SlideShare a Scribd company logo
1 of 52
Download to read offline
Genome-Wide View of Breeding History and Selection in North American Maize
Dr. Jeffrey Ross-Ibarra
University of California at Davis
Patterns of diversity across the genome are reflective of the historical processes of
selection, drift, and mutation that have acted on a species during its evolution. Here, we
apply population genetic analysis of two genomic datasets to assess the spatial and
temporal signatures of evolution in the maize genome. We first describe genome-wide
patterns of evolution during two epochs of selection, domestication and modern breeding,
using full-genome resequencing of a number of maize and teosinte lines. Then, using a
chronologically-stratified panel of corn belt lines, we dissect temporal patterns of ancestry
and selection in greater detail across the 80+ years of North American corn breeding. We
find distinct impacts of selection during domestication and modern breeding on diversity
and gene expression, identify candidate regions targeted by selection, and describe changes
in genetic structure and ancestry during the evolution of modern corn belt lines.
HISTORICAL GENOMICS OF MAIZE
Jeffrey Ross-Ibarra
www.rilab.org
Acknowledgements
Ed Buckler (Cornell, USDA)
Jeff Glaubitz (Cornell)
Major Goodman (NC State)
Mike McMullen (Missouri, USDA)
Chi Song (BGI)
Nathan Springer (Minnesota)
Doreen Ware (CSHL, USDA)
Xun Xu (BGI)
Matthew Hufford Joost van Heerwaarden Tanja Pyhäjärvi Jer-Ming Chia
HISTORICAL GENOMICS OF MAIZE






3
%





3


3
%
02




%
22222222222222222222222222222222222222222222222222222222222222200000000000000000000000000000000222222000022022222022200000000000000000000000000000000000000000000000000000000000000000022222200000000000000000000000020000000
%
2+
Spatial resolution -- genome resequencing
Temporal resolution -- SNP genotyping
HISTORICAL GENOMICS OF MAIZE






3
%





3


3
%




%
2222222222222222222222222222222222222222222222222000000000000000000022022020020222200200000000000000000000000000000000000000222220000000000000000020000000
%
2+
Spatial resolution -- genome resequencing
Temporal resolution -- SNP genotyping
ρ
0.0020.0060.01
00.010.020.030.04
ρρ
ππ
lower 1% cent10
cM/Mb
ρ
Chr 10
Maize Hapmap I: low-copy fraction of genome
Gore et al. 2009 Science
• Re-sequenced low-copy portion of genome of 27 inbreds
• High diversity genome, but large regions of low recombination
• Identified low diversity putative targets of selection
• No comparison to older material
• Cannot distinguish domestication from modern improvement
Chia et al. Submitted
Maize Hapmap II: full genome resequencing
75 resequenced genomes  21M SNPs75 resequenced genomes  21M SNPs
Sequenced
Genomes
Sequence
Depth
Locality
Improved 35 5.04 X
13 Temperate, 13 Tropical,
3 Mixed, 6 Chinese
Landraces 23 5.34 X
9 South American,
14 North American
parviglumis 14 3.81 X
4 Jalisco/Nayarit,
9 Balsas, 1 Oaxaca
1 1Oaxaca
mexicana 2 6.83 X 1 Jalisco, 1 Morelos
Tripsacum 1 12.62 X Colombia
parviglumis
Landraces
Improved Lines
Domestication
Improvement
Hufford et al. Submitted
Maize
Parviglumis
Maize
Parviglumis
Genic Tajima's D
Tajima's D
Density
-4 -2 0 2 4
0.00.10.20.30.40.5
Hufford et al. Submitted
Maize
Parviglumis
Maize
Parviglumis
Genic Tajima's D
Tajima's D
Density
-4 -2 0 2 4
0.00.10.20.30.40.5
Density
0.00.10.20.30.40.5
Genome-wide Tajima's D
Tajima's D
Density
-4 -2 0 2 4
0.00.10.20.30.4
Hufford et al. Submitted
Chen et al. 2010
Genome Research
COG3-like FMP25-like frq PAC10-like SEC14-like NSL1-like NCU02261
B
Carib
LA
Out
Ellison et al. 2011 PNAS
• Cross-Population Composite Likelihood Ratio
• Identifies extended regions of differentiation
• Compares to simulated model
• Define selected regions, estimate s
Artificial selection in the maize genome
• Stronger selection during
domestication (s~0.015 vs. s~0.003)
• Selection strength consistent with
archaeology  natural selection
• Continued selection on domestication
genes (18% overlap)
• ~3,000 genes in selected regions.
Identified ~1100 candidates
• 5-10% of selected regions had no genes
Hufford et al. Submitted
he maize genome
log s
Frequency
-6 -5 -4 -3 -2
050001500025000
domestication
improvement
Gore et al. Science 2009
Mb chr 8
proportion
FST
Mb chr 8
proportion
0.00.20.40.60.81.0
fixed shared unique temperate unique tropicalemfixed shared unique temperate unique tropical
0 20 40 60 80 100 120 140 160 180
00.050.150.250.35
Population-specific selection
Population-specific selection
Hufford et al. Submitted
• Many population-specific loci
• Stronger overall estimate of selection
• Still weaker than domestication
• Lower power to rule out drift
Candidate genes for maize improvement
Hufford et al. Submitted
Selection on the transcriptome
• Expression at 18K genes of 27 maize and teosinte using Nimblegen array
• Domestication directly selected on candidate gene expression
• Breeding has worked with highly expressed genes
• Modern breeding selected for heterosis in expression
Candidates: DOM IMP
Change in
expression
* --
Decreased
variance
* *
Tissue
specificity#
--
overall higher
expression
Dominance in
crosses#
-- inter  intra
# Data from Stupar et al. 2008 BMC Plant Bio; Sekhon et al. 2011 Plant J.
Hufford et al. Submitted
Regions with no genes likely regulatory
teosintemaize
Swanson-Wagner et al. Submitted
Expression
Genetic load in modern maize
• ~1% of SNPs disrupts predicted
protein
• 10,117 premature stop-codons
• 2557 (or 12%) high-confidence genes
has a stop-codon in ≥1line
• 870 genes have a segregating stop-
codon in ≥ 2 of improved lines
Chia et al. Submitted
Artificial selection across the maize genome
HISTORICAL GENOMICS OF MAIZE






3
%





3


3
%
02




%
22222222222222222222222222222222222222222222222222222222222222200000000000000000000000000000000222222000022022222022200000000000000000000000000000000000000000000000000000000000000000022222200000000000000000000000020000000
%
2+
Spatial resolution -- genome resequencing
Temporal resolution -- SNP genotyping
Detailed historical look at selection during
modern breeding
• Larger sample size
• Increased chronological
resolution
• Take into account heterotic
group structure
• Focus on corn belt varieties
Resolve caveats of
resequencing approach
Genotyping and Sampling
• 400 N.American corn belt lines
• Genotype on public Illumina 55K
• Tracked population structure,
ancestry, and selection over time
99
94
70
137
land races
1950 inbreds
1960-1970 inbreds
ex-PVP
0
1
2
3
van Heerwaarden et al. Submitted
(Oh43,W22, B14)
(B73, 207, Mo17)
Population structure through time
van Heerwaarden et al. Submitted
Population structure through time
van Heerwaarden et al. Submitted
C
Population structure through time
van Heerwaarden et al. Submitted
C
C
Population structure through time
van Heerwaarden et al. Submitted
C
C
Population structure through time
van Heerwaarden et al. Submitted
C
C
Population structure through time
van Heerwaarden et al. Submitted
Iodent
NSS
SS
Number and diversity of ancestors decreases
through time
ancestry
van Heerwaarden et al. Submitted
c.
Iodent
NSS
SS
effective#ancestorsdiversityofancestors
SS
0.0
0.1
0.2
0.3
0.4
undefined
OH43
CI31A
HY
B8
B10
B14
B96
B37
I205
OH07
CI3A
CI187-2
CI540
I224
WD456
Oh3167B
Tr9-1-1-6
FE2
LE773
ND203
B73
A634
A635
N28
H100
B64
B14A
B68
CM105
B84
A632
N196
LH195
LH205
LH196
LH194
991
LH74
PHG39
LH132
G80
78004
78010
4676A
78002A
794
LP5
PHT55
H8431
1 2 3
Ancestral contributions of individual lines
Ancestral contributions of individual lines
IDT
1 2 3
0.0
0.1
0.2
0.3
0.4
undefined
OH43
WF9
A375
A509
A556
I198
B2
H5
HY
B164
G
ND203
B10
B14
L317
I205
CI187-2
C49A
I224
AH83
Tr9-1-1-6
C11
IDT
A638
M14
207
PHN34
PHP76
PHW86
PHG50
PHG35
PHG71
IB014
PHG83
LH150
PHG29
PHG72
PHG84
PHZ51
PHV78
PHK42
PHN11
PHH93
PHJ33
PHN73
PHR62
Weak landrace structure still evident in modern inbreds
ancestryproportion
Iodent
NSS
SS
Selection and frequency over time
Duvick et al. 2004 Plant Breeding Reviews
advantageous alleles show continual increase
in frequency over time
•Identify genetic structure across all time periods
(PCA,Tracy-Widom)
•Treat time period as a phenotype for association
mapping
•For each SNP, compare two models:
•M0: Frequency determined by drift and population
structure (covariance matrix of all SNPs)
•M1: Frequency determined by drift, population
structure AND environment (time)
•Bayes Factor for each SNP
Time as a phenotype to identify selection
0
1
2
3
Coop et al. 2010 Genetics
Temporal association identifies candidates of selection
• 236 Candidate regions (1021 genes) with consistent BF across runs
• Include stress response, lignin biosynthesis, auxin response
• Two of the top candidates ZmErecta (US7847158) and ZmArgos
(US7834240) patented for plant growth, yield
ssociation identifies candidates
te regions (1021 genes) with consistent BF
0 1 2 3
0.00.20.40.60.81.0
era
p
Temporal association identifies candidates of selection
• 236 Candidate regions (1021 genes) with consistent BF across runs
• Include stress response, lignin biosynthesis, auxin response
• Two of the top candidates ZmErecta (US7847158) and ZmArgos
(US7834240) patented for plant growth, yield
Diversity
Genome Sequence
Selective Sweep
Selective sweeps
No abnormal haplotypes, ancestry at selected sites
0 1 2 3
01234
p p p p p p p p
era
ratioobserved/randomized
No abnormal haplotypes, ancestry at selected sites
Genome-wide ancestry not determined by selection
Weak correlation between good alleles and
contribution to modern lines
1950’s inbreds
1980’s inbreds
ex-PVP lines
selected regions
%ancestry
Weak correlation between good alleles and
contribution to modern lines
Structure and selection in corn belt maize
Some final thoughts on adaptation...
modern corn
phenotypic
space
FISHER-ORR MODEL OF ADAPTATION
modern corn teosinte
phenotypic
space
FISHER-ORR MODEL OF ADAPTATION
modern corn teosinte
phenotypic
space
FISHER-ORR MODEL OF ADAPTATION
landraces
phenotypic
space
FISHER-ORR MODEL OF ADAPTATION
landraces
phenotypic
space
FISHER-ORR MODEL OF ADAPTATION
Brown et al. 2011 PLoS Genetics
• Effect sizes of ear vs. other
consistent with Fisher-Orr
model
• Larger effects → larger fitness
difference → stronger selection
ThankYou!

More Related Content

What's hot

Introgression and the origin of maize in Mexico and the Southwest US
Introgression and the origin of maize in Mexico and the Southwest USIntrogression and the origin of maize in Mexico and the Southwest US
Introgression and the origin of maize in Mexico and the Southwest USjrossibarra
 
Population genetics of maize domestication, adaptation, and improvement
Population genetics of maize domestication, adaptation, and improvementPopulation genetics of maize domestication, adaptation, and improvement
Population genetics of maize domestication, adaptation, and improvementjrossibarra
 
A bad genetic history of maize
A bad genetic history of maizeA bad genetic history of maize
A bad genetic history of maizejrossibarra
 
Farm animals in aquatic systems - Anna Troedsson-Wargelius
Farm animals in aquatic systems - Anna Troedsson-WargeliusFarm animals in aquatic systems - Anna Troedsson-Wargelius
Farm animals in aquatic systems - Anna Troedsson-WargeliusOECD Environment
 
Andrea Crisanti-Enfermedades transmitidas por vectores
Andrea Crisanti-Enfermedades transmitidas por vectoresAndrea Crisanti-Enfermedades transmitidas por vectores
Andrea Crisanti-Enfermedades transmitidas por vectoresFundación Ramón Areces
 
Deploying genome sequence information for pigeonpea improvement
Deploying genome sequence information for pigeonpea improvementDeploying genome sequence information for pigeonpea improvement
Deploying genome sequence information for pigeonpea improvementICARDA
 
An overview of agricultural applications of genome editing: Farm animals
An overview of agricultural applications of genome editing: Farm animals An overview of agricultural applications of genome editing: Farm animals
An overview of agricultural applications of genome editing: Farm animals OECD Environment
 
Enhancing Genetic Gains through Innovations in Breeding Approaches
Enhancing Genetic Gains through Innovations in Breeding ApproachesEnhancing Genetic Gains through Innovations in Breeding Approaches
Enhancing Genetic Gains through Innovations in Breeding ApproachesICARDA
 
Crop genetic improvement and utilization in china. xinhai li
Crop genetic improvement and utilization in china. xinhai liCrop genetic improvement and utilization in china. xinhai li
Crop genetic improvement and utilization in china. xinhai liExternalEvents
 
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision PhenotypingGenomic Selection & Precision Phenotyping
Genomic Selection & Precision PhenotypingCIMMYT
 
Genome size and adaptation in plants
Genome size and adaptation in plantsGenome size and adaptation in plants
Genome size and adaptation in plantsjrossibarra
 
THEME – 4 Genomic diversity of domestication in soybean
THEME – 4 Genomic diversity of domestication in soybeanTHEME – 4 Genomic diversity of domestication in soybean
THEME – 4 Genomic diversity of domestication in soybeanICARDA
 
23. validation of molecular markers linked to sterility and fertility restore...
23. validation of molecular markers linked to sterility and fertility restore...23. validation of molecular markers linked to sterility and fertility restore...
23. validation of molecular markers linked to sterility and fertility restore...Vishwanath Koti
 
Beyond GWAS QTL Identification and Strategies to Increase Yield
Beyond GWAS QTL Identification and Strategies to Increase YieldBeyond GWAS QTL Identification and Strategies to Increase Yield
Beyond GWAS QTL Identification and Strategies to Increase YieldKate Barlow
 
2015. Jesse Poland. Integration of physiological breeding and genomic selecti...
2015. Jesse Poland. Integration of physiological breeding and genomic selecti...2015. Jesse Poland. Integration of physiological breeding and genomic selecti...
2015. Jesse Poland. Integration of physiological breeding and genomic selecti...FOODCROPS
 
Application of genome editing in farm animals: Cattle - Alison Van Eenennaam
Application of genome editing  in farm animals: Cattle - Alison Van EenennaamApplication of genome editing  in farm animals: Cattle - Alison Van Eenennaam
Application of genome editing in farm animals: Cattle - Alison Van EenennaamOECD Environment
 
Tyler impact next gen fri 0900
Tyler impact next gen fri 0900Tyler impact next gen fri 0900
Tyler impact next gen fri 0900Sucheta Tripathy
 
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...Premier Publishers
 

What's hot (20)

Danforth 2015
Danforth 2015Danforth 2015
Danforth 2015
 
Introgression and the origin of maize in Mexico and the Southwest US
Introgression and the origin of maize in Mexico and the Southwest USIntrogression and the origin of maize in Mexico and the Southwest US
Introgression and the origin of maize in Mexico and the Southwest US
 
Population genetics of maize domestication, adaptation, and improvement
Population genetics of maize domestication, adaptation, and improvementPopulation genetics of maize domestication, adaptation, and improvement
Population genetics of maize domestication, adaptation, and improvement
 
A bad genetic history of maize
A bad genetic history of maizeA bad genetic history of maize
A bad genetic history of maize
 
Ecogen2013
Ecogen2013Ecogen2013
Ecogen2013
 
Farm animals in aquatic systems - Anna Troedsson-Wargelius
Farm animals in aquatic systems - Anna Troedsson-WargeliusFarm animals in aquatic systems - Anna Troedsson-Wargelius
Farm animals in aquatic systems - Anna Troedsson-Wargelius
 
Andrea Crisanti-Enfermedades transmitidas por vectores
Andrea Crisanti-Enfermedades transmitidas por vectoresAndrea Crisanti-Enfermedades transmitidas por vectores
Andrea Crisanti-Enfermedades transmitidas por vectores
 
Deploying genome sequence information for pigeonpea improvement
Deploying genome sequence information for pigeonpea improvementDeploying genome sequence information for pigeonpea improvement
Deploying genome sequence information for pigeonpea improvement
 
An overview of agricultural applications of genome editing: Farm animals
An overview of agricultural applications of genome editing: Farm animals An overview of agricultural applications of genome editing: Farm animals
An overview of agricultural applications of genome editing: Farm animals
 
Enhancing Genetic Gains through Innovations in Breeding Approaches
Enhancing Genetic Gains through Innovations in Breeding ApproachesEnhancing Genetic Gains through Innovations in Breeding Approaches
Enhancing Genetic Gains through Innovations in Breeding Approaches
 
Crop genetic improvement and utilization in china. xinhai li
Crop genetic improvement and utilization in china. xinhai liCrop genetic improvement and utilization in china. xinhai li
Crop genetic improvement and utilization in china. xinhai li
 
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision PhenotypingGenomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
 
Genome size and adaptation in plants
Genome size and adaptation in plantsGenome size and adaptation in plants
Genome size and adaptation in plants
 
THEME – 4 Genomic diversity of domestication in soybean
THEME – 4 Genomic diversity of domestication in soybeanTHEME – 4 Genomic diversity of domestication in soybean
THEME – 4 Genomic diversity of domestication in soybean
 
23. validation of molecular markers linked to sterility and fertility restore...
23. validation of molecular markers linked to sterility and fertility restore...23. validation of molecular markers linked to sterility and fertility restore...
23. validation of molecular markers linked to sterility and fertility restore...
 
Beyond GWAS QTL Identification and Strategies to Increase Yield
Beyond GWAS QTL Identification and Strategies to Increase YieldBeyond GWAS QTL Identification and Strategies to Increase Yield
Beyond GWAS QTL Identification and Strategies to Increase Yield
 
2015. Jesse Poland. Integration of physiological breeding and genomic selecti...
2015. Jesse Poland. Integration of physiological breeding and genomic selecti...2015. Jesse Poland. Integration of physiological breeding and genomic selecti...
2015. Jesse Poland. Integration of physiological breeding and genomic selecti...
 
Application of genome editing in farm animals: Cattle - Alison Van Eenennaam
Application of genome editing  in farm animals: Cattle - Alison Van EenennaamApplication of genome editing  in farm animals: Cattle - Alison Van Eenennaam
Application of genome editing in farm animals: Cattle - Alison Van Eenennaam
 
Tyler impact next gen fri 0900
Tyler impact next gen fri 0900Tyler impact next gen fri 0900
Tyler impact next gen fri 0900
 
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...
 

Similar to Historical Genomics of US Maize: Domestication and Modern Breeding

Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...Integrated Breeding Platform
 
Mpt 9 interpopulation improvement
Mpt 9 interpopulation improvementMpt 9 interpopulation improvement
Mpt 9 interpopulation improvementAndrew Hutabarat
 
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...FOODCROPS
 
2013 GRM: Improve chickpea productivity for marginal environments in sub-Sah...
2013 GRM: Improve chickpea productivity for marginal environments in  sub-Sah...2013 GRM: Improve chickpea productivity for marginal environments in  sub-Sah...
2013 GRM: Improve chickpea productivity for marginal environments in sub-Sah...CGIAR Generation Challenge Programme
 
Exploiting Wheat’s Distant Relatives
Exploiting Wheat’s Distant RelativesExploiting Wheat’s Distant Relatives
Exploiting Wheat’s Distant RelativesCIMMYT
 
Th1_New approaches, resources and tools for gene discovery
Th1_New approaches, resources and tools for gene discovery Th1_New approaches, resources and tools for gene discovery
Th1_New approaches, resources and tools for gene discovery Africa Rice Center (AfricaRice)
 
B4FA 2012 Nigeria: Cassava Research in Nigeria - Emmanual Okogbenin
B4FA 2012 Nigeria: Cassava Research in Nigeria - Emmanual OkogbeninB4FA 2012 Nigeria: Cassava Research in Nigeria - Emmanual Okogbenin
B4FA 2012 Nigeria: Cassava Research in Nigeria - Emmanual Okogbeninb4fa
 
Status of Cassava Genetic Transformation at CIAT.
Status of Cassava Genetic Transformation at CIAT. Status of Cassava Genetic Transformation at CIAT.
Status of Cassava Genetic Transformation at CIAT. CIAT
 
GRM 2011: Approaches, resources and tools for rice gene discovery and breeding
GRM 2011: Approaches, resources and tools for rice gene discovery and breedingGRM 2011: Approaches, resources and tools for rice gene discovery and breeding
GRM 2011: Approaches, resources and tools for rice gene discovery and breedingCGIAR Generation Challenge Programme
 
Drought tolerance genes and traits/Gene Index and markers
Drought tolerance genes and traits/Gene Index and markersDrought tolerance genes and traits/Gene Index and markers
Drought tolerance genes and traits/Gene Index and markersInternational Potato Center
 
Role of Biotechnology in Improving Productivity for Rice Producers in Asia fr...
Role of Biotechnology in Improving Productivity for Rice Producers in Asia fr...Role of Biotechnology in Improving Productivity for Rice Producers in Asia fr...
Role of Biotechnology in Improving Productivity for Rice Producers in Asia fr...apaari
 
Population Structure & Genetic Improvement in Livestock
Population Structure & Genetic Improvement in LivestockPopulation Structure & Genetic Improvement in Livestock
Population Structure & Genetic Improvement in LivestockGolden Helix Inc
 
Genetic Dissection of Compositional & Anatomical Characteristics Associated w...
Genetic Dissection of Compositional & Anatomical Characteristics Associated w...Genetic Dissection of Compositional & Anatomical Characteristics Associated w...
Genetic Dissection of Compositional & Anatomical Characteristics Associated w...Jonathan Clarke
 
Gene discovery application_rice
Gene discovery application_riceGene discovery application_rice
Gene discovery application_riceCIAT
 
Cisgenesis and Intragenesis
Cisgenesis and IntragenesisCisgenesis and Intragenesis
Cisgenesis and Intragenesissharadabgowda
 
Allele mining in crop improvement
Allele mining in crop improvementAllele mining in crop improvement
Allele mining in crop improvementGAYATRI KUMAWAT
 
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...ICRISAT
 

Similar to Historical Genomics of US Maize: Domestication and Modern Breeding (20)

Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
Our PAG XXVI Presentations: Integrating Marker-Assisted Selection into a Popu...
 
Mpt 9 interpopulation improvement
Mpt 9 interpopulation improvementMpt 9 interpopulation improvement
Mpt 9 interpopulation improvement
 
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
2017. Sarah M Potts. Identification of QTL and candidate genes for plant dens...
 
2013 GRM: Improve chickpea productivity for marginal environments in sub-Sah...
2013 GRM: Improve chickpea productivity for marginal environments in  sub-Sah...2013 GRM: Improve chickpea productivity for marginal environments in  sub-Sah...
2013 GRM: Improve chickpea productivity for marginal environments in sub-Sah...
 
Exploiting Wheat’s Distant Relatives
Exploiting Wheat’s Distant RelativesExploiting Wheat’s Distant Relatives
Exploiting Wheat’s Distant Relatives
 
2013 Cornell's Plant Breeding and Genetic Seminar Series
2013 Cornell's Plant Breeding and Genetic Seminar Series2013 Cornell's Plant Breeding and Genetic Seminar Series
2013 Cornell's Plant Breeding and Genetic Seminar Series
 
The Wheat Genome
The Wheat GenomeThe Wheat Genome
The Wheat Genome
 
Th1_New approaches, resources and tools for gene discovery
Th1_New approaches, resources and tools for gene discovery Th1_New approaches, resources and tools for gene discovery
Th1_New approaches, resources and tools for gene discovery
 
B4FA 2012 Nigeria: Cassava Research in Nigeria - Emmanual Okogbenin
B4FA 2012 Nigeria: Cassava Research in Nigeria - Emmanual OkogbeninB4FA 2012 Nigeria: Cassava Research in Nigeria - Emmanual Okogbenin
B4FA 2012 Nigeria: Cassava Research in Nigeria - Emmanual Okogbenin
 
Status of Cassava Genetic Transformation at CIAT.
Status of Cassava Genetic Transformation at CIAT. Status of Cassava Genetic Transformation at CIAT.
Status of Cassava Genetic Transformation at CIAT.
 
GRM 2011: Approaches, resources and tools for rice gene discovery and breeding
GRM 2011: Approaches, resources and tools for rice gene discovery and breedingGRM 2011: Approaches, resources and tools for rice gene discovery and breeding
GRM 2011: Approaches, resources and tools for rice gene discovery and breeding
 
Drought tolerance genes and traits/Gene Index and markers
Drought tolerance genes and traits/Gene Index and markersDrought tolerance genes and traits/Gene Index and markers
Drought tolerance genes and traits/Gene Index and markers
 
Role of Biotechnology in Improving Productivity for Rice Producers in Asia fr...
Role of Biotechnology in Improving Productivity for Rice Producers in Asia fr...Role of Biotechnology in Improving Productivity for Rice Producers in Asia fr...
Role of Biotechnology in Improving Productivity for Rice Producers in Asia fr...
 
Population Structure & Genetic Improvement in Livestock
Population Structure & Genetic Improvement in LivestockPopulation Structure & Genetic Improvement in Livestock
Population Structure & Genetic Improvement in Livestock
 
Genetic Dissection of Compositional & Anatomical Characteristics Associated w...
Genetic Dissection of Compositional & Anatomical Characteristics Associated w...Genetic Dissection of Compositional & Anatomical Characteristics Associated w...
Genetic Dissection of Compositional & Anatomical Characteristics Associated w...
 
005 gene discovery and its application in rice, mathias lorieux
005   gene discovery and its application in rice, mathias lorieux005   gene discovery and its application in rice, mathias lorieux
005 gene discovery and its application in rice, mathias lorieux
 
Gene discovery application_rice
Gene discovery application_riceGene discovery application_rice
Gene discovery application_rice
 
Cisgenesis and Intragenesis
Cisgenesis and IntragenesisCisgenesis and Intragenesis
Cisgenesis and Intragenesis
 
Allele mining in crop improvement
Allele mining in crop improvementAllele mining in crop improvement
Allele mining in crop improvement
 
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
ICRISAT Global Planning Meeting 2019:Research Program - Genetic Gains by Dr R...
 

More from jrossibarra

Adaptation in plant genomes: bigger is different
Adaptation in plant genomes: bigger is differentAdaptation in plant genomes: bigger is different
Adaptation in plant genomes: bigger is differentjrossibarra
 
Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea mays
Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea maysParallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea mays
Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea maysjrossibarra
 
Adaptive evolution of genome size across altitudinal clines in maize
Adaptive evolution of genome size across altitudinal clines in maizeAdaptive evolution of genome size across altitudinal clines in maize
Adaptive evolution of genome size across altitudinal clines in maizejrossibarra
 
Gene flow and cross-incompatibility in maize and teosinte
Gene flow and cross-incompatibility in maize and teosinteGene flow and cross-incompatibility in maize and teosinte
Gene flow and cross-incompatibility in maize and teosintejrossibarra
 
Demography and deleterious alleles in maize
Demography and deleterious alleles in maizeDemography and deleterious alleles in maize
Demography and deleterious alleles in maizejrossibarra
 
2015 SF Exploratorium Lecture: "Corn: Diversity and Origins"
2015 SF Exploratorium Lecture: "Corn: Diversity and Origins"2015 SF Exploratorium Lecture: "Corn: Diversity and Origins"
2015 SF Exploratorium Lecture: "Corn: Diversity and Origins"jrossibarra
 
Presentation at SMBEBA
Presentation at SMBEBAPresentation at SMBEBA
Presentation at SMBEBAjrossibarra
 
Advice for getting into grad school (in the biological sciences)
Advice for getting into grad school (in the biological sciences)Advice for getting into grad school (in the biological sciences)
Advice for getting into grad school (in the biological sciences)jrossibarra
 

More from jrossibarra (10)

Adaptation in plant genomes: bigger is different
Adaptation in plant genomes: bigger is differentAdaptation in plant genomes: bigger is different
Adaptation in plant genomes: bigger is different
 
Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea mays
Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea maysParallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea mays
Parallel Altitudinal Clines Reveal Adaptive Evolution Of Genome Size In Zea mays
 
Adaptive evolution of genome size across altitudinal clines in maize
Adaptive evolution of genome size across altitudinal clines in maizeAdaptive evolution of genome size across altitudinal clines in maize
Adaptive evolution of genome size across altitudinal clines in maize
 
Gene flow and cross-incompatibility in maize and teosinte
Gene flow and cross-incompatibility in maize and teosinteGene flow and cross-incompatibility in maize and teosinte
Gene flow and cross-incompatibility in maize and teosinte
 
Demography and deleterious alleles in maize
Demography and deleterious alleles in maizeDemography and deleterious alleles in maize
Demography and deleterious alleles in maize
 
2015 SF Exploratorium Lecture: "Corn: Diversity and Origins"
2015 SF Exploratorium Lecture: "Corn: Diversity and Origins"2015 SF Exploratorium Lecture: "Corn: Diversity and Origins"
2015 SF Exploratorium Lecture: "Corn: Diversity and Origins"
 
Presentation at SMBEBA
Presentation at SMBEBAPresentation at SMBEBA
Presentation at SMBEBA
 
FIsherOrr_maize
FIsherOrr_maizeFIsherOrr_maize
FIsherOrr_maize
 
Advice for getting into grad school (in the biological sciences)
Advice for getting into grad school (in the biological sciences)Advice for getting into grad school (in the biological sciences)
Advice for getting into grad school (in the biological sciences)
 
Drift
DriftDrift
Drift
 

Recently uploaded

Major project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategiesMajor project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategiesAmanpreetKaur157993
 
8 Tips for Effective Working Capital Management
8 Tips for Effective Working Capital Management8 Tips for Effective Working Capital Management
8 Tips for Effective Working Capital ManagementMBA Assignment Experts
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽中 央社
 
Andreas Schleicher presents at the launch of What does child empowerment mean...
Andreas Schleicher presents at the launch of What does child empowerment mean...Andreas Schleicher presents at the launch of What does child empowerment mean...
Andreas Schleicher presents at the launch of What does child empowerment mean...EduSkills OECD
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxheathfieldcps1
 
Improved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio AppImproved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio AppCeline George
 
How To Create Editable Tree View in Odoo 17
How To Create Editable Tree View in Odoo 17How To Create Editable Tree View in Odoo 17
How To Create Editable Tree View in Odoo 17Celine George
 
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUMDEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUMELOISARIVERA8
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project researchCaitlinCummins3
 
SPLICE Working Group: Reusable Code Examples
SPLICE Working Group:Reusable Code ExamplesSPLICE Working Group:Reusable Code Examples
SPLICE Working Group: Reusable Code ExamplesPeter Brusilovsky
 
How to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryHow to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryCeline George
 
diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....Ritu480198
 
ANTI PARKISON DRUGS.pptx
ANTI         PARKISON          DRUGS.pptxANTI         PARKISON          DRUGS.pptx
ANTI PARKISON DRUGS.pptxPoojaSen20
 
When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...Gary Wood
 
An Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppAn Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppCeline George
 
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...Nguyen Thanh Tu Collection
 

Recently uploaded (20)

Major project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategiesMajor project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategies
 
8 Tips for Effective Working Capital Management
8 Tips for Effective Working Capital Management8 Tips for Effective Working Capital Management
8 Tips for Effective Working Capital Management
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
 
Andreas Schleicher presents at the launch of What does child empowerment mean...
Andreas Schleicher presents at the launch of What does child empowerment mean...Andreas Schleicher presents at the launch of What does child empowerment mean...
Andreas Schleicher presents at the launch of What does child empowerment mean...
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptx
 
Improved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio AppImproved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio App
 
How To Create Editable Tree View in Odoo 17
How To Create Editable Tree View in Odoo 17How To Create Editable Tree View in Odoo 17
How To Create Editable Tree View in Odoo 17
 
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUMDEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project research
 
SPLICE Working Group: Reusable Code Examples
SPLICE Working Group:Reusable Code ExamplesSPLICE Working Group:Reusable Code Examples
SPLICE Working Group: Reusable Code Examples
 
How to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryHow to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 Inventory
 
IPL Online Quiz by Pragya; Question Set.
IPL Online Quiz by Pragya; Question Set.IPL Online Quiz by Pragya; Question Set.
IPL Online Quiz by Pragya; Question Set.
 
Mattingly "AI and Prompt Design: LLMs with NER"
Mattingly "AI and Prompt Design: LLMs with NER"Mattingly "AI and Prompt Design: LLMs with NER"
Mattingly "AI and Prompt Design: LLMs with NER"
 
Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
 
diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....
 
ANTI PARKISON DRUGS.pptx
ANTI         PARKISON          DRUGS.pptxANTI         PARKISON          DRUGS.pptx
ANTI PARKISON DRUGS.pptx
 
Including Mental Health Support in Project Delivery, 14 May.pdf
Including Mental Health Support in Project Delivery, 14 May.pdfIncluding Mental Health Support in Project Delivery, 14 May.pdf
Including Mental Health Support in Project Delivery, 14 May.pdf
 
When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...
 
An Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppAn Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge App
 
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
 

Historical Genomics of US Maize: Domestication and Modern Breeding

  • 1. Genome-Wide View of Breeding History and Selection in North American Maize Dr. Jeffrey Ross-Ibarra University of California at Davis Patterns of diversity across the genome are reflective of the historical processes of selection, drift, and mutation that have acted on a species during its evolution. Here, we apply population genetic analysis of two genomic datasets to assess the spatial and temporal signatures of evolution in the maize genome. We first describe genome-wide patterns of evolution during two epochs of selection, domestication and modern breeding, using full-genome resequencing of a number of maize and teosinte lines. Then, using a chronologically-stratified panel of corn belt lines, we dissect temporal patterns of ancestry and selection in greater detail across the 80+ years of North American corn breeding. We find distinct impacts of selection during domestication and modern breeding on diversity and gene expression, identify candidate regions targeted by selection, and describe changes in genetic structure and ancestry during the evolution of modern corn belt lines.
  • 2. HISTORICAL GENOMICS OF MAIZE Jeffrey Ross-Ibarra www.rilab.org
  • 3. Acknowledgements Ed Buckler (Cornell, USDA) Jeff Glaubitz (Cornell) Major Goodman (NC State) Mike McMullen (Missouri, USDA) Chi Song (BGI) Nathan Springer (Minnesota) Doreen Ware (CSHL, USDA) Xun Xu (BGI) Matthew Hufford Joost van Heerwaarden Tanja Pyhäjärvi Jer-Ming Chia
  • 4. HISTORICAL GENOMICS OF MAIZE 3 % 3 3 % 02 % 22222222222222222222222222222222222222222222222222222222222222200000000000000000000000000000000222222000022022222022200000000000000000000000000000000000000000000000000000000000000000022222200000000000000000000000020000000 % 2+ Spatial resolution -- genome resequencing Temporal resolution -- SNP genotyping
  • 5. HISTORICAL GENOMICS OF MAIZE 3 % 3 3 % % 2222222222222222222222222222222222222222222222222000000000000000000022022020020222200200000000000000000000000000000000000000222220000000000000000020000000 % 2+ Spatial resolution -- genome resequencing Temporal resolution -- SNP genotyping
  • 6. ρ 0.0020.0060.01 00.010.020.030.04 ρρ ππ lower 1% cent10 cM/Mb ρ Chr 10 Maize Hapmap I: low-copy fraction of genome Gore et al. 2009 Science • Re-sequenced low-copy portion of genome of 27 inbreds • High diversity genome, but large regions of low recombination • Identified low diversity putative targets of selection • No comparison to older material • Cannot distinguish domestication from modern improvement
  • 7. Chia et al. Submitted Maize Hapmap II: full genome resequencing 75 resequenced genomes 21M SNPs75 resequenced genomes 21M SNPs Sequenced Genomes Sequence Depth Locality Improved 35 5.04 X 13 Temperate, 13 Tropical, 3 Mixed, 6 Chinese Landraces 23 5.34 X 9 South American, 14 North American parviglumis 14 3.81 X 4 Jalisco/Nayarit, 9 Balsas, 1 Oaxaca 1 1Oaxaca mexicana 2 6.83 X 1 Jalisco, 1 Morelos Tripsacum 1 12.62 X Colombia parviglumis Landraces Improved Lines Domestication Improvement Hufford et al. Submitted
  • 8. Maize Parviglumis Maize Parviglumis Genic Tajima's D Tajima's D Density -4 -2 0 2 4 0.00.10.20.30.40.5 Hufford et al. Submitted
  • 9. Maize Parviglumis Maize Parviglumis Genic Tajima's D Tajima's D Density -4 -2 0 2 4 0.00.10.20.30.40.5 Density 0.00.10.20.30.40.5 Genome-wide Tajima's D Tajima's D Density -4 -2 0 2 4 0.00.10.20.30.4 Hufford et al. Submitted
  • 10. Chen et al. 2010 Genome Research COG3-like FMP25-like frq PAC10-like SEC14-like NSL1-like NCU02261 B Carib LA Out Ellison et al. 2011 PNAS • Cross-Population Composite Likelihood Ratio • Identifies extended regions of differentiation • Compares to simulated model • Define selected regions, estimate s
  • 11. Artificial selection in the maize genome • Stronger selection during domestication (s~0.015 vs. s~0.003) • Selection strength consistent with archaeology natural selection • Continued selection on domestication genes (18% overlap) • ~3,000 genes in selected regions. Identified ~1100 candidates • 5-10% of selected regions had no genes Hufford et al. Submitted he maize genome log s Frequency -6 -5 -4 -3 -2 050001500025000 domestication improvement
  • 12. Gore et al. Science 2009 Mb chr 8 proportion FST Mb chr 8 proportion 0.00.20.40.60.81.0 fixed shared unique temperate unique tropicalemfixed shared unique temperate unique tropical 0 20 40 60 80 100 120 140 160 180 00.050.150.250.35 Population-specific selection
  • 13. Population-specific selection Hufford et al. Submitted • Many population-specific loci • Stronger overall estimate of selection • Still weaker than domestication • Lower power to rule out drift
  • 14. Candidate genes for maize improvement Hufford et al. Submitted
  • 15. Selection on the transcriptome • Expression at 18K genes of 27 maize and teosinte using Nimblegen array • Domestication directly selected on candidate gene expression • Breeding has worked with highly expressed genes • Modern breeding selected for heterosis in expression Candidates: DOM IMP Change in expression * -- Decreased variance * * Tissue specificity# -- overall higher expression Dominance in crosses# -- inter intra # Data from Stupar et al. 2008 BMC Plant Bio; Sekhon et al. 2011 Plant J. Hufford et al. Submitted
  • 16. Regions with no genes likely regulatory teosintemaize Swanson-Wagner et al. Submitted Expression
  • 17. Genetic load in modern maize • ~1% of SNPs disrupts predicted protein • 10,117 premature stop-codons • 2557 (or 12%) high-confidence genes has a stop-codon in ≥1line • 870 genes have a segregating stop- codon in ≥ 2 of improved lines Chia et al. Submitted
  • 18.
  • 19.
  • 20. Artificial selection across the maize genome
  • 21. HISTORICAL GENOMICS OF MAIZE 3 % 3 3 % 02 % 22222222222222222222222222222222222222222222222222222222222222200000000000000000000000000000000222222000022022222022200000000000000000000000000000000000000000000000000000000000000000022222200000000000000000000000020000000 % 2+ Spatial resolution -- genome resequencing Temporal resolution -- SNP genotyping
  • 22. Detailed historical look at selection during modern breeding • Larger sample size • Increased chronological resolution • Take into account heterotic group structure • Focus on corn belt varieties Resolve caveats of resequencing approach
  • 23. Genotyping and Sampling • 400 N.American corn belt lines • Genotype on public Illumina 55K • Tracked population structure, ancestry, and selection over time 99 94 70 137 land races 1950 inbreds 1960-1970 inbreds ex-PVP 0 1 2 3 van Heerwaarden et al. Submitted (Oh43,W22, B14) (B73, 207, Mo17)
  • 24. Population structure through time van Heerwaarden et al. Submitted
  • 25. Population structure through time van Heerwaarden et al. Submitted C
  • 26. Population structure through time van Heerwaarden et al. Submitted C C
  • 27. Population structure through time van Heerwaarden et al. Submitted C C
  • 28. Population structure through time van Heerwaarden et al. Submitted C C
  • 29. Population structure through time van Heerwaarden et al. Submitted Iodent NSS SS
  • 30. Number and diversity of ancestors decreases through time ancestry van Heerwaarden et al. Submitted c. Iodent NSS SS effective#ancestorsdiversityofancestors
  • 32. Ancestral contributions of individual lines IDT 1 2 3 0.0 0.1 0.2 0.3 0.4 undefined OH43 WF9 A375 A509 A556 I198 B2 H5 HY B164 G ND203 B10 B14 L317 I205 CI187-2 C49A I224 AH83 Tr9-1-1-6 C11 IDT A638 M14 207 PHN34 PHP76 PHW86 PHG50 PHG35 PHG71 IB014 PHG83 LH150 PHG29 PHG72 PHG84 PHZ51 PHV78 PHK42 PHN11 PHH93 PHJ33 PHN73 PHR62
  • 33. Weak landrace structure still evident in modern inbreds ancestryproportion Iodent NSS SS
  • 34. Selection and frequency over time Duvick et al. 2004 Plant Breeding Reviews advantageous alleles show continual increase in frequency over time
  • 35. •Identify genetic structure across all time periods (PCA,Tracy-Widom) •Treat time period as a phenotype for association mapping •For each SNP, compare two models: •M0: Frequency determined by drift and population structure (covariance matrix of all SNPs) •M1: Frequency determined by drift, population structure AND environment (time) •Bayes Factor for each SNP Time as a phenotype to identify selection 0 1 2 3 Coop et al. 2010 Genetics
  • 36. Temporal association identifies candidates of selection • 236 Candidate regions (1021 genes) with consistent BF across runs • Include stress response, lignin biosynthesis, auxin response • Two of the top candidates ZmErecta (US7847158) and ZmArgos (US7834240) patented for plant growth, yield ssociation identifies candidates te regions (1021 genes) with consistent BF 0 1 2 3 0.00.20.40.60.81.0 era p
  • 37. Temporal association identifies candidates of selection • 236 Candidate regions (1021 genes) with consistent BF across runs • Include stress response, lignin biosynthesis, auxin response • Two of the top candidates ZmErecta (US7847158) and ZmArgos (US7834240) patented for plant growth, yield
  • 39. No abnormal haplotypes, ancestry at selected sites 0 1 2 3 01234 p p p p p p p p era ratioobserved/randomized
  • 40. No abnormal haplotypes, ancestry at selected sites
  • 41. Genome-wide ancestry not determined by selection
  • 42. Weak correlation between good alleles and contribution to modern lines 1950’s inbreds 1980’s inbreds ex-PVP lines selected regions %ancestry
  • 43. Weak correlation between good alleles and contribution to modern lines
  • 44. Structure and selection in corn belt maize
  • 45. Some final thoughts on adaptation...
  • 51. Brown et al. 2011 PLoS Genetics • Effect sizes of ear vs. other consistent with Fisher-Orr model • Larger effects → larger fitness difference → stronger selection