Timeline of plant domestication

Timeline of plant domestication Old World New World centuries b.p. 150 140 130 120 110 100 90 80 70 60 50 40 30 20 10 0

The domestication syndrome • Harvestable parts • number • size • Life history • determinate flowering • loss of perenniality • loss of outcrossing • Dispersal • loss of shattering • loss of seed dormancy

Domestication: a convenient model to study the genetics adaptation • Strong, long-term artificial selection • Phenotypes are well-characterized • Potential for genetic dissection • maps and markers • sequences • mutant stocks • transformation technologies, etc.

The genetic architecture of domestication:conventional wisdom • A few major genes with recessive gene action • “Sport” model for domestication alleles • Loss of function mutants • Too deleterious to be present in wild populations • De novo mutations that arose in cultivated, pre-domesticated populations

The genetic architecture of domestication:recent studies • Quantitative trait locus (QTL) mapping • Cross domesticated genotype with a wild relative • Follow inheritance of mapped markers in segregating progeny (F2, backcross, etc.) • Measure domestication-related phenotypes in the progeny • Identify genomic regions cosegregating with a phenotype → linked to domestication QTL • Estimate location, effect size, dominance, etc.

M M Q M m *** m m q m m *** M M Q M m X m M Q M M X M M Q m m X m m q M M X M m q m m X m m q m M X

QTL mapping: caveats • Locus number is underestimated • Only detect loci of largest effect • Conflate linked genes • Effect size is biased upward in small samples • Dominance • Cannot be detected in all crosses • May reflect combined effect of linked loci • Low power to detect interactions • Coarse resolution

Unexpected patterns in domestication QTL: clustering in sunflower

Unexpected patterns in domestication QTL:homologous loci in related species • Tomato fruit weight QTL • highly polygenic trait • the largest ones map to homologous genomic regions in eggplant and pepper Homologous fruit weight QTL in tomato (Grandillo 1999), pepper (Ben Chaim et al. 2001) and eggplant (Doganlar et al. 2002). %C is percent difference of homozygous introgression relative to control. %PVE is percent phenotypic variance explained. Entries marked by a period (.) indicate no significant QTL at the syntenic location.

Unexpected patterns in domestication QTL:ancient • Maize tb1 • ‘Domestication’ allele found in teosinte • Tomato fw2.2 also diverged prior to domestication Clark et al. (2003) PNAS 101: 700

Domestication QTL studies genus common name references Capsicum pepper Rao et al. 2003 Citrullus watermelon Hashizume et al. 1993 Gossypium cotton Jiang et al. 1998, Wright et al. 1999, Mei et al. 2003 Helianthus sunflower Burke et al. 2002 Lactuca lettuce Johnson et al. 2000 Lycopersicon tomato Grandillo & Tanksley 1996 Oryza rice Xiong et al. 1999, Cai & Morishima, 2000, Bres-Patry et al. 2001 Panicum pearl millet Poncet et al. 2000, 2002 Phaseolus common bean Koinange et al. 1996 Saccharum sugarcane Ming et al. 2001, 2002 Solanum eggplant Doganlar et al. 2002 Sorghum sorghum Lin et. al. 1995 Triticum wheat Peng et al. 2003 Vigna cowpea Ewa Ubi et al. 2000 Zea maize Doebley & Stec 1991 Zizania wildrice Kennard et al. 2002

Questions • What is the genetic architecture of a typical domestication trait? • Major gene • Polygenic • Origin of domestication alleles • New mutations or pre-existing alleles? • Clustering of QTL • Why does it occur? • Is there a potential relationship to QTL homology?

Experimental design variables • Type of experimental cross • F2 • Backcross • Recombinant inbred (no heterozygotes) • Doubled haploid (no heterozygotes) • Number of individuals • Density (and type) of markers • We reduce all these variables to one measure: statistical power

Estimating statistical power • Power • Loosely, the probability of detecting a QTL when it is present (ranges from 0 to 1) • Calculated by simulation • Assumptions • Single codominant QTL • Constant small additive effect • Constant environmental variance • Power for a “middle-of-the-road” QTL • Allows us to compare different experiments on a common yardstick

Power and number of QTL # QTL detected per trait Power

% phenotypic variance explained/QTL Power Power and effect size

Beavis effect:overestimating the effect size of detected QTL QTL effect size significance threshold Large Small Population size

Results from low power studies can be misleading • Numbers of QTL • Five or more QTL per trait are only detected when power > 0.8 • When power > 0.8, about half the traits have > 5 QTL • Effect sizes • QTL of less than 20% PVE are rarely seen unless power > 0.75-0.8 • Most QTL contribute <20% PVE when power is >0.8 • This may be due to both undetected QTL and biased estimates of effect size

Two models for the origin of domestication alleles • ‘Sports’, or new mutations (Lester 1989, Ladizinsky 1998) • Selected from standing variation (e.g. maize tb1, tomato fw2,2). • Contrasting the dominance of QTL between selfers and outcrossers allows us to distinguish these models (Orr and Betancourt 2001). • If adaptation uses • new mutations: selfers will fix more recessive alleles than outcrossers (consistent with the standard “sport model”) • standing variation: the probability of fixation of an allele is independent of dominance (assuming s- balance) and relatively insensitive to mating system • We compared the 8 QTL studies where dominance can be estimated

Genotype A2A2 A1A2 A1A1 -a 0 d +a Genotypic value Gene action (Overdominant) (Underdominant) Recessive Additive Dominant -1.00 -0.75 -0.25 0 0.25 0.75 1.00 1.25 -1.25 Gene action of A1 = d/a

Gene action of domestication alleles Two-tailed t-test of d/a: p<0.31

Origin of domestication alleles • The domesticated allele is recessive or dominant with roughly equal frequency • Dominance does not differ appreciably between selfers and outcrossers • Results are more compatible with the predictions of the ‘standing variation’ model • The spectrum of dominance resembles that seen in surveys of insecticide resistance alleles (Bourget and Raymond 1998)

QTL clustering: Three potential explanations • Introgression model • An artifact of measuring pleiotropic QTL • Gene density/recombination frequency

Introgression model for QTL clustering • Assuming • many different loci could produce variant alleles affecting domestication traits (Turner’s largesse of the genome) • introgression from wild relatives during fixation • Linked QTL were preferentially fixed (Le Thierry D’Ennequin et al. 1999 ) • Prediction: clustering should be stronger in outcrossers than selfers, since selfers will not have been affected by introgression

q q q Q Q Q q Q q Q q Q Wild Relative Crop Outcrosser q q q Q Q Q gene flow q q Q Q q Q Selfer q q q Q Q Q q q Q Q q Q

Is clustering an artifact of pleiotropy? • A single pleiotropic QTL could be detected multiple times (i.e. once for each trait) giving the false appearance of clustering • Prediction: if we examine a conservative set of non-pleiotropic QTLs, we will not see clustering • Easier said than done!

Does clustering reflect gene density? • Gene density and recombination frequency vary along the genome • QTL could be clustered because they map to regions with many genes/cM • Suggested for wheat (Peng et al. 2003) • Expected theoretically (Noor et al. 2001)

Gene density and recombination Noor et al. 2001 Genetics 159, 581

Tests for QTL clustering • Clustering among linkage groups • 2 test of independence • Clustering within linkage groups • Measured by simulation • Randomly assigned same number of QTL to linkage groups • Measured distance between neighboring QTL

Reducing pleiotropy Full data set Reduced data set

Relationship of clustering to outcrossing rate

Clustering in an inbreeder: common bean

Non-clustering in an outcrosser: maize

Is clustering of QTLs even specific to domestication traits?

Gene density and QTL density • Are QTLs found where transcripts are dense? • Used rice map of 6591 transcripts (Wu et al. 2002) • Counted number of markers in 5 cM windows • Results • Average no. markers/window: 4.41 • Weighted avg no. markers/window for QTL: 3.49 • Thus, QTL clusters in rice are not explained by transcript density

Relationship between clustering and QTL homology • Homologous QTL observed in several systems • Solanaceae (fruit size, shape) • Beans (seed size) • Cereals (grain size, daylength sensitivity, shattering) • Are these QTL underlain by variation at the same genes? • Implicitly assumed by many in the field • But it may not be the case • If • chromosomal regions have varying propensities to harbor QTL • these regions are conserved among species • Some correspondence in the location of QTL among related species is to be expected

Conclusions • What is the genetic architecture of domestication? • High power studies tend to reveal many minor QTL • Is this due to domestication bottlenecks? • Recessive domestication alleles of large effect are not the norm • Where do domestication alleles come from? • Similarity of dominance spectrum in selfers and outcrossers suggests many domestication alleles were selected from standing variation • Why are QTL clustered? • Does not appear to be entirely an artifact of pleiotropy • Lack of effect of mating system argues against introgression as a major factor • Appears to reflect inherent differences among regions of the genome - but not obviously gene density per centimorgan • A genome structural basis for clustering may contribute to the pattern of QTL homology

Thanks to Maria Chacon Zongli Xu John Burke (sunflower) Lizhong Xiong (rice) Valerie Poncet (pearl millet) Raymie Porter and Ron Phillips (wildrice)

Timeline of plant domestication