Download Genetics/Genomics Research

Combining
Genomics and
Natural Diversity to
Design Genomes
Edward Buckler
USDA-ARS
Cornell University
http://www.maizegenetics.net
Lessons from maize
genome
•
•
•
•
What is junk?
What is useful?
What do we want to get rid of?
How do we use this knowledge?
What happens
when you
change a base?
Adaptive
or GxE
GS &
GWAS
Models
Well
Deleterious
Neutral
Approximate Distribution
of Mutational Effects
Only 40% of the maize genome is
shared between two varieties
Plant 1
Person 1
99%
40%
Plant 2
Maize
Plant 3
Person 2
Person 3
Humans
Fu & Dooner 2002, Morgante et al. 2005, Brunner et al 2005
Numerous PAVs and CNVs - Springer, Lai, Schnable in 2010
WHERE ARE THE NEUTRAL
CHANGES? (I.E. THE JUNK)
Most DNA is
tightly bound
around proteins
• DNA of one cell would be a
3 feet long if not wound up
• Can empirically measure
how tightly bound every
base pair in the genome is.
• 99% is tightly bound – 1%
is making the difference
Figure from Vera et al. 2014
5% of the genome may explain nearly all
genetic variance for 42 traits.
We are testing if this is true?
Intergenic
Coding
Sequence
3’UTR
5’UTR
MNase
Hypersensitive
The current model of DNA
MNase
1-2%
Ignore this part
Coding + UTRs
2%
HOW DO WE FIND THE ADAPTIVE
CHANGES?
Map on 2000-10,000 genotypes
• Genetic mapping identifies causative
nucleotides
– Bi-parental crosses – NAM – 5000 lines
• Mining variation from other Vitis
– Breeding panels - 2000+ lines
– Landrace panels – crossed to hybrids and
evaluated – 4500 lines
SeeD experimental design
Top cross
provides
reasonable
CIMMYT maize
collection
agronomics and
~30,000
accessions
Restricting to
adaptation30
SeeD
two~5,000
gametes
25
accessions
per accessions
20
makes the
15
115
survey tenable
Alberto Romero,
GBS & GWAS
analysis
o
o
o
o
o
Hybrid
Landrace:
2 gametes per
accession
Leaf sample
110o 105o 100o 95o 90o 85o
Phenotypic evaluation
according to accession’s
adaptation zone
GBS
>20 Different Traits
being Scored –
development, yield,
disease resistance,
drought tolerance,
nitrogen use efficiency
Data
analysis
SEED (Landrace) GWAS directly
hits known genes …
Vgt1
ZCN8
Although still
missing many
probably missed
as we have not
sequenced the
entire genome
Plus candidate genes that have not
been seen as QTL in maize inbred
lines
Vgt1
ZCN8
ZCN26
ZMM5
Key features of successful mapping
studies to causative base pairs
• >1000 genotypes
• Complete knowledge of DNA sequence
• Replication in across environments
• High quality measurement
– Future: Daily measurement by robotics
(may reduce some environmental
replication)
HOW DO WE DEAL WITH
DELETERIOUS MUTATIONS?
Mutation happens
• Every generation new 20-60 mutations per
genome
• 5 mutations change an amino acid sequence
– Another 5 in highly conserved non-coding regions
• Over the age of a clone
– 100 to 5000 genes have novel mutations in their
genome that interact with the 100,000s of
existing polymorphisms
72% of Amino Acid Substitutions Are
Highly Deleterious in Maize
0-1
22%
Nes
Proportion
1-10
6%
>100
64%
10-100
8%
Strength of selection (Ne*s) against new
animo acid mutations
Clonal species accumulate mutations
Clones
degrade
Sex mixes
things up
Deleterious mutations are at the
heart of inbreeding depression and
heterosis
• Heterosis
P1 P2 F1 F2 F3 F4 F5 F6 F7 F8
Maize well known to show inbreeding depression
(Jones 1924; Neale 1935)
Deleterious mutations are
enriched in low recombination
regions (they are like clones)
GERP
Recombination Rate
Eli RodgersMelnick
Chromosome 5 Position
Rodgers-Melnick 2015 PNAS
How to spot deleterious
mutations?
• Mapping is NOT efficient for these
rare allele
• Compare genomes of target species
to related species
• Create biological models on what
each base contribute to phenotype
HOW IS THE VARIATION
DEPLOYED?
Genomics Assisted Breeding
yi = µ +
Σmzijujδj + ei
(Re)train model
Phenotype
Training cycle
(slower, expensive)
Genotype
Predict via model
Make crosses
Selection cycle
(faster, less expensive)
Biology Assisted Breeding
yi = µ +
Σmzijujδj + ei
(Re)train model
Phenotype
Molecular biology
Physiology
Training cycle
(slower, expensive)
Biochemistry
Genotype
Predict via model
Evolution
Make crosses
Selection cycle
(faster, less expensive)
CRISPR/CAS9
Environment
CRISPR Future (Genome
editing)
CRISPR/CAS9 Editing
• Opportunity to maintain clone quality
profiles BUT fix the problems
– Disease resistance – mapping
– Off flavors – mapping
– Low yield – deleterious mutations
• Requires knowledge of causative
nucleotides
What does grape need to do?
• Create mapping panel of >2000 varieties
– Sequence their genomes
– Map causative adaptive nucleotides
– Measure phenotype with robotics
• Score genome for the right molecular
characteristics
• Apply to DNA therapy to help repair the
genome