# Human indels as predictors of antibody responses to COVID-19 vaccines

## TL;DR

The study finds that genetic indels and factors like age and ethnicity influence antibody responses to COVID-19 vaccines, supporting personalized vaccination strategies.

## Contribution

Identifies specific indels and demographic factors linked to vaccine responses and develops a predictive machine learning model.

## Key findings

- Neutralizing antibody levels after the first dose predict infection risk within a year.
- Age, sex, Chinese ethnicity, and two indels are associated with antibody responses.
- A Random Forest model predicted vaccine responses with over 70% accuracy.

## Abstract

Vaccine efficacy varies significantly among adults. This variability underlies the limitation of a one-size-fits-all vaccination strategy and the need for more personalized approaches. We investigated factors influencing inter-individual variability in antibody responses to COVID-19 mRNA vaccine among adults. Neutralizing antibody (nAb) levels after the first vaccine dose were associated with infection outcomes within 1 year after vaccination, suggesting their potential as a correlate of protection. Age, sex, and Chinese ethnicity were associated with nAb and anti-spike protein antibody levels. Two indels located at chr1:31433042 and chr15:76311269 showed significant association with antibody responses. Leveraging these host factors, we developed a Random Forest model that predicted vaccine-induced antibody responses with 72.7% accuracy for mRNA vaccine and 76.9% for the Sinopharm COVID-19 inactivated virus vaccine. These findings support predictive modeling as a tool to identify individuals at risk of low vaccine responses, enabling more targeted and effective vaccination strategies.

•Vaccine responses vary, highlighting the need for personalized strategies•Early antibody levels were predictive of infection risk post vaccination•Age, sex, ethnicity, and two indels were associated with antibody responses•Machine learning models predicted vaccine responses with >70% accuracy

Vaccine responses vary, highlighting the need for personalized strategies

Early antibody levels were predictive of infection risk post vaccination

Age, sex, ethnicity, and two indels were associated with antibody responses

Machine learning models predicted vaccine responses with >70% accuracy

Virology; Public health