scholarly journals Publisher Correction: Genome-wide association study of individual differences of human lymphocyte profiles using large-scale cytometry data

Author(s):  
Daigo Okada ◽  
Naotoshi Nakamura ◽  
Kazuya Setoh ◽  
Takahisa Kawaguchi ◽  
Koichiro Higasa ◽  
...  
Author(s):  
Daigo Okada ◽  
Naotoshi Nakamura ◽  
Kazuya Setoh ◽  
Takahisa Kawaguchi ◽  
Koichiro Higasa ◽  
...  

AbstractHuman immune systems are very complex, and the basis for individual differences in immune phenotypes is largely unclear. One reason is that the phenotype of the immune system is so complex that it is very difficult to describe its features and quantify differences between samples. To identify the genetic factors that cause individual differences in whole lymphocyte profiles and their changes after vaccination without having to rely on biological assumptions, we performed a genome-wide association study (GWAS), using cytometry data. Here, we applied computational analysis to the cytometry data of 301 people before receiving an influenza vaccine, and 1, 7, and 90 days after the vaccination to extract the feature statistics of the lymphocyte profiles in a nonparametric and data-driven manner. We analyzed two types of cytometry data: measurements of six markers for B cell classification and seven markers for T cell classification. The coordinate values calculated by this method can be treated as feature statistics of the lymphocyte profile. Next, we examined the genetic basis of individual differences in human immune phenotypes with a GWAS for the feature statistics, and we newly identified seven significant and 36 suggestive single-nucleotide polymorphisms associated with the individual differences in lymphocyte profiles and their change after vaccination. This study provides a new workflow for performing combined analyses of cytometry data and other types of genomics data.


PLoS ONE ◽  
2017 ◽  
Vol 12 (1) ◽  
pp. e0167742 ◽  
Author(s):  
Paul S. de Vries ◽  
Maria Sabater-Lleal ◽  
Daniel I. Chasman ◽  
Stella Trompet ◽  
Tarunveer S. Ahluwalia ◽  
...  

2020 ◽  
Vol 52 (7) ◽  
pp. 669-679 ◽  
Author(s):  
Kazuyoshi Ishigaki ◽  
Masato Akiyama ◽  
Masahiro Kanai ◽  
Atsushi Takahashi ◽  
Eiryo Kawakami ◽  
...  

2019 ◽  
Vol 10 ◽  
Author(s):  
Jicai Jiang ◽  
Li Ma ◽  
Dzianis Prakapenka ◽  
Paul M. VanRaden ◽  
John B. Cole ◽  
...  

2021 ◽  
Author(s):  
Sebastian May-Wilson ◽  
Nana Matoba ◽  
Kaitlin H Wade ◽  
Jouke-Jan Hottenga ◽  
Maria Pina Concas ◽  
...  

Variable preferences for different foods are among the main determinants of their intake and are influenced by many factors, including genetics. Despite considerable twins' heritability, studies aimed at uncovering food-liking genetics have focused mostly on taste receptors. Here, we present the first results of a large-scale genome-wide association study of food liking conducted on 161,625 participants from UK Biobank. Liking was assessed over 139 specific foods using a 9-point hedonic scale. After performing GWAS, we used genetic correlations coupled with structural equation modelling to create a multi-level hierarchical map of food liking. We identified three main dimensions: high caloric foods defined as "Highly palatable", strong-tasting foods ranging from alcohol to pungent vegetables, defined as "Learned" and finally "Low caloric" foods such as fruit and vegetables. The "Highly palatable" dimension was genetically uncorrelated from the other two, suggesting that two independent processes underlie liking high reward foods and the Learned/Low caloric ones. Genetic correlation analysis with the corresponding food consumption traits revealed a high correlation, while liking showed twice the heritability compared to consumption. For example, fresh fruit liking and consumption showed a genetic correlation of 0.7 with heritabilities of 0.1 and 0.05, respectively. GWAS analysis identified 1401 significant food-liking associations located in 173 genomic loci, with only 11 near taste or olfactory receptors. Genetic correlation with morphological and functional brain data (33,224 UKB participants) uncovers associations of the three food-liking dimensions with non-overlapping, distinct brain areas and networks, suggestive of separate neural mechanisms underlying the liking dimensions. In conclusion, we created a comprehensive and data-driven map of the genetic determinants and associated neurophysiological factors of food liking beyond taste receptor genes.


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