The genetic architecture of crop domestication: a meta-analysis

María Chacón, Todd Vision, Zongli Xu

Department of BiologyUniversity of North Carolina at Chapel Hill

October 23, 2003

Domestication

• “Domestication involves genetic changes in populations tending to infer increased fitness for human-made habitats and away from fitness for wild habitats.” (Harlan 1995)

• Domestication syndrome: The stereotypical set of adaptations to human habitat seen in crops

Quantitative trait locus (QTL) mapping in wild x domesticated crosses

• Genetic architecture of domestication– Number of QTL– Effect sizes– Mode of action– Chromosomal locations

• Limitations– Underestimate QTL #– Overestimation of effect size in small samples– QTL are located to large chromosomal segments– Difficult to distinguish linked vs. pleiotropic QTL

• Mapping populations differ in– Statistical power– Ability to measure dominance

QTL mapping

XParents

F1

F2 genotype

F2 Phenotype

QTL

QTL

QTL map in rice (Cai and Morishima, 2002)

Received wisdom regarding domestication QTL (DQTL)

• Few loci of major effect

• Domestication alleles tend to be recessive

• DQTL tend to be clustered among and within linkage groups

• DQTL tend to be homologous among related crops (e.g. fruit weight QTL in the Solanaceae)

Crop systemsPulses Common bean

Cowpea

Fruit crops Eggplant

Pepper

Tomato

Watermelon

Vegetable crops Lettuce

Grain crops Maize

Pearl millet

Rice

Sorghum

Wheat

Wild rice

Industrial crops Cotton

Sunflower

Sugarcane

Questions

• What is the effect of study power on– The # DQTL per trait?– The effect sizes of the DQTL?

• Do DQTL tend to be recessive even for polygenic traits?– What is the effect of breeding system?– What does the pattern suggest about the origin of the

domestication alleles?

• Clustering of DQTL among and within linkage groups– Is it an artifact of pleiotropy?– Is the pattern of clustering consistent with the major hypothesis

concerning its origin

Questions

• What is the effect of study power on– The # DQTL per trait?– The effect sizes of the DQTL?

• Do DQTL tend to be recessive even for polygenic traits?– What is the effect of breeding system?– What does the pattern suggest about the origin of the

domesticated alleles?

• Clustering of DQTL among and within linkage groups– Is it an artifact of pleiotropy?– Is the pattern of clustering consistent with the major

hypothesis concerning its origin

Gene actionA2A2 A1A2 A1A1

-a +a0 d

Genotype

Genotypicvalue

d/a=gene action of the A1 allele

-1.25 -1.00 -0.75 -0.25 0 0.25 0.75 1.00 1.25

Additive DominantRecessive

Expectations for gene action of domestication alleles

• Domestication alleles are recessive (Lester, 1989, Ladizinsky, 1998)

• If adaptation uses new mutations autogamous are expected to fix more recessive alleles than allogamous (Orr and Betancourt, 2001)

• If adaptation uses standing variation, the probability of fixation of alleles is independent of dominance (Orr and Betancourt, 2001)

Crop Dominant Codominant Recessive d/a Mating system Rice 22 7 15 0.03 Autogamous Tomato 3 11 16 0.10 Autogamous Sorghum 2 1 6 1.40 Autogamous Eggplant 16 7 24 0.78 Autogamous Total Autogamous

54 (37%)

26 (18%)

67 (46%)

Maize 11 15 17 0.11 Allogamous Sunflower 32 17 29 0.13 Allogamous Pearl millet 22 14 31 0.25 Allogamous Wild rice 13 13 16 -0.43 Allogamous Total Allogamous

78 (34%)

59 (26%)

93 (40%)

Total 132 85 160

Gene action of domestication alleles

Average d/a = 0.570 (autogamous), 0.015 (allogamous)

Two-tailed paired t-test: p<0.31

Findings

• Domestication alleles are not always recessive

• Autogamous and allogamous crops have equal proportions of recessive and dominant domestication alleles

• Results are more compatible with the predictions of the ‘standing variation’ model than the ‘new mutation’ model

Questions

• What is the effect of study power on– The # DQTL per trait?– The effect sizes of the DQTL?

• Do DQTL tend to be recessive even for polygenic traits?– What is the effect of breeding system?– What does the pattern suggest about the origin of the

domesticated alleles?

• Clustering of DQTL among and within linkage groups– Is it an artifact of pleiotropy?– Is the pattern of clustering consistent with the major

hypothesis concerning its origin

Why might DQTL be clustered?

• Predicted from some population genetic models (Le Thierry D’Ennequin et al. 1999) – Assuming

• DQTL could arise throughout the genome• Introgression from wild relatives

– Selection will prefer linked QTL in disequilibrium– Clustering should be more apparent in allogamous than

autogamous crops

• Potential for methodological artifact– One pleiotropic QTL would be detected multiple times– This would give the false appearance of clustering– Conservative set of QTLs chosen to reduce problems of

pleotropic QTL (one per trait category per locus)

Classification of domestication traitsEarliness Growth habit Increase in yield Gigantism Seed dispersal

Days to flower

Heading date

Fruiting date

Ripening date

Plant height

No. tillers/plant

No. of branches

Average length of nodes

No. of nodes

Kernel No./spikelet

Kernel No./plant

Kernel No./panicle

Grain yield

Spikelet No./ spike

Spikelet No./panicle

Spikelet density

Spike No./panicle

Cupules/rank

No. of rows of cupules

Fruit number

Fruit yield

Seed size/weight

Panicle length/weight

Spike length/weight

Fruit diameter/weight

/length

Shattering rate

Brittle rachis

Awn length

Full data set

Reduced data set

How to test for QTL clustering

• Clustering among linkage groups– Measured by a X2 goodness of fit test

• Clustering within linkage groups– Measured by simulation (randomly

assigning same number of QTL and measuring distance between neighboring QTL)

Clustering of DQTL among and within LGs

Crop Outcrossing rate

Clustering

among

Clustering

within

Rice <1% yes no

Rice <1% yes yes

Rice <1% yes yes

Common bean 1-5% yes yes

Tomato 1-5% yes no

Tomato (r) 1-5% yes no

Wheat 1-5% yes yes

Wheat (r) 1-5% yes no

Pepper 12-15% yes yes

Pepper (r) 12-15% no yes

Cowpea 12-15% yes yes

Eggplant 12-15% yes yes

Eggplant (r) 12-15% no no

Sunflower 25-40% yes no

Sunflower (r) 25-40% yes no

Pearl millet 25-40% yes yes

Pearl millet 25-40% yes yes

Maize >40% no no

Wild rice >90% no yes

Clustering of DQTL in common bean

Non-clustering of DQTL in maize

Clustering of non-domestication QTL

Crop Cross type Outcrossing rate

Clustering among

Clustering within

Rice D x D <1% no yes

Sunflower D x D 25-40% no no

Maize D x D >40% no yes

Rice W x D <1% yes yes

Cowpea W x D 12-15% yes yes

Alternative explanations• Are QTL clustered because they map to gene dense

regions?– Suggested for wheat (Peng et al. 2003)

• Preliminary test in rice using high density transcript map (6591 ESTs, Wu et al. 2002)– Counted number of QTLs and markers in 5cM windows– Average # of markers/windows = 4.41– Weighted avg. # of markers/window for QTL = 3.49

QTL homology

• Observed for QTL in several systems – Cereals (grain size, flowering time, shattering)– Solanaceae (fruit size, shape)– Beans (seed size)

• Not necessarily domestication trait specific• If clusters reflect chromosomal regions that

are particularly liable to contain QTL– Some correspondence in the location of QTL

among related species is to be expected– So do homologous QTL really correspond to the

same genes?

Summary• DQTL number and effect size

– Trend toward less DQTL and larger effect sizes in low power studies

– Some major DQTL detected in powerful studies (e.g. sugarcane)

• Mode of gene action and origin of DQTL alleles– d/a is not significantly different between allogamous and

autogamous crops– Results consistent with ‘standing variation’ model

• Clustering of DQTL– Does not appear to be an artifact of pleiotropy– Not consistent with introgression hypothesis– Appears to reflect inherent differences among regions of the

genome

Acknowledgements

• All those who helped provide supplemental data from their QTL studies:– John Burke (sunflower)– Lizhong Xiong (rice)– Valerie Poncet (pearl millet)– Raymie Porter and Ron Phillips (wildrice)

Statistical power

• Power– Probability of rejecting the null hypothesis (absence of QTL) when

it is false = probability of detecting a QTL when it is present– Calculated by simulation

• Assumptions– Single codominant QTL– Constant small additive effect– Constant environmental variance

Power of study and # DQTL detected

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0 0.2 0.4 0.6 0.8 1 1.2

Power

# D

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Power and effect size of DQTL

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

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heno

typi

c va

rianc

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QT

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