qtl mapping - wright

Di truyền số lượng QTL Công nghệ sinh học chọn tạo giống các phương pháp xác định Quantitative trait loci bản đồ di truyền số lượng

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Institute of Agriculture Sciences – Biotechnology Division

Trait Mapping (Quantitative Trait Loci)

IAS – Biotech Division

Robert J Wright Texas Tech University

Genes explaining variation in simple or

complex traits can easily be mapped to

chromosomes or linkage groups with

minimal a priori information.

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What is a QTL?

• A quantitative trait loci (QTL) is the

location of a gene(s) that have an effect

on a trait A QTL is depicted as a

confidence interval on a genetic map

– Examples of quantitative traits

• plant height

• grain yield

– These traits are typically affected by more

than one genes, and also by the

environment

IAS – Biotech Division – Slide 3

environment

– Thus, QTL mapping is not as simple as

mapping a single gene that influences a

qualitative trait (such as flower color)

Why map QTL ?

• To provide knowledge towards a

fundamental understanding of heredity

and the gene(s) that control individual

traits

• To study individual gene(s), gene

actions and interactions

• To enable positional cloning of the gene

• To improve estimations of breeding

value and selection response through

marker assisted selection (Predictive

Breeding)

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DNA markers that are near a disease

resistance gene tend to be inherited

together (genetically linked).

Resistant Allele Susceptible Allele

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During gamete formation the segregation

of the alleles of one allelic pair is

independent of the segregation of the

alleles of another allelic pair (Mendel’s

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Localize Target Gene to a Delimited Region

D d

C c

R s

• Identify genomic regions (QTLs) that

contribute to phenotypic variation of a

trait.

• Delineate the QTL location within a

confidence interval.

• Estimate the effects of the QTL,

putative gene action, and contribution

IAS – Biotech Division – Slide 10 10

to the phenotypic variance.

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Factors critical to successful QTL mapping

1 Good genetic map

2 Good phenotyping

3 Rigorous statistical/genetic analysis

4 Validation of QTL

1 to 3 absolutely critical for genetic analysis;

1 to 4 are all critical for implementation in breeding

I Good Genetic Mapping

– Critically look at your map

– Identify/remove poor marker loci

(disequilibrium and missing data)

– Identify alleles mapped as independent loci

– Conduct graphical assessment of map

quality

– Investigate alignment with published maps

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Constructing a Genetic Map

• DNA Markers

– Restriction Fragment Length Polymorphism (RFLP)

– Amplified Fragment Length Polymorphism (AFLP)

– Simple Sequence Repeats (SSR)

– Single Nucleotide Polymorphism (SNP)

– Insertion/Deletion Mutations (INDEL)

• To be a informative genetic marker, the

DNA marker must meet two criteria:

– The marker must differentiate between the parents

(Polymorphic).

– The marker must be precisely transmitted to the

progeny (Mendelian Segregation).

(Doubled haploid line)

Created to minimize the confounding effect of heterogeneity of the parental genotypes (i.e a

RIL(Recombinant inbred line)

With polymorphic molecular markers and linkage maps as tools, mapping

QTL is simply a matter of growing and evaluating large populations of plants,

and of applying the appropriate statistical tools

pure line is not genetically homogeneous).

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Stringent Computational Analysis

• Data Analysis

– Marker loci density

• 10-20 cm for genome wide linkage analysis

• 50-200 kb for genome wide association studies

– Pre-selection of marker type

Critically look at your map!!

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Scoring Marker Loci

(3) P

Genotypes are scored as:

Scoring Marker Loci

AFLP

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Dominant Markers (P1 and P2)

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Activities to improve map quality

1 Identify/remove poor marker loci

(disequilibrium and missing data)

Demo

2 Identify alleles mapped as independent loci

3 Conduct a graphical assessment of map

quality

4 Investigate alignment with published maps

Conduct a graphical assessment of map quality

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AABbAaBB

Numbers under the genotype indicate expected

number of individuals (N = 400) at 40 cM and

(10 cM) linkages between marker loci.

Numbers under the genotype indicate expected

number of individuals (N = 400) at 40 cM and

(10 cM) linkages between marker loci.

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Investigate alignment with published maps

Figure 2 Colinearity of the G herbaceum × G arboreum linkage map with the Nguyen

et al 2004 tetraploid map (N).

Discussion

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II Good Phenotyping

– Heritability

– Population design

– Replication vs re-sampling

– Reproducibility (measure –equip.)p y ( q p )

– How to handle false phenotypes

Phenotype = Genotype + Environment + Epistasis

Phenotype - The observable properties of an organism

properties of an organism, produced by the interaction between the organism’s genetic potential (its genotype) and the environment in which it finds itself.

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Qualitative Traits (Simple Inheritance)

Qualitative traits have a few possible phenotypes that

fall into discrete classes ( Discrete Traits);

phenotypes are determined by one or a few genes

Mendel studied seven characters (traits) in the garden pea

during his breeding experiments.

phenotypes are determined by one or a few genes

with minimal environmental influence

Bimodal Distribution

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P12-20a pAR1-28PXP1-9apAR335a A1318b pAR179x

Empire B2b6 Chromosome 20

pAR988 A1318a pAR335b pGH239

G1219

P5-57 pVNC163a A1695b

Empire B2b6 LGD04

Empire B2b6 LGD02

A1666 M16-40 pAR648 pAR566 pAR044b (34.4 cM)Mapping Traits as QTLs

Empire B3 Chromosome 20

M16-150 pAR125 A1701b

pAR377 pAR3-37A1429pAR3-41 pAR1005

(7.9 cM)

pAR430 pAR827

pAR044b

pAR043

pAR3-24bpAR723

pAR043 pAR129 pAR723b

(11.4 cM)

Empire B2b6 Chromosome 14 S295

Chromosome 14

(14.8 cM)

pGH559b pGH510a

pAR1005

pAR545

pAR3-24b pAR451b G1012 pAR129 G1012

p 7 b pAR3-24b A1580

Wright et al., (1998) Genetics 149:1987-1996.

QTLs are the Genetic Contribution

37%

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• a measure of the degree to which the phenotype is

controlled by genetic factors and thus amenable to

genetic improvement in breeding

• Two main types of heritability:

– broad sense heritability - The proportion of

phenotypic variability that is due to all types of

genetic causes.

– narrow sense heritability - The proportion of

H2= VG/VP x 100

phenotypic variability that is due to heritable genetic

factors (e.g., may be passed from parents to

progeny).

h 2= VA/VP x 100

Why is good phenotyping important?

Response to selection and heritability

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Predicted Gain from Selection

increase after one cycle of selection

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(Markers density does not solve this problem)

The bias in estimated genetic variance (σ G2) occurs mainly due to sampling of small

populations; QTLs with small effects tend to not be detected but when detected the

estimated genetic effects appear much larger than they really are This

phenomenon is known as “The Beavis Effect”.

a=0.0001, power = 90%, F2population

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(Doubled haploid line)

RIL(Recombinant inbred line)

Individual plant phenotypes lead to lower heritability estimates,

thus more false positives and QTLs that escape detection

GRAIN YIELD (Mass/Area)

Simplifying Phenotypic Complexity

Yield Components – Grain Sorghum

Seed Number/Area Seed Size (g/1000 Seed)

Harvested Heads/Area Seed/ Head Volume Density

Plant Population Tillers

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+ 0.015 ( panicle / ha )

+ 96.79 ( g / 10 3 seed )

0.57 0.12 0.12 0.81

COTTON LINT YIELD (Mass/Area)

Simplifying Phenotypic Complexity

Yield Components – Cotton

Plant Population Bolls/Plant Lint/Boll

Seed/Boll Lint/Seed

Number of Fruiting Sites

Retention of Fruit

Seed/ o t/Seed

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Multiple Regression Analysis of Cotton Lint Yield

Lint Yield Intercept

Source Component Parameter

0.00 16.77 38.42

Boll plant Plants acre -1

6.43 1.51

0.374 0.376

Var A Check 98032

Var B

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Replication vs Resampling

Example: QTL mapping of enzyme activity

Measure Enzyme Activity Measure Enzyme Activity

Biological Replication Resampling

Beneficial to estimate heritability and QTL mapping

Technical Replication beneficial

for estimates of reproducibility

Discussion

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III Rigorous statistical/genetic analysis

– Critically assess each QTL region

– Identify an appropriate significant criteria

– Provide confidence intervals

– Fixing QTL affectsg Q

– Dominant – Additive effects

– If multiple generation of testing or replicated

testing – due QTL regions align? - are

additive and dominant effects similar?

– Does the full model (include all QTL regions)

agree with heritability values for each trait?

– Check alignment with prior research

– Don’t reinvent the annotation

Critically look at your QTLs!!

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• Regress phenotypes on codes

• Significance = marker linked to QTL

• Regression slope = estimate of QTL effect

• Interval mapping

– Maximum Likelihood Method

• Numeric codes Give to marker genotypes

(eg AA = 0, aa = 1)

• Association of the phenotypes on codes

• Significance = target interval contains a QTL

• Split plants into groups

according to their genotype

at a marker

• Do an ANOVA (or t-test)

• Repeat for each marker

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– Suffers in low density scans

y– Only considers one QTL at a time

51

Interval mapping

Lander and Botstein 1989

• Imagine that there is a single QTL, at position z.

• Let qi = genotype of mouse i at the QTL, and assume

• We won’t know qi, but we can calculate (by an HMM)

• yi, given the marker data, follows a mixture of normal

distributions with known mixing proportions (the pig).

• Use an EM algorithm to get MLEs of θ = (μAA, μAB, μBB, σ).

• Measure the evidence for a QTL via the LOD score, which is

the log10 likelihood ratio comparing the hypothesis of a single

QTL at position z to the hypothesis of no QTL anywhere

IAS – Biotech Division – Slide 52 52

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Qeffects

(crossover) event occurring between them in meiosis

the likelihood that they are not linked

A LOD score of three or more is generally taken to indicate that two gene loci are close to each other on the chromosome (A LOD score of three means the odds are a thousand to one in favor of genetic linkage).

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LOD thresholds

• To account for the genome-wide search,

compare the observed LOD scores to

the distribution of the maximum LOD

score, genome-wide, that would be

obtained if there were no QTL

anywhere.

• The 95th percentile of this distribution

is used as a significance threshold.

• Such a threshold may be estimated via

Such a threshold may be estimated via

permutations (Churchill and Doerge

• Repeat many times

• LOD threshold = 95th percentile of M*

• P-value = Pr(M* ≥ M)

56

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This interval is expected to contain a gene(s) that explain the phenotypic variation of the trait.

The bar along the linkage group indicates the 90%

(1 LOD) lik lih d i t l

(1-LOD) likelihood interval for the QTL, and whiskers indicate 99% (2-LOD) likelihood interval.

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Mapping QTLs

• The critical factor to successfully

mapping quantitative traits is a detailed

understanding of the traits and the

precision in which individuals can be

phenotyped. LOD = 3.6

PVE = 14.7

LOD = 10.2 PVE = 56.5

Population Xcm Race

Probable gene identity

Nearest DNA

Gene action

II Additive effects: the effects of homozygous.

Percentage of variation explained (PVE) is the percentage of phenotypic

variation that is explained by the QTL

This deviation will be detected in F2 population

It’s calculated as: Heterozygous – [(P1+P2)/2]

A positive effect reflects growth of the heterozygous that exceeds

the midparent

A negative effect reflects growth that is less than the midparent

It’s calculated as: (Homozygous for P1 – Homozygous for P2)/2

A positive effect reflects greater growth of the P1 homozygous

A negative effect reflects greater growth of the P2 homozygous

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QTLs Mapped in Multiple Generations

Figure 3 Chromosomal location of QTL conferring resistance to Thielaviopsis

basicola Bars along the linkage groups indicate 90% (1-LOD) likelihood intervals for

the QTLs, and whiskers indicate 99% (2-LOD) likelihood intervals Marker names and

genetic distances (in centi-Morgan) are shown at the right and left of each linkage

group.

QTLs Mapped in Multiple Generations

Table 1 Biometrical parameters of individual QTLs conferring resistance to Black Root

a Markers flanking the QTL likelihood peak

b Position of the QTL likelihood peak (centi-Morgan from top)

c Biometrical parameters were calculated using dominance and recessiveness to refer to

the behavior of the G herbaceum alleles

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Check QTL alignment with prior research

Figure 1 A CMap display of the

comparison between an individual map and a consensus map The left map is chromosome 16 from

the n2 population including one

QTL (MIC) shown on the left side

of the map The right map is consensus homoeologous group 4

The syntenic regions with

Arabidopsis duplicates and QTLs

in this chromosome were aligned

on the right side of the map The numbers after D or Ds represent

the different Arabidopsis duplicates

and the numbers after the dot represent the different syntenic regions on the cotton chromosome

The experimental treatment

name (J IANGet al 1998a) and

particular measurement for fiber length (C HEEet al 2005b) were

presented in parenthesis after each QTL name Detailed explanation of these descriptions can be found in the references cited

Discussion

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IV QTL Validation and Efficacy

– Selection of genotypes

– Testing

– Cross validation (split data set - map in

1st and validate in second)

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