QTL lecture for Bio4025

91
An introduction to quantitative genetics Dan Chitwood (with slides from Julin Maloof) March 16, 2015

Transcript of QTL lecture for Bio4025

Page 1: QTL lecture for Bio4025

An introduction to quantitative genetics

Dan Chitwood

(with slides from Julin Maloof)

March 16, 2015

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What is a QTL?

• QTL– Quantitative Trait Locus– A genetic locus that contributes to quantitative

variation in a trait

• What is a quantitative trait? What contributes to the concept of “trait?”– Genes?– Environment?– Cross/allele/species background?– The researcher?

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Is hypocotyl length a quantitative trait? No?

Qualitative: can classifyas tall or shortWT phyB

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Is hypocotyl length a quantitative trait? Yes?

Segregation Ler x Cvi RIL

Quantitative: must measure (quantify) differences

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What causes quantitative segregation?

• Signal to noise (allelic effect versus unexplained variance, environment, error)

• Multiple genes segregating, smaller effects (polygenic traits)

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What causes quantitative segregation?

• Signal to noise (allelic effect versus unexplained variance, environment, error)

• Multiple genes segregating, smaller effects (polygenic traits)

allele effect = 3 allele effect = 2

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Two Loci

+

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Why study development with QTL?

• Micro-evolution– what makes two strains/populations/species

different?

– (Teosinte/Maize; Mimmulus; etc)

• Plant Breeding– Fruit shattering

– Flowering

– etc.

• Human Disease

• Different spectrum of loci than available through forward genetics

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Single marker regression

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• Simplified version:

– Phenotype all individuals

QTL mapping: Single marker regression

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Marker “A” is linked to a hypocotyl QTL Marker “C” is unlinked

• Simplified version: – Phenotype all individuals– Genotype all individuals– Look for correlation

QTL mapping: Single marker regression

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-Repeat, analyzing correlation to trait for 100s of makers

-Limitations:

-Confounding: can not separate QTL effect (size) and location of the QTL (relative to the marker).

-Does not account for effect of other contributing loci/markers

QTL mapping: Single marker regression

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y = m*x + bhyp = m*gtB + mean + erroris m not equal to 0? If so, then we have a QTL

QTL mapping: Single marker regression

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y = m*x + bhyp = m*gtB + mean + erroris m not equal to 0? If so, then we have a QTL

QTL mapping: Single marker regression

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Simple interval mapping

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QTL mapping: recombinationincreases variance

• The QTL “Q” may be some distance from marker “B”

• Parent genotypes: B-Q and b-q

• Progeny genotypes: B-Q, b-q, B-q, b-Q

• genotype at QTL: Q or q

• Solution:– Interval mapping

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Simple Interval Mapping(SIM; Lander and Botstein)

• Evaluate intervals between markers rather than markers themselves

• Conceptually:

– Parents: B-Q-D and b-q-d

– Progeny:

• B-Q-D and b-q-d

• b-q-D b-Q-D B-Q-d B-q-d

• very rare: B-q-D b-Q-d

– Use B-D and b-d to estimate allelic effect size of QTL

– Use recombinants to estimate whether QTL is closer to B or D

– LOD score: Likelihood of linkage. Log10 of ratio of likelihood of linkage / likelihood unlinked

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Simple Interval Mapping(SIM; Lander and Botstein)

• Evaluate intervals between markers rather than markers themselves

• In reality– The position of the QTL in the interval is evaluated by maximum likelihood.

– At each position in the interval an iterative algorithm is used to determine the most likely model given the data.

– The likelihood of a model with the QTL is compared to the null model (no QTL).

– These two likelihoods are compared to give a LOD score

• LOD score = log10(Likelihood with QTL/Likelihood no QTL)– what does a LOD score of 2 indicate?

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Composite interval mapping

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QTL mapping: other loci increase variance

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QTL mapping: other loci increase variance

Problem with SIM: Linked and unlinked QTL affect the analysis

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QTL mapping: composite interval mapping (Zeng; Jansen and Stam)

Simplifying and ignoring the “interval” issue:hyp = mean + m1*gtB + m2*gtA* + error

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Composite IntervalPartial CIMSimple Interval Mapping

Zeng, Genetics, 1994

Comparison of QTL methods

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Practicalities of QTL experiments

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Practicalities—Backcross Population

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Practicalities—F2 population

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Practicalities:RIL population

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Practicalities: experimental design

Design:

Randomization,

Replication,

Measurer effects,

Positional effects,

Environmental effects

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y = m*x + bhyp = m*gtB +mean + erroris m not equal to 0? If so, then we have a QTL

QTL mapping: Single marker regression

J. MaloofPhotos: Charlie Rick, TGRC

Solanum pennellii(Peruvian desert)

Solanum lycopersicum(cultivated)

Desert tomato

Cactus

Single marker regression, sort of:Tomato introgression lines

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Single marker regression, sort of:Tomato introgression lines

X

Backcross,marker-assisted selection

Self

Look forphenotypicdifferences

S. lycopersicum (domesticated)

S. pennellii (desert)

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Single marker regression, sort of:Tomato introgression lines

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Ravi KumarAashish RanjanMike Covington

RNA-Seq (genic polymorphisms) RESCAN (genic/non-genic polymorphisms)

Genotyping using next-generation sequencing

Kumar et al., Front. Plant Sci. 2012

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A precise genetic map of thetomato introgression lines

Chitwood et al., Plant Cell (2013)

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A precise genetic map of thetomato introgression lines

Chitwood et al., Plant Cell (2013)

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Detecting subtle change takes field space andgenerates large phenomic datasets . . .

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Field Aggie Stadium

Medical Center

Detecting subtle change takes field space andgenerates large phenomic datasets . . .

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Detecting subtle change takes field space andgenerates large phenomic datasets . . .

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Detecting subtle change takes field space andgenerates large phenomic datasets . . .

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A QTLNetwork . . .

IntrogressionLines

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Classic examples ofQTL experiments

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Doebley et al. Genetics 1995

Crop Domestication

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Crop Domestication

tb1-ref tb1-refA158 A158

Doebley et al. The evolution of apical dominance in maize. Nature 1997

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A. Mimulus lewisii--Bee PollinatedC. Mimulus cardinalis--Humingbird Pollinated

Schemske and Bradshaw, PNAS 1999

Pollination Syndromes

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Variation in F2Schemske and BradshawPNAS 1999

M. lewisii M. cardinalisF1 Hybrid

F2

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• Measure visitation rates in F2 population

• Look for correlation between floral QTL and visitation

• One QTL increases carotenoids -> decreases bee visitation 80%

• Another QTL increases nectar 3-fold, double hummingbird visits (indpendent of color)

Which Traits affect pollinator visitation?

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Genetics of Reproductive Isolation

• 12 Traits…47 QTL

• 9/12 Traits had “major” QTL

• Therefore, major QTL can play a role in speciation.

– Contrasts with a very polygenic, additive small effect loci view of evolution

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Genetics of Reproductive Isolation

Kuhlemeier Lab

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Genetics of Reproductive Isolation

Hoballah et al. Plant Cell 2007

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Genetics of Reproductive Isolation

Hoballah et al. Plant Cell 2007

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Frary et al. Science 2000

Crop Breeding

S. pimpinellifolium S. lycopersicum

Transgenic forS. pennellii fw2.2candidate

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Fruit Weight

• Cross wild to domesticated

• 11 fruit mass QTL

• fw2.2 largest effect, modifying fruit size up to 30%

• Create NIL and backcross

• Large allele in domesticated partially recessive

• Transgenics with wild allele have smaller fruit

• Structural homology to ras oncogene

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Developmental effect of fw2.2?

• What makes 2 alleles different?

• Expression: temporal expression different

• Phenotypic effect: Reduced cell division in carpels

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Real QTL effects are often “wimpy”

--ie, polygenic, small effects

--Contrasts with tb1 and pollinator shifts

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Drastic differences in fruit and leaf phenotypesbetween wild and domesticated tomato species

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How do we measure leaf shape?Elliptical Fourier Shape Descriptors

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How do we measure leaf shape?Elliptical Fourier Shape Descriptors

-2 SD +2 SD Overlay

PC144.4%

S.penn(desert)

S.lyco(dom.)

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How do we measure shape?Elliptical Fourier Shape Descriptors

-2 SD +2 SD Overlay

PC144.4%

PC213.0%

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How do we measure shape?Elliptical Fourier Shape Descriptors

-2 SD +2 SD Overlay

PC144.4%

PC213.0%

PC36.9%

PC46.6%

PC54.1%

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-5.00E-02

-4.00E-02

-3.00E-02

-2.00E-02

-1.00E-02

0.00E+00

1.00E-02

2.00E-02

3.00E-02

4.00E-02

5.00E-02

-0.55 -0.45 -0.35 -0.25 -0.15 -0.05 0.05

S.lyco(dom.)

The genetic basis of natural variationin leaflet morphology: an example

PC1

PC2

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-5.00E-02

-4.00E-02

-3.00E-02

-2.00E-02

-1.00E-02

0.00E+00

1.00E-02

2.00E-02

3.00E-02

4.00E-02

5.00E-02

-0.55 -0.45 -0.35 -0.25 -0.15 -0.05 0.05

S.penn(desert)

IL4-3

S.lyco(dom.)

PC1

PC2

The genetic basis of natural variationin leaflet morphology: an example

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-5.00E-02

-4.00E-02

-3.00E-02

-2.00E-02

-1.00E-02

0.00E+00

1.00E-02

2.00E-02

3.00E-02

4.00E-02

5.00E-02

-0.55 -0.45 -0.35 -0.25 -0.15 -0.05 0.05

S.penn(desert)

IL4-3

S.lyco(dom.)

PC1

PC2

The genetic basis of natural variationin leaflet morphology: an example

How to explain shapedifferences between tomatoes?

--Polygenic trait or epistasis

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-5.00E-02

-4.00E-02

-3.00E-02

-2.00E-02

-1.00E-02

0.00E+00

1.00E-02

2.00E-02

3.00E-02

4.00E-02

5.00E-02

-0.55 -0.45 -0.35 -0.25 -0.15 -0.05 0.05

S.penn(desert)

IL4-3

S.lyco(dom.)

PC1

PC2

The genetic basis of natural variationin leaflet morphology: an example

How to explain shapedifferences between tomatoes?

--Polygenic trait or epistasis--Additive effects?

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QTL Advances

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The Punctate phenotype:An example of bulk-segregant approaches

with next-generation sequencing

S. pennellii, low magnification S. penn., high magnification

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The Punctate locus lies on chromosome 10

S. lycopersicumIL10-3, chrom. 10

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The Punctate locus lies on chromosome 10

IL10-3, chrom. 10

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The Punctate locus lies on chromosome 10

IL10-3, chrom. 10

Chromosomes1 2 3 4 5 6 7 8 9 10 11 12

Chromosome 10

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The Punctate locus lies on chromosome 10

IL10-3, chrom. 10

Chromosomes1 2 3 4 5 6 7 8 9 10 11 12

Chromosome 10

2.65 Mbp; ~300 genes

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X

S. lycopersicum (domesticated)

S. pennellii (desert)

BackcrossedIntrogression Lines (BILs)

Backcrosses

Self

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BIL-460

BIL-430

BIL-263

BIL-274

BIL-202

BIL-040

BIL-218

BIL-176

BIL-466

BIL-405

BIL-057

BIL-194

BIL-376

BIL-232

BIL-127

BIL-347

1 2 3 4 5 6 7 8 9 10 11 12Chromosome

Genotypes of Punctate BILs share chrom. 10 region

Aashish Ranjan

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BIL-224

BIL-067

BIL-039

BIL-215

BIL-176

BIL-031

BIL-427

BIL-007

BIL-003

BIL-289

BIL-422

BIL-433

BIL-066

BIL-439

BIL-275

BIL-360

BIL-046

Genotypes of Punctate BILs share chrom. 10 region

1 2 3 4 5 6 7 8 9 10 11 12Chromosome

Aashish Ranjan

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On the Pn interval are four related MYBs,one of which is is Anthocyanin 1 (ANT1)

“MYB250” ANT1 “MYB270” Heavy metal-associateddomain gene

“MYB290”

S. lycopersicum:

Aashish Ranjan

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This et al. TAG 2007Foumier-Level et al. Genetics 2009

. . . but berry color in grape is also causedby a set of tandemly duplicated MYBs!

MYBA2 MYBA1V. vinifera:

MYBA3

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Relatedness of Vitis, Solanum, andArabidopsis MYBs

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Relatedness of Vitis, Solanum, andArabidopsis MYBs

V. vinifera (Grape)

S. lycopersicum (Tomato)

Arabidopsis

Quattrocchio et al. Plant Cell 2006

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The Arabidopsis MYB homolog also affects trichome pigmentation

Dissertation of Antonio Gonzalez

35S:MYB114

MYB113 MYB114 PAP2/MYB90

Arabidopsis

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Evolution at work:from berries to trichomes

V. vinifera (Grape)

S. lycopersicum (Tomato)Arabidopsis

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• 4,523 eQTL for 4,066 genes

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A QTLNetwork . . .

IntrogressionLines

IL4-3

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Gene expression as phenotype: eQTL, cis- and trans-relationships, and transcriptional networks

IL4-3:I

II

III

IV

VVIVII

VIII

IX

X

XIXII

S. pennellii (desert)

S. lycopersicum (domesticated)

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Gene expression as phenotype: eQTL, cis- and trans-relationships, and transcriptional networks

S. pennellii (desert)

S. lycopersicum (domesticated)

IL4-3:

IV

Differentially expressed:IL4-3 <-> S. lycopersicum

cis- regulation

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Gene expression as phenotype: eQTL, cis- and trans-relationships, and transcriptional networks

S. pennellii (desert)

S. lycopersicum (domesticated)

IL4-3:

IV

Differentially expressed:IL4-3 <-> S. lycopersicum

cis- regulation

trans-regulation

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Adaxial Abaxial

S. ly

co.

(do

mes

tica

ted

)S.

pen

n.

(de

sert

)

Another phenotype of IL4-3:Increased pavement cell size

Pavement cellsize:

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The molecular mechanismsunderlying cellular natural variation

Histone H3A, Histone H3B, Histone H2B, MCM3, MCM4,

MCM5,ssDNA replication binding

protein,Cyclin B1, Cyclin B2,

CDC20

Up-regulatedgenes:

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The molecular mechanismsunderlying cellular natural variation

Histone H3A, Histone H3B, Histone H2B, MCM3, MCM4,

MCM5,ssDNA replication binding

protein,Cyclin B1, Cyclin B2,

CDC20

Cell cycle

Up-regulatedgenes:

Sig. GO terms,trans-regulatedgenes:

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The molecular mechanismsunderlying cellular natural variation

Histone H3A, Histone H3B, Histone H2B, MCM3, MCM4,

MCM5,ssDNA replication binding

protein,Cyclin B1, Cyclin B2,

CDC20

Cell cycle

E2F binding

site

Up-regulatedgenes:

Sig. GO terms,trans-regulatedgenes:

Promoter motifs,trans-regulatedgenes

CDS

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E2F promotes endoreduplication

G1

SG2

M

E2F

Mitosis

Endocycle

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E2F promotes endoreduplication

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

SKP2A expression is reducedin S. penn. and IL4-3

G1

SG2

M

SKP2A E2F

Mitosis

Endocycle

Exp

ress

ion

leve

l

Lauren Headland