Genomic DNA was isolated28 from 125 F2 individual plants that produced the 125 F3 families. The quality of genomic DNA was checked by electrophoresis in 1% agarose gel. DNA was quantified in a NanoDrop ND-1000 Spectrophotometer (Thermo Fisher Scientific, Wilmington, DE).

Genomic DNA was sent to Data2Bio, LLC (Ames, Iowa) for GBS and SNP discovery. Data2Bio used the tunable genotyping-by-sequencing (tGBS) technology to sequence the parents and F2 plants, identify SNPs, and genotype the entire population with the SNPs. Briefly, genomic DNA samples were digested with two restriction endonucleases (RE; NspI and BfuCI), followed by a single-stranded barcode oligonucleotide (oligo A) ligation in one site, while the other site was ligated with another single-stranded oligonucleotide (Oligo B) complementary to amplification primer. The RE NspI and BfuCI recognize a degenerate 5 bp sequence; RCATG where R is A or G, and 4 bp sequence; GATC, respectively. The low quality and redundant SNPs were deleted according to error tolerance rate ≤ 3%.

At the outset of this study, whole genome sequence information was lacking publicly. Thus, SSR markers were used in the study to anchor linkage groups to prior studies. A total of 42 genomic SSR primer pairs from Rauscher and Simko29 were tested for parental screening following the protocol in microsatellite applications manual30. A regular reverse primer, a M13 forward-tailed primer (5′—CACGACGTTGTAAAACGAC—3′), and a M13 forward-labeled primer were used for amplification and subsequent visualization of polymerase chain reaction (PCR) products. A total of seven SSRs primer pairs (17%) out of 42 primer pairs tested were added to the genetic map; these seven markers amplified polymorphic PCR products from parental screening (Supplementary file 1; Table S1).

A genetic linkage map was constructed using MapDisto 2.0 (Ref31). The command ‘Find linkage groups’ was used to construct linkage groups. The minimum log-of-odds (LOD) threshold of 3 and maximum recombination fraction (r) of 0.35 were used to search for linkage groups. The commands ‘Compare all orders’ and ‘Order a linkage group’ were used to compute best order of sequences respectively for linkage groups with short sequences (< 10 loci) and long sequences (> 10 loci). The ‘Sum of adjacent recombination frequencies (SARF)’ option was chosen for ordering sequences. The commands ‘Ripple order’ and ‘Check inversions’ were used to refine the order of sequences generated by ‘Order a linkage group’ command. Double recombinants were removed using ‘Show double recombinants’ command followed by ‘Replace error candidates with missing data’ command. Finally, ‘Bootstrap order’ command with 1000 permutations was used to evaluate the stability or robustness of a given order.

Because many SNPs were very closely linked and did not provide additional information about the QTL locations, we selected only 251 SNPs that were evenly distributed across nine linkage groups for final linkage map construction and QTL mapping. The final linkage map contained 251 SNPs and seven SSRs. Linkage groups were aligned with the pseudo-molecules in the lettuce genome (Genome ID 28333) by BLASTing sequences containing SSRs and SNPs against the lettuce whole genome sequence32. The sequences for SSRs were retrieved from NCBI database33. The data were used to perform a QTL analysis using QTL Cartographer v2.5 (Ref34). Composite interval mapping (CIM) was performed to build an initial model for multiple interval mapping (MIM) procedure. The CIM analysis was implemented using the following criteria: standard model, ‘forward and backward method’ for automatic cofactor selection, a 10-cM window size, automatic selection of 5 control markers, walking speed of 1 cM, and threshold LOD score estimated empirically with 1000 permutations. The MIM analysis was conducted using QTL peaks with empirical LOD threshold value of 2.0 and minimum 5-cM interval between QTL as the initial model. Because the QTL analysis is an iterative process, QTL were searched and refined in a cyclic stepwise fashion using ‘Searching for new QTL’, ‘Testing for existing QTL’, and ‘Optimizing QTL positions’ commands. QTL detected at large marker intervals were deleted as they could be ‘ghost QTL’. New QTL models were only accepted into the current model if they reduced the Bayesian Information Criterion (BIC) values. The epistatic interactions between each pair of QTL were tested using the option ‘QTL interaction’. Non-significant epistatic interactions between QTL were deleted from the model. The model with the least BIC was selected to report the statistically significant putative QTL. QTL effects were estimated using the ‘summary’ option. Sequences for markers in the vicinity of detected QTL were BLASTed35 to identify the putative functions of the QTL. The QTL was visualized by MapChart 2.32 (Ref36).

Note: The content above has been extracted from a research article, so it may not display correctly.



Q&A
Please log in to submit your questions online.
Your question will be posted on the Bio-101 website. We will send your questions to the authors of this protocol and Bio-protocol community members who are experienced with this method. you will be informed using the email address associated with your Bio-protocol account.



We use cookies on this site to enhance your user experience. By using our website, you are agreeing to allow the storage of cookies on your computer.