Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep 15:14:1204415.
doi: 10.3389/fpls.2023.1204415. eCollection 2023.

GWAS with principal component analysis identify QTLs associated with main peanut flavor-related traits

Affiliations

GWAS with principal component analysis identify QTLs associated with main peanut flavor-related traits

Hui Zhang et al. Front Plant Sci. .

Abstract

Peanut flavor is a complex and important trait affected by raw material and processing technology owing to its significant impact on consumer preference. In this research, principal component analysis (PCA) on 33 representative traits associated with flavor revealed that total sugars, sucrose, and total tocopherols provided more information related to peanut flavor. Genome-wide association studies (GWAS) using 102 U.S. peanut mini-core accessions were performed to study associations between 12,526 single nucleotide polymorphic (SNP) markers and the three traits. A total of 7 and 22 significant quantitative trait loci (QTLs) were identified to be significantly associated with total sugars and sucrose, respectively. Among these QTLs, four and eight candidate genes for the two traits were mined. In addition, two and five stable QTLs were identified for total sugars and sucrose in both years separately. No significant QTLs were detected for total tocopherols. The results from this research provide useful knowledge about the genetic control of peanut flavor, which will aid in clarifying the molecular mechanisms of flavor research in peanuts.

Keywords: GWAS; PCA; QTL; flavor; peanuts.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The genetic structure of 102 genotypes mainly comes from the U.S. peanut mini-core collection. (A) Δ K information from STRUCTURE analysis of the U.S. peanut mini-core collection. (B) Population structure inferred by STRUCTURE analysis. The bar plot for K = 2 was created from 102 accessions and was ordered by Q values. A single vertical line represents each collection and each color represents one cluster. (C) Linkage disequilibrium (LD) decade over distance.
Figure 2
Figure 2
Frequency distribution of total sugars, sucrose, and total tocopherols in peanuts in years 2013 and 2014.
Figure 3
Figure 3
Presentation of Manhattan and Q-Q plots for total sugars, sucrose, and total tocopherols in peanut. The red horizontal line indicates the genome-wide significant threshold: − log10 (P value) = 4.67. The blue horizontal line indicates the threshold for the significance of “suggestive association”: − log10 (P value) = 3.37.
Figure 4
Figure 4
QTL analysis for AX-147221247 located on ChrA04 that is associated with sucrose and total sugars.
Figure 5
Figure 5
The potential flavor-related DEGs that are around significant QTLs and expressed at different development stages in four peanut genotypes: AABB, aaBB, AAbb, and aabb. YL, Yellow; OR, Orange; BR, Brown; and BL, Black.

Similar articles

Cited by

References

    1. Adeyemo A. A., Johnson T., Acheampong J., Oli J., Okafor G., Amoah A., et al. . (2005). A genome wide quantitative trait linkage analysis for serum lipids in type 2 diabetes in an African population. Atherosclerosis 181, 389–397. doi: 10.1016/j.atherosclerosis.2004.12.049 - DOI - PubMed
    1. Bradbury P. J., Zhang Z., Kroon D. E., Casstevens T. M., Ramdoss Y., Buckler E. S. (2007). TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635. doi: 10.1093/bioinformatics/btm308 - DOI - PubMed
    1. Cairncross S. E., Sjöström L. B. (2004). Flavor profiles: a new approach to flavor problems. In Descriptive Sensory Analysis in Practice, M.C. Gacula (Ed.). doi: 10.1002/9780470385036.ch1b - DOI
    1. Gordon D., Finch S. J. (2005). Factors affecting statistical power in the detection of genetic association. J. Clin. Invest. 115, 1408–1418. doi: 10.1172/JCI24756 - DOI - PMC - PubMed
    1. Gu X., Feng C., Ma L., Song C., Wang Y., Da Y., et al. . (2011). Genome-wide association study of body weight in chicken F2 resource population. PloS One 6, e21872. doi: 10.1371/journal.pone.0021872 - DOI - PMC - PubMed

LinkOut - more resources