1. Mapping Autotetraploid Alfalfa Joseph G. Robins and E. Charles Brummer

2. 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.

3. Problem Lack of gain in alfalfa forage yield since the early 1980s. Robins and Brummer. CAIC. 2003. Courtesy: Riday and Brummer, 2002.

4. 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.

5. 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.

6. Experiment Created F 1 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.

7. Forage Yield Results Population exhibits large amount of genetic variation for forage yield. 1)Broad-sense heritability = 0.57 ± 0.06. a)H 2 = σ 2 G / σ 2 P. Where σ 2 G = σ 2 A + σ 2 D + σ 2 F + σ 2 T + σ 2 I. a)Based on entry means across years and locations. 2)Identified high and low transgressive segregants. Robins and Brummer. CAIC. 2003.

8. 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.

9. 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.

10. 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.

11. Possible QTL Robins and Brummer. CAIC. 2003. Associations based on average forage yield (g plant -1 ) across locations and years. ParentMarkerYield (marker present/absent)P-value ABI-408UGA189a175 / 1890.004 Vg2D11a174 / 1870.007 AGC/CAC216177 / 1950.0007 WISFAL-6Vg2D11186 / 1690.005 UGA83185 / 1680.007

12. ABI-408 QTL Mapping Markers (highlighted in red) associated with forage yield in the sativa parent. Robins and Brummer. CAIC. 2003. Only three of fourteen consensus linkage groups shown.

13. UGA85b 0.0 UGA219 32.7 ACG/CTA142 54.2 ACG/CTG27760.1 AGC/CTT167 62.7 UGA28 74.6 UGA449 82.1 UGA792 88.4 UGA189a92.3 UGA671 99.2 UGA83106.5 RC2B-63BV8 110.1 ARC3D6110.3 afct32 127.6 AGC/CTT175 136.1 ACG/CTG122161.2 AGC/CTT276 180.7 65.7 109.5 UGA380 0.0 Vg2D11 15.4 ACG/CTG21140.2 afctt1 63.7 ACG/CAC324 UGA744 73.4 afct45 84.9 MSAICB 103.9 RC-1-51dT23V20 UGA540 116.1 WISFAL-6 QTL Mapping Markers (highlighted in red) associated with forage yield in the falcata parent. Robins and Brummer. CAIC. 2003. Only two of fourteen consensus linkage groups shown.

14. QTL x Environment Our 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.

15. 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.

16. Dr. Charlie Brummer Dr. Diane Luth Dr. Heathcliffe Riday Meenakshi Santra Baldomero Alarcón-Zúñiga ISU-Forage Breeding Group Acknowledgements Iowa State University Plant Science Institute USDA-NRI Competitive Grants Program Robins and Brummer. CAIC. 2003.