10.1002_tpg2.20560_GenotypicData.csv

This study was completed to improve our understanding of the genetics of carotenoid accumulation in carrots. Important loci related to qualitative color score were identified as well as a locus for quantitative carotenoid accumulation. Additionally, genomic prediction analyses were completed to provide a preliminary estimate of model accuracy and number of markers required.

All methods are fully described in the publication linked below: “Combining Genome-Wide Association and Genomic Prediction to unravel the Genetic Architecture of Carotenoid Accumulation in Carrot” doi: 10.1002/tpg2.20560

Phenotypic data is available as part of the associated publication in supplemental tables 1 and 2.
Genotypic data is available here.

All analyses used in this publication are described in: https://github.com/WRRolling/CarrotColorome

Briefly methods include:

1^st) Carrots were grown in the field in Wisconsin (44.117850° N, -89.552265° W) or California (32.816363, -115.441595.
2^nd) Carrot color was visually scored (orange, red, yellow, white).|
3^rd) Carotenoid concentrations in carrot tissue were quantified using high-performance liquid chromatography (HPLC). Samples were prepared by extracting carotenoids from lyophilized and pulverized mid-root tissue with petroleum ether. The extracted carotenoids were analyzed on a C18 column using a photodiode array detector. Lutein, lycopene, α-, β-carotene, ζ- carotene, and phytoene were quantified based on their absorbance at specific wavelengths. Carotenoid concentrations were reported in μg/g dry or fresh tissue weight and were normalized using a synthetic β-carotene standard. Quality control measures were implemented to ensure data accuracy, including technical replicates and filtering out samples with excessive variation or inconsistencies.
4^th) Genomic DNA was extracted from lyophilized leaf tissue using a commercial kit. DNA samples were subjected to restriction enzyme digestion, adapter ligation, and amplification for sequencing library preparation. Sequencing was performed on a NovaSeq6000 platform, generating millions of reads per sample. Raw sequencing data was processed and analyzed using a standard pipeline, resulting in the identification of genetic variants. The identified variants were filtered based on quality control criteria, including allele frequency, missing data, and depth, to ensure the accuracy of the genetic data.