análisis de ligamiento y mapas genéticos · chromosome theory of heredity thomas h. morgan sex...

Análisis de ligamiento y mapas genéticosAntonio J. MonforteIBMCP (UPV-CSIC)

[email protected] Almería, 28 de Enero de 2019

Análisis de ligamiento y mapas genéticos

Genetic segregation

• Gregor Mendel (1822-1884). Genetic Laws

• Hugo de Vries, Carl Correns, Erich von

Tschermak-Seysenegg. RE-Discovery of

Mendel laws. Beginning XX century

X

X

1 : 3


Independent

segregation



Chromosome theory of heredity

Thomas H. Morgan

Sex linkage of eye´s colour


Genes are located in

the chromosomesSegregation analysis

• Linkage: Association of

genes among them and to

chromosomes

• Mapping: Sequence of the

genes on a chromosome

and the distance among

them


Meiosis y recombinación


Linkage

A

a

B

b

Two genes in the same chromosome are linked

There is linkage between genes

C

c

Gametes

ABC

ABc

abC

abc

abcABC

abcABc

abcabC

abcabc

X a b c

a b c


Crossover

A

A

a

a

A

A

a

a

B

B

b

b

A

A

a

a

B

B

b

b

B

B

b

b

A

A

a

a

B

B

b

b

Meiotic chromosomes Meiotic products

Recombi-

nant

Crossover

No

Crossover

Exchange of chromosome segments among

homologue chromosomes during meiosis



LINKED LOCI

r = Recombination fraction

½ (1-r) AB

½ r Ab

½ r aB

½ (1-r) ab


½ (1-r) AB

½ r Ab

½ r aB

½ (1-r) ab

r = 0,30

= 35

= 15

= 15

= 35

(N = 100)


½ (1-r) AB

½ r Ab

½ r aB

½ (1-r) ab

r = 0,10

= 45

= 5

= 5

= 45

(N = 100)


Linkage testing


Two point. Linkage

• BC, DHL, CP… 1 recombination event AABB x aabb

χ2

MLE


Crossover and map distance


Multipoint recombinations

A B C15

18

5

A B C

a b c

A

Aa

a

B

Bb

b

C

cC

c

rac= rab + rbc - (2*rab*rbc)


GENETIC DISTANCE

• r tends to a maximum of 0.5

• recombination is not additive

double recombinations are not

considered

INTERFERENCE: When a crossover occurs, it is

less probable that another one will occur at the

same region of the chromosome.

r is not the optimal parameter for map construction


Map Distance: average number of recombinations between two genes

Recombination and map distance: Map functions


Genetic distances and recombination fraction


Construction of linkage maps: directions

• Choose parents

• Polymorphic markers (depends on genome

size)

• Mapping population

• Score markers

• Segregation analysis

• Linkage mapping


Choosing parents

• Previous information about interesting traits

• Maximun polymorphism for

markers


Choosing markers

• First choice. Codominant markers

– Anchor points: RFLPs, CAPs, SCARs,

Microsatellites, SNPs

• Second choice. (Actually, to avoid)

Dominant markers

– RAPDs, AFLPs

• Number of markers

– Good resolution >20 markers per chromosome

– High resolution >100 markes per chromosome


Codominance


Dominance


What markers?

• In-house

• Bibliography

• Databases:– Gramene (rice, wheat, maiz, rye…) www.gramene.org

– Tomato and Solanaceae: Solgenomics.net

– Melon and cucurbits: http://cucurbitgenomics.org/

• DNA sequence (own or public data bases)

• Genotype by Sequence or other NGS-

based as Target SNP, SPET


Next-Generation sequencing

• No cloning

• DNA or RNA

• Shotgun sequencing

• Sequence up to 1 Tb in few days.

• Tomato 950 MB, Melon 450, Arabidopsis 150 MB

• Short reads from 700 to 25 nucleotides. Millions of

them

• Requires huge computer power and bioinformatics


SNP discovery

• DNA/RNA from several genotypes

• Sequence

• Assemble to obtain contigs

• Compare contigs and find SNPs

• SNPs are very consistent because each

position is usually read several times

• Timing: a few weeks


Output and assembling

ChromosomeReference

sequence

Reads


• RFLP, CAPS, Allele specific PCR

• Denaturing HPLC (DHPLC)- Transgenomics

• Hi-resolution melting (HRM) by quantitative PCR.

• Pyrosequencing

• Single-base extension (Snapshot, TDI-FP )

• Microarrays (Affymetrix, Illumina, millions of SNP)

• Taqman probes, Kaspar

• Cel I digestion

• Sequenom iPLEX assay

• Genotype by sequencing

• The machines are usually very expensive. Cost-effective

when analyzing a large number of SNPs and samples, HRM

is affordable for low scale genotyping

SNP detection technologies


Genotype by sequencing

Indiv A

Indiv B

Indiv C

Nex Generation Sequencing

Sequence alignment

SNP discovery

SNP genotyping

Genotyping by sequencing (GBS) in any large genome

species requires reduction of genome complexity


Davey et al 2011;

Nat Rev Genet 12:499- 510


Multiplexing and barcoding

Sequencing

one “run”


Barcode

sequence

Individual

sequence

SNPs

AA BB BB

BB AB AA

AA AA BB

BB BB AA


Mapping populations

• Inbred crosses

– Backcross

– F2

– Double Haploid Lines (LDH)

– Recombinant Inbred Lines (RIL)

• Outbred crosses

– Pseudo-test cross

• Size: 80-200

Immortal

1-2 recombinant

events

Multiple

recombinant

events

Immortal 1 recombination events


Outbred crosses or Cross-Pollinator (CP)

F1 “pseudo-testcross”

- One meiosis within each parent

- Phase unknown

- Two linkage maps, one for each parent

- Multiple possible segregations

X

P1 P2


Data collection

A = Homozygote for allele of parent A

B = Homozygote for allele of parent B

H = Heterozygote


(aa) (ab) (bb)

A H B

D C

(a-) (b-)


A B B A B B B B A A A A A A A A A A A A B B A A B A A B B

P1 P2

DHL

-Watch out!!

Do not guess genotypes.

If one marker genotype

is not clear, record it as

missing

Pla

nt

*CS

AT

42

5B

*NR

21

*CM

GA

15

*NR

35

*CM

AT

35

*NR

9

*NR

3

*NR

2

*OID

IO

*CS

WTA

02

*CM

TA

A1

66

*CM

AC

C1

46

*CM

AG

59

*CM

AT

14

1

*NR

25

*CM

TC

16

0

*CS

WC

T1

8B

*CM

GA

10

4

3 A A A A B B B B B B B A A A B B A A6 B B B A B B A A A A A B B B - B B A7 - A - - - A A A B - B A A - A B A A

10 A A A A A A A - A A A A A A - B B B11 A A - B B B B - B - B A A A A A A A14 A A B B B B B B B B B A B A - B B B15 B B B B A A A A A A A - A A A B A A17 B B B A A A A B B B B A A B A A A A19 A A A B B B B B - B B A A A A A A A22 B B B A A B A B B B B A A B B B B B23 A A B B B B B B - B B B B A A B B B24 B B B B B B B B B B B B B A A A A A26 A A A A B - - B B B B A A A A B B B29 A A B B B B B B - B B A A A A A A A

Genotype scoring


• DHL, RIL A:B 1:1

• BC A:H 1:1

• F2 A:H:B 1:2:1

• CP abxcd 1:1:1:1

abxab 1:2:1

abxaa 1:1

- 2 test with 2 (1:1) or 3 (1:2:1) d. f.

- Statistical threshold? p< 0.05, 0.01, 0.005?

- My suggestion: 0.05/2*N chromosomes

Segregation analysis


• Technical:

– Human/machine errors

– Problematic markers causing score errors

– Duplicated loci

• Biological

– Gametic differential viability

– Zygotic differential viability: Self-incompatibility,

reproductive barriers, fitness…….

– Several linked markers show distortion

• Include or remove from linkage analysis?

Segregation distortion


Data file

*pit 3 6 7 10 11 14 15 17 19 22 23 24 26 29 39 40 41

*CSGA057 A B B B A B B A A B A B A A A B B

*TJ3 A B B B A B B A A - A B A A A B B

*MC301 A - B B A B B A A B A B A A A B A

*MC68 A B B B A B B A A B A B A A A B -

*TJ2 A A B B A B B A A B A B A A B B A

*MC356 A A A A A A - A A B B B A A B B A

*MC11 A A A A A A A A A - B B A A B A A

*MC78 A B A A A A A A A B B B A A B A A

*CMACC146 A B A A A A A A A A B B A A B A A

*MC208 A B A A A A A A A A B B A A B A A

*CMAG59 A B A A A - A A A A B B A A B B A

*CMAT141 A B B A A A A A A - B B A A B B A

*MC138 A A B A A A A A B - B B B B B B A

*NR56 A A B A A A A A B A B B B B - - B

*MC127 B A B B A A A A B A A B B A B A A

*CSWCT10 A A B B A A A A B A A B B A B A A

*TJ12b A A - B A A A - B A A B B - A A A

*nr66a A A A - A A A A B - A B B A A A A


Linkage testing and genetic distance

estimation with MAPMAKER 3.0

1. Two point analysis. Linkage testing.

Grouping markers.

- 2

- Logarythm of ODDS (LOD score)

LOD=log[prob(linkage)/prob(not linkage)]

2. Three point analysis. Determination of

marker order.

3. Calculation of genetic distance (Haldane

or Kosambi)


Groups

6> group

Linkage Groups at min LOD 3.00, max Distance 50.0

group1= 1 2 3 4 5 6 7 8 9 10 11 12 13 14

-------

group2= 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

-------

group3= 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

55 56 57 58 59 60 61 62 140 141 142 143 144 145 146 147 148 149 150 151 152 153

169 170 171 172 173 174 175 176 177 178 179 180 181 182

-------

group4= 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79


Three point analysis to estimate marker order

Which is the most likely order?

- The order that needs less number of recombination events

# Rec: 4 6 6

Plant CM37 CM45 CM46 CM46 CM37 CM45 CM37 CM46 CM45

1 A B B B A B A B B

2 A A A A A A A A A

3 A A B B A A A B A

4 A A A A A A A A A

5 B B A A B B B A B

6 B A A A B A B A A


8> three point

Linkage Groups at min LOD 3.00, max Distance 50.0

Triplet criteria: LOD 3.00, Max-Dist 50.0, #Linkages 2

'triple error detection' is off.

counting...107 linked triplets in 1 linkage group

log-likelihood differences

count markers a-b-c b-a-c a-c-b

1: 1 2 3 0.00 -2.85 -1.30

2: 1 2 4 0.00 -1.01 -5.24

3: 1 2 5 0.00 -1.41 -13.23

4: 1 3 4 0.00 -0.25 -4.34

5: 1 3 5 0.00 -1.46 -9.28

6: 1 4 5 0.00 -4.38 -2.47

7: 1 4 8 0.00 -1.96 -5.12

8: 1 5 6 0.00 -6.57 -2.54

9: 1 5 7 0.00 -4.22 -2.45

10: 1 5 8 0.00 -4.28 -2.01

11: 2 3 4 -0.04 0.00 -5.33

12: 2 3 5 0.00 -0.55 -10.75

13: 2 4 5 0.00 -3.24 -3.81

14: 2 4 8 0.00 -1.71 -7.06

15: 2 5 6 0.00 -5.91 -2.76

16: 2 5 7 0.00 -4.48 -3.76

17: 2 5 8 0.00 -4.34 -3.33

18: 3 4 5 0.00 -3.17 -1.89

19: 3 4 8 0.00 -1.64 -5.16

20: 3 5 6 0.00 -4.39 -2.11

group1= 1 2 3 4 5 6 7 8 9 10 11 12 13 14

3 or 2 LOD

threshold

Most likely order

Comparison with

most likely order


Linkage group 1, 14 Markers:

1 CSGA057 2 TJ3 3 MC301 4 MC68 5 TJ2

6 MC356 7 MC11 8 MC78 9 CMACC146 10 MC208

11 CMAG59 12 CMAT141 13 MC138 14 NR56

All markers are informative...

Searching for a starting order containing 5 of all 14 loci...

Got one at log-likelihood 3.05

Placing at log-likelihood threshold 3.00...

Start: 2 5 6 7 9

Npt-7: 2 (4) 5 6 7 9

Npt-End: 2 4 5 6 7 9 (12)

Npt-5: 2 4 5 6 7 9 (11) 12

Npt-End: 2 4 5 6 7 9 11 12 (14)

No unique placements for 5 remaining markers

Map:

Markers Distance

2 TJ3 12.5 cM

4 MC68 14.7 cM

5 TJ2 15.9 cM

6 MC356 9.4 cM

7 MC11 15.3 cM

9 CMACC146 7.6 cM

11 CMAG59 14.6 cM

12 CMAT141 23.6 cM

14 NR56 ----------

113.6 cM 9 markers log-likelihood= -124.24

Markers placed relative to above map:

2 4 5 6 7 9 11 12 14

:-12-:-15-:-16-:--9-:-15-:--8-:-15-:-24-:

8 2 ...:....:....:....:..*.:.**.:....:....:....:...

1 2 **.:..*.:....:....:....:....:....:....:....:...

13 2 ...:....:....:....:....:....:....:....:.**.:.*.

3 2 .*.:.**.:....:....:....:....:....:....:....:...

10 2 ...:....:....:....:....:..*.:.**.:....:....:...

Distance

calculation


Placing at log-likelihood threshold 2.00...

Start: 2 4 5 6 7 9 11 12 14

Npt-3: 2 4 5 6 7 9 11 12 (13) 14

Npt-4: 2 4 5 6 7 9 (10) 11 12 13 14

No unique placements for 3 remaining markers

Map:

Markers Distance

2 TJ3 12.5 cM

4 MC68 14.6 cM

5 TJ2 15.9 cM

6 MC356 9.3 cM

7 MC11 15.4 cM

9 CMACC146 2.4 cM

10 MC208 5.0 cM

11 CMAG59 14.6 cM

12 CMAT141 17.5 cM

13 MC138 6.3 cM

14 NR56 ----------

113.5 cM 11 markers log-likelihood= -130.80

Markers placed relative to above map:

2 4 5 6 7 9 10 11 12 13 14

:-13-:-15-:-16-:--9-:-15-:--2-:--5-:-15-:-17-:--6-:

8 2 ...:....:....:....:..*.:.**.:....:....:....:....:....:...

1 2 **.:..*.:....:....:....:....:....:....:....:....:....:...

3 2 .*.:.**.:....:....:....:....:....:....:....:....:....:...

order1= 2 4 5 6 7 9 10 11 12 13 14

other1= 8 1 3


CTC/AAG-323CMTA170aMC202MC54

OPP07-1.07OPP19-1.23

MC296

MG19CTA/AGC-105MC127MC235

MC221MC209BMC148OPX11-1.01

MC53OPP08-0.64MC244OPY16-0.49OPK03-0.67CTA/AGG-186CAT/AAC-62OPO15-1.78MC331BCM11CM88AMG57MC27CMGA128

MC124MC298MC215MC207B

0

2320

29

40

44

54

5760

71

79

85

91

95

AEST90B

CTA/ACG-302

MC365MC32

50

CMGA36B

MC373

MC311

Npr1MC44

ACTG4-0.60CTC/AAG-136CTA/AGG-200CAC/AAC-78CTA/AAC-62CAC/AAC-79

OPJ18-1.24

OPX11-0.59MC253Acs2CACG4-1.23CSAT425BCAC/AAC-119CAC/AAC-77MC317

CM139

CMGA15

MC387OPG04-1.54MC217MC125

MC249

CAC/ACG-139

0

6

26

47

42

51

74

97

102

112

28

61

CMGA21

55

66

106

CTA/AGG-256CAC/AAC-52MC63MC259AEST239BMC255AMC234

MC146

Mlf 1

MC326CAC/AAG-264MC388

OPL04-0.75Icl1MG23Mas1MC264

CAA/AGC-74CAT/AAG-66

MC375

MC331AMC277OPK03-2.02

MC20MC231MC40MC118

0

24

16

30

35

5558

69

76

83

95

101

CAT/AAC-150

MC16MC82

CAT/AAC-55

MC291Acs1MC349CMGA104MG34BMC93

MC278MC265

CAC/AAG-130

5

12

40

47

OPR12-0.75

MC301

MC319MC68CAC/ACC-86

Aox2

CMTC13

MC356CM173MC11AMC11BMC316MC78

Mdr1

CMACC146

MC208

CMAG59

CMAT141

AEST135A

MC138AEST59

CAC/AAG-58

0

6

16

20

33

38

44

54

58

65

73

79

87

93

Aco-2

29

MC287MC233MC7

GATA4-0.71

MC33

CAC/AAC-217

CAG/AAC-236

CCT/AAC-173

MC4MC223OPM11-1.52CAT/AAC-141OPL04-1.30

CCT/AAC-176MC261AMC256CMAT35

GACA4-0.52

CAA/AGG-129MC76

59

54

41

32

17

10

0

MC99B

36

22

CMTC928

MC247CAA/AGG-98

OPW08-1.83

MC279

AEST144

CAA/AGC-214

CTC/AAG-68Msf 1CTC/AAC-71CTC/AAG-115MC120

MG1MC133BAEST23AEST1BCAT/AAC-92MC85CM101A

CMCT505CAT/AAG-132CMCAA145OPQ17-1.25MC309MC210OPU01-1.36

0

23

17

36

46

53

61

72

78

94

MC134

MC216

49

67 MC22A


MAPMAKER 3.0

• Free

• Command line. DOS

• Manual analysis

• Handle a limited

number of markers

• Very robust

JOINMAP 4.0

• License Fee

• Windows

• Automatic analysis

• Handle large number of

markers

• Allows map merging

• Sensitive to

missing/errors

Mapping software


Improving the efficiency of genetic mapping

• Saturation of specific genomic regions. Bulk

Segregant Analysis

• Map Merging

• Application of Next Generation Sequencing


Bulk

Segregant

AnalysisMichelmore et al.

(1991) PNAS

88:9828-9832

S R


BS

A i

n P

op

ulu

sC

erver

a e

t al.

TA

G93:

733-7

37 (

1996)


Map

Merging


Application of map merging in melon

• Baudracco-Arnas & Pitrat 1996:. RFLP, RAPDs, and

morphological traits

• Complete genetic maps (12 Linkage groups):

– Oliver et al. 2001. RFLPs, RAPDs, AFLP, SSR

– Perin et al. 2002. AFLP, RAPDs, IMAs, RFLP, traits

• Partial linkage maps with SSRs, RFLPs, RAPDs, AFLPs:

Wang et al. 1997 Danin-Poleg et al. 2002, Silberstein et al.

2003

• Dense SSR maps: Gonzalo et al. 2005, Nobuko et al. 2008,

Fernandez-Silva et al. 2008

• SSR and SNP maps:: Deleu et al. 2009, Harel-Beja et al.

2010


Final map merging statistics

• 1.593 markers

– 641 gSSRs and EST-SSRs

– 335 SNP. Candidate genes for sugar, organic acid, carotenoid and ethylene synthesis, ripening, flesh texture.

– 252 AFLP

– 239 RFLP most of them from cDNAs

– 89 RAPD

– 15 IMA

– 11 indel

– 11 traits

• 0.72 cM/marker


RFLP

SSR

SNP

AFLP


Application of NGS in map construction

NGS Bioinformatic

analysis

SNP

Markers

High density map

Founder

Parents

Mapping population

High-throuput

genotyping

x


GBS

Bioinformatic

analysis

SNP

Genotype

imputation

High density map

Founder

Parents

Mapping populationApplication of NGS in map construction


¿Why maps?

• Anchor physical maps and sequence

• Comparison physical and genetic maps

• Comparison among genomes: Synteny

• Mapping genes and QTLs

• Positional cloning

• Marker Assisted Selection


Physical location of markers on chromosomes

Cheng et al. 2001 Genetics

157: 1749–1757

5 m


Comparison

Genetic -

physical maps

Tomato exampleSim et al. 2012

PLoS ONE 7: e40563


Comparison Genetic:Physical distance


Structure of tomato chromosomes

Citogenetics Recombination Maps Physical Maps


Synteny

among

species

Cucumber vs

melon


Grasses

genomes show

extensive

synteny


Genetic dissection of Quantitative Traits by QTL analysis


Marker Assisted Selection (MAS)

What is the heritability of a molecular marker?


Linkage Disequilibrium (LD) in natural

populations

• Non-random association of alleles at

different loci

• Many possible causes

– mutation

– drift / inbreeding / founder effects

– population stratification

– multiloci selection


Confusing terminology

• Allelic association can exist without physical linkage

• “Gametic phase disequilibrium” is proper term

(Hedrick 1987)


LD due to gametic phase disequilibrium


Detection of LD

• Given two alleles A, B at two different loci:

– No LD pAB=pApB

– With LD pAB>pApB

– Linkage disequilibrium

• D=pApB-pApB


Representation of LD across the genome

Distance between marker pairs


Chromosome variability in natural populations

1: 1.0 2: 2.0 3: 3.0 4: 4.0 5: 5.0 6: 6.0 7: 7.0 8: 8.0 9: 9.0 10: 10.0 11: 11.0 12: 12.0

MM

LZ1

LZ5

LZ10

LZ11

LZ7

LZ2

LZ8

LZ3

LZ9

LZ4

LZ6

LZ16

LZ45

LZ34

LZ12

LZ20

LZ21

LZ22

LZ23

LZ24

LZ25

LZ26

LZ27

LZ28

LZ13

LZ14

LZ15

LZ17

LZ18

LZ19

LZ43

LZ29

LZ30

LZ31

LZ32

LZ33

LZ40

LZ44

LZ35

LZ36

LZ37

LZ41

LZ42

LZ38

LZ39

Consensus

Legend . A B D


LD is dinamic. Decay with time

θ= genetic distance

Recent populations have higher LD than old populations


Association mapping

- 1 marker every 100 to 1 Kb

or less, depending on the

genetic structure of the species

. Large population size

. Complex statistical analysis

Study linkage

directly in natural

populations

análisis de ligamiento y mapas genéticos · chromosome theory of heredity thomas h. morgan sex...

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