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A bad genetic history of maize
Jeffrey Ross-Ibarra
@jrossibarra
Photo: anthocyaninless (anl1) lethal, Gerry Neuffer
photos Gerry Neuffer (courtesy MaizeGDB)
Flowering Time
Plant Height
Leaf Angle
Leaf Length
Yield
Ear Length
GWAS Enrichment for
Deleterious Alleles
0.9 1 1.1 1.2 1.3
Kremling et al. 2018 Nature Mezmouk & Ross-Ibarra 2014 G3
Flowering Time
Plant Height
Leaf Angle
Leaf Length
Yield
Ear Length
GWAS Enrichment for
Deleterious Alleles
0.9 1 1.1 1.2 1.3
Kremling et al. 2018 Nature
3.0
2.5
2.0
1.5
ng
ls
-
ng
ls
ng
ls
Upstream SNP with MAF ≤ 0.05
b c
Meanupstreamrare-allelecount
Meanupstreamrare-allelecount
Expression rankExpression rankLow High Low High
0.2
1.0
0.6
1.6
2.0
2.4
2.6
2.2
1.8
0 25050 100 150 2000 25050 100 150 200#ofrare(deleterious)mutations
Mezmouk & Ross-Ibarra 2014 G3
Flowering Time
Plant Height
Leaf Angle
Leaf Length
Yield
Ear Length
GWAS Enrichment for
Deleterious Alleles
0.9 1 1.1 1.2 1.3
Kremling et al. 2018 Nature
3.0
2.5
2.0
1.5
ng
ls
-
ng
ls
ng
ls
Upstream SNP with MAF ≤ 0.05
b c
Meanupstreamrare-allelecount
Meanupstreamrare-allelecount
Expression rankExpression rankLow High Low High
0.2
1.0
0.6
1.6
2.0
2.4
2.6
2.2
1.8
0 25050 100 150 2000 25050 100 150 200#ofrare(deleterious)mutations
Mezmouk & Ross-Ibarra 2014 G3
East 1910 Pop Sci. Mon
pollinated, some 15 to 20 plants ineach of the paired lines,D1 and D*,and
if this characterwere present inthe stock it would surely have been noted.
Defective seeds have reappeared in this mutant line every year since,
now three generations in succession,and segregatingplants out-crossed to
FIGURE1.-A germinal change which occurredin the thirteenth generationof self-fertilization.
The ear on the right is segregating fora lethal factor that produces defective seeds;on the left
isa normal ear from the same progeny.
Jones 1924 Genetics
G→T
SNP model by David Eccles (gringer) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 4.0 (http://creativecommons.org/licenses/by/4.0)], via Wikimedia Commons
By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
3 x 10-8
per bp
3 x 10-8
per bp
By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
x 1013 plants
3 x 10-8
per bp
3 x 10-8
per bp
By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
x 1013 plants
3 x 10-8
per bp
3 x 10-8
per bp
= 500,000
mutations
By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
2.3 gigabase
genome
x
x 1013 plants
3 x 10-8
per bp
3 x 10-8
per bp
= 500,000
mutations
By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
2.3 gigabase
genome
x
x 1013 plants
3 x 10-8
per bp
3 x 10-8
per bp
= 500,000
mutations
~ 140 mutations per meiosis
By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
2 nonsynonymous mutations
2.3 gigabase
genome
x
x 1013 plants
3 x 10-8
per bp
3 x 10-8
per bp
= 500,000
mutations
~ 140 mutations per meiosis
By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons
2 nonsynonymous mutations
2.3 gigabase
genome
x
x 1013 plants
3 x 10-8
per bp
3 x 10-8
per bp
= 500,000
mutations
~ 140 mutations per meiosis
mean: 8%
median: 0.3%
Ross-Ibarra unpub.
Mezmouk & Ross-Ibarra 2014 G3
ATA
Mezmouk & Ross-Ibarra 2014 G3
ATA
TTA
mild
nonsynonymous
Mezmouk & Ross-Ibarra 2014 G3
ATA
TTA
mild
nonsynonymous
AGA
strong
nonsynonymous
Mezmouk & Ross-Ibarra 2014 G3
ATA
TTA
mild
nonsynonymous
AGA
strong
nonsynonymous
mild nonsynonymous
strong nonsynonymous
synonymous
allele frequency in 282
#ofSNPs
high
low
GERP (conservation) score:
Davydov et al., 2010 PLoS Comp Bio
high
low
B73 REF
GERP (conservation) score:
Davydov et al., 2010 PLoS Comp Bio
high
low
B73 REF
strongly
deleterious
weakly
deleterious
G
ATA
ATT
A A
ATA
ATTT
T
DATA
GERP (conservation) score:
Davydov et al., 2010 PLoS Comp Bio
Yang et al. 2017 PLoS Genetics
deleteriousness (GERP score)
Hapmap3allelefrequency
Yang et al. 2017 PLoS Genetics
D
D
T
A
P
0.000.010.020.03
0.0 0.5 1.0 1.5 2.0
GERP Score
AdditiveEffect
−06
c
f
1.5 2.0
P Score
1.5 2.0
P Score
0.0
0.0 0.5 1.0 1.5 2.0
GERP Score
3e−064e−065e−066e−067e−06
0.0 0.5 1.0 1.5 2.0
GERP Score
TotalVariance
h
yield
ear height
plant height
0.000.010.020.03
0.0 0.5 1.0 1.5 2.0
GERP Score
AdditiveEffect
e−067e−06
ce
f
0.00
DTS
DTP
TW
ASI
PHT
0.000.010.020.03
0.0 0.5 1.0 1.5 2.0
AdditiveEffect
c
0.00
DTS
DTP
TW
ASI
PHT
EHT
GY
0.000.010.020.03
0.0 0.5 1.0 1.5 2.0
AdditiveEffect
0.000.010.020.03
0.0
DominantEffect
c d
Traits
deleteriousness (GERP score)deleteriousness (GERP score)
Hapmap3allelefrequency
additiveeffect
~150,000 deleterious GERP SNPs per inbred
Yang et al. 2017 PLoS Genetics
Lorant et al. In prep
~2M nonsynonymous SNPs per plant
Jones 1939 Genetics
maize
Darwin1876 The Effects of Cross and Self Fertilisation
in the Vegetable Kingdom
Jones 1939 Genetics
maize
Jones 1939 Genetics
maize
homozygosity
plantheight
Schulz et al. P206
teosinte
LOCUS A
LOCUS A
LOCUS A
LOCUS B
LOCUS A
LOCUS B
few
many
deleterious
alleles
colonization
few
many
deleterious
alleles
colonization
expansion
few
many
deleterious
alleles
colonization
expansion
colonization
expansion
few
many
deleterious
alleles
colonization
expansion
colonization
expansion
few
many
deleterious
alleles
Lorant et al. P2
0.05Na
Na
Na 3Na
maizeteosinte
Beissinger et al. 2016 Nature Plants
Wang et al. 2017 Genome Biology
present
past
0.05Na
Na
Na 3Na
maizeteosinte
Beissinger et al. 2016 Nature Plants
Wang et al. 2017 Genome Biology
fixedsegregating
#deleteriousallelespergenome
maize teosinte
present
past
0.05Na
Na
Na 3Na
maizeteosinte
Beissinger et al. 2016 Nature Plants
Wang et al. 2017 Genome Biology
fixedsegregating
#deleteriousallelespergenome
maize teosinte
present
past
0.05Na
Na
Na 3Na
maizeteosinte
Beissinger et al. 2016 Nature Plants
Wang et al. 2017 Genome Biology
present
past
Wang et al. 2017 Genome Biology
Wang et al. 2017 Genome Biology
Wang et al. 2017 Genome Biology
Wang et al. 2017 Genome Biology
colonization
expansion
colonization
expansion
few
many
deleterious
alleles
Wang et al. 2017 Genome Biology
colonization
expansion
colonization
expansion
few
many
deleterious
alleles
Gates, Janzen et al. In Prep
field trial sites
landrace collections
Gates, Janzen et al. In Prep
temperature diff. from trial
field trial sites
landrace collections
Sawers, Flint-Garcia, Rellán, unpublished
lowland highland
Sawers, Flint-Garcia, Rellán, unpublished
lowland highland
Sawers, Flint-Garcia, Rellán, unpublished
lowland highland
Gates, Janzen et al. In Prep
Gates, Janzen et al. In Prep
Domestication
10,000BP
Domestication
10,000BP
Mexican Highlands
6,000BP
S. American lowlands
6,000BP
Domestication
10,000BP
Mexican Highlands
6,000BP
S. American lowlands
6,000BP
Andes
4,000BP
Domestication
10,000BP
Mexican Highlands
6,000BP
S. American lowlands
6,000BP
Andes
4,000BP
High
SAm Mex SAm Mex
Low
Flint-Garcia (unpublished)
floweringtime
Takuno et al. 2015 Genetics
ACTCCTG
ACTGCTG
Takuno et al. 2015 Genetics
ACTGCTG
ACTCCTG
ACTGCTG
Takuno et al. 2015 Genetics
ACTGCTG
ACTCCTG
ACTGCTG
population size
selection in lowlands
dispersal
distance (Km)
Takuno et al. 2015 Genetics
ACTGCTG
ACTCCTG
ACTGCTG
Takuno et al. 2015 Genetics
high-lowFstS.America(-log10p)
high-low Fst Mexico (-log10 p)
shared SNPs
unique to
S.America
unique to
Mexico
ACTGCTG
ACTCCTG
ACTGCTG
Takuno et al. 2015 Genetics
b
All Sites
Fixed Segregating
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbp
LR IN LR IN LR IN
deleteriousallelesperbp
Yang et al. 2017 PLoS Genetics
b
All Sites
Fixed Segregating
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbp
LR IN LR IN LR IN
deleteriousallelesperbp
Yang et al. 2017 PLoS Genetics
b
All Sites
Fixed Segregating
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbp
LR IN LR IN LR IN
deleteriousallelesperbp
Yang et al. 2017 PLoS Genetics
b
All Sites
Fixed Segregating
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbp
LR IN LR IN LR IN
deleteriousallelesperbp
Yang et al. 2017 PLoS Genetics
b
All Sites
Fixed Segregating
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbp
LR IN LR IN LR IN
sampling
deleteriousallelesperbp
Yang et al. 2017 PLoS Genetics
b
All Sites
Fixed Segregating
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbp
LR IN LR IN LR IN
sampling
inbreeding
deleteriousallelesperbp
Yang et al. 2017 PLoS Genetics
b
All Sites
Fixed Segregating
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbp
LR IN LR IN LR IN
sampling
inbreeding
purging
deleteriousallelesperbp
Yang et al. 2017 PLoS Genetics
BSSS1
BSCB11
Iowa Stiff Stalk
(BSSS)
Iowa Corn Borer
(BSCB1)
BSSS1
BSCB11
Iowa Stiff Stalk
(BSSS)
Iowa Corn Borer
(BSCB1)
BSSS1
BSCB11
yield trials
Iowa Stiff Stalk
(BSSS)
Iowa Corn Borer
(BSCB1)
BSSS1
BSCB11
yield trials
Iowa Stiff Stalk
(BSSS)
Iowa Corn Borer
(BSCB1)
BSSS1
BSCB11
yield trials
S1
S1
N=20
Iowa Stiff Stalk
(BSSS)
Iowa Corn Borer
(BSCB1)
BSSS1
BSCB11
yield trials
S1
S1
N=20 BSSS2
BSCB12
Iowa Stiff Stalk
(BSSS)
Iowa Corn Borer
(BSCB1)
Genetic change within a single
program: BSSS/BSCB1
1935 1945 1955 1965 1975 1985 1995 2005
B10
B14
B37 B73 B97 B104
0481216
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0481216
0 50 100 150 200
Position(Mb)
Heterozygosity
BSCB1, Chromosome 2
B
position (Mb)heterozygosity
cycle
0481216
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0481216
0 50 100 150 200
Position(Mb)
Heterozygosity
BSCB1, Chromosome 2
B
cycle
position (Mb)
heterozygosity
BSCB1
BSSS
overdominance
BSCB1
BSSS
overdominance
BSCB1
BSSS
overdominance
BSCB1
BSSS
overdominance
BSCB1 BSSS
complementation
BSSS
overdominance
BSCB1 BSSS
complementation
complement
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
12345678910
0 100 200 300
Position (Mb)
Heterozygosity
P < 0.001
BSCB1
BSSS
Both
Physical Space
A
0.5
Genetic Space
B
12345678910
P < 0.001
BSCB1
BSSS
Both
Physical Space
heterozygosity
chromosome
position (Mb)
heterozygote
high
recombination
inbreeding
low
recombination
Teosinte Inbred Line 5 — S6
Ross-Ibarra unpublished
heterozygote
high
recombination
inbreeding
low
recombination
heterozygote
high
recombination
inbreeding
low
recombination
Gore et al. 2009 Science
NAM RILs
position Chr. 10 (Mb)
heterozygosity
log Mb/cM
Kremling et al. 2018 Nature
Baldauf et al. 2018 Current Bio.
3.0
2.5
2.0
1.5
b c
Meanupstreamrare-allelecount
Meanupstreamrare-allelecount
Expression rankExpression rankLow High Low High
1.6
2.0
2.4
2.6
2.2
1.8
0 25050 100 150 2000 25050 100 150 200
#ofrare(deleterious)mutations
three developmental stages, hybrids express on average
(stage I), 593 (stage II), and 660 (stage III) genes more
their parental inbred lines. Moreover, the total number of
0
B X B X B X
I II III
B73xA554 B73xH84 B73xH99 B73xMo17
B X B X B X
I II III
B X B X B X
I II III
B X B X B X
I II III
#ofexpressedgenes
F1 hybrids
Inbred
lines
25,000
25,500
26,000
26,500
27,000
Stage IIIStage IIStage I
0
B
#ofexpressedgenes
developmental stage
inbreds
hybrids
degree of dominance (k) of GERP-SNPs for traits per se. (a) Total per-SNP variance explained for grain yield trait
s) and randomly sampled SNPs (grey beanplots). (b) Density plots of the degree of dominance (k). Extreme values of k
Linear regressions of additive effects (c), dominance effects (d), and degree of dominance (e) of seven traits per se
d and dashed lines represent significant and nonsignificant linear regressions, with grey bands representing 95%
dominance(d/a)ofbeneficialallele
deleteriousness (GERP score)
B73 Mo17 PHZ51
B73
Mo17
PHZ51
Yang et al. 2017 PLoS Genetics
additive effectphenotype
Yi = µ +
X
j=1
Xij↵j +
X
j=1
Wijdj
dominant effect
genotype
grain yield
photo: Jim Birchler
MMMBBB BMMBBM
Yao et al. 2013 PNAS
triploid B73, Mo17, F1
photo: Jim Birchler
MMMBBB BMMBBM
Yao et al. 2013 PNAS
6080100120140160180
GrainYield
BreedingValue
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diploid hybrid (AB)
best inbred (AA or BB)
grainyield
AAB
ABB
simulated triploids
nYield
●
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triploid B73, Mo17, F1
Larièpe et al. 2012 Genetics
Larièpe et al. 2012 Genetics
B73 Mo17 PHZ51
B73
Mo17
PHZ51
Yang et al. 2017 PLoS Genetics
Yi = µ +
X
j=1
rjIij + ✏
haplotype GERP scorephenotype
B73 Mo17 PHZ51
B73
Mo17
PHZ51
Yang et al. 2017 PLoS Genetics
Yi = µ +
X
j=1
rjIij + ✏
haplotype GERP scorephenotype
heterosishybrid
phenotypicvarianceexplained
grain yield
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b)
additive
dominant
partial dom
B73 Mo17 PHZ51
B73
Mo17
PHZ51
Yang et al. 2017 PLoS Genetics
Yi = µ +
X
j=1
rjIij + ✏
haplotype GERP scorephenotype
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance
under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d)
cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of in
values for each GERP-SNP under an incomplete dominance model is shown on the left (red
Fig 3. Genomic prediction models incorp
under models of additivity (red), dominance
cross-validation experiments for traits per se
values for each GERP-SNP under an incom
on models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b)
(red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent prediction accuracy estimated from
nts for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Prediction accuracy using estimated
P under an incomplete dominance model is shown on the left (red) and permutated values on the right (grey). Horizontal bars
permutations
GERP
predictionaccuracy
heterosishybrid
predictionaccuracy
grain yield
heterosishybrid
phenotypicvarianceexplained
grain yield
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b)
additive
dominant
partial dom
Troyer & Wellin 2009
Troyer & Wellin 2009
Troyer & Wellin 2009
colonization
expansion
colonization
expansion
few
many
deleterious
alleles
sampling patterns deleterious
alleles across pops & among taxa
b
d
All Sites
Fixed Segregating
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0 2 4 6 8 10 12
1234
Recombination Rate (cM/Mb)
GERPScore
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−10 −5 0 5
GERP Score
●●●
●●●●●●
●
TW
DTP
DTS
PHT
EHT
ASI
GY
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbpdeleteriousallelesperbp
colonization
expansion
colonization
expansion
few
many
deleterious
alleles
sampling patterns deleterious
alleles across pops & among taxa
b
d
All Sites
Fixed Segregating
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0 2 4 6 8 10 12
1234
Recombination Rate (cM/Mb)
GERPScore
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−10 −5 0 5
GERP Score
●●●
●●●●●●
●
TW
DTP
DTS
PHT
EHT
ASI
GY
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbpdeleteriousallelesperbp
high-lowFstS.America(-log10p)
high-low Fst Mexico (-log10 p)
shared SNPs
unique to
S.America
unique to
Mexico
GxE fitness consequences
important for local adaptation
colonization
expansion
colonization
expansion
few
many
deleterious
alleles
sampling patterns deleterious
alleles across pops & among taxa
b
d
All Sites
Fixed Segregating
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0 2 4 6 8 10 12
1234
Recombination Rate (cM/Mb)
GERPScore
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−10 −5 0 5
GERP Score
●●●
●●●●●●
●
TW
DTP
DTS
PHT
EHT
ASI
GY
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbpdeleteriousallelesperbp
high-lowFstS.America(-log10p)
high-low Fst Mexico (-log10 p)
shared SNPs
unique to
S.America
unique to
Mexico
GxE fitness consequences
important for local adaptation
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se
under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent pred
cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Pred
values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on
indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the be
accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are sh
https://doi.org/10.1371/journal.pgen.1007019.g003
Fig 3. Genomic prediction models incorporating GERP. (a-b) Tota
under models of additivity (red), dominance (green), and incomplete do
cross-validation experiments for traits per se (c) and heterosis (MPH) (
values for each GERP-SNP under an incomplete dominance model is
indicate mean accuracy for each trait and the grey dashed lines indicat
accuracies significantly (FDR < 0.05) higher than permutations. Result
https://doi.org/10.1371/journal.pgen.1007019.g003
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b)
under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent prediction accuracy estimated from
cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Prediction accuracy using estimated
values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on the right (grey). Horizontal bars
indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the beans indicate prediction
accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are shown in S13 Fig.
https://doi.org/10.1371/journal.pgen.1007019.g003
permutations
GERP
heterosishybrid
predictionaccuracy
grain yield
Deleterious alleles and heterosis in maize
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained f
under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots
cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete d
values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permu
indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Sta
accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominanc
partial dom
heterosishybrid
phenotypicvarianceexplained
grain yield
additive
dominant
0 50 100 150
Position(cM)
0 50 100 150
Position(cM)
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0481216
0 50 100 150 200
Position(Mb)
Heterozygosity
BSSS, Chromosome 2
A
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
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0.0
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0.5
0.0
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0.4
0.5
0481216
0 50 100 150 200
Position(Mb)
Heterozygosity
BSCB1, Chromosome 2
B
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
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0.5
0.0
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0.5
0481216
0 50 100
Position(cM)
Heterozygosity
C
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
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0.5
0.0
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0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0481216
0 50 100
Position(cM)
Heterozygosity
D
3
deleterious alleles underlie
hybrid vigor
Panzea
Peter Bradbury
Ed Buckler
John Doebley
Sherry Flint-Garcia
Jim Holland
Mike McMullen
Sharon Mitchell
Qi Sun
Doreen Ware
HiLo
Graham Coop
Sherry Flint-Garcia
Matt Hufford
Rubén Rellán-Álvarez
Dan Runcie
Ruairidh Sawers
Collaborators
Felix Andrews

Justin Gerke
Li Wang
Lab Alumni
Tim Beissinger

Matt Hufford
Sofiane Mezmouk

Tanja Pyhäjärvi

Shohei Takuno
Joost van Heerwaarden
Jinliang Yang
colonization
expansion
colonization
expansion
few
many
deleterious
alleles
sampling patterns deleterious
alleles across pops & among taxa
b
d
All Sites
Fixed Segregating
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0 2 4 6 8 10 12
1234
Recombination Rate (cM/Mb)
GERPScore
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−10 −5 0 5
GERP Score
●●●
●●●●●●
●
TW
DTP
DTS
PHT
EHT
ASI
GY
●
●
LR MZ LR MZ LR MZ
0.080.120.160.20
DeleteriousLoadperbpdeleteriousallelesperbp
high-lowFstS.America(-log10p)
high-low Fst Mexico (-log10 p)
shared SNPs
unique to
S.America
unique to
Mexico
GxE fitness consequences
important for local adaptation
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se
under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent pred
cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Pred
values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on
indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the be
accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are sh
https://doi.org/10.1371/journal.pgen.1007019.g003
Fig 3. Genomic prediction models incorporating GERP. (a-b) Tota
under models of additivity (red), dominance (green), and incomplete do
cross-validation experiments for traits per se (c) and heterosis (MPH) (
values for each GERP-SNP under an incomplete dominance model is
indicate mean accuracy for each trait and the grey dashed lines indicat
accuracies significantly (FDR < 0.05) higher than permutations. Result
https://doi.org/10.1371/journal.pgen.1007019.g003
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b)
under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent prediction accuracy estimated from
cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Prediction accuracy using estimated
values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on the right (grey). Horizontal bars
indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the beans indicate prediction
accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are shown in S13 Fig.
https://doi.org/10.1371/journal.pgen.1007019.g003
permutations
GERP
heterosishybrid
predictionaccuracy
grain yield
Deleterious alleles and heterosis in maize
Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained f
under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots
cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete d
values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permu
indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Sta
accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominanc
partial dom
heterosishybrid
phenotypicvarianceexplained
grain yield
additive
dominant
0 50 100 150
Position(cM)
0 50 100 150
Position(cM)
0.0
0.1
0.2
0.3
0.4
0.5
0.0
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0.5
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0.0
0.1
0.2
0.3
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0.5
0481216
0 50 100 150 200
Position(Mb)
Heterozygosity
BSSS, Chromosome 2
A
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
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0.0
0.1
0.2
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0.5
0481216
0 50 100 150 200
Position(Mb)
Heterozygosity
BSCB1, Chromosome 2
B
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
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0.5
0.0
0.1
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0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0481216
0 50 100
Position(cM)
Heterozygosity
C
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0.0
0.1
0.2
0.3
0.4
0.5
0481216
0 50 100
Position(cM)
Heterozygosity
D
3
deleterious alleles underlie
hybrid vigor

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A bad genetic history of maize

  • 1. A bad genetic history of maize Jeffrey Ross-Ibarra @jrossibarra Photo: anthocyaninless (anl1) lethal, Gerry Neuffer
  • 2. photos Gerry Neuffer (courtesy MaizeGDB)
  • 3. Flowering Time Plant Height Leaf Angle Leaf Length Yield Ear Length GWAS Enrichment for Deleterious Alleles 0.9 1 1.1 1.2 1.3 Kremling et al. 2018 Nature Mezmouk & Ross-Ibarra 2014 G3
  • 4. Flowering Time Plant Height Leaf Angle Leaf Length Yield Ear Length GWAS Enrichment for Deleterious Alleles 0.9 1 1.1 1.2 1.3 Kremling et al. 2018 Nature 3.0 2.5 2.0 1.5 ng ls - ng ls ng ls Upstream SNP with MAF ≤ 0.05 b c Meanupstreamrare-allelecount Meanupstreamrare-allelecount Expression rankExpression rankLow High Low High 0.2 1.0 0.6 1.6 2.0 2.4 2.6 2.2 1.8 0 25050 100 150 2000 25050 100 150 200#ofrare(deleterious)mutations Mezmouk & Ross-Ibarra 2014 G3
  • 5. Flowering Time Plant Height Leaf Angle Leaf Length Yield Ear Length GWAS Enrichment for Deleterious Alleles 0.9 1 1.1 1.2 1.3 Kremling et al. 2018 Nature 3.0 2.5 2.0 1.5 ng ls - ng ls ng ls Upstream SNP with MAF ≤ 0.05 b c Meanupstreamrare-allelecount Meanupstreamrare-allelecount Expression rankExpression rankLow High Low High 0.2 1.0 0.6 1.6 2.0 2.4 2.6 2.2 1.8 0 25050 100 150 2000 25050 100 150 200#ofrare(deleterious)mutations Mezmouk & Ross-Ibarra 2014 G3 East 1910 Pop Sci. Mon
  • 6. pollinated, some 15 to 20 plants ineach of the paired lines,D1 and D*,and if this characterwere present inthe stock it would surely have been noted. Defective seeds have reappeared in this mutant line every year since, now three generations in succession,and segregatingplants out-crossed to FIGURE1.-A germinal change which occurredin the thirteenth generationof self-fertilization. The ear on the right is segregating fora lethal factor that produces defective seeds;on the left isa normal ear from the same progeny. Jones 1924 Genetics G→T SNP model by David Eccles (gringer) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY 4.0 (http://creativecommons.org/licenses/by/4.0)], via Wikimedia Commons
  • 7. By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons 3 x 10-8 per bp 3 x 10-8 per bp
  • 8. By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons x 1013 plants 3 x 10-8 per bp 3 x 10-8 per bp
  • 9. By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons x 1013 plants 3 x 10-8 per bp 3 x 10-8 per bp = 500,000 mutations
  • 10. By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons 2.3 gigabase genome x x 1013 plants 3 x 10-8 per bp 3 x 10-8 per bp = 500,000 mutations
  • 11. By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons 2.3 gigabase genome x x 1013 plants 3 x 10-8 per bp 3 x 10-8 per bp = 500,000 mutations ~ 140 mutations per meiosis
  • 12. By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons 2 nonsynonymous mutations 2.3 gigabase genome x x 1013 plants 3 x 10-8 per bp 3 x 10-8 per bp = 500,000 mutations ~ 140 mutations per meiosis
  • 13. By File:Chromosome-es.svg: KES47 (talk) derivative work: KES47 (File:Chromosome-es.svg) [CC BY 3.0 (http://creativecommons.org/licenses/by/3.0)], via Wikimedia Commons 2 nonsynonymous mutations 2.3 gigabase genome x x 1013 plants 3 x 10-8 per bp 3 x 10-8 per bp = 500,000 mutations ~ 140 mutations per meiosis mean: 8% median: 0.3% Ross-Ibarra unpub.
  • 14. Mezmouk & Ross-Ibarra 2014 G3 ATA
  • 15. Mezmouk & Ross-Ibarra 2014 G3 ATA TTA mild nonsynonymous
  • 16. Mezmouk & Ross-Ibarra 2014 G3 ATA TTA mild nonsynonymous AGA strong nonsynonymous
  • 17. Mezmouk & Ross-Ibarra 2014 G3 ATA TTA mild nonsynonymous AGA strong nonsynonymous mild nonsynonymous strong nonsynonymous synonymous allele frequency in 282 #ofSNPs
  • 18. high low GERP (conservation) score: Davydov et al., 2010 PLoS Comp Bio
  • 19. high low B73 REF GERP (conservation) score: Davydov et al., 2010 PLoS Comp Bio
  • 21. Yang et al. 2017 PLoS Genetics deleteriousness (GERP score) Hapmap3allelefrequency
  • 22. Yang et al. 2017 PLoS Genetics D D T A P 0.000.010.020.03 0.0 0.5 1.0 1.5 2.0 GERP Score AdditiveEffect −06 c f 1.5 2.0 P Score 1.5 2.0 P Score 0.0 0.0 0.5 1.0 1.5 2.0 GERP Score 3e−064e−065e−066e−067e−06 0.0 0.5 1.0 1.5 2.0 GERP Score TotalVariance h yield ear height plant height 0.000.010.020.03 0.0 0.5 1.0 1.5 2.0 GERP Score AdditiveEffect e−067e−06 ce f 0.00 DTS DTP TW ASI PHT 0.000.010.020.03 0.0 0.5 1.0 1.5 2.0 AdditiveEffect c 0.00 DTS DTP TW ASI PHT EHT GY 0.000.010.020.03 0.0 0.5 1.0 1.5 2.0 AdditiveEffect 0.000.010.020.03 0.0 DominantEffect c d Traits deleteriousness (GERP score)deleteriousness (GERP score) Hapmap3allelefrequency additiveeffect
  • 23. ~150,000 deleterious GERP SNPs per inbred Yang et al. 2017 PLoS Genetics Lorant et al. In prep ~2M nonsynonymous SNPs per plant
  • 24. Jones 1939 Genetics maize Darwin1876 The Effects of Cross and Self Fertilisation in the Vegetable Kingdom
  • 31.
  • 37. 0.05Na Na Na 3Na maizeteosinte Beissinger et al. 2016 Nature Plants Wang et al. 2017 Genome Biology present past
  • 38. 0.05Na Na Na 3Na maizeteosinte Beissinger et al. 2016 Nature Plants Wang et al. 2017 Genome Biology fixedsegregating #deleteriousallelespergenome maize teosinte present past
  • 39. 0.05Na Na Na 3Na maizeteosinte Beissinger et al. 2016 Nature Plants Wang et al. 2017 Genome Biology fixedsegregating #deleteriousallelespergenome maize teosinte present past
  • 40. 0.05Na Na Na 3Na maizeteosinte Beissinger et al. 2016 Nature Plants Wang et al. 2017 Genome Biology present past
  • 41. Wang et al. 2017 Genome Biology
  • 42. Wang et al. 2017 Genome Biology
  • 43. Wang et al. 2017 Genome Biology
  • 44. Wang et al. 2017 Genome Biology colonization expansion colonization expansion few many deleterious alleles
  • 45. Wang et al. 2017 Genome Biology colonization expansion colonization expansion few many deleterious alleles
  • 46. Gates, Janzen et al. In Prep field trial sites landrace collections
  • 47. Gates, Janzen et al. In Prep temperature diff. from trial field trial sites landrace collections
  • 48. Sawers, Flint-Garcia, Rellán, unpublished lowland highland
  • 49. Sawers, Flint-Garcia, Rellán, unpublished lowland highland
  • 50. Sawers, Flint-Garcia, Rellán, unpublished lowland highland
  • 51. Gates, Janzen et al. In Prep
  • 52. Gates, Janzen et al. In Prep
  • 56. Domestication 10,000BP Mexican Highlands 6,000BP S. American lowlands 6,000BP Andes 4,000BP High SAm Mex SAm Mex Low Flint-Garcia (unpublished) floweringtime
  • 57. Takuno et al. 2015 Genetics
  • 60. ACTGCTG ACTCCTG ACTGCTG population size selection in lowlands dispersal distance (Km) Takuno et al. 2015 Genetics
  • 62. high-lowFstS.America(-log10p) high-low Fst Mexico (-log10 p) shared SNPs unique to S.America unique to Mexico ACTGCTG ACTCCTG ACTGCTG Takuno et al. 2015 Genetics
  • 63. b All Sites Fixed Segregating ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbp LR IN LR IN LR IN deleteriousallelesperbp Yang et al. 2017 PLoS Genetics
  • 64. b All Sites Fixed Segregating ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbp LR IN LR IN LR IN deleteriousallelesperbp Yang et al. 2017 PLoS Genetics
  • 65. b All Sites Fixed Segregating ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbp LR IN LR IN LR IN deleteriousallelesperbp Yang et al. 2017 PLoS Genetics
  • 66. b All Sites Fixed Segregating ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbp LR IN LR IN LR IN deleteriousallelesperbp Yang et al. 2017 PLoS Genetics
  • 67. b All Sites Fixed Segregating ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbp LR IN LR IN LR IN sampling deleteriousallelesperbp Yang et al. 2017 PLoS Genetics
  • 68. b All Sites Fixed Segregating ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbp LR IN LR IN LR IN sampling inbreeding deleteriousallelesperbp Yang et al. 2017 PLoS Genetics
  • 69. b All Sites Fixed Segregating ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbp LR IN LR IN LR IN sampling inbreeding purging deleteriousallelesperbp Yang et al. 2017 PLoS Genetics
  • 72. BSSS1 BSCB11 yield trials Iowa Stiff Stalk (BSSS) Iowa Corn Borer (BSCB1)
  • 73. BSSS1 BSCB11 yield trials Iowa Stiff Stalk (BSSS) Iowa Corn Borer (BSCB1)
  • 74. BSSS1 BSCB11 yield trials S1 S1 N=20 Iowa Stiff Stalk (BSSS) Iowa Corn Borer (BSCB1)
  • 75. BSSS1 BSCB11 yield trials S1 S1 N=20 BSSS2 BSCB12 Iowa Stiff Stalk (BSSS) Iowa Corn Borer (BSCB1)
  • 76. Genetic change within a single program: BSSS/BSCB1 1935 1945 1955 1965 1975 1985 1995 2005 B10 B14 B37 B73 B97 B104
  • 78. 0481216 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 150 200 Position(Mb) Heterozygosity BSCB1, Chromosome 2 B cycle position (Mb) heterozygosity
  • 79. BSCB1
  • 85. 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 12345678910 0 100 200 300 Position (Mb) Heterozygosity P < 0.001 BSCB1 BSSS Both Physical Space A 0.5 Genetic Space B 12345678910 P < 0.001 BSCB1 BSSS Both Physical Space heterozygosity chromosome position (Mb)
  • 87. Teosinte Inbred Line 5 — S6 Ross-Ibarra unpublished heterozygote high recombination inbreeding low recombination
  • 88. heterozygote high recombination inbreeding low recombination Gore et al. 2009 Science NAM RILs position Chr. 10 (Mb) heterozygosity log Mb/cM
  • 89. Kremling et al. 2018 Nature Baldauf et al. 2018 Current Bio. 3.0 2.5 2.0 1.5 b c Meanupstreamrare-allelecount Meanupstreamrare-allelecount Expression rankExpression rankLow High Low High 1.6 2.0 2.4 2.6 2.2 1.8 0 25050 100 150 2000 25050 100 150 200 #ofrare(deleterious)mutations three developmental stages, hybrids express on average (stage I), 593 (stage II), and 660 (stage III) genes more their parental inbred lines. Moreover, the total number of 0 B X B X B X I II III B73xA554 B73xH84 B73xH99 B73xMo17 B X B X B X I II III B X B X B X I II III B X B X B X I II III #ofexpressedgenes F1 hybrids Inbred lines 25,000 25,500 26,000 26,500 27,000 Stage IIIStage IIStage I 0 B #ofexpressedgenes developmental stage inbreds hybrids
  • 90. degree of dominance (k) of GERP-SNPs for traits per se. (a) Total per-SNP variance explained for grain yield trait s) and randomly sampled SNPs (grey beanplots). (b) Density plots of the degree of dominance (k). Extreme values of k Linear regressions of additive effects (c), dominance effects (d), and degree of dominance (e) of seven traits per se d and dashed lines represent significant and nonsignificant linear regressions, with grey bands representing 95% dominance(d/a)ofbeneficialallele deleteriousness (GERP score) B73 Mo17 PHZ51 B73 Mo17 PHZ51 Yang et al. 2017 PLoS Genetics additive effectphenotype Yi = µ + X j=1 Xij↵j + X j=1 Wijdj dominant effect genotype grain yield
  • 91. photo: Jim Birchler MMMBBB BMMBBM Yao et al. 2013 PNAS triploid B73, Mo17, F1
  • 92. photo: Jim Birchler MMMBBB BMMBBM Yao et al. 2013 PNAS 6080100120140160180 GrainYield BreedingValue ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● diploid hybrid (AB) best inbred (AA or BB) grainyield AAB ABB simulated triploids nYield ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● triploid B73, Mo17, F1
  • 93. Larièpe et al. 2012 Genetics
  • 94. Larièpe et al. 2012 Genetics
  • 95. B73 Mo17 PHZ51 B73 Mo17 PHZ51 Yang et al. 2017 PLoS Genetics Yi = µ + X j=1 rjIij + ✏ haplotype GERP scorephenotype
  • 96. B73 Mo17 PHZ51 B73 Mo17 PHZ51 Yang et al. 2017 PLoS Genetics Yi = µ + X j=1 rjIij + ✏ haplotype GERP scorephenotype heterosishybrid phenotypicvarianceexplained grain yield Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b) additive dominant partial dom
  • 97. B73 Mo17 PHZ51 B73 Mo17 PHZ51 Yang et al. 2017 PLoS Genetics Yi = µ + X j=1 rjIij + ✏ haplotype GERP scorephenotype Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of in values for each GERP-SNP under an incomplete dominance model is shown on the left (red Fig 3. Genomic prediction models incorp under models of additivity (red), dominance cross-validation experiments for traits per se values for each GERP-SNP under an incom on models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b) (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent prediction accuracy estimated from nts for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Prediction accuracy using estimated P under an incomplete dominance model is shown on the left (red) and permutated values on the right (grey). Horizontal bars permutations GERP predictionaccuracy heterosishybrid predictionaccuracy grain yield heterosishybrid phenotypicvarianceexplained grain yield Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b) additive dominant partial dom
  • 101. colonization expansion colonization expansion few many deleterious alleles sampling patterns deleterious alleles across pops & among taxa b d All Sites Fixed Segregating ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 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● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ●● ●● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● 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(cM/Mb) GERPScore ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●●● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ●● ●●●● ●●● ● ● ● ●●● ●● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●●●●● ●●● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● −10 −5 0 5 GERP Score ●●● ●●●●●● ● TW DTP DTS PHT EHT ASI GY ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbpdeleteriousallelesperbp
  • 102. colonization expansion colonization expansion few many deleterious alleles sampling patterns deleterious alleles across pops & among taxa b d All Sites Fixed Segregating ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 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(cM/Mb) GERPScore ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●●● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ●● ●●●● ●●● ● ● ● ●●● ●● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●●●●● ●●● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● −10 −5 0 5 GERP Score ●●● ●●●●●● ● TW DTP DTS PHT EHT ASI GY ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbpdeleteriousallelesperbp high-lowFstS.America(-log10p) high-low Fst Mexico (-log10 p) shared SNPs unique to S.America unique to Mexico GxE fitness consequences important for local adaptation
  • 103. colonization expansion colonization expansion few many deleterious alleles sampling patterns deleterious alleles across pops & among taxa b d All Sites Fixed Segregating ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ●●● ● ●● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●●●● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ● ● ● ● ●● ●● ● ●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●●● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ●● ● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ● ● ●●● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ●●● ● ●●● ● ●● ● ● ● ● ●●● ●● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ● ● ● ●● ● ●●● ● ● ● ●●● ● ● ●● ● ● ● ●●● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 2 4 6 8 10 12 1234 Recombination Rate (cM/Mb) GERPScore ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●●● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ●● ●●●● ●●● ● ● ● ●●● ●● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●●●●● ●●● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● −10 −5 0 5 GERP Score ●●● ●●●●●● ● TW DTP DTS PHT EHT ASI GY ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbpdeleteriousallelesperbp high-lowFstS.America(-log10p) high-low Fst Mexico (-log10 p) shared SNPs unique to S.America unique to Mexico GxE fitness consequences important for local adaptation Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent pred cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Pred values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the be accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are sh https://doi.org/10.1371/journal.pgen.1007019.g003 Fig 3. Genomic prediction models incorporating GERP. (a-b) Tota under models of additivity (red), dominance (green), and incomplete do cross-validation experiments for traits per se (c) and heterosis (MPH) ( values for each GERP-SNP under an incomplete dominance model is indicate mean accuracy for each trait and the grey dashed lines indicat accuracies significantly (FDR < 0.05) higher than permutations. Result https://doi.org/10.1371/journal.pgen.1007019.g003 Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b) under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent prediction accuracy estimated from cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Prediction accuracy using estimated values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on the right (grey). Horizontal bars indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the beans indicate prediction accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are shown in S13 Fig. https://doi.org/10.1371/journal.pgen.1007019.g003 permutations GERP heterosishybrid predictionaccuracy grain yield Deleterious alleles and heterosis in maize Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained f under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete d values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permu indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Sta accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominanc partial dom heterosishybrid phenotypicvarianceexplained grain yield additive dominant 0 50 100 150 Position(cM) 0 50 100 150 Position(cM) 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 150 200 Position(Mb) Heterozygosity BSSS, Chromosome 2 A 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 150 200 Position(Mb) Heterozygosity BSCB1, Chromosome 2 B 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 Position(cM) Heterozygosity C 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 Position(cM) Heterozygosity D 3 deleterious alleles underlie hybrid vigor
  • 104. Panzea Peter Bradbury Ed Buckler John Doebley Sherry Flint-Garcia Jim Holland Mike McMullen Sharon Mitchell Qi Sun Doreen Ware HiLo Graham Coop Sherry Flint-Garcia Matt Hufford Rubén Rellán-Álvarez Dan Runcie Ruairidh Sawers Collaborators Felix Andrews
 Justin Gerke Li Wang Lab Alumni Tim Beissinger
 Matt Hufford Sofiane Mezmouk
 Tanja Pyhäjärvi
 Shohei Takuno Joost van Heerwaarden Jinliang Yang
  • 105.
  • 106. colonization expansion colonization expansion few many deleterious alleles sampling patterns deleterious alleles across pops & among taxa b d All Sites Fixed Segregating ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 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(cM/Mb) GERPScore ● ● ● ● ● ● ● ●● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ●● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●●● ●●● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ●● ●●●● ●●● ● ● ● ●●● ●● ● ● ● ●●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ●●●● ● ●● ● ● ● ● ●●● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●●●●● ●●● ● ●● ● ● ● ● ●● ●●● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ● ● −10 −5 0 5 GERP Score ●●● ●●●●●● ● TW DTP DTS PHT EHT ASI GY ● ● LR MZ LR MZ LR MZ 0.080.120.160.20 DeleteriousLoadperbpdeleteriousallelesperbp high-lowFstS.America(-log10p) high-low Fst Mexico (-log10 p) shared SNPs unique to S.America unique to Mexico GxE fitness consequences important for local adaptation Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent pred cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Pred values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the be accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are sh https://doi.org/10.1371/journal.pgen.1007019.g003 Fig 3. Genomic prediction models incorporating GERP. (a-b) Tota under models of additivity (red), dominance (green), and incomplete do cross-validation experiments for traits per se (c) and heterosis (MPH) ( values for each GERP-SNP under an incomplete dominance model is indicate mean accuracy for each trait and the grey dashed lines indicat accuracies significantly (FDR < 0.05) higher than permutations. Result https://doi.org/10.1371/journal.pgen.1007019.g003 Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained for traits per se (a) and heterosis (MPH) (b) under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots represent prediction accuracy estimated from cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete dominance. Prediction accuracy using estimated values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permutated values on the right (grey). Horizontal bars indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Stars above the beans indicate prediction accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominance models are shown in S13 Fig. https://doi.org/10.1371/journal.pgen.1007019.g003 permutations GERP heterosishybrid predictionaccuracy grain yield Deleterious alleles and heterosis in maize Fig 3. Genomic prediction models incorporating GERP. (a-b) Total phenotypic variance explained f under models of additivity (red), dominance (green), and incomplete dominance (blue). (c-d) Beanplots cross-validation experiments for traits per se (c) and heterosis (MPH) (d) under a model of incomplete d values for each GERP-SNP under an incomplete dominance model is shown on the left (red) and permu indicate mean accuracy for each trait and the grey dashed lines indicate the overall mean accuracy. Sta accuracies significantly (FDR < 0.05) higher than permutations. Results for pure additive and dominanc partial dom heterosishybrid phenotypicvarianceexplained grain yield additive dominant 0 50 100 150 Position(cM) 0 50 100 150 Position(cM) 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 150 200 Position(Mb) Heterozygosity BSSS, Chromosome 2 A 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 150 200 Position(Mb) Heterozygosity BSCB1, Chromosome 2 B 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 Position(cM) Heterozygosity C 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.5 0481216 0 50 100 Position(cM) Heterozygosity D 3 deleterious alleles underlie hybrid vigor