This F₂ population segregates for plant height, shoot biomass, root shape, and root color. This population was derived from a cross between L8708, an orange inbred line with a medium-long storage root and compact shoots, and Z020, a yellow, cultivated landrace from Uzbekistan with a short, blunt-tipped storage root with broad, prostrate leaves. A single F₁ plant was selected from this cross and selfed to produce the F₂ population.
- Turner, S.D. Genetic influences on shoot architecture in carrot (Daucus carota L.). Ph.D. Thesis. 2017. University of Wisconsin-Madison
- Turner SD, Ellison SL, Senalik DA, Simon PW, Spalding EP, Miller ND. An Automated Image Analysis Pipeline Enables Genetic Studies of Shoot and Root Morphology in Carrot (Daucus carota L.).. Frontiers in plant science. 2018; 9:1703.
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The accession, Daucus carota Uzbekistan, is a maternal parent of population, L8708×Z020. |
The breeding_research_material, L8708B, is a paternal parent of population, L8708×Z020. |
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Prior field screening led to the identification of an F₂ population, L8708 x Z020, which segregates for plant height, shoot biomass, root shape, and root color. This population was derived from a cross between L8708, an orange inbred line with a medium-long storage root and compact shoots, and Z020, a yellow, cultivated landrace from Uzbekistan with a short, blunt-tipped storage root with broad, prostrate leaves. A single F₁ plant was selected from this cross and selfed to produce the F₂ population used in this study. Linkage maps were constructed using the JoinMap 4.1 software (Van Ooijen 2011). Markers and genotypes with more than 20% missing data and which deviated from expected segregation ratios based on a Chi-square test (P < 0.001) were excluded. All linkage groups were obtained at a LOD threshold greater than 10. The regression mapping algorithm was used with Haldane’s mapping function to calculate the distance between markers (Haldane 1919). Haldane’s function, which assumes no crossover interference, was chosen over Kosambi’s mapping function based on previous observations in carrot that Haldane’s function provides a more accurate marker placement relative to the physical map (Parsons et al. 2015). Marker names corresponded to physical locations in the genome, allowing assessment of accurate marker placement and identification of inconsistencies. After initial mapping, markers defined as having insufficient linkage were flipped to the opposite phase and remapped. Maps from round 2 were used for QTL analysis. QTL analysis was conducted in R 3.3.2 (R Core Team 2016) using the R/qtl package (Broman and Sen 2009). Genotype probabilities were calculated using a step value of one for the entire linkage map and an assumed genotyping error rate of 0.001. Missing genotype data was replaced with the most probable values using the Viterbi algorithm (method = ‘argmax’) in the ‘fill.geno’ function. Multiple QTL mapping (MQM) (Jansen and Stam 1994) was performed in R/qtl using the ‘mqmscan’ function with an additive model and cofactor significance set to 0.001 (Arends et al. 2010). Cofactors were set at a fixed marker interval of 5 cM. Genome-wide LOD significance thresholds (α = 0.01) were determined separately for each phenotype after running 1,000 random permutations (Churchill and Doerge 1994) with the assumed genotyping error rate set to 0.001. For each QTL, confidence intervals were determined using the 1.5 LOD drop off flanking the highest peak of the QTL. QTL were named using an abbreviation for the trait (e.g. ht, height) suffixed with the linkage group (1-9), and finally the serial number of QTLs in the linkage group (e.g. ht-2.1, ht-2.2). | cM | |
This map was constructed using an F₂ population L8708×Z020 that segregates for plant height, shoot biomass, and storage root shape. Linkage maps were constructed using the JoinMap 4.1 software (Van Ooijen, 2011). Markers and genotypes which deviated from expected segregation ratios based on a Chi-square test (P < 0.001) were excluded. All linkage groups were obtained at a LOD threshold greater than 10. The regression mapping algorithm was used with Kosambi’s mapping function to calculate the distance between markers (Kosambi, 1943). Linkage groups were achieved by aligning GBS sequences to the carrot genome (Iorizzo et al., 2016) and corresponded to nine chromosomes. After initial mapping, markers defined as having insufficient linkage were flipped to the opposite phase and remapped. Two rounds of the regression mapping algorithm were used to increase the number of loci incorporated into the map. QTL analysis was conducted in R 3.3.2 (R Core Team, 2016) using the R/qtl package (Broman and Sen, 2009). Individuals included 316 F₂ plants from the CA2016 environment. Genotype probabilities were calculated using a step value of one for the entire linkage map and an assumed genotyping error rate of 0.001. Missing genotype data was replaced with the most probable values using the Viterbi algorithm (method = ‘argmax’) in the ‘fill.geno’ function. Multiple QTL mapping (MQM) (Jansen and Stam, 1994) was performed in R/qtl using the ‘mqmscan’ function with an additive model and cofactor significance set to 0.001 (Arends et al., 2010). Cofactors were set at a fixed marker interval of 5 cM. Following scripts developed by Moore et al. (2013), genome-wide LOD significance thresholds were determined for each phenotype using parallel computing on the Open Science Grid (OSG) (Pordes et al., 2007; Sfiligoi et al., 2009). Significance thresholds were based on 10,000 random permutations (Churchill and Doerge, 1994) with the assumed genotyping error rate set to 0.001 and α = 0.01. For each QTL, confidence intervals were determined using the 1.5 LOD drop off flanking the most significant peak of the QTL. | cM |