Mapping Autotetraploid AlfalfaJoseph 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.
Robins and Brummer. CAIC. 2003.
2
4
6
8
10
12
1915 1930 1945 1960 1975 1990 2005Year
Yiel
d (M
g/ha
) UpperMidwestIowa
USA
Courtesy: Riday and Brummer, 2002.
Autopolyploid GeneticsForage 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 ApproachA 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.
ExperimentCreated 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 = σ2
G / σ2P.
Where σ2G = σ2
A + σ2D + σ2
F + σ2T + σ2
I.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.
Robins and Brummer. CAIC. 2003.
1) Autopolyploid genetics complicate mapping.
2) Used RFLPs, AFLPs, and SSRs.a) Single and double
dose alleles.3) Developed maps of
both parents.
Mapping Summary
Both parental maps are preliminary and currently composed of fourteen
consensus linkage groups.
1) ABI-408: 120 RFLPs, 201 AFLPs, 7 SSRsa) 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.
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.
QTL Analysis
Robins and Brummer. CAIC. 2003.
Possible QTL
Robins and Brummer. CAIC. 2003.
Associations based on average forage yield (g plant-1) across locations and years.
Parent Marker Yield (marker present/absent) P-valueABI-408 UGA189a 175 / 189 0.004
Vg2D11a 174 / 187 0.007
AGC/CAC216 177 / 195 0.0007
WISFAL-6 Vg2D11 186 / 169 0.005
UGA83 185 / 168 0.007
AGC/CAA1410.0
AGC/CAG27615.3AGC/CAG14118.8
AGC/CAA28835.2
AGC/CAA20146.5
AGC/CAA23063.0ACG/CAT43364.0
AGC/CAC29675.9ACG/CAT15581.8AGC/CAC21686.5ACG/CAT46797.5ACG/CTA156102.6AGC/CAC201102.8AGC/CAC230111.3AGC/CAC251121.6AGC/CAC366127.8ACG/CAT264135.2
AGC/CAC177147.4
AGC/CAC148173.6
AGC/CTC1200.0
AGC/CTT16233.9
ACG/CAA38049.5
UGA522b65.3ACG/CAC22769.8AGC/CAA25376.9ACG/CTG32583.5MS1485.2UGA564100.9UGA1208105.4Vg2D11a107.1UGA328111.6AGC/CAG241121.6UGA5124.5ACG/CAT283129.1AGC/CAG239133.5AGC/CAG304135.7
ACG/CAC130156.0
UGA189a0.0
UGA24622.6
UGA54353.8ACG-CTC17758.3
UGA28689.5
ACG/CTG283107.9
UGA189b126.6
ABI-408 QTL MappingMarkers (highlighted in red) associated with
forage yield in the sativa parent.
Robins and Brummer. CAIC. 2003.
Only three of fourteen consensus linkage groups shown.
UGA85b0.0
UGA21932.7
ACG/CTA14254.2ACG/CTG27760.1AGC/CTT16762.7UGA2874.6UGA44982.1UGA79288.4UGA189a92.3UGA67199.2UGA83106.5RC2B-63BV8110.1ARC3D6110.3
afct32127.6AGC/CTT175136.1
ACG/CTG122161.2
AGC/CTT276180.7
65.7
109.5
UGA3800.0
Vg2D1115.4
ACG/CTG21140.2
afctt163.7ACG/CAC324UGA74473.4
afct4584.9
MSAICB103.9RC-1-51dT23V20UGA540116.1
WISFAL-6 QTL MappingMarkers (highlighted in red) associated with
forage yield in the falcata parent.
Robins and Brummer. CAIC. 2003.
Only two of fourteen consensus linkage groups shown.
QTL x EnvironmentOur next step will be to analyze QTL as they change over the different locations and years.
1) The extent of our phenotypic data will allow us to identify QTL that are specific to individual locations, years, or location/year combinations.
2) 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.
3) 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.
Dr. Charlie BrummerDr. Diane LuthDr. Heathcliffe RidayMeenakshi SantraBaldomero Alarcón-ZúñigaISU-Forage Breeding Group
Acknowledgements
Iowa State UniversityPlant Science Institute
USDA-NRI Competitive Grants Program
Robins and Brummer. CAIC. 2003.