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.