2017. Sarah M Potts. Identification of QTL and candidate genes for plant density tolerance in maize
1. Identification of QTL and candidate genes for plant density tolerance in maize
Sarah M. Potts, Rita H. Mumm, and Martin O. Bohn, Department of Crop Sciences; University
of Illinois at Urbana-Champaign.
Though demand for grain continues to rise, the amount of available agricultural land is
unlikely to change in the near future. To continue to increase crop yields to meet these growing
demands, it is suggested that improving yield will need to be achieved by increasing yield on a
per-unit basis. This can be achieved by raising plant density while retaining current ‘per plant’
yields. Furthermore, a portion of historical yield increases have been suggested as resulting from
increased plant density tolerance (Duvick 2005).
An initial plant density tolerance survey was conducted to ensure comprehensive
coverage of the many traits that may possibly be involved in plant density tolerance (Mansfield
and Mumm 2014). This survey identified six parent inbreds that were high yielders at high
density and 30 traits that were associated with plant density tolerance. Recombinant inbred line
(RIL) families derived from these inbreds were used to create a connected population of 320
testcross hybrids for the purposes of quantitative trait loci (QTL) mapping and candidate gene
identification. Yield trials of the hybrids were conducted at two locations in 2012 and three
locations in 2013, to evaluate the traits found to be associated with plant density tolerance in the
initial survey. The RIL parents of the 320 hybrids were genotyped using genotype-by-
sequencing (GBS) technology, and hybrid genotypes were inferred. Phenotypic and genotypic
analyses have been conducted and numerous QTL have been identified for traits correlated with
grain yield in the phenotypic analyses.
2. Identification of plant
density tolerance QTL in
maize
Sarah Potts| PhD Candidate
Martin Bohn, Rita Mumm| Advisors University of Illinois at Urbana-Champaign
6. The five categories hypothesized to
underlie plant density tolerance
• Photosynthetic capacity
• Growth response
• Plant architecture
• Source-sink relationship
• General stress tolerance
7. Initial Density Survey
12 ex-PVP parents
North Carolina Design II
6 densities (19k-54k ppa)
48 phenotypic traits measured
3 environments over 2 years
8. Initial Density Survey
48 phenotypic traits 30 traits
5 top yielding hybrids
Yields over 195 bu/acre
Mansfield and Mumm (2014, Crop Sci)
Mean hybrid grain yield across environments and densities
9. Objectives
Detect QTL for plant density tolerance
Identify candidate genes for plant density tolerance
10. Materials and Methods
Connected population
Alpha design, IBD
Field evaluation
30 phenotypic traits measured
47,000 ppa
5 environments over 2 years
Genotyping
QTL and GWAS methodology
11. Connected Population
Top yielding inbred
parents of 5 top yielding
survey hybrids
216 unique parents
90% DH
10% SSD
320 testcross hybrids
Top yielding female inbreds Top yielding male inbreds
XY XZ YZ
AB
AX AY AX AZ AY AZ
BX BY BX BZ BY BZ
AC
AX AY AX AZ AY AZ
CX CY CX CZ CY CZ
BC
BX BY BX BZ BY BZ
CX CY CX CZ CY CZ
12. Field Design and Model
Experimental design
Incomplete block design- alpha (0,1)
20 blocks of 16 genotypes (2x8 plots = 40’x40’)
Model
𝑦 = 𝑢 + 𝑒𝑖 + 𝑟𝑗(𝑖) + 𝑏 𝑘(𝑖𝑖) + 𝑔𝑙 + 𝑔𝑙 𝑥 𝑒𝑖 + 𝑔𝑙 𝑥 𝑟𝑗(𝑖)
Photos by Tim Mies
15. Materials and Methods
Genotype by sequencing data
DNA extracted from 216 unique RILs
Quality control
High concentration
Low shearing
Processed by Cornell Institute for Genomic Diversity
Run included over 32,000 genotypes
More genotypes in run = more powerful
~ 2.2 million SNPs per RIL in original data set
Hybrids inferred from inbred genotypes
16. Materials and Methods
QTL mapping – collaboration with NRGene
Average coverage of x 0.01 for each genotype
Imputed with Hidden Markov Model (HMM)
Selected only markers which differed between SSS
and NSS parents of each subpopulation
Between 7,500 and 8,168 markers per subpopulation
T-tests for marker trait associations, with false
discovery rate (FDR) test to determine reliable QTL
17. Materials and Methods
Genome Wide Association Study
Filtered for 20% missing marker data in TASSEL
2,673 SNPs
Also working on imputation
GAPIT software for GWAS
Using both K matrix (GAPIT) and Q matrix (STRUCTURE)
18. Results
Tassel attributes
Trait H2
Tassel branch number 0.95
Days to anthesis 0.95
Days to silking 0.94
Central spike length 0.93
Leaf angle 0.91
Kernel width 0.90
Ear height 0.89
Total leaf area 0.89
Kernel length 0.86
Tassel weight 0.85
Staygreen 0.85
Number of rows per ear 0.85
Kernel depth 0.84
Ear width 0.83
Plant height 0.83
Ear length 0.80
Broad sense heritability
Highest heritability
Kernel dimensions
Leaf measurements
Heights
Flowering time
20. Correlations with Grain Yield
Trait 2012 r value 2013 r value Survey r value
Leaf area to prod. 1 g grain -0.83*** -0.82*** -0.62***
Percent barren plants -0.63*** -0.30*** -0.52***
Zipper effect -0.17** NS -0.44***
Percent root lodged 0.20*** NS -0.43***
Anthesis-silking interval -0.58*** NS -0.42***
Kernel width -0.22*** -0.25*** NS
Kernel depth -0.39*** NS NS
Kernel length NS 0.33*** 0.50***
Kernels/row 0.40*** NS 0.42***
Rows/ear 0.23*** 0.27*** 0.54***
Kernels per plant 0.41*** 0.33*** 0.51***
Staygreen -0.25*** 0.22*** 0.45***
Days to canopy closure NS -0.32*** 0.54***
Upper stem diameter NS NS 0.69***
Leaf angle NS 0.30*** 0.71***
** Significant at the 0.01 probability level
***Significant at the 0.001 probability level
21. QTL Results
243
6 5 8
Single env, single pop Multi env QTL
Multi pop QTL Multi env & pop
31
21
24
33
29
13
39
16
23
14
0 10 20 30 40
1
2
3
4
5
6
7
8
9
10Chromosome
Number of QTL
Number of QTL
Preliminary QTL analysis: Collaboration with NRGene
22. QTL Results
Env Family (Subpopulation) Trait Chromosome σ2 var explained LOD
MF1500 B73PHG39 x PHG47PHG84 Rows/ear Chrom 2 0.232 1.78
MF1500 B73PHG39 x LH82PHG84 Rows/ear Chrom 2 0.361 3.21
MF400 B73PHG39 x PHG47PHG84 Rows/ear Chrom 2 0.382 3.24
MF400 B73PHG39 x LH82PHG47 Plant height Chrom 6 0.368 3.29
S600 B73PHG39 x PHG47PHG84 Plant height Chrom 6 0.424 3.84
MF400 B73PHG39 x PHG47PHG84 Plant height Chrom 6 0.428 3.76
S800 B73PHG39 x LH82PHG47 Staygreen Chrom 4 0.394 3.15
MonA6 B73PHG39 x PHG47PHG84 Staygreen Chrom 4 0.559 3.73
MF400 B73PHG39 x PHG47PHG84 ASI Chrom 9 0.488 3.05
S800 B73PHJ40 x LH82PHG47 ASI Chrom 9 0.62 3.78
S600 B73PHG39 x LH82PHG47 Ear height Chrom 3 0.409 3.43
MonA6 B73PHJ40 x PHG47PHG84 Ear height Chrom 3 0.66 3.51
MF400 B73PHJ40 x LH82PHG47 Staygreen Chrom 9 0.526 3.08
MonA6 B73PHG39 x PHG47PHG84 Staygreen Chrom 9 0.705 5.04
26. Concluding Remarks
Verified findings of Mansfield and Mumm (2014)
Similar correlations between Yield and Leaf Area to
Produce 1 g Grain, Percent Barren Plants, Anthesis
Silking Interval, Kernel Length, Kernels/Row,
Rows/Ear, Kernels/Plant, Leaf Angle
Discrepancies between this study and initial survey
Upper stem diameter NS
Percent root lodged was 0.20*** and NS correlated
with yield in 2012 and 2013, but -0.43*** in survey
Staygreen was -0.25*** in 2012, but 0.22*** in 2013
Days to canopy closure was NS and -0.32*** in 2012
and 2013, but 0.54*** in survey
27. Concluding Remarks
Most important traits for plant density tolerance:
Low leaf area to produce 1 g grain (LATP)
Long, narrow kernels
In dry years, short ASI is beneficial
In wetter years, upright leaf angle is beneficial
QTL identified in QTL analysis
Over 240 QTL identified
6 high confidence QTL selected for further study
1 NRGene QTL confirmed in GWAS
2 GWAS QTL identified for candidate gene analysis
28. Future Work
Ongoing collaboration with NRGene
Refining marker filtering and GWAS study
Examine alignment, especially near telomeres
Quantitative genetics analysis
Candidate gene analysis
Fine mapping
Candidate gene validation
29. Acknowledgements
Committee
Dr. Martin Bohn
Dr. Rita Mumm
Dr. Fred Below
Dr. Pat Brown
Staff
Nicole Yana
Graduate students
Undergraduate workers
UIUC farm crew
Others
NRGene
Cornell IGD
Below lab
Bradley lab
Brian Mansfield
Funding
This project is funded by the USDA National
Institute of Food and Agriculture. The
authors would also like to gratefully
acknowledge the Illinois Plant Breeding
Center, and student funding from the
Illinois Corn Marketing Board Fellowship in
Plant Breeding, the Pioneer Hi-Bred Plant
Breeding Fellowship, and the Illinois
Chapter of ARCS® (Achievement Rewards
for College Scientists) Foundation, Inc.