Project Number: 2090-21000-036-22-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2019
End Date: Apr 30, 2021
Developing high density markers and Implementing association mapping using phenotypic data and a GBS marker platform to identify biomass, forage quality, and fall dormancy genes/QTL at high mapping resolution in drought-stressed alfalfa.
1) Researchers are currently developing a genetically broad-based alfalfa training population consisting of 200 individuals. The parents of the training population were derived from four NMSU and industry-derived elite germplasms and cultivars. All plant materials have experienced one or more cycles of phenotypic and/or DNA marker assisted selection for improved forage productivity in deficit irrigated environments. Seed produced from each of the 200 plants of the training population will be used to plant replicated field plots of maternally derived half-sib families at Las Cruces, NM and Prosser, WA. Field plots at both sites will be irrigated at 50% the normal rate during three years, and shoot biomass production will be measured over multiple forage regrowth cycles in each year. Researchers will also collect forage samples Forage quality determination. High density markers will be developed using genotyping by sequencing (GBS). Genotypic data generated by GBS for the 200 elite C0 plants (Objective 2), and forage yield, quality, and fall dormancy phenotypic data collected from their 200 respective half-sib families (Objective 1) grown under drought-stressed field conditions in WA (biomass only) and NM (biomass, quality, and fall dormancy), will be used for genome-wide association analysis. A HapMap will be generated from genotyping calls after filtration. Genotypic variants will be digitalized and used for trait-marker association based upon linkage disequilibrium analysis. 2) Data analysis and expected outcomes: The analysis will use SAS PROC PRINCOMP (SAS Institute Inc. 2011, SAS OnlineDoc 9.3, Cary, NC). A Kinship matrix will be obtained and used to correct for any structure that may be present in the population. Marker-trait association will be analyzed using TASSEL. Linkage disequilibrium (LD) between markers will be assessed by calculation of r2 between markers, LD statistics will be calculated and LD decay will be evaluated by an exponential probability density function using PROC NLIN in SAS software (Yu et al. 2011). A mixed linear model will be used in association mapping and markers significantly associated with forage yield, quality, and dormancy related traits during drought will be identified. Comparative genomics with the annotated M. truncatula genome assembly will also be used to compare our results with those of previous QTL mapping projects conducted in other alfalfa populations. We will also search the M. truncatula genome assembly with sequencing tags that flank significant QTL to identify candidate genes underlying the forage yield, quality, and fall dormancy traits. The technology roadmap of the proposed research will use multiple genomics tools including genome-wide association mapping, second generation sequencing and comparative genomics to identify genes/QTLs associated with drought resistance, forage quality, and fall dormancy. We will also identify high throughput diagnostic markers tightly linked to the loci influencing these traits, and use them in future MAS efforts to develop alfalfa varieties with improved drought resilience, nutritive value, and appropriate fall dormancy.