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Genome-wide association study of individual differences of human lymphocyte profiles using large-scale cytometry data.

ABSTRACT: Human 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.

SUBMITTER: Okada D

PROVIDER: S-EPMC8144016 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Genome-wide association study of individual differences of human lymphocyte profiles using large-scale cytometry data.

Okada Daigo D Nakamura Naotoshi N Setoh Kazuya K Kawaguchi Takahisa T Higasa Koichiro K Tabara Yasuharu Y Matsuda Fumihiko F Yamada Ryo R

Journal of human genetics 20201123 6

Human 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 associatio ...[more]

PMID: 33230199

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Project description:BackgroundDaily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks.ObjectiveUse data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex.MethodsWe used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model.ResultsAlthough the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females.ConclusionWeb-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.

Dataset Information

Genome-wide association study of individual differences of human lymphocyte profiles using large-scale cytometry data.

Publications

Genome-wide association study of individual differences of human lymphocyte profiles using large-scale cytometry data.

Similar Datasets

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