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The genetic architecture of crop domestication: a meta-analysis María Chacón, Todd Vision, Zongli Xu Department of Biology University 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 Parents X F1 QTL F2 genotype QTL F2 Phenotype 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 systems Pulses 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 action Genotype A2A2 Genotypic value -a 0 Additive Recessive -1.25 -1.00 -0.75 -0.25 0 A1A2 A1A1 d +a Dominant 0.25 d/a=gene action of the A1 allele 0.75 1.00 1.25 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) Gene action of domestication alleles Crop Dominant Rice 22 Tomato 3 Sorghum 2 Eggplant 16 Total 54 Autogamous (37%) Maize 11 Sunflower 32 Pearl millet 22 Wild rice 13 Total 78 Allogamous (34%) Total 132 Codominant Recessive d/a Mating system 7 15 0.03 Autogamous 11 16 0.10 Autogamous 1 6 1.40 Autogamous 7 24 0.78 Autogamous 26 67 (18%) (46%) 15 17 0.11 Allogamous 17 29 0.13 Allogamous 14 31 0.25 Allogamous 13 16 -0.43 Allogamous 59 93 (26%) (40%) 85 160 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 traits Earliness 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 Rice DxD Sunflower D x D Outcrossing rate Clustering among Clustering within <1% no yes 25-40% no no Maize DxD >40% no yes Rice WxD <1% yes yes Cowpea WxD 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 # DQTL detected per trait 20 18 16 14 12 10 8 6 4 2 0 0 0.2 0.4 0.6 Power 0.8 1 1.2 Power and effect size of DQTL 100 90 80 70 60 50 40 30 20 10 0 0 0.1 0.2 0.3 0.4 0.5 Power 0.6 0.7 0.8 0.9 1