Transcript Yield in Alfalfa (Medicago sativa L.)

Mapping Autotetraploid Alfalfa
Joseph G. Robins and
E. Charles Brummer
Objective
Determine the genetic basis of forage
yield in alfalfa.
1) Develop a genetic linkage map of tetraploid
alfalfa.
2) Map quantitative trait loci (QTL) associated
with forage yield.
3) Implement a marker-assisted selection
(MAS) program for alfalfa improvement.
Robins and Brummer. CAIC. 2003.
Problem
Lack of gain in alfalfa forage yield since
the early 1980s.
Yield (Mg/ha)
12
10
Upper
Midwest
8
Iowa
Courtesy:
Riday and
Brummer,
2002.
6
USA
4
2
1915
1930
1945
1960
1975
1990
2005
Year
Robins and Brummer. CAIC. 2003.
Autopolyploid Genetics
Forage yield gain is complicated by the
complexities of alfalfa genetics.
1) Complementary gene action (Bingham et al.
1994).
2) Irregular meiosis, when compared to diploids,
with non-conventional segregation patterns.
a) Potential multivalent pairing.
b) Potential double reduction.
Robins and Brummer. CAIC. 2003.
Our Approach
A potential solution is to identify genomic
regions associated with forage yield.
1) Create genetic map of a segregating
population using molecular markers.
2) Combine marker and phenotype data to
identify associations between markers and
phenotype (QTL)
3) Utilize QTL in a marker-assisted breeding
program to increase forage yield.
Robins and Brummer. CAIC. 2003.
Experiment
Created F1 mapping population
by crossing
WISFAL-6 (M. sativa subsp. falcata) x
ABI-408 (M. sativa subsp. sativa).
1) Placed at Ames, IA, Nashua, IA & Ithaca,
NY for forage yield analysis from 1999 2001.
2) Measurements were also taken for a variety
of other traits.
3) Lsmeans across years and locations.
Robins and Brummer. CAIC. 2003.
Forage Yield Results
Population exhibits large amount of
genetic variation for forage yield.
1) Broad-sense heritability = 0.57 ± 0.06.
a) H2 = σ2G / σ2P.
Where σ2G = σ2A + σ2D + σ2F + σ2T + σ2I.
a) Based on entry means across years and
locations.
2) Identified high and low transgressive
segregants.
Robins and Brummer. CAIC. 2003.
Genetic Mapping
Developed a genetic map of the population
using RFLPs, AFLPs, and SSRs.
1) Autopolyploid
genetics complicate
mapping.
2) Used RFLPs, AFLPs,
and SSRs.
a) Single and double
dose alleles.
3) Developed maps of
both parents.
Robins and Brummer. CAIC. 2003.
Mapping Summary
Both parental maps are preliminary and
currently composed of fourteen
consensus linkage groups.
1) ABI-408: 120 RFLPs, 201 AFLPs, 7 SSRs
a) 179 single-dose, 32 double-dose, 120
distorted.
2) WISFAL-6: 106 RFLPs, 139 AFLPs, 4 SSRs
a) 115 single-dose, 50 double-dose, 84
distorted.
Robins and Brummer. CAIC. 2003.
QTL Analysis
Utilized single-marker analysis (ANOVA)
to identify molecular markers
significantly associated with forage
yield.
1) ABI-408: Identification of three potential
forage yield QTL.
2) WISFAL-6: Identification of two potential
forage yield QTL.
Robins and Brummer. CAIC. 2003.
Possible QTL
Associations based on average forage yield
(g plant-1) across locations and years.
Parent
Marker
Yield (marker present/absent) P-value
ABI-408
UGA189a
175 / 189
0.004
Vg2D11a
174 / 187
0.007
AGC/CAC216
177 / 195
0.0007
Vg2D11
186 / 169
0.005
UGA83
185 / 168
0.007
WISFAL-6
Robins and Brummer. CAIC. 2003.
ABI-408 QTL Mapping
Markers (highlighted in red) associated with
forage yield in the sativa parent.
0.0
AGC/CAA141
15.3
18.8
AGC/CAG276
AGC/CAG141
35.2
AGC/CAA288
46.5
AGC/CAA201
63.0
64.0
AGC/CAA230
ACG/CAT433
75.9
81.8
86.5
AGC/CAC296
ACG/CAT155
AGC/CAC216
97.5
102.6
102.8
111.3
ACG/CAT467
ACG/CTA156
AGC/CAC201
AGC/CAC230
121.6
127.8
135.2
AGC/CAC251
AGC/CAC366
ACG/CAT264
147.4
AGC/CAC177
173.6
AGC/CAC148
0.0
AGC/CTC120
33.9
AGC/CTT162
49.5
ACG/CAA380
65.3
69.8
76.9
83.5
85.2
UGA522b
ACG/CAC227
AGC/CAA253
ACG/CTG325
MS14
100.9
105.4
107.1
111.6
121.6
124.5
129.1
133.5
135.7
UGA564
UGA1208
Vg2D11a
UGA328
AGC/CAG241
UGA5
ACG/CAT283
AGC/CAG239
AGC/CAG304
156.0
ACG/CAC130
0.0
UGA189a
22.6
UGA246
53.8
58.3
UGA543
ACG-CTC177
89.5
UGA286
107.9
ACG/CTG283
126.6
UGA189b
Only three
of fourteen
consensus
linkage
groups
shown.
Robins and Brummer. CAIC. 2003.
WISFAL-6 QTL Mapping
Markers (highlighted in red) associated with
forage yield in the falcata parent.
0.0
32.7
UGA85b
0.0
UGA380
15.4
Vg2D11
40.2
ACG/CTG211
63.7
65.7
73.4
afctt1
ACG/CAC324
UGA744
84.9
afct45
UGA219
54.2
60.1
62.7
74.6
82.1
88.4
92.3
99.2
106.5
110.1
110.3
ACG/CTA142
ACG/CTG277
AGC/CTT167
UGA28
UGA449
UGA792
UGA189a
UGA671
UGA83
RC2B-63BV8
ARC3D6
127.6
afct32
136.1
AGC/CTT175
161.2
ACG/CTG122
180.7
AGC/CTT276
103.9
109.5
116.1
MSAICB
RC-1-51dT23V20
UGA540
Only two of
fourteen
consensus
linkage
groups
shown.
Robins and Brummer. CAIC. 2003.
QTL x Environment
Our next step will be to analyze QTL as
they change over the different
locations and years.
1)
2)
3)
The extent of our phenotypic data will allow us to
identify QTL that are specific to individual locations,
years, or location/year combinations.
This should allow us to identify QTL that are
important in the developmental process of alfalfa
(as the plant ages, it is possible that QTL may
change) and QTL that are or are not influenced by
environmental factors.
We hope to have results from these analyses
shortly.
Robins and Brummer. CAIC. 2003.
Summary
We have:
1) Developed preliminary linkage maps of
ABI-408 and WISFAL-6.
a) We are continuing to add SSRs.
2) Used single-marker analysis to identify
potential QTL associated with forage yield in
both parents.
a) Associations will be further verified with
permutation testing.
3) We then hope to incorporate the results for
alfalfa forage yield improvement.
Robins and Brummer. CAIC. 2003.
Acknowledgements
Dr. Charlie Brummer
Iowa State University
Dr. Diane Luth
Plant Science Institute
Dr. Heathcliffe Riday
Meenakshi Santra
Baldomero Alarcón-Zúñiga USDA-NRI Competitive
Grants Program
ISU-Forage Breeding Group
Robins and Brummer. CAIC. 2003.