaf: ligule Absent,present

Leaf: shape of ligule truncate ,acute ,split

Leaf: colour of ligule green ,light purple,purple

Leaf: length of blade short ,medium,long

Leaf: width of blade   narrow ,medium,broad

Culm  Erect, semi-erect , open spreading

Flag leaf erect ,semi-erect ,horizontal deflexed

Spikelet: density of pubescence of lemma

absent ,weak ,medium ,strong, very strong

Spikelet: colour of stigma White, light green, yellow light purple, purple

Stem: thickness thin ,medium ,thick

Stem: length (excluding panicle; excluding floating rice)

very short (<91 cm) short (91-110 cm) medium (111-130 cm) long (131-150 cm) very long (>150 cm)

Panicle: length of main axis very short (<16 cm)  short (16-20 cm)  medium (21-25 cm)  long (26-30 cm)  very long (>30 cm)

Panicle: curvature of main axis Straight, semi-straight,  drooping, deflexed

Panicle: number per plant few (<11) medium (11-20) many (>20)

Spikelet : colour of tip of lemma White, yellowish, brown  red,purple,black

Lemma and Palea: colour straw ,gold and gold furrows on straw background ,brown spots on straw brown furrows on straw  brown ( tawny) ,reddish to light purple, purple spots on straw ,purple furrows on straw ,purple ,black

Panicle : awns Absent, present

Panicle: colour of awns (late observation)

yellowish white ,yellowish brown ,brown, reddish brown, light red  ,red  light purple, purple  black

Panicle: length of longest awn very short, short, medium  long ,very long   

Panicle: distribution of awns tip only, upper half only  

whole length 

Panicle: presence of secondary branching

absent ,present 

Panicle: secondary branching weak ,strong ,clustered

Panicle: attitude of branches

 

erect , erect to semi-erect  semi-erect, semi-erect to spreading , spreading

Time of maturity very early ,early medium late ,very late

Grain: length very short ,short ,medium  long ,very long

Grain: width very narrow, narrow, ,medium ,broad ,very broad

Decorticated grain: aroma absent present

Germinating seed

When the seed germinates in well-drained and well-aerated soil, the coleorhiza, a covering enclosing the radicle or primary root, protrudes first.

Fig. 1 - The coleorhiza protrudes first breaks

Fig. 2 - Radicle or primary root through the covering.

Shortly after the coleorhiza appears, the radicle or primary root breaks through the covering.Two or more sparsely branched seminal roots follow. These roots eventually die and are replaced by many secondary adventitious roots.

Fig. 3 - Seminal roots a tapered Fig. 4 - Coleoptile emerging as cylinder

  If the seed germinates in water, the coleoptile, a covering enclosing the young shoot, emerges ahead of the coleorhiza. The coleoptile emerges as a tapered cylinder.

 Seedling

The mesocotyl or basal portion of the coleoptile elongates when the seed germinates in soil, and in darkness. It pushes the coleoptile above the soil surface.

Fig. 5 - Mesocotyl pushing the coleoptile

Fig. 6 - First and second seedling above the soil surface

Tiller

The first seedling leaf, or primary leaf, emerges from the growing seed. It is green and shaped like a cylinder. It has no blade. The second leaf is a complete leaf. It is differentiated into a leaf blade and a leaf sheath. The seedling will grow and develop branched tillers. Parts of the rice tiller include the roots, culm and leaves. Mature roots of the rice plant are fibrous and produce smaller roots called rootlets. All roots have root hairs to absorb moisture and nutrients.

Fig. 7 - Parts of the rice tiller. Fig. 8 - Types of roots.

There are two kinds of mature roots: secondary adventitious roots, adventitious prop roots. Secondary adventitious roots are produced from the underground nodes of young tillers.

 

Fig. 9 - Secondary adventitious roots.  

Fig. 10 - Adventitious prop roots

  As the plant grows, coarse adventitious prop roots often form above the soil

Surface in whorls from the nodes of the culm.

Culm

The culm, or jointed stem of the rice, is made up of a series of nodes and internodes.

Fig. 11 - Culm, nodes, and internodes.

Fig. 12 - Young and mature internodes

Young internodes are smooth and solid. Mature internodes are hollow and finely grooved with a smooth outer surface. Generally, internodes increase in length from the lower to the upper portions of the plant. The lower internodes at the plant base are short and thick. The node is the solid portion of the culm. The node or nodal region bears a leaf and a bud. The bud is attached to the upper portion of the node and is enclosed by the leaf sheath. The bud may give rise to a leaf or a tiller.

Fig. 13 - Leaf, node, and bud. Fig. 14 - Primary tillers.

  Early tillers arise from the main culm in an alternate pattern. Primary tillers originate from the lowermost nodes and give rise to secondary tillers. Secondary tillers produce tertiary tillers.

 

Fig. 15 - Secondary tillers. Fig. 16 - Tertiary tillers.Leaf

The node or nodal region of the culm will bear a leaf.

Fig. 17 - Leaf. Fig. 18 - Leaves – alternate arrangement

Culm

Leaves are borne alternately on the culm in opposite directions. One leaf is produced at each node. Varieties differ in the number of leaves produced. The topmost leaf below the panicle is the flag leaf. The flag leaf contributes largely to the filling of grains because it supplies photosynthetic products, mainly to the panicle.

Fig. 19 - Flag leaf  Fig. 20 Leaf sheath and leaf blade The leaf sheath and leaf blade are continuous. A circular collar joins the leaf blade and the leaf sheath.

Fig. 21 - Leaf collar Fig. 22 - Leaf sheath and culm

The leaf sheath is wrapped around the culm above the node. The swelling at the base of the leaf sheath, just above the node, is the sheath pulvinus. It is sometimes incorrectly referred to as the node.

Fig. 23 - Sheath pulvinus Fig. 24 - Different varieties with varying blade characteristics.

Leaf blades are generally flat. Varieties differ in blade length, width, thickness, area, shape, color, angle and pubescence. With many parallel veins on the upper surface of the leaf, the underside of the leaf blade is smooth with a prominent ridge in the middle; the midrib.

 Fig. 25 - Parallel veins on upper surface

Fig. 26 - Leaf midrib

Most leaves possess small, paired ear-like appendages on either side of the base of the blade. These appendages are called auricles. Auricles may not be present on older leaves. Another leaf appendage is the ligule, a papery membrane at the inside juncture between the leaf sheath and the blade. It can have either a smooth or hair-like surface. The length, color, and shape of the ligule differ according to variety.

Fig. 27 - Ligule and auricle Fig. 28 - Rice and grassy weed comparison

  Panicle

Although similar, rice seedlings are different from common grasses. While rice plants have both auricles and ligules, common grassy weeds found in rice fields normally do not have these features. These characteristics are often helpful in identifying weeds in rice fields when the plants are young.

Spikelets

The terminal component of the rice tiller is an inflorescence call the panicle. The inflorescence or panicle is borne on the uppermost internode of the culm. The panicle bears rice spikelets, which develop into grains.

 Fig. 29 - Rice panicle Fig. 30 - Panicle base (neck)

The panicle base often appears as a hairlike ring and is used as a dividing point in measuring culm and panicle length. The panicle base is often called the neck. The panicle axis is continuous and hollow except at the nodes where branches are borne.The swellings at the panicle axis where the branches are borne are referred to as the panicle pulvinus. Each node on the

main panicle axis gives rise to primary branches which in turn bears secondary branches. Primary branches may be arranged singly or in pairs.

 

Fig. 31 - Secondary and primary branch

Fig. 32 – Spikelets

The panicles bear spikelets, most of which develop into grains. These spikelets are borne on the primary and secondary branches. The spikelet is the basic unit of the inflorescence and panicle. It consists of the pedicel and the floret. The floret is borne on the pedicel.

Fig. 33 - Floret and pedicel Fig. 34 - Rudimentary glumes, sterile lemmas, and rachilla

The rudimentary glumes are the laterally enlarged, cuplike apex of the pedicel. The rudimentary glumes are the lowermost parts of the spikelet. During threshing, the rudimentary glumes are separated from the rest of the spikelet. The sterile lemmas are small, bractlike projections attached to the floret. The rachilla is a small axis that bears the single floret. It is between the sterile lemmas and the floret.

Floret

The rachilla, sterile lemmas and the rudimentary glumes all support the floret. The floret includes the lemma, palea, and the flower.

Fig. 35 – Floret Fig. 36 - Palea and lemma

The larger protective glume covering the floret is called the lemma and the smaller one is referred to as the palea. Both the lemma and palea have ridges referred to as nerves. The lemma has five while the palea has three. The middle nerve of the lemma can be either smooth or hairy.

Fig. 37 - Nerves Fig. 38 - Awn and keel

  The lemma has a constricted structure at its end called the keel. In some varieties, the keel is elongated into a thin extension, the awn.

Flower

The floret contains a flower. The flower consists of a pistil (female organ) and six stamens (male organs).

Fig. 39 - Pistil Fig. 40 – Stamens

The stamens have two-celled anthers borne on slender filaments.

Fig. 41 - Anthers and filaments Fig. 42 - Stigma, style, and ovule

The pistil contains one ovule and bears a double-plumed stigma on a short style. At the flower’s base near the palea are two transparent structures known as lodicules. The lodicules thrust the lemma and palea apart at flowering to enable the elongating stamens to emerge out of the open floret. The lemma and palea close after the anthers have shed their pollen.

Fig. 43 - Lodicule Fig. 44 - Rice grainRice grain

The rice grain is the ripened ovary, with the lemma, palea, rachilla, sterile lemmas and the awn firmly attached to it. The rice hull includes the lemma and palea and their associated structures – the sterile lemmas, rachilla, and awn.

Fig. 45 - Rice hulls Fig. 46 - Tagmen, pericap, and aleurone layers

The dehulled rice grain is called caryopsis, commonly referred to as brown rice because of three brownish pericarp layers that envelope it. Next to the pericarp layers are the two tegmen layers and the aleurone layers. The remaining part of the grain consists of the endosperm and the embryo. The endosperm provides nourishment to the germinating embryo. The embryo lies on the belly side of the grain and is enclosed by the lemma. It is the embryonic organ of the seed.

Fig. 47 - Endosperm and embryo Fig. 48 - Plumule and radicle 

The embryo contains the plumule (embryonic leaves) and the radicle (embryonic primary root).

Case Study: Morphological and SSR diversity in Rice Varieties released from TRRI, Aduthurai, TNAU.

Morphological, Simple Sequence Repeats (SSR) allele diversity analysis was performed using 27 morphological traits and 25 SSR primers in 35 Aduthurai (ADT) rice varieties. A wide range of morphological diversity was noticed for

7 quantitative and 20 qualitative traits viz., the duration ranged from 90 (ADT 30) to 220 (ADT 6) days, the plant height varied from 90 cm (ADT 48) to 155cm (ADT 1). Factor analysis (factor1) revealed that plant height (0.812), days to flowering (0.562), number of productive tillers (0.501), and thousand seed weight (0.574) contributed maximum variability among the morphological traits. In morphological clustering the cluster IV consisted of varieties (ADT 11, ADT 43, ADT48, and ADT 45) which were differentiated into different clusters instead of cluster IV in case of SSR marker clustering and the SSR marker clustering differentiated the ADT 10 (cluster VII) into a separate cluster which was clustered in cluster I of morphological traits. The narrow genetic base of Aduthurai varieties is evidenced from cluster XI, where ADT 36 (Triveni/IR20) and ADT 39 (IR8/IR20), having common parentage and hence clustered together in SSR markers data. Hence SSR marker will be useful for the characterization of germplasm accessions. The iron content analysis also indicated a wide variation in the iron content among the different Aduthurai rice varieties. It varies from 15.76 ppm (ADT 25) to 3.77 ppm (ADT 42).

Input data file in a spreadsheet

Output phylogenic tree

Ex. No: 2

Date:

Isolation of plant genomic DNA and Quantification

The plant tissues are notoriously difficult material for DNA isolation due to the presence of various Secondary plant products. Many different methods are available for the isolation of genomic DNA. In general, all methods involve disruption and lysis of the starting material followed by the removal of proteins and other contaminants and finally recovery of the DNA. Here, organic extraction method is followed in which C-TAB buffer is used CTAB is a cationic detergent which aids in the lyses of cell membranes and will form complexes with nucleic acids. Sodium chloride aids in the formation

Coefficient0.28 0.38 0.48 0.58 0.68

ADT1 ADT2 ADT25 ADT4 ADT6 ADT7 ADT15 ADT29 ADT26 ADT12 ADT30 ADT16 ADT31 ADT14 ADT40 ADT20 ADT22 ADT27 ADT10 ADT32 ADT35 ADT38 ADT8 ADT41 ADT11 ADT37 ADT36 ADT39 ADT42 ADT43 ADT47 ADT28 ADT44 ADT45 ADT48

I

II

III

IVV

VI

VIIVIIIIX

X

XI

XII

Dendrogram of rice genotypes based on the SSR marker data

of nucleic acid – CTAB complexes. EDTA will chelate the magnesium ion which is an essential cofactor for DNase and β-Mercaptoethanol is a reducing agent which protects DNA from degradation by various oxidants. RNA, protein, polysaccharides, pigments and tannins in plant cells will be removed by treating the extract with RNase, chloroform and phenol, respectively.

MATERIALS REQUIRED:

CTAB extraction buffer

Tris base (pH 8.0) 100m M

Sodium chloride 1.4 m M

EDTA 20 m M

CTAB 2.0%(w/v)

β mercapto ethanol 0.1%

Chloroform: isoamyl alcholol(24:1) Ethanol (70%) 0.1X TE buffer RNase A (10 mg/ml) 3 M Sodium acetate (pH 5.2)

PROTOCOL

Grind 0.3 g of leaf tissue in liquid nitrogen to a fine powder using a prechilled mortar and pestle.

Transfer the powder to a 15ml centrifuge tube and add 10ml of extraction buffer and incubated at 65°C for20 mins.

Add equal volume of chloroform: isoamyl alcohol (24:1, v/v) and gently mix the tube.

Spin the mixture at 12000 rpm for 20 minutes and remove the aqueous layer carefully into new 15 ml tube.

Add 0.7 volume of ice cold isopropanol. Mix carefully and keep at room temperature for 15 minutes (or overnight). Spin the mixture for 5 minutes @ 12000rpm.

Transfer the DNA with a glass rod to clean tube and air dry the pellet.

Dissolve the DNA pellet with a minimal volume of TE buffer Supernatant is mixed with 10µl RNase (10 mg/ml) in a fresh tube

and incubated at 37º C for 15 minutes. Add equal volume of chloroform: isoamyl alcohol (24:1) mixture

and mix well.Centrifuge at 10000 rpm for 20 minutes. Transfer the aqueous layer to a fresh tube and add 2.5 volume of

absolute alcohol and 100µl of 3M sodium acetate and incubate at -70 º C for 1 h.

Spin at 10000 rpm for 10 min, to pellet the DNA. Wash the pellet with 70% ethanol. Air dry the pellet and dissolve in small quantity of 1 X TE buffer.

EX.No.3

Date:

Analysis of purity of DNA and Quantification

The purity and the concentration of the extracted DNA can be analyzed using spectrophotometer or Nanodrop. Both RNA and DNA absorb UV light very efficiently making it possible to detect and quantify even at concentrations as low as 2.5 ng/µl and it can also be quantified by cutting the DNA with a restriction enzyme, running it on an agarose gel, staining with ethidium bromide and comparing the intensity of the DNA with a DNA marker of known concentration

Quantification by spectrophotometer:

The spectrophotometer works on the principle of Beer lambert’s law in which the intensity of light absorbed by the molecule is directly proportional to the number of molecule present in the solution.. This method is however limited to relatively pure preparations that are devoid of significant amounts of contaminants such as proteins, polysaccharides, and other nucleic acids.

Determination by UV absorption:A pure solution of double-stranded DNA at 50 g/ml has an optical

density of 1.0 at 260 nm and an OD260/OD280 ratio of 1.8. Contamination with protein or phenol will give OD260/OD280 values significantly less than 1.8 and contamination with RNA gives a ratio greater than 1.8 (For pure RNA, OD260/OD280 = 2.0). Thus, the OD260/OD280 value is obtained first, and, if it approaches 1.8, an accurate estimation of DNA concentration can be determined from the absorption at 260 nm.

Dilute the DNA (1:10) in 1X TE buffer (pH 8.0) and measure the OD260 and OD280 using spectrophotometer.

Procedure:

Plug the instrument and wait for the self checkup to be completed. Warm up the machine for at least 30 minutes until main menu

display on screen. Remove the cell from its compartment and discard the TE. Put 2.0 μl of the DNA sample in the cell. Add 998 μl of TE. Mix

solution thoroughly by press-release method. Introduce the cell into the compartment and close the cover

tightly to avoid any leak of light. Read the O.D value directly from the screen. This gives the

concentration of DNA when multiplied by 1000.

Nanodrop quantification:The NanoDrop is a full-spectrum (220-750nm) spectrophotometer that measures 1 ul samples with high accuracy and reproducibility. It utilizes

surface tension to hold the sample in place. This eliminates the need for cuvettes and other sample containment devices and allows for clean up in seconds. In addition, it has the capability to measure highly concentrated samples without dilution (50X higher concentration than the samples measured by a standard cuvette spectrophotometer).

Procedure:

A 1 ul sample is pipetted onto the end of a fiber optic cable (the receiving fiber).

A second fiber optic cable (the source fiber) is then brought into contact with the liquid sample causing the liquid to bridge the gap between the fiber optic ends.

The gap is controlled to both 1mm and 0.2 mm paths. A pulsed xenon flash lamp provides the light source and a

spectrometer utilizing a linear CCD array is used to analyze the light after passing through the sample.

The instrument is controlled by special software run from a PC, and the data is logged in an archive file on the PC.

Quantification and quality checking by gel electrophoresis

This method requires a DNA sample on an agarose gel next to a known quantity of a DNA molecular weight standard. A rough estimation of DNA quantity can be obtained by visually comparing the fluorescence of a specific "band" or DNA fragment that has been subjected to agarose gel electrophoresis and stained with EtBr to the fluorescence of another standard "band" (of known size and amount).

Procedure

a. Gel Preparation:

Weigh 0.8% of agarose and melt it in 1 X TAE by heating with continuous swirling till a clear solution is obtained.

Seal the casting template using cellophane tape and place the comb into the tray. Pour the molten agarose onto the gel casting platform with a comb inserted, ensuring that no air bubbles have been trapped underneath the comb.

After the gel hardens, the gel is transferred to the electrophoresis tank filled with sufficient volume of electrophoresis buffer in which gel should be submerged.

b. Loading and running the gel:

DNA samples are mixed with gel loading dye and loaded into the wells with micropipette.

Electrophoresis is carried out at a constant voltage of 80 Volts. Gel is allowed to run for about 45 minutes.

After the completion of electrophoresis, gel is stained with ethidium bromide (5 µg /ml) for 10-15 min at RT. Then the gel is destained with deionized water and documented using alpha imager.

Good quality DNA with intact bands Bad

quality DNA-sheared and RNA contaminated

Ex. No: 4

Date:

RAPD markers –agarose gel electrophoresis

RAPD markers are PCR-based markers generated by using arbitrary primers (Williams et al., 1991).They are dominant markers and uses only a single primer. A single, short oligonucleotide primer, which binds to many different loci, is used to amplify random sequences from a complex DNA template. . It is usually 10mer rather than a 20mer and the aim is to amplify several segments of the target genome in a 'random' fashion. This depend upon the fact that 10mer sequences are sufficiently common in a large genome so that just by chance at several unpredictable locations, two primers will anneal sufficiently close to one another on opposite strands of the template to amplify the intervening region. The primers are available commercially (Operon Technologies Inc., California, USA) as 10-base primer kits, and we need to select a kit that has a (G+C) content of 60 to 70% and they have no self-complimentary ends.

The PCR amplification process involves 3 major steps.

DNA denaturation at 94˚C

Annealing of primer to single stranded DNA at 37˚C

Primer extension catalyzed by ‘Taq DNA polymerase' at 72˚C

The genomic DNA is extracted from the etiolated leaf samples and purified. The purified DNA is tested for its intactness by electrophoresis. The DNA is quantified and diluted to a concentration of 25 ng/l. Then the DNA is used for RAPD analysis. The cocktail for the amplification is prepared as follows using 0.2 ml/0.5 ml test PCR tubes depending on the PCR machine’s specification.

Reaction mixture (15 µl) contains (slightly modified from Williams et al., 1991):

The reaction mixture is given a momentary spin for thorough mixing of the cocktail components. Then the 0.2 ml PCR tubes are loaded on to a thermal cycler.

The thermal cycler is programmed as follows:

Profile 1: 94°C for 2 min – Initial denaturation

Profile 2: 94°C for 1 min – Denaturing

Profile 3: 37°C for 1 min – Annealing

Profile 4: 72°C for 1 min – Extension

Profile 5: 72°C for 5 min – Final extension

Reaction mixture:Stock

Aliquot Final concentration

DNA 50 ng/µl 1.00µl 50 ngdNTPs (2.5 mM) 1.20 µl 200 µMPrimer (20 µM) 0.50 µl 0.60 µM10X assay buffer 1.50 µl 1XTaq polymerase (3 units/µl)

0.18 µl 0.036units

Sterile distilled H20 10.42 µlTotal 15.00 µl

Profile 6: 4°C for infinity - Hold

Profile 2, 3 and 4 are programmed to run for 35 cycles.

Standardization of PCR reactions

The reagents and sterile water are divided into aliquots to minimize the number of samplings.

To avoid cross contamination, via the electrophoresis equipment, the gel combs and casting trays are washed using 3% acetic acid..

PCR in the absence of exogenously added DNA is used as negative control to identify DNA-free status of reagents and solutions.

PCR with positive control DNA is used to check the completeness of PCR mixture. Negative and positive controls are helpful to check for spurious background bands and reaction specificity.

Metaphor agarose gel electrophoresis

Metaphor agarose is a high resolution agarose that can resolve PCR products and small DNA fragments that differ in size by 2%. It has an intermediate melting temperature (75°C) with twice the resolution capabilities of the finest-sieving agarose products. It is suggested by the manufacturer that 3% Metaphor agarose in 1X TAE buffer is sufficient enough to resolve 50-250 bp DNA fragments.

Materials required:

a. Loading dye:

Glycerol 50% (V/V)

Bromophenol blue 0.5% (W/V)

b. 10X TAE (Tris Acetate EDTA) buffer:

Tris base 107.8 g

Boric acid 55.03 g

EDTA (Na2.2H2O) 8.19 g

(Dissolved in 800 ml milli Q water filtered through 0.22µm filter paper and made up to 1000 ml and stored at 4°C).

The following steps are followed for gel casting.

A beaker with two to four times the volume of the gel solution is chosen and 1X TAE buffer is added.

The solution is stirred with constant speed and the Metaphor agarose powder is sprinkled slowly and mixed well with Teflon coated magnetic stir bar.

Metaphor agarose is soaked in the buffer for 15 minutes before heating (This reduces the tendency of the agarose solution to foam during heating).

The solution level is marked in the beaker and then heated in a microwave oven on medium power for 3 minutes.

The beaker is removed from microwave oven and gently swirled to resuspend any settled powder and gel pieces.

The solution is reheated on high power for 2 minutes or until all of the particles are dissolved.

The beaker is removed and gently swirled and sufficient hot distilled water is added to get the initial volume and mixed thoroughly.

The solution is allowed to cool to 50-60°C and then casted on a clean template.

The molten Metaphor agarose is allowed to cool in room temperature for 15 minutes and then placed at 4°C for 15 minutes to obtain optimum resolution and gel handling characteristics.

7µl of PCR products and 3µl of agarose gel loading dye is used to load the samples into the well and the gel is run @ 100V.

Then the gel is stained in Ethidium bromide solution for 20 minutes and documented (AlphaImager1200, Alpha Innotech Corporation,

California, USA). The gel is reused for four times or until it gives poor quality in documentation.

Ex. No: 5

Date:

MICROSATELLITE MARKERS-PAGE AND SILVER STAINING

Micro satellites or simple sequence repeats (SSRs) are small repeats of one or few tandemly arranged nucleotides spread throughout eukaryotic genomes. They are single locus markers and are co-dominant . PCR amplification

The cocktail for PCR amplification of respective SSR fragments is prepared as follows.

Reaction mixture (15μl) contains:

Stock Aliquot Final concentration

DNA (50ng) 2.0 μl 50.0 ng

dNTPs (2.5mM) 0.6 μl 100.0 μM

Forward Primer (20μM) 0.15 μl 0.2 μM

Reverse Primer (20μM) 0.15μl 0.2 μM

Taq DNA polymerase (3 units/μl)

0.1 μl 0.02units

Buffer (10X)1.5 μl

1X

Sterile distilled water

Total10. 5 μl15 μl

The reaction mixture is given a momentary spin for thorough mixing of the cocktail components. Then 0.2ml PCR tubes are loaded in a thermal cycler. The thermal cycler is programmed as follows

Profile 1: 94°C for 5 min - Initial denaturation

Profile 2: 94°C for 45 sec - Denaturation

Profile 3: 55°C for 45 sec - Annealing

Profile 4: 72°C for 1 min - Extension

Profile 5: 72°C for 5 min - Final extension

Profile 6: 4°C for infinity to hold the sample.

Profile 2, 3 and 4 are programmed to run for 35 cycles.

Electrophoretic analysisAfter PCR amplification, products are separated by electrophoresis on

metaphor agarose gels and visualized by ethidium bromide staining. Usually, for better resolution and detection of smaller differences in amplified products, polyacrylamide gel and silver staining is preferred.

PolyAcrylamide Gel Electrophoresis (PAGE)

Acrylamide based gel is used to analyze the DNA molecules that are having shorter than 500 bp and when it is required to resolve the nucleotides differing in size by one nucleotide. Two types of polyacrylamide gels are in commonly used. Non-denaturing polyacrylamide gels for separation and purification of double stranded DNA and denaturing polyacrylamide gels for the separation of and purification of single stranded fragments of DNA.

Polyacrylamide gels can range in length from 10-100 cm, depending upon separation required. They invariably run in a vertical position and have three major advantages over agarose gels:

Their resolving power is so great that they can separate molecules of DNA whose length differ as little as 0.2% (i.e. 1 bp in 500 bp)

They can accommodate much larger quantity of DNA than agarose gel. Up to 10 ug of DNA can be applied to a typical polyacrylamide gel without significant loss of resolution.

DNA recovered from polyacrylamide gels is extremely pure and can be used for most demanding purposes.

Polyacrylamide gels are chemically cross linked gels formed by the polymerization of acrylamide with a cross-linking agent, usually N, N’-methylene bisarylamide (Bis). The polymerization is initiated by free radical formation usually carried out with ammonium per sulfate (APS) as the initiator and N, N, N’, N’ – tetramethylene diamine (TEMED) as a catalyst. The length of chain is determined by the concentration of acrylamide in the polymerization reaction. One molecule of cross linker is included for every 29 monomers of acrylamide. Denaturing gels are polymerized in the presence of an agent (urea or sometimes with formamide) that supresses base pairing in nucleic acids. However, alkali conditions should not be used as denaturing agent. Denatured DNA migrates through these gels at a rate that is almost completely independent of its base composition and sequence.

CHEMICAL PREPARATION FOR PAGE GEL:

Chemicals For 500ml For 1000ml99.9% Ethanol 497.5ml 995ml0.5% Acetic acid 2.5ml 5mlBind Silane 1µl 1.5ml

(BS – Methacryloxy Prophyl dimethoxy Silane)

Repellent: For IPC chamber (Rain X) 40% Acrylamide: (100ml) (38:2 ratio)

Acrylamide 38g (40g in 100ml distilled water) Bis-Acrylamide 2g 6% Acrylamide solution: (Denaturing Gel)

Chemicals 6%Acrylamide solution (100ml)

6%Acrylamide solution (60ml)

8%Acrylamide solution (100ml)

8%Acrylamide solution(60ml)

40% Acrylamid

15ml 9ml 20ml 12mlUrea 42g 25.2g 42g 25.2g10 X TBE Buffer

10ml 6ml 10ml 6ml

Distilled water

100ml 60ml 100ml 60ml

Both APS and TEMED should be in 10:1 ratio and should be added just before casting the gel.

PAGE DYE: Formaldehyde 50ml Xylene cyanol 50mg Bromophenol blue 50mg 0.5M EDTA 1ml

Chemicals For 100ml For 60ml10% APS 600 µl (60mg APS in 600ul

dist H2O)360 µl (50mg in 500 µl dist H2O)

TEMED 60 µl 36 µl

Plate preparation:

Clean the both IPC chamber (integral plate chamber) and outer glass plate with distilled water and remove by wiping with tissue paper.

Again wipe with absolute alcohol (100-70%) by kim wipe paper. Coat IPC chamber (integral plate chamber) with Rain X or Repellent

(1ml). Coat outer glass plate with Bind silane (1ml) Wipe the side clamps, bottom assembly gasket, spacer, and comb with

70% alcohol. Place the spacer (0.4mm thickness) on IPC chamber and keep the

outer glass plate over it i.e., coated side of the outer glass plate should be towards spacer.

Assemble the unit, with side clamp, bottom assembly gasket and lock the unit.

Wipe the comb with 70% alcohol and insert ¼ of the comb bet7ween the plates.

Gel matrix preparation Measure 10ml of 40% acrylamide solution and 10ml 10X TBE buffer in

measuring cylinder. Weigh required amount of urea in a beaker, add 5ml of distilled water

to it and warm for 30sec and dissolve it. Add the measured 10ml of 40% acrylamide solution and 10ml 10X TBE

buffer to the dissolved urea solution and make the volume to 100ml by distilled water and filter the acrylamide solution through filter paper.

Prepare 10% APS (Ammonium per Sulphate) and TEMED in 10:1 ratio and add both at the same time to the filtered acrylamide solution.

Take the filtered acrylamide solution in syringe immediately and spread solution uniformly in-between the plates, once slowly reaches other side, insert the comb immediately (wells should be inside).

Allow the unit undisturbed for After 1hr take this assembly and keep it over base chamber filled with

1X TBE buffer. Place the stabilizer bar for tightening the assembly unit and fit the

bottom safety cover. Pour 1X TBE buffer between the plates and remove the comb and

close the IPC chamber by fitting the upper safety cover and allow it for pre-running for 30 min to1 hr. Flash the well with ink filler to remove unpolymerized urea.

Sample loading and gel running

Add 3 µl of dye to each pcr tube containing 15 µl of amplified product. Clean each well using P-100 pipette man to remove the deposited urea

and load 3µl of Amplified DNA sample. Run the gel at 100W – constantly for 1-2 hrs depending on size of PCR

products. Attach the temperature sensor on the outer glass plate for

temperature indication. Allow it to run for the estimated time and terminate the reaction,

remove the buffer and disassemble the unit and take outer glass plate for staining.

Staining of Gel

After careful removal of the glass plate with gel from the assembly,

Fixer for 15 minutes or till the dye disappears

Double distilled water for 5 minutes (twice). Staining solution for 25 minutes. Double distilled water for 10 seconds.

Developer for 5 minutes or till band appears. Acetic acid for 2-3 minutes. Double distilled water for 2 minutes. Then the gel is carefully air dried and documented

SILVER STANING SOLUTION:

Fixer: (10%)

200ml Acetic acid + 1800ml of Dist. H2OStaining: 2g AgNO3 + 2000ml of dist. H2O +3ml formaldehyde (Keep it in brown bottle)

Developer: 60gm Na2CO3 in 2000ml dist. H2O was mixed with 400 µl sodium thiosulfate (10mg/ml)and 3ml 37% formaldehydeGel plate cleaning: NaOH pellets in 2000ml dist. H2O to few hours to remove the stained gel and washing the plate with soap solution.Notes:

Glass plates must be meticulously clean. Detergent microfilms left on the glass plates may result in a high background (brown) upon staining the gel.

Care should be taken not to heat the solution at a higher temperature (> 60 °C), as the urea will crystallize as the mixture cools, which will affect the uniformity of the gel thickness.

TEMED and APS should be mixed thoroughly with the acrylamide solutions, to ensure homogeneous polymerization.

The voltage should be typically adjusted to maintain the run temperature at 50-60°C. This temperature is hot enough to keep DNA fragments denatured without cracking the gel plates.

Acrylamide is a potent neurotoxin and is absorbed through the skin. The effects of acrylamide are cumulative. Wear gloves and a mask while weighing acryl amide and methylene-bisacrylamide. Although polyacrylamide is considered to be nontoxic, it should be handled with care because of the possibility that it might contain small quantities of unpolymerized acryl amide. It is recommended to wear safety gloves while doing this step.

Ex. No: 6

Date:

STS marker analysis

A sequence-tagged site (or STS) is a short (200 to 500 base pair) unique DNA sequence that occurs but once in a genome and whose location and base sequence are known. STSs can be detected by the polymerase chain reaction (PCR), and are useful for localizing and orienting the mapping and sequence data reported from many different laboratories. They also serve as landmarks on the developing physical map of a genome.

Sequenced tagged site (STS) markers are PCR based markers generated by a pair of primers that are designed according to the known DNA sequences. STS markers are reproducible and specific than the original RAPD marker. Partial sequencing of clones would, however provide enough

information for the recovery of any desired marker by means of PCR. The following steps are involved:

DNA isolation from plant tissues PCR amplification using RAPD markers Eluting DNA from agarose gel Cloning the eluted DNA fragment and DNA sequencing Primer design and PCR amplification for STS

DNA isolation from plant tissues (CTAB METHOD)

Plant tissues from young seedling at different stages to maturity can be used for DNA isolation. A healthy leaf blade (about 2 cm long) is collected in 1.5 ml tube. The tube is capped and placed on ice. The leaf tissue is cut into half cm long and placed again on the same tube.

About 400 µl of CTAB extraction buffer is added. The tissue is ground using a thick glass rod or pestle.

Again 400 µl of the CTAB extraction buffer is added and mixed. Then 400 µl chloroform (containing 4% v/v isoamyl alcohol) is added

and mixed well, spun for 30 seconds in micro-centrifuge with full speed and the supernatant is decanted.

The supernatant is transferred to another 1.5 ml tube. To the supernatant, 800 µl of absolute alcohol is added and mixed gently.

The tube is spun for 3 min in micro-centrifuge with full speed and the supernatant is decanted.

The pellet is washed with 70 % ethanol and air dried. DNA is suspended in 50 µl TE buffer and stored at -20º C.

The DNA can be directly used in PCR without quantification. About 1 µl of DNA is used for PCR analysis.

PCR amplification using RAPD markers (Refer Ex No. 6)

Eluting DNA from agarose gel

Different types of agarose are available in which hydroyethyl groups have been introduced into the polysaccharide chain. This substitution causes the agarose to gel at approximately 30º C and to melt at approximately 65ºC well below the melting temperature of most double-stranded DNAs. These properties have been exploited to develop a simple technique for the recovery of DNA from gels. This protocol works best for DNA fragments ranging in size from 0.5 kb to 5 kb.

MATERIALS

Ammonium acetate (10 M) Chloroform Ethanol 6x Gel-loading buffer LMT elution buffer (20 mM Tris-Cl (pH 8.0). 1 mM EDTA (pH 8.0). Phenol:chloroform (1:1, v/v) Phenol, equilibrated to pH 8.0

METHOD

1. Prepare a gel containing the appropriate concentration of low-melting-temperature agarose in 1x TAE buffer.

2. Cool the gel to room temperature, and then transfer it and its supporting glass plate to a horizontal surface in a gel box.

3. Mix the samples of DNA with gel-loading buffer, load them into the slots of the gel, and carry out electrophoresis at 3-6 V/cm.

4. Stain the agarose gel with ethidium bromide and locate the DNA band of interest.

5. Use a sharp scalpel or razor blade to cut out a slice of agarose containing the band of interest and transfer it to a clean Eppendorf tube.

6. After cutting out the band, photograph the gel to record which band of DNA was removed.

7. Add approx. 5 volumes of elution buffer to the slice of agarose, close the top of the tube, and melt the gel by incubation for 5 minutes at 65°C.

8. Cool the solution to room temperature, and then add an equal volume of equilibrated phenol.

9. Vortex the mixture for 20 seconds, and then recover the aqueous phase by centrifugation at 4000g for 10 minutes at 20°C.

10. Extract the aqueous phase once with phenol: chloroform and once with chloroform.

11. Transfer the aqueous phase to a fresh centrifuge tube. Add 0.2 volume of 10 M ammonium acetate and 2 volumes of absolute ethanol at 4°C. Store the mixture for 10 minutes at room temperature, and then recover the DNA by centrifugation at 5000g for 20 minutes at 4°C.

12. Wash the DNA pellet with 70% ethanol and dissolve in an appropriate volume of TE (pH 8.0).

Cloning the eluted DNA fragment and DNA sequencing.

Ligation of a stretch of DNA to a linearized plasmid vector involves the formation of new bonds between phosphate residues located at the 5’ termini of double-stranded DNA and adjacent 3’-hydroxyl moieties. When both strands of the plasmid vector carry 5’-phosphate residues, four new phosphodiester bonds are generated. The formation of phosphodiester bonds between adjacent 5’-phosphate and 3’-hydroxyl residues can be catalyzed in vitro by DNA ligases.

Ligation of PCR amplified purified DNA fragment was performed using pTZ57R/T vector (InsT/A clone™ PCR Product Cloning Kit, MBI Fermentas Inc., USA) as described in supplier’s manual. Vector (pTZ57R/T) and then

amplified products were taken in 1:3 ratios for ligation reaction. Ligation mixture was incubated at 16°C for 16 hrs.

Plasmid vector pTZ57R/T DNA (0.165µg, 0.18pmol ends)3.0 µlPurified PCR fragment (approx. 0.54pmol ends) 4.0 µl10X ligation buffer 3.0 µlPEG 4000 solution 3.0 µlDeionized water 29µlT4 DNA Ligase, 5 U 1µl

Transformation of E. coli strains by heat shock

Transformation is the alteration of the genotype of a cell by the direct introduction of foreign DNA into the cell. The cells which are capable of taking up DNA are called competent. The following method of transformation is very fast and easy and the cells can be readily frozen for later use. As in most methods of transformation the exact mechanism by which the procedure works is unclear.

Preparation of competent cells

Competent cells for plasmid transformation were prepared following the protocol suggested by Mandel and Higa (1970). Four ml of sterile LB broth was inoculated with a single colony of E. coli DH5α cells and incubated overnight at 37 ºC on a shaker set at 160 rpm. One ml of overnight grown culture was diluted into 30 ml of sterile LB broth, kept at 37 ºC on a shaker set at 160 rpm until OD600 reaches 0.4-0.5 (3-3.5 h only). The cell

suspension was transferred to 50 ml sterile centrifuge tubes and kept on ice for 20 min. The cell suspension was centrifuged at 5,000 rpm for 10 min at 4 ºC. The supernatant was carefully discarded and the pellet was gently resuspended in 10 ml of sterile ice-cold 50 mM CaCl2 using the tip of the pipette. The tubes were kept on ice for 20 min. The cell suspension was centrifuged at 5,000 rpm for 10 min at 4 ºC. The supernatant was carefully discarded and the pellet was very gently resuspended in two ml of sterile ice-cold 100 mM CaCl2 + 15% glycerol using the tip of the pipette. 100 l aliquots of resuspended cells were dispensed into pre-chilled tubes and then kept at 0ºC for 1 h, at -20 ºC for 1 h and finally dipped in liquid nitrogen before storage at -70 ºC.

Transformation

The ligation mixture (15l) was mixed to 100 l of competent cells after thawing the vial on ice. Tube was inverted few times to mix the DNA and cells gently. Tube with cells and DNA was kept on ice for 1 h. Tube was rapidly taken from ice, heat shock was given in a 42 ºC water bath exactly for 90 sec without shaking and placed back on ice for 10 min. Under aseptic conditions, 500 l of LB broth pre-warmed to 37 ºC was added and the tube was inverted twice to mix the cells and LB broth. The tube was incubated at 37 ºC. Aliquots (50, 100 and 200 l) of the transformed cells were plated on LB [ampicillin (100 ppm) / IPTG (100 ppm) / X-gal (160 ppm) / agar (2%)] plates and incubated overnight at 37 ºC. The recombinant clones were selected based on blue-white screening.

Design of STS Primer and PCR amplification:

STS primer pairs were designed for each sequenced species – specific RAPD marker using the primer 3 program. The selected new primer sites may or may not partially overlap with the original RAPD primer sites. PCR was carried out in a reaction mixture (25 l) containing

Sterile double distilled water 12.375 µl

10 x buffer 2.5 µl

8 Mm dNTPS 2.0 µl

Designed primer 2.0 µl

50 mM Mgcl2 2.0µl

Taq polymerase 0.125 µl

Template DNA 2µl

STS markers were optimally amplified under various PCR programs.

Ex.No:7

Date:

ISSR markers –Agarose Gel Electrophoresis

ISSR involves amplification of DNA segments present at an amplifiable distance in between two identical microsatellite repeat regions oriented in opposite direction. The technique uses microsatellites as primers in a single primer PCR reaction targeting multiple genomic loci to amplify mainly inter simple sequence repeats of different sizes.It does not require any prior knowledge of genome sequence This allows amplification of the genomic segments between inversely oriented repeats (ISSRs) . Theamplified products are usually 200–2000 bp long and amenable to detection by both agarose and polyacrylamide gel electrophoresis.Materials required

Genomic DNA dNTP’s Forward primer

Reverse primer 10 X PCR buffer Taq DNA polymerase Sterile Water PCR machine/thermocycler)Procedure1. Extract genomic DNA from respective samples 2. Estimate the concentration of the DNA samples3. Perform PCR in a final volume o 15 l

Genomic DNA : 3.0 l (50-100 ng)dNTP’s : 0.5 l (10 M each)Forward primer : 1.0 l (70 ng)Reverse primer : 1.0 l (70 ng)10 X PCR buffer : 1.5 l Taq DNA polymerase : 0.2- 0.5lDouble distilled water : to make up to 15 l

Temperature profile:

Step 1 : 94 °C for 5 minStep 2 : 94 °C for 1 minStep 3 : 50 °C for 2 minStep 4 : 72 °C for 2 minStep 5 : Step 2 to 4; 45 cyclesStep 6 : 72 °C for 5 minStep 7 : maintained at 4 °C

4. Run the products in an agarose gel along with DNA size marker5. Observe the gel under UV transilluminator.

Ex. No: 8

Date:

Southern hybridization and RFLP analysis

The technique was developed by E.M. Southern in 1975. The Southern blot is used to detect the presence of a particular piece of DNA in a sample. The DNA detected can be a single gene, or it can be part of a larger piece of DNA such as a viral genome. The key to this method is hybridization. Hybridization-process of forming a double-stranded DNA molecule between a single-stranded DNA probe and a single-stranded target patient DNA.

There are 2 important features of hybridization:

The reactions are specific-the probes will only bind to targets with a complementary sequence.

The probe can find one molecule of target in a mixture of millions of related but non-complementary molecules.

1 Steps for hybridization The mixture of molecules is separated. The molecules are immobilized on a matrix. The probe is added to the matrix to bind to the molecules. Any unbound probes are then removed. The place where the probe is connected corresponds to the

location of the immobilized target molecule.2 DNA Fragmentation

Cut the DNA into different sized pieces. Use restriction endonucleases (RE) Bacterial proteins In vivo, they are involved in DNA metabolism and repair or in

bacterial host defense.3 Gel Electrophoresis

Sorts the DNA pieces by size Gels are solid with microscopic pores Agarose or polyacrilamide Gel is soaked in a buffer which controls the size of the pores Standards should also be run

4 Blotting Transfer the DNA from the gel to a solid support. The blot is

usually done on a sheet of nitrocellulose paper or nylon. DNA is partially depurinated with dilute HCL which promotes higher efficiency transfer by breaking down fragments into smaller pieces. DNA is then denatured with an alkaline solution such as NAOH. This causes the double stranded to become single-stranded. DNA is then neutralized with NaCl to prevent re-hybridization before adding the probe. Transferred by either electrophoresis or capillary blotting. Capillary blotting-fragments are eluted from the gel and deposited onto the membrane by buffer that is drawn through the gel by capillary action.

The blot is made permanent by:

Drying at ~80°C

Exposing to UV irradiation

1) Blocking Buffer binds to areas on the blot not occupied by patient DNA.

Blocks the empty sites from being bound during hybridization

2) Hybridization The labeled probe is added to the blocked membrane in buffer and incubated for several hours to allow the probe molecules to find their targets.

3) Washing

Excess probe will have bound nonspecifically to the membrane despite the blocking reagents.

Blot is incubated with wash buffers containing NaCl and detergent to wash away excess probe and reduce background.

4) Detection Radioactive probes enable autoradiographic detection.

USES• Identify mutations, deletions, and gene rearrangements• Used in prognosis of cancer and in prenatal diagnosis of genetic

diseases• Leukemias• Diagnosis of HIV-1 and infectious disease

Applications of DNA fingerprinting include:• Paternity and Maternity Testing• Criminal Identification and Forensics

Ex. No: 9

Date:

AFLP (Amplified Fragment Length Polymorphism)-

Marker analysis

Amplified fragment length polymorphism (AFLP) is a technique for fingerprinting genomic DNA. It has a number of applications such as monitoring inheritance of agronomic traits in plant and animal breeding, diagnostics of genetically inherited diseases etc., the most important aspect of AFLP is that it can be used to inspect the entire genome for polymorphism and its reproducibility. It is to selectively amplify the restricted fragment of the total genomic DNA using polymerase chain reaction (Zabeau and Vos, 1993).Molecular genetic polymorphisms are identified by the presence or absence of DNA fragment following the restriction and amplification of genomic DNA.

Tha DNA is digested with restriction endonucleases, and double stranded DNA adapters are ligated to the ends of the DNA fragments to generate template DNA for amplification. Thus, the sequence of the adapters and the adjacent restriction site serve as the primer binding site for the amplification of restricted fragment by PCR. Selective nucleotides extending into the restriction fragments are added to the 3’ends of the PCR primers such that only a subset of the restriction fragments are recognized. The subset of the amplified fragments is then analyzed by denaturing polyacrylamide electrophoresis. It involves three main steps:

Restriction endonuclease digestion of DNA and ligation of adapters. Amplification of restricted fragments Gel analysis of Amplified fragmentsTypically, 50-100 restriction fragments are coamplified in each AFLP reaction and detected by electrophoresis.

Restriction Digestion of Genomic DNA

To prepare an AFLP template, genomic DNA is isolated and digested with two restriction endonucleases simultaneously. This step generates the required substrate for ligation and subsequent amplification.

The restriction fragment for amplification is generated by two restriction endonucleases; EcoRI and MseI. EcoRI is a 6-bp recognition site, and MseI has a 4-bp recognition site. When used together, these enzymes generate small fragment of DNA that will amplify well .

Components Stock Final 1 reaction (µl)10X 1X 5

Mse I 4U/µl 5U(0.1U/µl) 1.25EcoRI 10U/µl 5U (0.1U/µl) 0.5BSA (comes with Mse I)

10U/µl 0.1 ug/µl 0.5

ddH2O - - 32.75Genomic DNA 50-250 ng/µl - 10

Total Volume

50

1. Heat one water bath to 70°C and the other to 37°C.

2. Distribute 40 µl of the cocktail into each labeled tube.

3. Add 10 µl of DNA to each tube.

4. Vertex and spin the sample down in the centrifuge.

5. Incubate the tubes at 37°C for 3 hours. Agitate every hour by vortexing.

6. Inactivate the enzymes at 70°C for 15 min.

II. Adapter Preparation: complete during or before digestion.

EcoRI Adapter (120 reactions):

120 reactions

EcoRI.1 oligo (1ug/µl) 3.4 µl

EcoRI.2 oligo (1ug/µl) 3.0 µl

OPA 6.0 µl

ddH2O 107.6 µl

MseI Adapter (120 reactions):

120 reactions

MseI.1 oligo (0.5 µg/µl) 64.0 µl

MseI.2 oligo (0.5 µg/µl) 56.0 µl

OPA 7.0 µl

Mix the reagents in tubes and incubate.

65°C 10 min

37°C 10 min

25°C 10 min

Store tubes at -20°C.

III. Ligation of Adapters.

1 reaction

EcoRI adapter 1.0 µl

MseI adapter 1.0 µl

T4 DNA ligase 10X buffers 1.0 µl

T4 DNA ligase (3U/µl 0.33 µl

ddH2O 6.7 µl

Total 10.03µl

Add 10 µl of the ligation mix to 50 µl of digested DNA. Vortex briefly and spin

Incubate at room temperature for 3 hrs. Agitate every hour.

IV. Pre-amplification Reactions:

1 reaction

EcoRI + A oligo (50 ng/µl) 0.5 µl

MseI + C oligo (50 ng/µl) 0.5 µl

dNTPs (5mM, Gibco as 100mM) 2.0 µl

10X PCR buffer (comes with Taq) 2.0 µl

MgCl2 (comes with Taq) 1.2 µl

ddH2O 11.9 µl

Template DNA from 2.0 µl

restriction/ligation

Taq polymerase (5U/µl) 0.1 µl

Total 20.2µl

Run the following PCR profile:

94°C 2 min

94°C 1 min

56°C 1 min 26 cycles

72°C 1 min

72°C 5 min

4°C soak

Add 100 µl of sterile water.

V. Selective Amplification:

1 reaction

EcoRI + ANN oligo (50ng/µl) 0.5 µl

MseI + CNN oligo (50 ng/µl) 0.6 µl

dNTps (5mM, Gibco as 100mM) 0.8 µl

10X PCR buffer (comes with Taq) 2.0 µl

MgCl2 (comes with Taq) 1.2 µl

ddH2O 13.82 µl

Diluted template DNA from pre- 1.0 µl

selective PCR

Taq polymerase (5U/µl) 0.08 µl

Total 20 µl

Run the following PCR profile:

94°C 2 min

94°C 30s

65°C 30s 12 cycles, decrease

72°C 1 min Tm by 0.7°C each

94°C 30s

56°C 30s 23 cycles

72°C 1 min

72°C 2 min

4°C soak

Add 8 µl of formamide-loading buffer to the PCR product.

Oligo sequences:

EcoRI linker 1 CTC GTA GAC TGC GTA CC

EcoRI linker 2 AAT TGG TAC GCA GTC TAC

EcoRI + A GAC TGC GTA CCA ATT CA

PstI linker 1 CTC GTA GAC TGC GTA CAT GCA

Pst1 linker 2 TGT ACG CAG TCT AC

PstI + A GAC TGC GTA CAT GCA GAC A

MseI linker 1 GAC GAT GAG TCC TGA G

MseI linker 2 TAC TCA GGA CTC AT

MseI + C GAT GAG TCC TGA GTA AC

VI . Run the products in an agarose gel along with DNA size markerVII . Observe the gel under UV transilluminator.

Ex. No. 11. Development of Mapping Population: II. F2, RILs and BCs

The construction of genetic maps involves the linkage analysis of genes/markers segregating among progenies of a sexual cross. To get more recombination of the genes, different kinds of mapping population are being used. Most of the mapping populations in plants have been derived from crosses between homozygous parents. However, in crops like potato, sugarcane and alfalfa, which are not favours inbreeding, must rely on crosses between heterozygous individuals and genetic analysis of such plant populations relies on segregation of ‘single dose restriction fragments’. This exercise demonstrates how to develop F2, recombinant inbred lines (RILs) and back cross (BCs) progenies in cotton.

F2 ProgeniesBy simply selfing or intermating F1 hybrids, F2 progenies or populations can be made.

Each F2 individual is recombinant along both of each homologous chromosome but like back cross, it undergoes only one cycle of meiosis. Because of the F2 population harbouring all possible recombinations of parental alleles (i.e., AA, Aa and aa) not like in backcross where only Aa and aa are involved, the F2 populations are most widely used for genetic linkage map construction.

Recombinant inbred lines (RILs)Homozygous populations can also be made by traditional means i.e., recombinant

inbred (RILs) in which the progeny of an F2 cross are selfed or inbred to homozygosity. RILs have been developed in many crop plants and used in linkage map construction, since most of the crops are self-pollinated, homozygosity can be reached very quickly and easily by

single seed descent (SSD) method. Selfing is intensified form of inbreeding than sib-mating (sib-mating is usually followed in maize), which results in more rapid achievement of homozygosity; as a consequence, the heterozygosity is lost very rapidly.

The use of RILs simultaneously exploits the advantage offered by genetic segregation and recombination on one hand and of inbreeding on the other hand. Genetic segregation and recombination have revealed the particulate nature of genes and the characteristics of linkage, while inbreeding, which represents the opposite of segregation and recombination, represents the means of producing replicable genotypes.

From the below figure, it is obvious that RILs are lines which are derived from the cross of two unrelated but highly inbred progenitor strains and which have been maintained independently under a regimen of strict inbreeding since the F2 generation. This procedure genetically fixes the chance recombinants that occur in the generation following the F1 in ever decreasing amounts as full homozygosity is approached. The resulting population of lines can be looked upon as a replicate recombinant population.

Backcross (BC) populations

Backcross progenies can easily be obtained by crossing the F1 to one of its parent (recurrent parent) which segregates alleles derived from the donor parent (non-recurrent parent). Each back cross individual is recombinant along only one cycle of meiosis, hence association between alleles from a common parent remain strong. In effect, a donor parent (DP), which is homozygous for a mutant (or alternative) allele at some conventional marker locus of interest is mated to a recurrent parent (RP), which is homozygous for the wild type (or standard) allele at the same locus. The resultant F1 individuals are back crossed to the RP.

This kind of BC procedure is used to develop near isogenic lines (NILs) which are considered as genetic resource that can be used to identify linkages between molecular and conventional markers. The near isogenicity of a NIL to its RP provides a unique resource that can be exploited to identify presumptive linkages between hitherto unknown molecular markers and the various conventional marker that have been introduced in to the NILs.

Choice of the mapping populationChoice of the mapping population depends on the objective of the project. For

example, initial construction of a primary genetic linkage map, which has not been previously studied – F2 and backcross population are useful to detect linkage between widely scattered markers. If the objective is resolving closely linked markers, we need to use F2

intermatted population. For genomic analysis of complex genetic traits, RILs has to be employed since it permits replicated trails during phenotyping which minimize the effect of non-genetic factors on the data.

Ex. No. 12. Development of Mapping Population: III. DHLs

Doubled Haploid Lines (DHLs)In any hybridization breeding program, the ultimate goal is to obtain a homozygous

line. To achieve this, normally selfing is repeated for several generations following F1 hybrid. But in anther culture technique, the haploids of hybrid are produced. It is well known that after chromosome doubling of these haploids (by treating with chemicals such as colchicines), doubled haploids might be rapidly obtained. Thus the homozygosity could be obtained in shorter period, that too for all the gene loci. Further, among doubled haploids, recombination events are fixed resulting in stable recombinational values and there is less chance of inadvertent selection resulting in skewed allele distribution than RILs. The amount of recombinational information in the case of DH lines is equal to the back cross progenies because the plant has two identical homologues.

The homozygous lines, thus evolved, are stable and may be used directly in plant breeding and genetical studies. Though the strategy sounds good, evolving dihaploid population is a genotype-dependant process to the in vitro culture conditions i.e., all the crop plants are not amenable (e.g. cotton) for the production of DHLs and only few species (e.g. rice, tomato) are responsive to the anther culture protocol.

Ex. No. 13. Genotyping of different mapping populations

Selection of parents• Parents must have sufficient variation both at phenotypic and molecular levels. Variation at molecular level is essential to trace recombination events • Completely inbred lines are ideal as parents. They create marker-trait association due to their F1’s being in complete linkage disequilibrium for genes differing between linesLD = Non-random association of alleles from two loci on the same chromosome Most widely used mating designs to generate a mapping population are F2, BC, RIL, DH• A great advantage of RILs and DHs is their eternity; they can be multiplied without loosing genetic identity; This is important for QTL detection as it enables using replicates and to collect data in multiple environments• Low proportion of heterozygotes in an RIL population produces higher recombination frequency between marker-pairs and higher resolution of QTL mapping

Representative sample of mapping population• Number of individuals, ng ≈ 500-1000 [h2, QTL effect, size, …]• ng affects map resolution and marker order – more is better• Small ng - Poor quality linkage map

- Low power to detect QTL- QTL effects are overestimated

Types of Error

Selection of marker system • Different marker systems have different levels of resolution for detecting genomic variation

- Select a system that allows experimental detection of heritable genomic variation among individuals

of the mapping population - Co-dominant markers provide more power for QTL detection; Dominant markers

are generally less informative- For eternal RIL and DH, both types of markers are equally informative

Number of markers vis-a-vis mapping population size• How many markers (nm)? For locating QTLs, a dense map (large nm) is preferable to a sparse map (small nm) as this allows greater precision of QTL location• However … increasing nm beyond a certain average density makes little sense if ng is not increased at the same time• QTL mapping, like linkage mapping, relies on the frequency of detectable recombination events, which, beyond a given marker density (average inter-marker distance of ≈15 cM), can only be increased by increasing ng• A possible approach is to initially use an evenly spaced sparse map to detect significant chromosomal regions to which more markers could be subsequently saturated for fine-scale localization of QTLs

Genotyping of mapping population Identify the parental polymorphism Screen the mapping population for the polymorphic markers and score the data as

shown in the next exercise

Ex. No. 14. Scoring Principles and Methods

Codominant markers

With codominant markers, such as allozymes, RFLP and SSR, each recognizable allele at a given locus is ordinarily associated with a single band at a unique position in the gel. Thus, in the case of diploid organisms for a given locus a homozygote will have one band and a heterozygote will have two. Null alleles (no band) are rarely seen. Also, if there are multiple alleles per locus, as expected for SSRs, which are highly variable, the total number of bands expressed by all the individuals in a sample will likely be much greater than the number of loci involved.

Dominant markers

For dominant markers such as RAPDs, AFLPs and ISSRs, it is generally assumed that each band represents a different locus and that the alternative to a band at the gel position characteristic of that locus is the absence of a band anywhere in the gel.

ScoringWith a codominant marker, the genotypes of the three genotypic classes can be

observed for the two homozygotes and the heterozygote. In the drawing above, top centre, we see a gel image with the banding pattern of a codominant marker for a single locus of a

diploid organism. We need to score the bands in the gel and convert them to the respective scores as described in next section.

With a dominant marker, only two genotypic classes can be observed: AA + Aa and aa, that is, one of the homozygote classes is confounded with the heterozygote. The gel image with the banding pattern of a dominant marker for a single locus will show either one band or no band for each individual. The bands are scored in a way that is explained in the following section.

Scoring for “Mapmaker” Analysis / Preparation of Raw Data File

The very first line of your raw data file should read like:

data type xxxx

where xxxx is one of the allowed data types, either:

f2 intercross f2 backcross f3 self ri self ri sib

The second line of the raw file should contain a list of three numbers, separated by spaces, such as:

46 362 2

The first of these values indicates the number of progeny for which data are included in the file (in this case, 46). The second indicates the number of genetic loci for which data are supplied (362). The third indicates the number of quantitative traits in the data set (here 2, although this may be zero, of course).

Additional information may be optionally supplied at the end of this line. In particular, you may specify the coding scheme you use for genotypes. By default, the codes used for F2 backcross (a.k.a. BC1) data are:

'A' Homozygote for the recurrent parent genotype. 'H' Heterozygote. '-' Missing data for the individual at this locus.

For F2 intercross data, the default codes are:

'A' Homozygote for the allele from parental strain a of this locus. 'B' Homozygote for the allele from parental strain b of this locus. 'H' Heterozygote carrying both alleles a and b. 'C' Not a homozygote for allele a (either bb or ab genotype.) 'D' Not a homozygote for allele b (either aa or ab genotype.) '-' Missing data for the individual at this locus

For RI data, the default codes are:

'A' Homozygote for parental genotype a. 'B' Homozygote for parental genotype b.

'-' Missing data for the individual (or line) at this locus.

Also by default, MAPMAKER will match genotype characters in a case-insensitive manner (that is 'a' and 'A' indicate the same genotypes).

Howver, you can tell MAPMAKER to use whatever conventions you like, so long as you use the same conventions for the entire data file. First off, if you follow the numbers on the second line with the word "case", then MAPMAKER will match genotype characters in a case sensitive manner (that is 'a' and 'A' can be used to indicate different genotypes). For example:

46 362 2 case

If you do not wish to use case-sensitive genotypes, do not include the word "case".

To specify the coding scheme itself, include on the end of the above line the word "symbols" followed by the coding scheme you wish to use, defined in terms of the coding scheme above. For example, if you wish to use the following scheme with an RI data set:

'1' Homozygote for parental genotype a. '2' Homozygote for parental genotype b. '0' Missing data for the individual (or line) at this locus.

then you would use a second line like:

46 362 2 symbols 1=A 2=B 0=-

Note that when interpreting this line, MAPMAKER is in fact quite finickey about spaces and case distinctions (in order to keep MAPMAKER from ever misunderstanding exactly what you mean). In particular, NO SPACES should surround the "=" signs.

To use with a backross data set the scheme:

'a' Homozygote for parental genotype a. 'A' Heterozygote. '-' Missing data for the individual (or line) at this locus.

you should use a line like:

46 362 2 case symbols a=A A=H

The main restriction on coding schemes are that the only allowed symbols are letters, numbers, and the characters '-' and '+'.

After the first two header lines, the raw file should then present the genetic locus data, in the following simple format: For each locus, you list (1) the name of the locus, preceded by an asterisk ("*"); (2) one or more spaces (or tabs etc.); and (3) the genotypic data for all individuals, in order. For example:

*locus1 BA-HHHAAABBB-HHAA

would provide data for a locus named "locus1" with individual #1 having the B

genotype, individual #2 having the A genotype, and so forth. Data for each new locus should begin on a new line (with blank lines allowed), although the genetic data for any one locus may be "broken" by any number of spaces, tabs, and line breaks. This means that, among other things, tab-delimited-text files (such as those often exported by spreadsheet programs) will work well, for example:

*L2 B A - H H H A A A B B B - H

There is a system-dependednt maximum line length, although it is fairly large (at least 1,000 characters, where a tab counts as one character).

Locus names should be kept to at most 8 characters, and must be limited to alphabetic and numeric characters, along with the underscore character ('_') and periods ('.'). No other characters are allowed (although any dashes in locus names ('-') will be converted to underscores). Locus names must start with a alphabetic character (so that they are not confused with locus numbers in MAPMAKER sequences).

Any quantitative trait data should come after the genetic locus data. These data follow a similar format, except that the trait values for each individual must be separated by at least one space, tab, or line break. A dash ('-') alone indicates missing data. For example:

*weight 6.3 7.7 8.0 6.2 8.6 - 7.5 9.0 5.5 - - 8.4 7.7 7.4 6.9 -

would correspond to a trait named "weight", for which individual #1 has a value of 6.3, individual #2 has a value of 7.7, and so on. The sixth individual is missing data for this trait (and will be ignored for all analyses involving these trait data). As for the genotypes, a new trait should begin on a new line, and line breaks are allowed. Tab-delimted-text files work well here too.

Traits may also be specified as functions of other existing trait data. For example:

*weight1 6.3 7.7 8.0 6.2 8.6 6.9 7.5 9.0 *weight2 6.7 7.9 7.5 6.8 8.0 7.3 7.5 9.5 *mean= (weight1 + weight2)/2

The format of these equations is described under the "make trait" command. Such traits must be included in the number of traits indicated on the file's second line.

Note that genetic maps (particularly for MAPMAKER/QTL) are no longer included in the raw file, as they were with MAPMAKER Version 2.0. Instead, use a ".prep" initialization file, described below.

Finally, note that comments may be inserted on any line starting with a number sign character ("#").

Genotyping of mapping population Identify the parental polymorphism Screen the mapping population for the polymorphic markers and score the data as

shown in the following table

The data: How do they look like?• Example: 500 F8:9 RILs, 200 SSR markers

A: Parent 1 , B: Parent 2 (one high, one low); (A,B) can also be coded as (1,0)

• Error-free genotyping of ‘ng’ mapping population individuals with respect to each of the ‘nm’ markers• Marker genotype data contain information on segregation at various positions of the genome• Genotyping errors may lead to distorted segregation, inflated linkage map, biased QTL inferences

An example of a complete raw file follows:

data type f2 intercross 20 5 2 # tiny data set for practical class demonstration

*locus1 BBBHH-AAABBBHHH-AABA *locus2 AB-ABHABHAB-ABHABHBH *locus3 ABBAHHHBHABHABHBBHH- # Locus3 may be mis-scored in individual 12! *locus4 ABHABAAAHAB-ABHABHHB *locus5 ABHABHAA-ABHABHAHHHB

*trait1 6.3 7.7 8.0 6.2 8.8 6.2 4.1 6.5 5.4 7.3 8.7 9.0 5.2 6.8 7.2 7.1 7.6 8.3 8.1 7.5 *trait2 5.5 5.5 5.5 4.5 4.5 4.5 3.5 3.5 3.5 - 5.5 5.5 4.5 4.5 4.5 3.5 5.2 6.8 7.2 7.1

Ex. No. 15 & 16 Analysis and Interpretation of marker data

The genetic segregation ratio at maker locus is jointly determined by the nature of marker (dominant / codominant) and types of mapping populations (Table). Therefore, a thorough understanding of the nature of markers and mapping population is crucial for any mapping projects. Markers such RFLPs, microsatellites and CAPS etc. are codominant in nature, while AFLP, RAPD, ISSR are often scored as dominant markers. Mapping populations such as RILs and DHs equalize marker type because of fixation of parental alleles at marker locus in homozygous condition. These populations result in 1: 1 segregation ratio at marker locus irrespective of genetic nature of markers, while an F2 population segregates in 1: 2: 1 ratio for a codominant marker and in 3:1 ratio for dominant marker. Depending upon the segregation pattern, statistical analysis of marker data will vary.

Table. Genetic segregation ratio at marker locus in different marker–population combinations.

Characterization of Mapping PopulationsPrecise molecular and phenotypic characterization of mapping population is vital for

success of any mapping project. Since the molecular genotype of any individual is independent of environment, it is not influenced by G x E interaction. However, trait phenotype could be influenced by the environment, particularly in case of quantitative characters. Therefore, it becomes important to precisely estimate the trait value by evaluating the genotypes in multi-location testing over years using immortal mapping populations to have a valid marker-trait association.

Segregation Distortion of Markers in Linkage MappingSignificant deviation from expected segregation ratio in a given marker-population

combination is referred to as segregation distortion. There are several reasons for segregation distortion, including: gamete/zygote lethality, meiotic drive/preferential segregation, sampling/selection during population development and differential responses of

parental lines to tissue culture in case of DHs. Segregation distortion can also be specific with respect to some markers in an otherwise normal mapping population. It is therefore important that the ‘goodness of fit’ of segregation ratio must be tested for individual marker locus and if necessary, the marker showing high degree of segregation distortion be eliminated from the analysis.

Χ2 test to analyze the segregation ratio using the program ANTMAPThe chi-square test is most commonly used to test hypothesis concerning the

frequency distribution of one or more population. Χ2 = ∑ (O-E) 2

EWhere, O is observed frequency and E is expected frequency. The computed Χ2

value with the tabulated Χ2 value and reject the hypothesis of goodness of fit to the given ratio, if the computed Χ2 value exceeds the corresponding Χ2 value at given level of significance (i.e., 1% or 5%)

Step 1: Open an input file.

Fig. 2

Open an input file in MapMaker format (*.raw) through “File-Open” menu (Fig. 2).

Here, open “sample.raw” contained in the “antmap” folder.

Fig. 3

After opening the file, contents of the file will appear in the “Data” panel (Fig. 3).

Fig. 4

Click the “Log” tab, and you can see a summary of the input data (Fig. 4).

Step 2: Segregation ratio test.

Fig. 5

Select “Segregation Test” from the “Analysis” menu (Fig. 5). Then you can see the results of segregation ratio tests in the “Result” panel (Fig. 6). If P value is < 0.01, it will have **

(indicates that highly significant) and 0.01 to 0.05, it will have * (indicates significant). In other words, the above said P value indicates the data set fit the hypothesized frequency distribution at 1% and 5% level of significance.

Fig. 6

Ex. No. 17, 18 and 19 Linkage analysis, Interpretation of Data and Map Position Using Mapmaker

LinkageTwo genes are said to be linked if they are located on the same chromosome. We

assume that different chromosomes segregate independently during meiosis. Therefore, for two genes located at different chromosomes, we may assume that their alleles also segregate independently. The chance that an allele at one locus co-inherits with an allele at another locus of the same parental origin is then 0.5 and such genes are unlinked.

(Parent 1) AABB x aabb (parent 2)

F1 AaBb (100%)

F1-gametes AB Ab aB abIfA and B are unlinked: frequency (%) 25 25 25 25A and B linked: e.g. frequency (%) 35 15 15 35A and B tightly linked e.g. frequency (%) 48 2 2 48

The chance that A/B or a/b co-inherit to the offspring is 0.5 in case the genes are unlinked. This chance increases if the genes are linked. We can observe a degree of linkage. The reason is that even if genes are located on the same chromosome, they have a chance of not inheriting as in the parental state. This is due to recombination. During meiosis, the chromosome often breaks and the rejoins with the homologue chromosome, such that new chromosomal combinations appear (indicated as crossover). In the example, the

combination aB and Ab did not appear in the parental cells. These new combinations are the result of recombination, therefore indicated as recombinants.

In real life we can not observe gametes (at least, not the haplotypes), but the result from meiosis in an F1 can be checked in a testcross, which is a classical genetic test of linkage. This is achieved by crossing an F1 back to the homozygote recessive parent. The recombinants can easily be identified among the phenotypes in the offspring of a testcross.A testcross is

(F1) AaBb x aabb (parent 2)

Offspring AaBb Aabb aaBb aabb

If the A and B alleles are dominant, the composition of the gamete produced by the F1 sire can be determined from the offspring’s phenotype. In Drosophila, such linkage studies have been carried out during most of the 20th century. The further the distance between two genes, the more frequently there will be crossover, the higher the number of recombinations. Therefore, the recombination fraction is calculated from the proportion of recombinants in the gametes produced.

Recombination fraction = number of recombinants / total number of individuals

Note that the combinations aB and Ab are not always the recombinants. If the F1 was made from a parental cross AAbb x aaBB, than the recombinant gametes would be AB and ab. Therefore, for each testcross, we have to determine how the alleles were joined in the parental generation. This is known as the phase. If AB and ab were joined in the parental gametes, the gene pairs are said to be in coupling phase (as in first example). Otherwise, as in the cross AAbb x aaBB, the gene pairs are in repulsion phase. (These terms can be somewhat arbitrary if there are no dominant or mutant alleles).

Linkage disequilibriumLinkage equilibrium and its opposite: linkage disequilibrium, are terms used for the

chance of co-inheritance of alleles at different loci. Alleles that are in random association are said to be in linkage equilibrium. The chance of finding one allele at one locus is independent of finding another allele at another locus. In the previous example, suppose in the testcross progeny we observe the A allele. If the chance of finding either the B-allele or the b-allele is 50%, the genes are in linkage equilibrium.

Hence, if we look at the gamete-frequencies, then we speak of linkage equilibrium if the freq(AB) = freq (Ab) = freq (aB) = freq (ab). And the amount of disequilibrium is measured as

D = freq(AB).freq(ab) – freq(Ab).freq(aB).

Linkage disequilibrium is somewhat a confusing term. It can be the result of physical linkage of genes. However, even if the genes are on different chromosomes, there can be linkage disequilibrium. This can be due to selection. If A and B both affect a characteristic positively, and the characteristic is selected for, than in the selected offspring there will be a negative association between A and B. This is also known as Bulmer effect, as Bulmer (1971) described it to (partly) explain loss of variation due to selection.

Linkage disequilibrium can also be the result of crossing or migration. If a new individual with AB gametes come into a population with ab gametes, then in the offspring there will be more AB and ab gametes if the genes are linked. However, after a number of generations, the number of AB and ab gametes will approach that of the recombinant aB and Ab gametes, indicating linkage equilibrium. If the linkage is closer, this process will take longer. But ultimately, even if the distance between two genes is less than 1 cM, genes will become in linkage equilibrium (with no selection).

Hence, linkage disequilibrium is due to- recent migration or crossing- selection- recent mutation.

Linkage disequilibrium is essential for mapping. We may expect full disequilibrium between linked genes within a family, as the number of recombinants is the result of one meiosis event. Similarly, the same disequilibrium exists between a cross of inbred lines, such as in the testcross example above. However, in most other cases, at population level, genes are in linkage equilibrium. The important consequence is that if we find a particular allele at one gene (e.g. a marker) we cannot say which allele at another gene (e.g. at a QTL) should be expected. However, such statements are possible within families or across all families in a population if it was a recent cross from inbred lines, as in such cases there is linkage disequilibrium. Population-wide linkage disequilibrium exist in the case of selection, or with linked loci short after crossing or migration, or when two genes are so close that hardly any recombinations occur.

Mapping functionsThe distance between two genes is determined by their recombination fraction. The

map-units are Morgans. One Morgan is the distance over which, on average, one crossover occurs per meiosis. When considering the mapping of more than two points on the genetic map, it would be very handy if the distances on the map were additive. However, recombination fractions themselves are not additive. Consider the loci A, B and C. The recombination fraction between A-C is not equal to the sum of the recombination fractions AB and BC.

Say, the distance A-B is r1, the distance B-C is r2, and the distance A-C = r12 depends on the existence of interference.

If the recombination between A and B (with probability r1) is independent from the event of recombination between B and C (with probability r2), we say that there is no interference. In that case, the recombination between A and C is equal to r12 = r1 + r2 - 2*r1*r2. Interference is the effect in which the occurrence of a crossover in a certain region reduces the probability of a crossover in the adjacent region.

The last term is a reflection of the double crossovers. If there is complete interference the event of a crossover in one region completely suppressed recombinations in adjacent regions. In that case r12 = r1 + r2, i.e. the recombination fractions are additive. Also within small distances, the term 2r1r2 may be ignored, and recombination fractions are nearly additive. More generally, double recombinants can not be ignored, and recombination fractions are not additive. If distances were not additive, it would be necessary to redo a genetic map each time when new loci are discovered. To avoid this problem, the distances

on the genetic map are mapped using a mapping function. A mapping function translates recombination frequencies between two loci into a map distance in cM.

A mapping function gives the relationship between the distance between two chromosomal locations on the genetic map (in centiMorgans, cM) and their recombination frequency.Two properties of a good mapping function is that– Distances are additive, i.e. the distance AC should be equal to AB + BC if the order is ABC– A distance of more than 50 cM should translate into a recombination fraction of 50%.

In general, a mapping function depends on the interference assumed. With complete interference, and within small distances, a mapping function is simply:

distance (d) = r (recombination fraction).With no interference, the Haldane mapping function is appropriate:d = - ½ ln(1-2r).and given the map distance (d) the recombination fraction can be calculated asr = ½ (1-e-2d)Kosambi’s mapping function allows some interference:d = ¼ ln[(1+2r)/(1-2r)]

The different mapping functions are depicted in Figure.

Below 15 cM there is little difference between the different mapping

functions, and we can safely assume that d= c.Note: There is no general relationship between genetic distance and physical distance (in base pairs) The is a large variability between species for the average number of kilo base pairs (Kb) per centiMorgan. For humans this average is about 1000 kb per cM. Even within chromosomes there is variation, with some regions having less crossovers, and therefore more Kb per cM, than other.

The number of recombinations is not equal in the two sexes. It is usually lower in the heterogametic sex. In mammals, the female map is longer than the male map, as in females there are more recombinations for a certain stretch of DNA.

Mapping of genetic markersGenetic markers can be mapped relative to each other by

- Determining recombination fractions- Using a mapping function

Such genetic mapping can only place markers on the genetic map, relative to each other. For a whole genome map, some markers need to be anchored to their physical position, using in-situ mapping. Several molecular techniques are available, e.g. FISH (Fluorescent In-Situ Hybridization) Recombination fractions between genetic markers can be

estimated from mapping experiments (as in a test cross). Since we can observe complete marker genotypes, we do not fully rely on such specific designs as in a testcross. However, some designs are more efficient for mapping than other designs, determining the percentage of meiosis observed that is actually informative.

Estimation of the recombination fractionRecombination fractions are estimated from the proportion of recombinant gametes.

This is relatively easy to determine if we know - Linkage phase in parents - The haplotype of the gamete that was transmitted from parent to offspring If the linkage phase is known in parents, we can know which gametes are recombinants, and which ones are non-recombinant. However, in practice, linkage phases are not always known. This is especially the case in animals, as it is hard to create inbred lines. And markers are often in linkage equilibrium, even across breeds. If the linkage phase is not known, we can usually infer the parental linkage phase, as the number of recombinants is expected to be smaller than the number of nonrecombinants.

However, there is some chance that by chance there are more recombinants. Maximum Likelihood is used to determine the most likely phase, and therefore, to determine the most likely recombination fraction. Information about the gamete that was received by an offspring depends on the genotypes on offspring, parents. If parents and offspring are all heterozygous (e.g. Aa), then we don’t know which allele was paternal and which was maternal. If marker genotypes of parents are not heterozygous, we have no information about recombination events during their meiosis. For example, if the sire has genotype AB/Ab we cannot distinguish between recombinant gametes. However, if one parent is homozygous, it increases the chance of having informative meiosis on the other parent.

Testing for linkage: LOD scoresBesides estimating the most likely recombination fraction, we usually also want to

test those estimates statistically. In particular we want to test whether or not two loci are really linked. Therefore, the statistical test to perform is the likelihood versus a certain recombination fraction (r) vs the likelihood of no linkage (r=0.5). Different likelihoods are usually compared by taking the ratio of the likelihood. The 10log ratio of this likelihood ratio is indicated by LOD-score (abbreviation of log off odds) (Morton, 1955)

A LOD-score above 3 is generally used a critical value. A LOD-score >3 imply that the null-hypothesis (r = 0.5) is rejected. This value implies a ratio of likelihoods of 1000 to 1. This seems like a very stringent criterion. However, it accounts for the prior probability of linkage. Due to the finite number of chromosomes, there is a reasonable probability (5% in humans with 23 chromosome pairs) that two random loci are linked.

Running “Mapmaker” program with sample file

Precisely how you should start MAPMAKER depends on your computer. Just get into the mapmaker folder and double click the mapmaker icon to get into the command prompt.

When MAPMAKER starts running, you will first see its start-up banner and a prompt "1>" for the first command.

Command that should be typed into MAPMAKER is represented in bold italics, while MAPMAKER output is presented in regular type.

The first step in almost every MAPMAKER session is to load a data file for analysis. If you are starting out an analysis on a new data set, or if you have modified the raw data in an existing data set, you will do this using MAPMAKER's "prepare data" command, as described in the previous exercise. If instead you are resuming an analysis of a particular (unmodified) data set, you may use the "load data" command, which preserves many of the results from your previous session. If you are just starting out, use MAPMAKER's "prepare data" command to load sample file "sample.raw". From this file MAPMAKER extracts: The type of cross, number of markers, and number of scored progeny The genotype for each marker in each individual (if available)

Other information may be present in the data files, such as quantitative trait data and pre-computed linkage results. These issues will be addressed later.

Before performing any analyses of data set, first instruct MAPMAKER to save a transcript of this session in a text file for later reference. Using the "photo" command, a transcript named "tutorial.out" is started. Note that if the file already exists, MAPMAKER appends new output to this file. ************************************************************************ * Output from: * * * * MAPMAKER/EXP * * (version 3.0b) * * * ************************************************************************ Type 'help' for help. Type 'about' for general information. 1> prepare data sample.raw preparing data from 'sample.raw'... F2 intercross data (333 individuals, 12 loci)... ok saving genotype data in file 'sample.data'... ok 2> photo tutorial.out 'photo' is on: file is 'tutorial.out' Finding Linkage Groups by Two-Point Linkage

Initially begin the linkage map construction analysis by performing a classical "two-point" , or pairwise, linkage analysis of data set. While we generally do not use two-point analysis for ordering markers, we usually do find two-point analysis helpful for identifying linkage groups of markers in preliminary analyses. To then order markers within a group, we use other more powerful techniques.

First, we need to tell MAPMAKER which loci we wish to consider in our two-point analysis. We do this using MAPMAKER's "sequence" command. When you type something like: sequence locus1 locus2 locus3 ...

MAPMAKER is told which loci (and, in some cases, which orders of those loci) any following analysis commands should consider (for ex: locus1, locus2, locus3, etc.). Since almost all of MAPMAKER's analysis functions use the "current sequence" to indicate which loci they should consider, you will find that the "sequence" command must be entered before performing almost any analysis function. The sequence of loci in use remains unchanged until you again type the "sequence" command to change it.

In this two point analysis we want to examine all 12 of the loci in our sample data set. Thus, we now type into MAPMAKER: sequence 1 2 3 4 5 6 7 8 9 10 11 12

Note that for two-point analysis, the order in which the loci are listed is unimportant. Then type MAPMAKER's "group" command, instructing the program to divide the

markers in the sequence into linkage groups. To determine whether any two markers are linked, MAPMAKER calculates the maximum-likelihood distance and corresponding LOD score between the two markers: If the LOD score is greater than some threshold, and if the distance is less than some other threshold, then the markers will be considered linked. By default, the LOD threshold is 3.0, and the distance threshold is 80 Haldane cM.

For the purpose of finding linkage groups, MAPMAKER considers linkage transitive. That is, if marker A is linked to marker B, and if B is linked to C, then A, B, and C will be included in the same linkage group.

As you see, MAPMAKER has divided data set into two linkage groups, which it names "group1" and "group2". Moreover, there are no unlinked markers in this data set. 3> sequence 1 2 3 4 5 6 7 8 9 10 11 12 sequence #1= 1 2 3 4 5 6 7 8 9 10 11 12 4> group Linkage Groups at min LOD 3.00, max Distance 50.0 group1= 1 2 3 5 7 ------- group2= 4 6 8 9 10 11 12 Exploring Map Orders by Hand

To determine the most likely order of markers within a linkage group, we could imagine using the following simple procedure: For each possible order of that group, we calculate the maximum-likelihood map (e.g. the distances between all markers given the data), and the corresponding map's likelihood. We then compare these likelihoods and choose the most likely order as the answer. This type of exhaustive analysis may be performed using MAPMAKER's "compare" command.

In practice however, this sort of "exhaustive" analysis is not practical for even medium sized groups: a group of N markers has N!/2 possible orders, a number which becomes unwieldy (for most computers) when N gets to be between 6 and 10. (In practice, one needs to order subsets of the linkage group and then overlap those subsets, mapping any remaining markers relative to those already mapped, a process we will illustrate later).

Luckily, group1, consisting of markers 1, 2, 3, 5, and 7 is small enough that we can use a fully exhaustive analysis.

To do this, we first change MAPMAKER's sequence to "{1 2 3 5 7}". Here, the set-braces indicate that the order of the markers contained within them is unknown, and thus that all possible orders need to be considered.

We then type the "compare" command, instructing MAPMAKER to compute the maximum likelihood map for each specified order of markers, and to report the orders sorted by the likelihoods of their maps. Note that while MAPMAKER examines all *** orders, only the 20 most likely ones are reported (by default).

For each of these 20 orders, MAPMAKER displays the log-likelihood of that order relative to the best likelihood found. Thus the best order: 1 3 2 5 7 is indicated as having a relative log-likelihood of 0.0. The second best order: 3 1 2 5 7 is significantly less likely than the best, having a relative log-likelihood of -6.0. Said a different way, the best order of this group is supported by an odds ratio of roughly 1,000,000:1 (10 to the

6th power to one), over any other order. We consider this good evidence that we have found the right order.

5> sequence {1 2 3 5 7} sequence #2= {1 2 3 5 7} 6> compare Best 20 orders: 1: 1 3 2 5 7 Like: 0.00 2: 3 1 2 5 7 Like: -6.00 3: 5 7 2 3 1 Like: -20.20 4: 5 7 2 1 3 Like: -26.26 5: 2 5 7 3 1 Like: -27.25 6: 2 5 7 1 3 Like: -28.39 7: 2 3 1 5 7 Like: -28.85 8: 5 2 3 1 7 Like: -32.33 9: 2 1 3 5 7 Like: -34.12 10: 5 7 1 3 2 Like: -35.55 11: 5 2 1 3 7 Like: -37.61 12: 1 3 5 2 7 Like: -37.76 13: 3 1 5 2 7 Like: -39.09 14: 5 7 3 1 2 Like: -40.38 15: 1 3 5 7 2 Like: -40.87 16: 3 1 5 7 2 Like: -41.55 17: 5 2 7 3 1 Like: -43.67 18: 5 2 7 1 3 Like: -44.78 19: 5 1 3 2 7 Like: -47.63 20: 2 5 3 1 7 Like: -52.28 order1 is set

Displaying a Genetic Map When we used the "compare" command previously, MAPMAKER calculated the map

distances and log-likelihood for each of the 60 orders we were considering. The "compare" command however only reports the relative log-likelihoods, and afterwards forgets the map distances. To actually display the genetic distances we must instead use the "map" command.

Like "compare", the "map" command instructs MAPMAKER to calculate the maximum likelihood map of each order specified by the current sequence. If the current sequence specifies more than one order (for example, the sequence "{1 2 3 5 7}" specifies 60 orders) then the maps for all specified orders will be calculated and displayed.

Because we found one order of this group to be much more likely than any other, we probably only care to see the map distances for this single order. First, we set MAPMAKER's sequence, putting the markers in their best order and doing away with the set brackets.

Next, we simply type "map" to display this order's maximum likelihood map. As you can see, the distances between neighboring markers are displayed. Note however,

that these distances may be considerably different than the "two-point" distances between those markers: This is because MAPMAKER's so-called multipoint analysis facility can take into account much more information, such as flanking marker genotypes and some amount of missing data. This is precisely the reason that we use multipoint analysis rather than two point analysis to order markers: Because more data is taken into account, you have a smaller chance of making a mistake.

7> sequence 1 3 2 5 7

sequence #3= 1 3 2 5 7 8> map ===================================================================== Map: Markers Distance 1 T175 4.2 cM 3 C35 15.0 cM 2 T93 11.9 cM 5 C66 12.2 cM 7 T50B ---------- 43.2 cM 5 markers log-likelihood= -424.94 ==================================================================== Mapping a Slightly Larger Group

As we mentioned earlier, exhaustive analyses of large linkage groups are not practical. Instead, to find a map order of a larger group, we need to find a subset of markers on which we can perform an exhaustive "compare" analysis. Thus, to map group2 we could pick a subset of its 8 markers at random, although we might do better if we pick markers which are likely to be ordered with high likelihood. Generally, this is true for sets of markers which have (i) as little missing data as possible, and (ii) do not have many closely spaced markers.

To quickly see how much data is available for the markers in this group, we set MAPMAKER's "sequence" appropriately and use MAPMAKER's "list loci" command.

MAPMAKER prints a list of loci, showing each marker by both its MAPMAKER-assigned number as well as it's name in the data file. For each marker, MAPMAKER prints the number of informative progeny (out of the 333 in the data set), and the type of scoring. In this case all loci have been scored using "co-dominant" markers (e.g. RFLP-like genotypes in an F2 intercross), although clearly markers 4 and 6 are the least informative. To also look for markers which may be too close, we use MAPMAKER's "lod table" command.

MAPMAKER prints both the distance and LOD score between all pairs of markers in the current sequence. Unfortunately, the closest pair is separated by over 6.0 cM, a distance which should almost always be resolvable in a data set with so many informative meioses. Given the results of these two analyses, a good subset to try might be: 8 9 10 11 12

Note that the above two tests could have been automatically performed using MAPMAKER's "suggest subset" command, documented in the reference section. 9> sequence 4 6 8 9 10 11 12 sequence #4= 4 6 8 9 10 11 12 10> list loci Linkage Num Name Genotypes Group 4 T24 273 codom group2 6 T209 275 codom group2 8 T125 306 codom group2 9 T83 327 codom group2 10 T17 297 codom group2 11 C15 324 codom group2 12 T71 319 codom group2 11> lod table Bottom number is LOD score, top number is centimorgan distance: 4 6 8 9 10 11

6 63.1 3.33 8 16.8 56.0 39.06 4.33 9 56.3 17.8 54.8 6.77 36.70 7.68 10 106.3 27.7 - 43.3 0.89 22.51 15.08 11 14.9 74.0 6.3 65.4 - 43.78 2.20 80.87 5.76 12 28.2 43.1 18.4 24.1 89.1 30.1 22.24 9.13 39.84 32.39 2.22 23.90

As before, we now change MAPMAKER's sequence to specify the subset we wish to test, and then type the "compare" command. This time, the results are even more conclusive, with one order at least 10 to the 14.6th more likely than any other.

We also again select the best order as the current sequence using MAPMAKER's "sequence" command. This time however, we so this using a special shortcut, "order1", which is a name MAPMAKER often sets to indicate the results of its analyses.

To determine the map position of the remaining two markers in group2, we will use the following procedure: Starting with the known order of 5 markers, we will place the other two (one at a time) into every interval in this order and then recalculate the maximum-likelihood map of each resulting 6 marker order. In this analysis, MAPMAKER recalculates all recombination fractions for all intervals in each map (not just the ones involving the newly placed markers).

This function is performed by MAPMAKER's "try" command. In its output, MAPMAKER again displays relative log-likelihood of each position for the inserted markers. The relative log-likelihood of 0 indicates the best position, while the negative log-likelihoods indicate the odd against placement in each other interval.

In this case, we see that marker 6 strongly prefers to be in-between markers 9 and 10. Even the next most likely position for marker 6 is more than 10 to the 21.09th power times less likely.

The "try" command not only tries to place markers in each interval in the framework, but also tries to place each marker infinitely far away (that is, forced 50% recombination between it and the framework). The relative log-likelihoods for this position are indicated following the "INF" entry in the MAPMAKER output. In the same way that a two-point LOD score indicates the odds of linkage between two loci when they are separated by their maximum likelihood distance, these relative log-likelihoods indicate the odds supporting linkage between one locus and a framework of loci when the locus is placed in its most likely position. In the above test, we see that a log-likelihood of 44.66 supports linkage between 4 and the rest of the group. 12> sequence {8 9 10 11 12} sequence #5= {8 9 10 11 12} 13> compare Best 20 orders: 1: 11 8 12 9 10 Like: 0.00 2: 10 11 8 12 9 Like: -14.57 3: 8 11 12 9 10 Like: -15.23 4: 10 9 11 8 12 Like: -27.20 5: 11 8 12 10 9 Like: -29.97 6: 10 8 11 12 9 Like: -30.14 7: 9 10 11 8 12 Like: -32.23 8: 8 11 10 9 12 Like: -39.80

9: 10 9 8 11 12 Like: -39.91 10: 9 11 8 12 10 Like: -40.05 11: 11 8 10 9 12 Like: -40.25 12: 11 8 9 12 10 Like: -44.73 13: 8 11 12 10 9 Like: -45.21 14: 10 11 8 9 12 Like: -46.57 15: 8 11 9 12 10 Like: -47.46 16: 9 10 8 11 12 Like: -47.94 17: 10 8 11 9 12 Like: -49.61 18: 8 11 10 12 9 Like: -52.71 19: 9 8 11 12 10 Like: -52.74 20: 11 8 10 12 9 Like: -53.07 order1 is set 14> sequence order1 sequence #6= order1 15> try 4 6 4 6 --------------- | 0.00 -42.68 | 11 | | |-35.57 -118.6 | 8 | | |-19.65 -70.19 | 12 | | |-46.80 -28.09 | 9 | | |-51.35 0.00 | 10 | | |-43.40 -21.09 | |---------------| INF |-44.66 -45.03 | --------------- BEST -619.33 -612.03

As a last step, we now type the complete sequence for this group, adding markers 4 and 6 into their most likely positions. Then we type "map" to see the complete map of all markers in this group. 16> sequence 4 11 8 12 9 6 10 sequence #7= 4 11 8 12 9 6 10 17> map ========================================================================= Map: Markers Distance 4 T24 14.8 cM 11 C15 6.4 cM 8 T125 18.9 cM 12 T71 24.0 cM 9 T83 18.1 cM 6 T209 28.6 cM 10 T17 ---------- 110.8 cM 7 markers log-likelihood= -688.99 =========================================================================

ReferencesBovenhuis, H. and T.H.E. Meuwissen. 1996. Detection and mapping of quantitative trait loci. Animal Genetics and Breeding Unit. UNE, Armidale, Australia. ISBN 186389 323 7Bulmer, M.G. 1971. The effect of selection on genetic variability. Amer. Nat. 105:201.Morton, N.E. 1955. Sequential tests for the detection of linkage. American Journal of Human Genetics. 7:277-318.Mapmaker tutorial

Ex. No. 20, 21 and 22 Linkage analysis, Interpretation of Data and Map Position Using AntMap

Constructing genetic linkage maps by ant colony optimization algorithm - AntMap Ver. 1.2

Locus ordering is an essential procedure in genome mapping. When the number of loci is large, it is quite difficult to determine the optimum order with an exhaustive search of all possible orders. The problem of searching for the optimum order has been recognized as a special case of the traveling salesman problem (TSP), i.e., given a set of cities and distances for each pair of them, find a roundtrip of minimal total length visiting each city exactly once. In recent years, Ant Colony Optimization (ACO) (Dorigo and Stützle 2004), which is a set of algorithms inspired by the behavior of real ant colonies, has been successfully used to solve discrete optimization problems, such as TSP. We developed a novel system based on ACO for locus ordering in genome mapping (Iwata and Ninomiya in preparation). In our system, loci and absolute value of log likelihood (or recombination fraction) between loci were regarded as TSP cities and distance between cities, respectively. We tested the system using a simulated segregation population, and found it is highly efficient for linkage grouping as well as locus ordering in genome mapping (Iwata and Ninomiya 2004).

To commoditize our newly-developed system, we developed software named AntMap for constructing linkage map by the system. AntMap performs segregation test, linkage grouping and locus ordering, and constructs a linkage map quite rapidly and nearly automatically. Rapidity of the algorithm based on ACO enables us to conduct a bootstrap

test of estimated order. With the aid of this software, researchers can save their time and labor, and can obtain a linkage map whose reliability is indicated by bootstrap values. Another advantage of AntMap is the fact that AntMap is open source; that is, source code and executables of AntMap are available under GNU General Public License (GPL). Java and C++ objects that code our newly-developed system will be utilized effectively for other applications as well as AntMap.

Input File FormatInput file format of AntMap is identical to *.raw files required by MapMaker (Lander et al. 1987). A concise description of the format of *.raw files can be found in http://www.rfcgr.mrc.ac.uk/Registered/Help/mapmaker/. AntMap can analyze data derived from progeny of several types of crosses, including:(1) F2 intercross(2) F2 backcross (e.g., BC1)(3) Recombinant inbred lines by self-mating(4) Doubled haploid lines

The first line of input file indicates the crossing types as:data type xxxx

where xxxx should be one of the followings:f2 intercrossf2 backcrossri selfdh

The current version of AntMap does not support two types of cross, F3 intercross by self-mating (f3 self) and recombination inbred lines by sib-mating (ri sib), which are supported by MapMaker EXP.

Step 0: Start AntMap

Fig. 1When you use AntMap on Windows, start AntMap with double-clicking the “AntMap” icon (Fig. 1). For other operating systems (i.e., platforms), See below. AntMap can also be executed by using the executable jar file “AntMap.jar” on any platforms (Linux, Solaris and Mac OS as well as Windows). To execute the jar file, run: java –jar AntMap.jar on your command line system. Some platforms may have bindings already set up such that you can execute the jar file just by clicking on the “AntMap.jar” file icon, which will run the command line equivalent. Note that you should change mode of the jar file to be executable when you are on “Linux” or “Solaris” platforms as described in Box 1.

Box 1.Linux and SolarisBefore executing “AntMap-linux” or “AntMap-solaris”, you should change mode of these files to be executable. To do that, type chmod 755 AntMap-xxxx on your command line system (“xxxx“ should be ”linux“ or “solaris”). After changing the mode of files, you can execute AntMap by clicking the “AntMap-linux” or “AntMap-solaris” file icon.Mac OS X

Note that you can execute “AntMap-macx” from the command line, but cannot execute by clicking the “AntMap-macx” file icon.

Step 1: Open an input file.

Fig. 2Open an input file in MapMaker format (*.raw) through “File-Open” menu (Fig. 2). Here, open “sample.raw” contained in the “antmap” folder.

Fig. 3After opening the file, contents of the file will appear in the “Data” panel (Fig. 3).

Fig. 4Click the “Log” tab, and you can see a summary of the input data (Fig. 4).

Step 2: Segregation ratio test.

Fig. 5Select “Segregation Test” from the “Analysis” menu (Fig. 5). Then you can see the results of segregation ratio tests in the “Result” panel (Fig. 6).

Fig. 6

Step 3: Linkage grouping

Fig. 7Click the “Options” tab. Then you can see the “Grouping” option panel (Fig. 7). You can choose one of the two grouping methods: “nearest neighboring locus” and “all combinations”. The former makes a group by sequentially combining a locus which shows the smallest recombination value against it. This algorithm has been implemented by MAPL (Ukai et al. 1991). The latter will produce similar results with “group” command of MapMaker. You can also choose the grouping criterion, threshold value and the minimum number of markers for a single group. Here, we will keep these options unchanged except for the threshold value.

Fig. 8

Type “0.25” into an input area, and push the “Alter” button. Then you can change the threshold value from 0.3 to 0.25.

Fig. 9

Select the “Linkage Grouping” from the “Analysis” Menu. Then you can see the results of linkage grouping in the “Result” panel (Fig. 10).

Fig. 10

When you analyze your data, you may not be able to achieve a good separation of markers to linkage groups from the start. In such a case, please find a good set of the threshold value, criterion and method through try-and-errors.

Step 4: Locus ordering

Fig. 11Click the “Options” tab, and click the “Ordering” tab. Then you can see the “Ordering” option panel (Fig. 11).

In the locus ordering, you can choose one of the two criteria: “LL” and “SARF”. “LL” is an abbreviation for “Log Likelihood”. “SARF” is an abbreviation for “Sum of Adjacent Recombination Fractions” (Liu 1998). AntMap will search a locus order which maximizes log-likelihood or minimizes “SARF”. You can also choose the number of runs of locus ordering. You can find the meaning of this option in the “AntMap Options” section of the AntMap use’s manual.A map function for calculating a map distance between adjacent markers can be selected from “Haldane” (Haldane 1919) or “Kosambi” (Kosambi 1944) functions. Here, we will keep these options unchanged.

Fig. 12Select the “Locus Ordering” from the “Analysis” Menu. Then you can see the results of locus ordering in the “Result” panel (Fig. 13).

Fig. 13You can also obtain a graphic of linkage map in the “Map” panel (Fig. 14).

Fig. 14

Step 5: One-step mapping

Fig. 15Select “Full Course” from the “Analysis” Menu. Then, you can overall process from segregation ratio test (Step 2) to locus ordering (Step 4) at once.Step 6: Redraw a linkage map

Fig. 16Click the “Options” tab, and click the “Draw map” tab. Then you can see the “Draw map” option panel (Fig. 16). Here, we will change the “Scale factor” option. Drawing size of linkage map can be changed through this option. Here, type “2” into an input area, and click the “Alter” button (Fig. 17).

Fig. 17

After changing the option value from 4 to 2, select “Redraw Map” from the “Analysis” menu. Then, you can obtain a smaller linkage map than one obtained previously (Fig. 18).

Fig. 18

Step 7: Bootstrap test for locus orderYou can evaluate the reliability of estimated locus order by using bootstrap test. Bootstrap test (or bootstrapping) is a method for estimating the sampling distribution of an estimator by resampling with replacement from the original sample. In a bootstrap test, a random sample of size n is drawn from the original sample of size n, and estimates are obtained from the random sample. After repeating (iterating) this operation many times (e.g., 100-1000 times), the stability of estimates (e.g., standard error or confidence interval of estimators) is evaluated. For the details of bootstrap test, please see a good textbook such as Manly (1998). In the bootstrap test for locus order, we can obtain probability thata locus is located at its estimated order (Liu 1998).

Fig. 19

Click the “Options” tab, and click the “Ordering” tab. Then you can see the “Ordering” option panel (Fig. 19). You can change the number of iterations (repeats) of bootstrapping. To get a good estimate of percentage of correct locus order, 100 may be sufficient. You can also choose a group which is targeted in the bootstrap test. Here, we will choose only Group3 to save our time (Fig. 20).

Fig. 20

Fig. 21

Select the “Bootstrap Test” from the “Analysis” Menu (Fig. 21). Then you can see the results of bootstrap test for locus order in the “Result” panel (Fig. 22).

Fig. 22You can also obtain a graphic of linkage map with bootstrap values in the “Map” panel (Fig. 23).

Fig. 23Caution! : The bootstrap test for all linkage groups may take long time even by high-end PC. Thus, you have better set your computer to perform this test at your lunch time or after going home.

Step 8: Save results of linkage mapping

Fig. 24

You can save information in “Result”, “Log” and “Map” panels through the “Save” submenu in the “File” Menu. The information in “Result” and “Log” is saved as a text file. The information in “Map” (i.e., a graphic of linkage map) is saved as a JPEG (*.jpg) file.

Ex. No. 23 & 24 Germplasm Characterization and Allele Mining

Conservation of genetic resources entails several activities, many of which may greatly benefit from knowledge generated through applying molecular marker technologies. This is the case for activities related to the acquisition of germplasm (locating and describing the diversity), its conservation (using effective procedures) and evaluation for useful traits. In all, the availability of sound genetic information ensures that decisions made on conservation will be better informed and will result in improved germplasm management. Of the activities related to genetic resources, that involving germplasm evaluation and the addition of value to genetic resources are particularly important as they help identify genes and traits, and thus provide the foundation on which to enhance use of collections.

‘Characterization’ is the description of a character or quality of an individual. The word ‘characterize’ is also a synonym of ‘distinguish’, that is, to mark as separate or different, or to separate into kinds, classes or categories. Thus, characterization of genetic resources refers to the process by which accessions are identified or differentiated. This identification may, in broad terms, refer to any difference in the appearance or make-up of an accession. In the agreed terminology of gene banks and germplasm management, the term ‘characterization’ stands for the description of characters that are usually highly heritable, easily seen by the eye and equally expressed in all environments. In genetic terms, characterization refers to the detection of variation as a result of differences in either DNA sequences or specific genes or modifying factors.

Standard characterization and evaluation of accessions may be routinely carried out by using different methods, including traditional practices such as the use of descriptor lists of morphological characters. They may also involve evaluation of agronomic performance under various environmental conditions. In contrast, genetic characterization refers to the description of attributes that follow a Mendelian inheritance or that involve specific DNA sequences. In this context, the application of biochemical assays such as those that detect differences between isozymes or protein profiles, the application of molecular markers and the identification of particular sequences through diverse genomic approaches all qualify as genetic characterization methods.

Because of its nature, genetic characterization clearly offers an enhanced power for detecting diversity (including genotypes and genes) that exceeds that of traditional methods. Likewise, genetic characterization with molecular technologies offers greater power of detection than do phenotypic methods (e.g. isozymes). This is because molecular methods reveal differences in genotypes, that is, in the ultimate level of variation embodied by the DNA sequences of an individual and uninfluenced by environment. In contrast,

differences revealed by phenotypic approaches are at the level of gene expression (proteins).

Using molecular characterization to make informed decisions on the conservation of crop genetic resources

Information about the genetic make-up of accessions helps decision making for conservation activities, which range from collecting and managing through identifying genes to adding value to genetic resources. Well-informed sampling strategies for germplasm material destined for ex situ conservation and designation of priority sites (i.e. identifying specific areas with desirable genetic diversity) for in situ conservation are both crucial for successful conservation efforts. In turn, defining strategies is dependent on knowledge of location, distribution and extent of genetic diversity. Molecular characterization, by itself or in conjunction with other data (phenotypic traits or geo-referenced data), provides reliable information for assessing, among other factors, the amount of genetic diversity, the structure of diversity in samples and populations, rates of genetic divergence among populations and the distribution of diversity in populations found in different locations.

A recent study on the genetic diversity of cultivated Capsicum species in Guatemalan home gardens compared the diversity present in an array of home gardens in the Department of Alta Verapaz with a countrywide representative sample of 40 accessions conserved ex situ in the national collection. The results showed that home gardens of Alta Verapaz (H = 0.251) contained as much diversity as the entire national ex situ collection (H = 0.281). These results thus suggest that,

(1) home gardens are indeed an extremely important resource for in situ conservation of Capsicum germplasm in Guatemala, and as such they should not be neglected;

(2) if further collecting activities were to be undertaken, special emphasis should be given to collecting in Alta Verapaz; and (3) additional collecting in Alta Verapaz alone could disclose novel genetic diversity that is absent from the national collection.

Conservation of clonally propagated crops demands more complex and expensive procedures. If these crops are maintained on-farm, their existence is endangered by several factors, one of which being the introduction of alternative improved varieties. Conservation efforts need then to be based on solid knowledge of clonal diversity. This was the case for Abyssinian banana or ensete (Ensete ventricosum (Welw.) Cheesman) from Ethiopia, which was analysed with AFLP markers. Of the 146 clones from five different regions, only 4.8% of the total genetic variation was found between regions, whereas 95.2% was found within regions. The results led to a reduced number of clones for conservation and indicated the existence of a common practice of exchange of local types between regions, which, in its turn, emphasizedthe need to collect further in different farming systems.

A study on taro (Colocasia esculenta (L.) Schott) genetic diversity in the Pacific, using SSR markers, showed that many of the accessions from countries of the Pacific region were identical to those of Papua New Guinea. This indicates that originally the cultivars may have been introduced throughout the region from Papua New Guinea and that collection of taro genetic diversity could focus on Papua New Guinea alone. Molecular characterization also helps determine the breeding behaviour of species, individual reproductive success and the existence of gene flow, that is, the movement of alleles within and between populations of the same or related species, and its consequences. Molecular data improve or even allow the elucidation of phylogeny, and provide the basic knowledge for understanding taxonomy,

domestication and evolution. As a result, information from molecular markers or DNA sequences offers a good basis for better conservation approaches.

Management of germplasm established in a collection (usually a field, seed or in vitro gene bank) comprises several activities. Usually, such activities seek to ensure the identity of the individually stored and maintained samples, to ensure the safeguarding of genetic integrity and genetic diversity and to have the material available for distribution to users. These tasks are primarily a responsibility of gene bank managers and curators, and involve the control of accessions on arrival at the facilities, as well as their continuous safeguarding for the future through regeneration and multiplication. For all these routine activities, information about the genetic constitution of samples or accessions is critical and provides possibly the most important means of measuring the quality of the work being performed.

Bulk seed of wheat accessions was analysed to test their genetic integrity after 24 cycles of regeneration and after more than 50 years of storage at room temperature in a gene bank. They found neither contamination nor incorrect manipulation effects such as mechanical mixtures, but did identify one case of genetic drift in one accession. The fact that IPK-Gatersleben gene bank (Germany) splits its germplasm samples into either almost or completely pure lines, i.e. accessions, is expected to have contributed to this very positive finding. However, in the same gene bank, a study examined the genetic constitution of rye accessions that underwent frequent regeneration. Results showed that

(1) a significant number of alleles present in the original sample was lacking in the newly regenerated material, and

(2) new alleles in the new material were not present in the first regeneration sample. Thus, the use of molecular markers can quickly help check whether changes in alleles

or allele frequencies are taking place. Molecular information has been used to weigh the need for decreasing the size of germplasm collections, which otherwise would add costs to the long-term conservation of germplasm. For instance, microsatellite markers were used to analyse the genetic diversity and structure of 19 sorghum accessions known as ‘Orange’ in the USDA’s national sorghum collection. They found two redundant groups (involving five entries) among the 19 accessions evaluated. They also found that much of the total genetic variation was partitioned among accessions. As a result, it was concluded that the number of accessions held by the US National Plant Germplasm System (NPGS) could be significantly reduced without risking the overall amount of genetic variation contained in these holdings.

Markers were also helpful in examining genetic identities and relationships of Malus accessions. Eight primer pairs unambiguously differentiated 52 of 66 genotypes in a study that calculated the probability of any two genotypes being similar at all loci analysed as being about 1 in 1,000 million. The results not only discriminated among the genotypes, but were also shown to be useful for designing strategies for the collection and in situ conservation of wild Malus species. Selected molecular technologies render cost-effective and comprehensive genotypic profiles of accessions (‘fingerprints’) that may be used to establish the identity of the material under study. Simultaneously, these technologies can detect contaminants (and, in the case of material mixtures, contamination with introgressed genes from other accessions or commercial varieties), as well as the presence of redundant materials (or ‘duplicates’).

Moreover, molecular data provide the baseline for monitoring natural changes in the genetic structure of the accession, or those occurring as a result of human intervention (e.g. seed regeneration or sampling for replanting in the field). Whatever the case, analysis of molecular information allows the design of strategies for either purging the consequences of inappropriate procedures or amending them to prevent future inconveniences.

A small number of potential duplicates were identified in a core collection of cassava (Manihot esculenta Crantz) when isozyme and AFLP profiles were compared. The core collection had been assembled with information from traditional markers, which proved to be highly effective for selecting unique genotypes. Molecular data were used for efficiently verifying the previous work on the collection and ensure minimum repetition. The taro core collection for the Pacific region was treated in a similar manner. Thus, gene bank managers can easily realize the potential value of using molecular methods to support and possibly modify or improve a gene bank’s operations.

A special and increasingly important role of genetic characterization is that of identifying useful genes in germplasm, that is, of maximizing conservation efforts. Because the major justification for the existence of germplasm collections is use of the conserved accessions, it is important to identify those valuable genes that can help develop varieties that will be able to meet the challenges of current and future agriculture.

Characterization has benefited from several approaches resulting from advances in molecular genetics such as genetic and QTL mapping, and gene tagging. Research in this field has led to the acknowledgement of the value of wild relatives, in which modern techniques have discovered useful variation that could contribute to varietal improvement.

Knowledge of molecular information in major crops and species and of the synteny of genomes, especially conservation of gene order, has also opened up prospects for identifying important genes or variants in other crop types, particularly those that receive little attention from formal research.

Future trendsMost marker technologies target genomic regions, which are selectively neutral;

some technologies, however, target specific genes. The neutrality of markers is suitable for most uses in germplasm conservation and management. However, when the interest of conservation lies specifically in the diversity of traits of agronomic importance, some questions remain about the markers’ representativeness. In such a case, those markers able to detect functional diversity are more suitable for characterizing germplasm collections.

Germplasm in collections can undergo molecular characterization that is structural, meaning ‘based on the building blocks of the DNA sequence’, and functional, that is, based on the identification of genes and their functions. Such characterization permits access to the raw materials—the genes—for nearly all the objectives of today’s and tomorrow’s breeding programmes. The information gathered from structural characterization not only provides increased clarity on existing genetic diversity and its organization in individuals, but it determines sample and population organization that ultimately may form the basis for functional characterization.

The increasing number of sequencing projects has resulted in an increased opportunity to produce expressed sequence tags (or ESTs) to which gene functions may be assigned. Moreover, such projects are making possible the compilation of an enormous amount of sequence data that can be used to develop markers linked to specific genes, which, in turn, may help identify novel functional variation.

In addition, the development of novel technologies continues. This usually means decreased costs—a very significant point for their application in the tasks of conserving genetic resources, which tend to involve large numbers of samples and to have difficulties in sourcing needed funds. Other improvements involve increasing the throughput—both in number of markers analysed and in number of samples—and simplifying technologies. New developments are also taking place in designing better approaches to access new and useful

genetic variation in collections, namely, allele mining and association genetics. Allele mining focuses on the detection of allelic variation in important genes and/or traits within a germplasm collection. If the targeted DNA (either a gene of known function or a given sequence) is known, then the allelic variation (usually point mutations) in a collection can be identified, using methods developed for the purpose.

Association studies of artificial progenies are an alternative to segregation analysis for identifying useful genes by correlation of molecular markers and a specific phenotype. Association studies can be performed on a germplasm collection and also on other materials, as long as significant linkage disequilibrium (LD) exists, for example, breeding materials. It may be especially useful for those crops where appropriate populations for genetic analysis cannot be obtained or their production is too time-consuming. It is also useful for those crops for which sequence information does not exist and is unlikely to be available soon.

The challenges aheadThe importance of the variation captured in genetic resources in allowing evolution

and/or facilitating plant breeding has been long recognized. However, appreciating the variation held in collections is not sufficient. Conservation of genetic resources needs to go hand in hand with enhanced use of the conserved material. Identifying and making available the allelic variation that makes up the genotype and phenotype provides the groundwork on which genetic resources can be used in, for example, plant breeding. The number of accessions held collectively by all CGIAR gene banks is estimated as being almost 600 000. Together with the collections established by national programmes worldwide, this number reaches almost 6 million. Without doubt, these genetic resources collections, together with uncollected germplasm and that held in situ and on-farm, harbor abundant quantities of hidden allelic variants. The challenge is to unravel the mysteries of this variation so it can be used for the benefit of humankind.

Gene banks hold large numbers of accessions, particularly of staple crops. Modern improvements in equipment and procedures allow considerable sample throughput. This can be costly. However, the more a technology develops, the lower its costs will be per data point and per sample. Nevertheless, the higher the throughput used, the higher the number of data points obtained. This requires adequate equipment—for handling and storing—and expertise—for handling and analysing—to draw adequate results from the investment. One possible avenue for ensuring broader benefits from molecular characterization is the establishment of international collaboration for particular crops. Although, currently, equipment and expertise cannot be readily available worldwide, characterization networks arepossible. In addition to carrying out the laboratory work, such networks would also facilitate access to information, thus fostering closer links between curators, breeders and molecular scientists. At the same time, those countries with little expertise or equipment can make steady progress in both areas and, hence, can make better use of the genetic resources that they hold.

More and more, technologies have increased throughputs, which generally means the generation of progressively larger amounts of data. Such data should not languish unused. If gene banks equip themselves with the latest technologies, then they should be able to translate such data into scientific knowledge. To do so, they need not only laboratory technical expertise, but also need bioinformatics staff. This means that through their molecular work, gene banks may keep not only live plant materials but also DNA and data.

Hence, the banks may develop appropriate new roles as providers of genetic resources and their accompanying data in an array of forms. Such broadening of the gene banks’ roles implies that their clientele will also expand from plant breeders to include molecular geneticists, molecular biologists and even bioinformaticists. The expanded range of roles may even lead to including activities related to phenotyping, a type of characterization beyond the traditional description of morphology and general field performance. Phenotyping is very much linked to the usefulness of good molecular characterization as, together, they form the basis of progress in modern genomics research.

Allele MiningThe sequencing of the entire rice genome by Syngenta, the world’s largest

agribusiness, has raised apprehension about the effect of this advance on poorer countries that rely on rice as a staple food. The results of the sequencing will be available only through contracts and will not be published. However, the data from the publicly funded International Rice Genome Sequencing Project (IRGSP), which aims to complete the sequence of the Oryza sativa L cv. Nipponbare genome is more accurate and freely available. The japonica cultivar Nipponbare is the reference genotype. The genomewide sequence will provide a directory for all the genes of rice, whether their function is currently known or unknown.

Existing and emerging tools of genomics will be used to determine the function of unknown genes, to relate genotype to phenotype, and to identify the genes conditioning important traits in the rice germplasm as a whole. In extending rice genomics beyond Nipponbare, an essential step will be allele mining: the use of Nipponbare sequence data to isolate the corresponding alleles from other rice accessions. Within the limits of genome synteny and sequence conservation, the data for Nipponbare will also assist allele mining in other cereals.

One among many applications of this sequence information will be to devise rapid and inexpensive polymerase chain reaction (PCR) strategies to isolate useful alleles of rice genes from a wide range of rice cultivars, related species, and genera. This capability will be important for giving rice breeders direct access to key alleles conferring

(1) resistance to biotic stresses, (2) tolerance of abiotic stresses, (3) greater nutrient use efficiency, (4) enhanced yield, and (5) improved quality, including human nutrition. This approach to allele mining is already available as a result of existing sequence

databases but will be greatly enhanced when IRGSP provides both the sequence and the physical map location of each Nipponbare gene to the public.

Case Study -1This Allele mining study exploited the deoxyribonucleic acid (DNA) sequence of one

genotype to isolate useful alleles from related genotypes. The international project to sequence the genome of Oryza sativa L cv. Nipponbare make allele mining possible for all genes of rice and possibly related cereals. Latha et al., 2004 used a rice calmodulin gene, a rice gene encoding a late embryogenesis-associated protein, and salt-inducible rice gene to optimize the polymerase chain reaction (PCR) for allele mining of stress tolerance genes on identified accessions of rice and related germplasm. Two sets of PCR primers were designed for each gene. Primers based on the 5′ and 3′ untranslated region of genes were found to be

sufficiently conserved so as to be effective over the entire range of germplasm in rice for which the concept of allelism is applicable. However, the primers based on the adjacent amino (N) and carboxy (C) termini amplify additional loci.

Case Study – 2Allele mining facilitates the discovery of novel resistance (R) genes that can be used

in breeding programs and sheds light on the evolution of R genes. Here we focus on two R genes, Rpi-blb1 and Rpi-blb2, originally derived from Solanum bulbocastanum. The Rpi-blb1 gene is part of a cluster of four paralogues and is Xanked by RGA1-blb and RGA3-blb. Highly conserved RGA1-blb homologues were discovered in all the tested tuber-bearing (TB) and non-tuber-bearing (NTB) Solanum species, suggesting RGA1-blb was present before the divergence of TB and NTB Solanum species. The frequency of the RGA3-blb gene was much lower. Interestingly, highly conserved Rpi-blb1 homologues were discovered not only in S. bulbocastanum but also in Solanum stoloniferum that is part of the series Longipedicellata. Resistance assays and genetic analyses in several F1 populations derived from the relevant late blight resistant parental genotypes harbouring the conserved Rpi-blb1 homologues, indicated the presence of four dominant R genes, designated as Rpi-sto1, Rpi-plt1, Rpi-pta1 and Rpi-pta2. Furthermore, Rpi-sto1 and Rpi-plt1 resided at the same position on chromosome VIII as Rpi-blb1 in S. bulbocastanum. Segregation data also indicated that an additional unknown late blight resistance gene was present in three populations. In contrast to Rpi-blb1, no homologues of Rpi-blb2 were detected in any material examined. Hypotheses are proposed to explain the presence of conserved Rpi-blb1 homologues in S. stoloniferum. The discovery of conserved homologues of Rpi-blb1 in EBN 2 tetraploid species offers the possibility to more easily transfer the late blight resistance genes to potato varieties by classical breeding.References

1. M.C. de Vicente, F.A. Guzmán, J. Engels and V. Ramanatha Rao. 2005. Genetic characterization and its use in decision making for the conservation of crop germplasm. Proceedings of the role of biotechnology, villa Gualino, Turin, Italy – 5-7 March, 2005.

2. R. Latha, L. Rubia, J. Bennett, and M. S. Swaminathan. 2004. Allele Mining for Stress Tolerance Genes in Oryza Species and Related Germplasm. Molecular biotechnology 27:121.

3. Miqia Wang · Sjefke Allefs · Ronald G. van den Berg, Vivianne G. A. A. Vleeshouwers , Edwin A. G. van der Vossen · Ben Vosman. 2008. Allele mining in Solanum: conserved homologues of Rpi-blb1 are identiWed in Solanum stoloniferum. Theor Appl Genet (2008) 116:933–943.