Mohsen Mohammadi, Assistant Professor of Wheat Breeding and Quantitative Genetics, Purdue University Genetic variation in yield and yield-related traits in an elite population of soft red winter wheat was studied using field-based low-throughput phenotyping and genotyping-by-sequencing markers. QTL conditioning grain yield, grain number per unit area, and kernel weight were identified. QTL result was mined to identify prospects of parents’ complementarity. Strategies for further improvements of grain yield of SRWW populations will be discussed.

1. 5TH PLANT GENOMICS & GENE
EDITING CONGRESS: USA
Mohsen Mohammadi
Purdue Agronomy
Nov 3, 2017
Beyond GWAS
QTL Identification and Strategies to
Increase Yield

2.  Why we do what we do is not just presenting Manhattan plot
 The goal is finding ways to increase yield
 GWAS/Manhattan plots, if successful, is just the beginning of
the journey
 Need to focus on allelic composition of germplasm
 Prospects of further improvement
Themes

3.  Describe a GWAS experiment for wheat yield
 Dissect the GWAS results for understanding promising crosses
 Discuss failures in transferability of QTL results
 The missing heritability and next-gen GWAS
 The journey from GWAS results to candidate genes
 Molecular traits
Outline

4. Germplasm: 270 elite soft red winter wheat lines (red areas). Seed obtained from Colleague Dr.
Clay Sneller at the Ohio State University.
GWAS experiment for wheat yield

5. GWAS experiment for wheat yield
Phenotyping: Field-based low-throughput phenotyping, by evaluating grain yield and its
components (grain number (GN) and kernel weight (KW). GN and KW determine ultimate sink
strength.

6. GWAS experiment for wheat yield
Genotyping:
A combination of 90K SNP array and GBS
Libraries created with Pst1-Msp1 restriction enzymes
96-plex on a single lane of Illumina Hi-Seq 2500
Wheat IWGSC_WGAv0.4 (IWGSC) genome
TASSEL 5 pipeline for SNP calling
MAF ≥ 5% and missing ≤ 50% retained.
Imputation: Linkage Disequilibrium K-number neighbor
imputation (LDKNNi) in Tassel 5.0
5483
6815
2093
4845
7007
1346
A B D
Relative contribution of
GBS and 90K
GBS 90K

7. GWAS experiment for wheat yield
Results:
Histogram of YLD
YLD
Frequency
3 4 5 6 7 8
0510152025
Histogram of GN
GN
Frequency
10000 15000 20000 25000 30000 35000 40000
0510152025
Histogram of KW
KW
Frequency
30 35 40 45
05101520
GN KW GYgrains/m2 Tons/hamg
Trait Mean Range Heritability
GN (grains/m2) 19690 9680-41220 0.49
KW (mg) 35.2 28.0-46.2 0.72
GY (Kgh-1) 5640 2970-8130 0.82

8. GWAS experiment for wheat yield
GYi = β0+β1GNi + β2 KWi + ɛi
KW GY
GN 0.186** 0.582***
KW 0.280***
Variable Coefficient P-value
Intercept β0 1.63 0.003456
GN β1 0.0001052 2.00E-16
KW β2 0.05485 0.000521
Multiple regression expressing changes in GY in
response to GN and KW
Pair-wise Pearson correlations between
traits

9. GWAS experiment for wheat yield

10. Q7DQ7D q7Dq7D
Q6AQ6A 19 (7.2) 170 (5.75)
q6Aq6A 0 (NA) 81 (5.02)
There are 170 individuals
with only one favorable alleles
Q6AQ6Aq7Dq7D. The best
performing lines in this set
can be crosses into
Q6AQ6AQ7DQ7D individuals in
order to transfer Q7D .
Histogram of g
g
Frequency
3 4 5 6 7 8
05101520253035
x Q6AQ6AQ7DQ7DQ6AQ6Aq7Dq7D
Dissect the GWAS results for promising crosses
Design promising crosses

11. Q7A.1Q7A.1q7A.2q7A.2
33.5 Mbp apart
q7A.1q7A.1Q7A.2Q7A.2x
Q7A.1Q7A.1Q7A.2Q7A.2
25K 24K
MAS
Q7DQ7D
Q7DQ7D
Dissect the GWAS results for promising crosses
Design promising crosses
Changing repulsion to coupling

12.  The ultimate goal of identifying QTL is deployment and often
transferability of QTL effect is issue.
 Despite considerable investment in QTL identification
approaches, few successes stories in QTL deployment have been
achieved.
 Several challenges limit the transferability
Confounding effects of genetic background
QTL x environment interaction
The trait plasticity and reaction norm
Discuss failures in transferability of QTL results

13.  When the favorable allele of the QTL is fixed in the elite
germplasm, the QTL cannot be used for selection.
 When the unfavorable allele is fixed, introgression could be
an option. The question to be answered is whether or not the
QTL exhibits population specific effect, indicating the role for
genetic background effect.
 Background effect manifests itself by population structure,
which is the systematic differences in allele frequencies
between sub-populations, or by epistasis interaction.
Fail in transferability: Background effects

14.  One consequence of introgressing a QTL discovered in
another population is the effect of genetic background and
mainly epistasis.
 In the presence of epistasis in a discovery population, the
effects of single-locus QTL is typically dependent on the
genotypes of other loci, which may have a positive or negative
effect on the net effect of underlying QTL.
 Therefore, the QTL itself with the interacting epistatic effect
of other loci act as a package within specific genetic
background of the discovery panel
Fail in transferability: Background effects

15.  The expectation that GWAS results are transferable from one
environment to another requires that a) the phenotype of
interest is not plastic and b) GEI is absent and plays no role in
the expression of trait of interest – simply not true because
P = G+E+GEI.
 Is it realistic (& logical) to discover QTL in one environment
and expect to deploy it in another environment?
 With analogy to traditional GEI, QTL x environment (QEI)
analysis can be conducted to characterize whether QTL is
interacting with diverse environments.
Fail in transferability: Plasticity and GEI

16.  NAM offers historic,
recent recombination, high
resolution, high power but
a severe covariation
between parents and
maturity zones.
Fail in transferability: Plasticity and GEI
 The question is, how application are these QTL outside of the
“real” environments in which the data were collected?

17.  Transferability is also under certain ecological
boundaries. Many phenotyping experiments are
conducted in a highly controlled environment,
which investigates a single plant in isolation,
ignoring what the effect of plants in community
level might be.
Fail in transferability: Plant-plant interactions

18.  Can produce millions of SNPs, but SNPs are not the only genetic
variants. Structural variants i.e., INDELs, substitutions,
inversions, copy-number variants are also a significant
amount of inter-varietal variation.
 Need tools that enable parsing out the effects of
homeologous loci in allopolyploid organisms such as wheat
or accounting for gene dosage in autotetraploid organisms
such as potato.
Next-gen GWAS: What is next?

19.  Capture the value of every phene and trait throughout
plant growth cycle. Are there traits in seedling stage
that are effective but followed by weak phenes /
traits in adult stage? Can we capture the value of all
phenes in a complementary manner?
 Molecular traits such as genes involved in nitrogen
uptake and metabolism or sink source manipulation.
 Augmenting phenotype from a single organism
perspective to a definition under mutualism or
symbiosis conditions.
Next-gen GWAS: What is next?

20. Grand challenges for plant breeders
 Genetic base is narrow. Business makes it narrower.
 It is hard to get to a fine genomic resolution as human
population in plant germplasm (specially in self pollinated
breeding germplasm).
 As such, higher number of germplasm, even with the advent of
high-throughput phenotyping, is not going to solve the low
genomic resolution problem.
 As such no matter hard we try still we speak with a lot of
ambiguity about candidate genes.

21. Grand challenges for plant breeders
 The challenge remains for trait discovery and deployment for
on-the-ground breeders with limited budgetary resources that
do not allow sufficient investment in preparation sizeable
genetic populations with adequate genomic resolution.
Thank you

22. Acknowledgment
Blake Russell
Clay Sneller
Gina Brown-Guedira
IWGSC